Ozark-Ouachita

Highlands Assessment

Air Quality

United States

Department of

Agriculture

Forest Service

Southern

Research Station

General Technical

Report SRS-32

REPORT

2

OF 5
Cover photo: Clear day in the Ozarks

Photo by A.C. Haralson, Arkansas Department of Parks and Recreation, Little Rock, AR.

Natural resource specialists and research scientists worked together to produce the five General

Technical Reports that comprise the Ozark-Ouachita Highlands Assessment:

• Summary Report

• Air Quality

• Aquatic Conditions

• Social and Economic Conditions

• Terrestrial Vegetation and Wildlife

For information regarding how to obtain these Assessment documents, please contact: USDA Forest

Service, P.O. Box 1270, Hot Springs, AR 71902 or telephone 501-321-5202.

To limit publication costs, few color maps and figures were used in the Assessment reports. For color

versions of some of the Assessment figures and supplemental material, please see the Assessment’s home

page on the Internet at <http://www.fs.fed.us/oonf/ooha/welcome.htm>. The Assessment reports will

be online for about 2 years after the date on this publication; then they will be archived.

Please note: When “authors” are agency or business names, most are abbreviated to save space in the

citations of the body of the report. The “References” at the end of the report contain both the full name

and abbreviations. Because abbreviations sometimes are not in the same alphabetical order as the references, for clarifications of abbreviations, consult the “Glossary of Abbreviations and Acronyms.”

The use of trade, firm, or corporation names in this publication is for the information and convenience of

the readers. Such use does not constitute an official endorsement of any product or service by the USDA

Forest Service.

December 1999

Southern Research Station

P.O. Box 2680

Asheville, North Carolina 28802
Ozark-Ouachita Highlands Assessment:

Air Quality

Eastern Region

U.S. Department of Agriculture

Forest Service

Milwaukee, WI

North Central Research Station

U.S. Department of Agriculture

Forest Service

St. Paul, MN

Southern Region

U.S. Department of Agriculture

Forest Service

Atlanta, GA

Southern Research Station

U.S. Department of Agriculture

Forest Service

Asheville, NC

Contents

Page

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Contributors to This Report. . . . . . . . . . . . . . . . . . vi

Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . viii

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . ix

Chapter 1: Major Air Pollutants . . . . . . . . . . . . . . 1

Air Quality in the Assessment Area . . . . . . . . . . . . 1

Primary Pollutants . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Data Sources and Methods of Analysis . . . . . . . . . 2

Patterns and Trends . . . . . . . . . . . . . . . . . . . . . . . . . 4

Particulate Matter . . . . . . . . . . . . . . . . . . . . . . . . 4

Nitrogen Oxides. . . . . . . . . . . . . . . . . . . . . . . . . . 6

Volatile Organic Compounds . . . . . . . . . . . . . . . . 8

Sulfur Dioxide . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Pollution Exposure Index Results . . . . . . . . . . . 11

Emission Trends. . . . . . . . . . . . . . . . . . . . . . . . . 12

Implications and Opportunities . . . . . . . . . . . . . . . 13

Chapter 2: Particulate Matter

 (PM10) in the Air . . . . . . . . . . . . . . 15

Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Data Sources and Methods of Analysis . . . . . . . . 16

Patterns and Trends . . . . . . . . . . . . . . . . . . . . . . . . 17

Implications and Opportunities . . . . . . . . . . . . . . . 20

Page

Chapter 3: Visibility . . . . . . . . . . . . . . . . . . . . . . . . 23

Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Data Sources and Methods of Analysis . . . . . . . . 24

Patterns and Trends . . . . . . . . . . . . . . . . . . . . . . . . 25

Implications and Opportunities . . . . . . . . . . . . . . . 31

Chapter 4: Ground-Level Ozone . . . . . . . . . . . . . 33

Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Data Sources and Methods of Analysis . . . . . . . . 34

Patterns and Trends . . . . . . . . . . . . . . . . . . . . . . . . 36

Implications and Opportunities . . . . . . . . . . . . . . . 38

Chapter 5: Acid Deposition . . . . . . . . . . . . . . . . . 43

Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Data Sources and Methods of Analysis . . . . . . . . 44

Patterns and Trends . . . . . . . . . . . . . . . . . . . . . . . . 44

Implications and Opportunities . . . . . . . . . . . . . . . 47

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Glossary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . 52

Glossary of Abbreviations and Acronyms . . . . 54

List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

iii

Preface

Change is evident across the Ozark and Ouachita Highlands. Whether paying attention to State and

regional news, studying statistical patterns and trends, or driving through the Highlands, one cannot escape

signs that growth may be putting strains on the area’s natural resources and human communities. How

people regard these changes varies widely, however, as does access to reliable information that might help

them assess the significance of what is happening in the Highlands. The Assessment reports provide

windows to a wealth of such information.

The Air Quality report is one of five that document the results of the Ozark-Ouachita Highlands

Assessment. Federal and State natural resource agency employees and university and other cooperators

worked together to produce the four technical reports that examine air quality; aquatic conditions; social

and economic conditions; and terrestrial vegetation and wildlife. Dozens of experts in various fields

provided technical reviews. Other citizens were involved in working meetings and supplied valuable ideas

and information. The Summary Report provides an overview of the key findings presented in the four

technical reports. Data sources, methods of analysis, findings, discussion of implications, and links to

dozens of additional sources of information are included in the more detailed technical reports.

The USDA Forest Service initiated the Assessment and worked with other agencies to develop a

synthesis of the best information available on conditions and trends in the Ozark-Ouachita Highlands.

Assessment reports emphasize those conditions and trends most likely to have some bearing on the future

management of the region’s three national forests—the Mark Twain, Ouachita, and Ozark-St. Francis.

People who are interested in the future of the region’s other public lands and waters or of this remarkable

region as a whole should also find the reports valuable.

No specific statutory requirement led to the Assessment. However, data and findings assembled in the

reports will provide some of the information relevant for an evaluation of possible changes in the land and

resource management plans of the Highland’s three national forests. The National Forest Management

Act directs the Forest Service to revise such management plans every 10 to 15 years, which means that

the national forests of Arkansas, Missouri, and Oklahoma are slated to publish revised plans in the year

2001. Due to restrictions in the 1998 appropriations bill that provides funding for the Forest Service, it is

uncertain when these revisions can begin.

The charter for the Ozark-Ouachita Highlands Assessment established a team structure and listed

tentative questions that the teams would address. Assembled in mid-1996, the Terrestrial, Aquatic and

Atmospheric, and Human Dimensions (Social-Economic) Teams soon refined and condensed these

questions and then gathered and evaluated vast quantities of information. They drafted their key findings in

late 1997 and refined them several times through mid-1999. In addition to offering relevant data and key

findings in the reports, the authors discuss some of the possible implications of their findings for future

public land management in the Highlands and for related research. The Assessment reports, however, stop

well short of making decisions concerning management of any lands in the Highlands or about future

research. In no way do the reports represent management plans. Instead, the findings and conclusions

offered in the Assessment reports are intended to stimulate discussion and further study.

v
Contributors to This Report

Assessment Team Leader

William F. Pell, Ecologist, USDA Forest Service, Hot Springs, AR

Aquatic-Atmospheric Team Leader

J. Alan Clingenpeel, Forest Hydrologist, USDA Forest Service, Ouachita National Forest, Hot Springs, AR

Authors

Chapter 1: Major Air Pollutants

William A Jackson

Chapter 2: Particulate Matter in the Air

Cliff F. Hunt, Warren E. Heilman

Chapter 3: Visibility

Cliff F. Hunt

Chapter 4: Ground-Level Ozone

William A Jackson

Chapter 5: Acid Deposition

Cliff F. Hunt, Warren E. Heilman

Author Information

Warren E. Heilman, Research Meteorologist, USDA Forest Service, North Central Research Station, East

Lansing, MI.

Cliff F. Hunt, Air Resource Management Specialist (now retired), USDA Forest Service, National Forests

in Arkansas, Oklahoma, and Texas, Hot Springs, AR.

William A Jackson, Air Resource Management Specialist, USDA Forest Service, National Forests in

North Carolina, South Carolina, and Tennessee, Asheville, NC.

vi
Other Contributors

The Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort

Collins, CO—two figures that include IMPROVE data.

Scott Copeland, Visibility Data Analyst, CIRA, Colorado State University, Fort Collins, CO—the visibility

data analysis.

Jeffrey W. Grimm, Research Assistant, The Pennsylvania State University, University Park, PA—acid

deposition data modeling.

Janna E. Hummel, Environmental Engineer, E. H. Pechan & Associates, Inc., Durham, NC—emissions

data.

Allen S. Lefohn, President, A.S.L. & Associates, Helena, MT—data to conduct the ground-level ozone

analysis.

James A. Lynch, Professor of Forest Hydrology, The Pennsylvania State University, University Park,

PA—acid deposition data modeling.

Michael V. Miller, Senior Planner/Analyst, Geographic Information System, CH2M Hill, Portland, OR—

emissions data and construction of the Pollution Exposure Index model.

Kent Norvill, Atmospheric Scientist, CH2M Hill, Portland, OR—emissions data and construction of the

Pollution Exposure Index model.

Ruth A. Tatom, Environmental Protection Specialist, U.S. Environmental Protection Agency, Region 6,

Dallas, TX—PM10 data.

Jeremy S. Vestal, Hydrologist Co-op, USDA Forest Service, Ouachita National Forest, Hot Springs, AR—

final figure refinement.

Robert C. Weih, Associate Professor, University of Arkansas, School of Forest Resources, Monticello,

AR—critical Geographic Information System (GIS) work, advice, and assistance.

Ozark-Ouachita Highlands Assessment Steering Team

G. Samuel Foster, Assistant Station Director of Research Programs–West, USDA Forest Service,

Southern Research Station, Asheville, NC.

Randy Moore, Forest Supervisor, USDA Forest Service, Mark Twain National Forest, Rolla, MO.

Lynn C. Neff, former Forest Supervisor, USDA Forest Service, Ozark-St. Francis National Forests,

Russellville, AR.

Alan G. Newman, Forest Supervisor, USDA Forest Service, Ouachita National Forest, Hot Springs, AR.

Editorial Team

Donna M. Paananen, Technical Publications Writer-Editor, USDA Forest Service, North Central

Research Station, East Lansing, MI.

Ariana M. Mikulski, Editorial Assistant, USDA Forest Service, North Central Research Station, East

Lansing, MI.

Amy Susan Buckler, Intern, USDA Forest Service, North Central Research Station, East Lansing, MI.

Louise A. Wilde, Production Editor, USDA Forest Service, Southern Research Station, Asheville, NC.

vii
Acknowledgments

The Atmospheric Team would like to thank members of the public who attended meetings and/or

provided written or oral input during the Assessment process. The team also wishes to thank the previously named organizations and individuals who contributed information used to write this report.

In addition, the Atmospheric Team appreciates the help of the many individuals who reviewed all or

part of this general technical report. Their comments and suggestions contributed to the preparation of this

final document; information about them follows.

Reviewers

Bruce A. Bayle, Program Manager, Air Resources, USDA Forest Service, Southern Region, Atlanta,

GA—entire air quality report.

Scott Copeland, Visibility Data Analyst, Cooperative Institute for Research in the Atmosphere, Fort

Collins, CO—Chapter 3.

Ken Luckow, Soil Scientist, USDA Forest Service, Ouachita National Forest, Hot Springs, AR—entire air

quality report.

Keith Michaels, Air Division Chief, Arkansas Department of Pollution Control and Ecology, Little Rock,

AR—entire air quality report.

Robert C. Musselman, Plant Physiologist, USDA Forest Service, Rocky Mountain Research Station, Fort

Collins, CO—Chapter 4.

Roger D. Ottmar, Research Forester, USDA Forest Service, Pacific Northwest Station, Seattle, WA—

entire air quality report.

Marc Pitchford, Chief, Applied Sciences Branch, Air Resources Laboratory, National Oceanic Atmospheric Administration, Las Vegas, NV—Chapter 3.

Brian E. Potter, Research Meteorologist, USDA Forest Service, North Central Research Station, East

Lansing, MI—entire air quality report.

Jerome Thomas, Program Manager, Air Resources, USDA Forest Service, Eastern Region, Milwaukee,

WI—entire air quality report.

Kathy Tonnessen, Ecologist, USDI National Park Service, Air Resources Division, Denver, CO—entire

air quality report.

Robert C. Weih, Associate Professor, University of Arkansas, School of Forest Resources, Monticello,

AR—entire air quality report.

viii
Executive Summary

This Assessment of the Ozark-Ouachita Highlands

area began in May of 1996 and was completed in May of

1998. It was designed as an interagency effort led by the

USDA Forest Service to collect and analyze ecological,

social, and economic data concerning the Highlands of

Arkansas, Missouri, and Oklahoma. The information

compiled will facilitate an ecosystem approach to management of the natural resources on public lands within

the Ozark Highlands, the Boston Mountains, the Arkansas River Valley, and the Ouachita Mountains. The

Atmospheric Team studied air quality conditions in these

and surrounding areas.

The Atmospheric Team, with input from scientists,

forest planners, and concerned citizens, identified five

questions that needed to be addressed in order to understand air quality conditions and trends in the OzarkOuachita Highlands. Following is a summary of the

team’s findings.

Chapter 1: Major Air Pollutants

What are the major emissions characteristics in

the Ozark-Ouachita Highlands Assessment area,

and what areas receive the greatest exposure to

pollutants?

• The major types of air-pollution emissions with the

potential to impact the natural resources of the OzarkOuachita Highlands are particulate matter, nitrogen

oxides, volatile organic compounds, and sulfur dioxide.

• Emissions of particulate matter are greatest along the

northern and western boundaries of the Assessment

area, where they are usually generated by fugitive dust

sources (e.g., sources of uncontrolled dust emissions

such as dirt roads or agriculture fields). Emissions in the

future are expected to remain constant unless wildland

fires or prescribed fires increase beyond current levels.

• Motor vehicles and electrical utilities are the usual

sources of nitrogen oxides nationally; however, in the

Assessment area, fuel combustion at industrial sources

is the major source of these emissions. Current

measures taken by the U.S. Environmental Protection

Agency (EPA) are likely to reduce emissions of

nitrogen oxides from electrical utilities and possibly

other sources.

• Nationally and in the Ozark-Ouachita Highlands,

motor vehicles are the main source of volatile organic

compounds caused by human activities. Available data

were insufficient to enable the Atmospheric Team to

project how volatile organic compounds will change in

the future.

• Fuel combustion from electrical utilities is the greatest

source of sulfur dioxide in the Highlands area; the

Atmospheric Team expects the amount of emissions

to decrease in the future due to the enactment of and

full compliance with the Clean Air Act amendments

of 1990.

Chapter 2: Particulate Matter (PM10) in the Air

What is the status of particulate matter in the

Ozark-Ouachita Highlands?

• Particulate matter (PM10) concentrations show a

definite seasonal trend over the Assessment area. The

highest concentrations between 1991 and 1995 were

during the summer months, with an average period concentration of 33.05 milligrams per cubic meter (μg m-3);

the average winter concentration was 19.84 μg m-3.

• Rural areas have lower PM10 concentrations than

urban areas.

• There is a spatial distribution of PM10 across the

Assessment area, with the lowest annual average

PM10 concentrations occurring in western Arkansas.

• The Assessment area is well within the National

Ambient Air Quality Standards (NAAQS) for PM10.

Implementation of the new PM2.5 regulations may

create a challenge to prescribed burning programs of

farmers and land management agencies such as the

USDA Forest Service.

Chapter 3: Visibility

How good is visibility in the Assessment area; how

does air pollution affect visibility?

• A definite seasonal pattern exists. The best visibility

occurs during the fall, and the worst visibility occurs

during the summer. (Summer is also the time of

highest PM2.5 concentrations.)

ix
• The Upper Buffalo Wilderness on the Ozark-St.

Francis National Forests has the best visibility of the

three Class I wilderness areas on national forests

within the Assessment area.

• Visibility impairment in the form of regional haze exists

within the Assessment area, but the team found that

there is insufficient data to identify trends.

• Sulfates are the primary aerosols responsible for

visibility impairment within the Assessment area.

• Compliance with the Clean Air Act amendments of

1990 should reduce sulfates and improve visibility.

Chapter 4: Ground-Level Ozone

What impact does ground-level ozone have on

forests?

• Using available ozone monitoring data, it appears that

ground-level ozone had a minimal impact on forest tree

growth between 1990 and 1995.

• There are few ozone monitors within the Assessment

area. Consequently, there could be localized areas

where growth losses occurred in trees that are highly

sensitive to ozone.

• Future ozone exposure may be less as Federal, State,

and local air pollution control agencies implement

strategies to reduce pollution, especially nitrogen oxide.

Chapter 5: Acid Deposition

To what extent are resources in the Highlands

being affected by acid deposition?

• Atmospheric wet acid loadings are less than the

loadings observed in the Southern Appalachian region.

Nitrate and sulfate loadings are expected to decrease

in the future.

• Most surface waters within the Assessment area do

not appear to be adversely impacted by the previous

and present rate of acid deposition.

• The low acid neutralizing capacity headwater areas of

the Ouachita Mountains make them most at risk while

the limestone areas of the Ozark Highlands are least at

risk.

Implications and Opportunities

Each chapter concludes with a section on the implications and opportunities that the key findings present for

management and research in the Assessment area.

Following is a brief summary of implications and opportunities for each subject.

Emissions

To discuss air quality within the Assessment area, the

Atmospheric Team needed to understand where pollution

releases are the greatest and what types of sources are

emitting specific pollutants. The team compiled countylevel data that can be used by land managers and others

to see how a proposed action may influence emissions.

Emissions estimates are critical for such determinations.

Particulate Matter (PM10)

Approximately 70 percent of the particulate matter

produced by wildland fuels is within the PM2.5 size class

(diameter of 2.5 microns or smaller). Proposed regulations call for the 24-hour standard to be less than 65 μg

m-3 and the annual average to be less than 15 μg m-3. The

1992 to 1995 annual average concentration of such fine

mass particles was between 9 and 11 μg m-3 over the

more rural parts of the Assessment area based on interpolated IMPROVE network data. These concentrations

represent 60 to 73 percent of the proposed standard annual

average of 15 μg m-3 and 18 to 22 percent of the current

annual standard of 50 μg m-3. Thus, even with the

implementation of the new PM2.5 standards, the more

rural sections of the Assessment area should still be in

compliance if current PM2.5 concentration averages

continue to characterize the region.

According to Forest Service records, most prescribed

burning occurs during March in the Assessment area.

Average PM10 concentrations in the Assessment area

during the month of March (1991 to 1995) ranged from

minimums of 10 to 20 μg m-3 to maximums of 30 to 40 μg

m-3, with a mean of 22.7 μg m-3. If prescribed fire

becomes a more prominent land management tool in the

Assessment area during the normal prescribed fire

season, total PM10 and PM2.5 emissions and concentrations in the atmosphere will likely increase during the

springtime.

x
Visibility

Title IV (Acid Deposition Control) of the Clean Air

Act amendments of 1990 specifies that sulfur dioxide

emissions be reduced by 10 million tons and nitrogen

oxide emissions by 2 million tons from 1980 emission

levels. When these reductions are fully implemented by

the year 2000, visibility should be improved. Sulfates are

the major factor in visibility reduction, especially during

the summer when visibility is poorest. Newly proposed

PM2.5 and ozone regulations, while targeted to improve

human health, should have the added benefit of improving

visibility through anticipated reductions in atmospheric

sulfate concentrations.

Ground-Level Ozone

Ozone exposures in the study area result from the

chemical reaction of nitrogen oxides and volatile organic

compounds. The volatile organic compounds are known

to be so abundant that it appears nitrogen oxides may be

the limiting factor in ozone formation. Implementation of

and compliance with the Clean Air Act Amendments of

1990 should reduce nitrogen oxide emissions nationally by

2 million tons and may reduce ozone exposures further

within the Assessment area. Other strategies that reduce

nitrogen oxides may also result in lower ozone exposures

for the Ozark-Ouachita Highlands area. Recently, the

EPA notified State and local air pollution control agencies

in 22 Eastern States that further reductions in nitrogen

oxides are needed for some urban areas to satisfy the

NAAQS for ground-level ozone. Included were Illinois,

Kentucky, Missouri, and Tennessee, where the needed

reduction of nitrogen oxides is between 35 and 43

percent. Implementation of nitrogen oxide reductions of

this magnitude likely will reduce the amount of groundlevel ozone in the Assessment area.

Acid Deposition

Acid deposition can pose a threat to forest ecosystems—especially on poorly buffered, higher elevation

watersheds. Acid deposition patterns in the Assessment

area are affected by emissions of sulfur dioxide and

nitrogen oxides and by the patterns of precipitation over

the region. Future reductions in the emissions of sulfur

dioxide and nitrogen oxides should lead to reduced

atmospheric sulfate and nitrate concentrations, thereby

reducing the potential for acid deposition episodes.

However, future changes in precipitation patterns as a

result of changes in regional climate may also influence

the amount of acid deposition over the Assessment area.

Comprehensive assessments of future acid deposition

patterns over the Assessment area will require the use of

coupled high-resolution models that take into account

complex atmospheric processes, cloud formation and

precipitation occurrence, surface-atmosphere interactions

as they relate to the hydrologic cycle, and the chemical

reactions that control the formation of sulfuric and nitric

acid in the atmosphere. Information from these models

should aid natural resource managers in the development

of management strategies for watersheds in the Assessment area that are sensitive to acid rain episodes.

xi
Figure 1.1—Counties in the Ozark-Ouachita Highlands Assessment area.

xii
Chapter 1: Major Air Pollutants

Question 1: What are the major emissions

characteristics in the Ozark-Ouachita Highlands

Assessment area, and what areas receive the

greatest exposure to pollutants?

Air Quality in the Assessment Area

The term “air pollution” calls up images of dark fumes

billowing from tailpipes, black smoke from factory

chimney stacks, or smog hanging over a city. Americans

depend on the combustion of fossil fuels for transportation, electricity, industrial processes, and heating homes

and businesses. This combustion of fossil fuels not only

generates energy, it also creates toxic gases and particulates. These pollutants can be transformed in the atmosphere and transported throughout the area of origin and

beyond. People and resources in the Ozark-Ouachita

Highlands Assessment area (fig. 1.1) cannot escape air

pollution and its effects.

Rarely can air pollution impacts be traced back to a

single source. Moreover, pollutants are generated from

both within and outside of the Assessment area—even

from hundreds of miles away. Air pollution is produced

by both human and natural activities and has three major

sources: (1) stationary or point sources such as powergenerating plants, service stations and industrial facilities;

(2) area sources such as dust from roads and smoke

from fires; and (3) mobile sources such as automobiles,

trucks, and aircraft. The primary pollutants (such as

sulfur dioxide or nitrogen oxides) emitted directly from

these sources are transformed in the atmosphere into

secondary pollutants such as sulfate or nitrates. In this

report, the secondary pollutants discussed are those that

most likely affect the forest environment.

The information presented in the following sections is

for a broad-scale assessment that focuses on air quality

issues concerning potential impacts to forest ecosystems.

The information and data presented should be used

cautiously and may not be appropriate for local planning;

that is, a statement that in general holds true for the

whole Ozark-Ouachita Highlands may not hold true for a

specific site in the Assessment area.

Primary Pollutants

Natural events such as dust storms, lightning-caused

wildfires, and volcanoes release pollutants into the

atmosphere as do human activities (also called anthropogenic sources) such as agriculture, industry, transportation, and prescribed fires.

In their analysis, the Atmospheric Team identified four

primary pollutants released from human activities that

eventually affect the Assessment area: particulate matter

(10 microns and smaller, called PM10), nitrogen oxides,

volatile organic compounds, and sulfur dioxide. These

pollutants represent some of the six “Criteria Pollutants”

recognized by the U.S. Environmental Protection Agency

(EPA) (U.S. EPA 1995). The team selected these primary

pollutants because secondary pollutants formed from

them are suspected of causing visibility reductions and

impacts to vegetation and aquatic ecosystems. Information presented on these primary pollutants includes the

location and intensity of emissions and likely future trends

in emissions.

Regional climate change resulting from emissions of

carbon dioxide and other greenhouse gases is not discussed in this report. Although the team recognized that

resources in the Ozark-Ouachita Highlands could be

susceptible to climatic change, uncertainty concerning the

nature of regional climatic changes and the global aspects

of the phenomenon led the team to conclude that a

comprehensive analysis of this issue was beyond the

scope of this Assessment. However, an overview of the

baseline climatic characteristics in the Assessment area

can be found in the “Climate” section of Chapter 1 of the

Ozark-Ouachita Highlands Assessment: Aquatic

Conditions (USDA FS 1999).
Key Findings

 1. The major types of air-pollution emissions with the

potential to impact the natural resources of the

Ozark-Ouachita Highlands are particulate matter,

nitrogen oxides, volatile organic compounds, and

sulfur dioxide.

 2. Emissions of particulate matter are greatest along

the northern and western boundaries of the Assessment area, where they are usually generated by

fugitive dust sources (e.g., sources of uncontrolled

dust emissions such as dirt roads or agriculture

fields). Emissions in the future are expected to

remain constant unless wildland or prescribed fires

increase beyond the current normal occurrences.

 3. Nationally, motor vehicles and electrical utilities are

the usual sources of nitrogen oxides; however, in the

Assessment area, fuel combustion at industrial

sources is the major source of these emissions.

Current measures taken by the U.S. Environmental

Protection Agency are likely to reduce emissions of

nitrogen oxides from electrical utilities and possibly

other sources.

 4. Nationally and in the Ozark-Ouachita Highlands,

motor vehicles are the main source of volatile

organic compounds caused by human activities.

Available data were insufficient to enable the

Atmospheric Team to project how volatile organic

compounds will change in the future.

 5. Fuel combustion from electrical utilities is the

greatest source of sulfur dioxide in the Highlands

area; the Atmospheric Team expects the amount of

emissions to decrease in the future due to the

enactment of and full compliance with the Clean Air

Act Amendments of 1990.

Data Sources and Methods of Analysis

States included in this analysis—Arkansas, Illinois,

Kansas, Kentucky, Louisiana, Mississippi, Missouri,

Nebraska, Oklahoma, Tennessee, and Texas—are

located within 120 miles of the Ozark-Ouachita Highlands Assessment boundary. Table 1.1 lists the main

categories of emissions used in the analysis for this

Assessment (Miller 1997).

The Atmospheric Team obtained data from the EPA

for county-level estimates (tons/year) of emissions from

point and area sources for the 11 States. The team used

the adjusted data from the EPA’s 1985 National Acid

Precipitation Assessment Program (NAPAP) inventory,

using economic activity data to estimate the emissions for

1994. The NAPAP inventory is organized by area and

point source emission categories (Placet and others 1991).

The EPA also provided 1995 data on emissions of

natural sources of nitrogen oxides and volatile organic

compounds for each county within the area (U.S. EPA

1996). A simple rating system developed by the EPA

(1996) was used to classify each county by the amount of

natural or anthropogenic emissions recorded there. The

team categorized each county by dividing the emissions

estimate for a pollutant by the area of the county, resulting in “tons/square mile” (tons/mi2

). They condensed the

five category system (table 1.2) used by the EPA (1996)

into three categories: (1) low or below average, (2)

average or above average, and (3) high. These categories

allow comparisons of the emissions from a particular

county with emissions from other areas in the United

States. For example, a county with high emissions of

nitrogen oxides in the Assessment area would have more

2

Table 1.1—Main categories of area, mobile, and

point source emissions in the Assessment area

Category

number Main category name

 1 Fuel combustion, electrical utility

 2 Fuel combustion, industrial

 3 Fuel combustion, other

 4 Chemical and allied product manufacturing

 5 Metals processing

 6 Petroleum and related industries

 7 Other industrial processes

 8 Solvent utilization

 9 Residential wood and other

10 Waste disposal and recycling

11 Highway vehicles

12 Off-highway vehicles

13 Natural sources

14 Miscellaneous

15 Off-highway, other

16 Agriculture and forestry

17 Fugitive dust

Source: Miller (1997).
emissions per square mile than most counties in the

United States.

It is difficult to define a specific boundary area for

“contributing” sources because the atmospheric processes that control pollutant formation and transport vary

by pollutant and as a function of weather conditions.

Sources within the designated 120-mi radius and more

distant sources could contribute to pollutant levels.

Atmospheric transport and dispersion models are traditionally used to simulate pollution exposures across the

landscape or to map the potential downwind impact of a

pollution source. The most accepted regional models are

very expensive to use and were beyond the financial

resources of this Assessment. Instead, a simplified

approach called statistical modeling was used for the

analysis. As shown in figure 1.2, the Atmospheric Team

located 28 receptors (spatial locations) over the Assessment area, using a 60-mi by 60-mi spacing. The team

selected a 120-mi radius to illustrate which sections of the

Ozark-Ouachita Highlands are likely to receive the

greatest pollution exposure. Only anthropogenic emissions

within 120 mi of the receptors were used in the simple

modeling exercise.

The statistical model used for this Assessment is called

the Pollution Exposure Index (PEI) (Miller 1997). The

model has been developed using ArcView® software as

an interface to perform the calculations. The team used

emissions from up to 405 counties in the analysis. If

emissions from a county were less than 40 tons/year, the

team did not use them in the PEI model. Separate calculations were performed for PM10, nitrogen oxides, sulfur

Table 1.2—Emissions categories described by the range of pollutants (tons per square mile)

resulting from natural sources and human activities

 Natural Human activity

Category NOx VOC PM10 NOx VOC SO2

 - - - - - - - - - - - - - - - - - - - - - - - - - - Tons per square mile - - - - - - - - - - - - - - - - - - - - - - - - -

Low 0 – 0.20 0 – 2.0 0 – 6.9 0 – 1.13 0 – 1.21 0 – 0.07

Below average 0.21– 0.30 2.1– 6.0 7.0 – 10.3 1.14 – 2.40 1.22– 2.61 0.08– 0.20

Average 0.31– 0.60 6.1– 15.0 10.4 – 14.2 2.41 – 4.60 2.62– 4.76 0.21– 0.70

Above average 0.61– 1.0 15.1– 25.0 14.3 – 19.7 4.61 –11.40 4.77–11.53 0.71– 4.80

High > 1.0 > 25.0 > 19.7 > 11.40 >11.53 > 4.80

NOx = nitrogen oxides; VOC = volatile organic compounds; PM10 = particulate matter 10 microns or smaller; SO2 = sulfur

dioxide.

Source: U.S. EPA (1996).

dioxide, and volatile organic compounds. The model used

the following equation:

PEIip = 

j

N

=

∑

1

[(Fij * Tij) * Qjp / Dij]

and calculated a value for each of the 28 receptors. (See

the sidebar for a complete explanation of the equation

used.)

Figure 1.2—Location of receptors used for statistical

modeling of pollution exposure within and near the

Assessment area.

3
Patterns and Trends

Particulate Matter

Particulate matter in the atmosphere includes windblown soil, soot, smoke, and liquid droplets. It also

includes fine particles of sulfates, nitrates, and organic

compounds that are 2.5 microns or smaller in size

(PM2.5). Particles are emitted into the air by sources

such as factories, power plants, construction activities,

automobiles, fires, and agricultural activities. Relative to

the United States as a whole, many counties on the

northern and western boundaries of the Assessment area

have high emissions of PM10 (fig. 1.3). Within the

The Atmospheric Team used the unpublished PEI model described by Miller (1997), which is as follows:

PEIip = 

j

N

=

∑

1

[(Fij * Tij) * Qjp / Dij]

where

PEIip = total index value for each receptor and pollutant of interest.

i = receptors.

p = pollutant of interest.

N = total number of sources.

j = source (i.e., the emissions from a county).

Fij = annual wind direction percent frequency (using a 22.5 degree “window”), using the nearest surface

 wind station between the receptor and source.

Tij = the calculated terrain factor between the receptor and the source (i.e., the county center).

Qjp = pollutant emission rate (tons per year) for a particular pollutant.

Dij = distance (kilometers) between the receptor and the source. Values less than or equal to 0.06 miles

 (0.1 km) were set to 0.06 miles (0.1 km).

The terrain factor (Tij) is an adjustment made if there are mountains between the receptor and

source. The calculation uses the following two equations:

Ee = max (Er, Es, Em)

Tij = Mh / Mh + (Ee - Es)

where

Ee = largest value for Er

, Es, or Em.

Er

 = maximum receptor elevation (feet).

Es = maximum source (i.e., county center) elevation (feet).

Em = highest elevation (feet) along a line drawn between the source and the receptor.

Mh = mixing height (average annual value of 2,689 feet was used).

The team then used the values at the receptors to

estimate the PEI values between the receptors. Spatial

Analyst® (an ArcView® extension) was used to perform

the estimates (also called interpolations). The team used

a spline technique for the interpolations to a 6-mi by 6-mi

grid. The nearest 12 points to each grid were used for the

spline interpolation. The results for the PEI model are

given in tons per year per kilometer, which lacks meaning

except as an index. Those areas with the highest values

are believed to have the highest risk of impact from a

particular pollutant. The results from the PEI model

should be used cautiously and in conjunction with available ambient monitoring data.

4
5

Figure 1.3—Estimates of emissions of particulate matter

10 microns or smaller (PM10) in Assessment area and

surrounding counties in 1994 (U.S. EPA 1995).

Figure 1.4—Particulate matter emissions 10 microns or smaller

(PM10) by source categories in 1994 (U.S. EPA 1995).

Natural sources

4%

Vehicles

1%

Agriculture and forestry

18%

Fugitive dust

71%

Miscellaneous

3%

Fuel combustion 

and other industries

3%

Arkansas portion of the Assessment area, most of the

counties have low or below average emissions compared

to the rest of the United States (U.S. EPA 1996).

Fugitive dust is the greatest emission source of PM10

(71 percent) from within and outside the Assessment

area (fig. 1.4). The second largest source of emissions is

from agriculture and forestry practices (18 percent).

Emissions of PM10 from fuel combustion and other

industries (stationary sources) comprise a small portion

(3 percent) of the total emissions within 120 mi of the

receptors (fig. 1.4).
Vehicles

22%

Other industries

7%

Fuel combustion, 

industrial

28%

Fuel combustion, other

9%

Fuel combustion, 

electrical utilities

20%

Miscellaneous 

14%

Nitrogen Oxides

More than 95 percent of nitrogen oxide emissions is in

the form of nitric oxide. In the presence of volatile

organic compounds and sunlight, this gas is rapidly

converted in the atmosphere to nitrogen dioxide, which

can subsequently be altered by sunlight to produce ozone.

Available evidence suggests that nitrogen oxides are a

controlling factor in the formation of ground-level ozone

in rural areas of the Southern United States (Chameides

and Cowling 1995). When trapped in sufficient quantities,

nitrogen dioxide can be seen as a brownish haze. Secondary pollutants formed from nitrogen oxides also reduce

visibility and contribute to acid deposition. The largest

contributors of nitrogen oxides within 120 mi of the

receptors (fig. 1.5) are fuel combustion from industrial

sources (28 percent), motorized vehicles (22 percent),

and electrical utilities (20 percent). Nationally, greater

portions of total nitrogen oxide emissions come from

motorized vehicles (45 percent) and electrical utilities (33

percent). On the other hand, national nitrogen oxide

emissions from industrial fuel combustion comprise a

smaller portion (14 percent) of the total than in the

Assessment area (U.S. EPA 1995).

Figure 1.6 shows which counties have the largest

releases of nitrogen oxides from anthropogenic sources.

The areas with high annual emissions correspond to the

larger cities within or outside the Assessment area

including Little Rock, AR; St. Louis, MO; Kansas City,

MO; Tulsa, OK; and Dallas, TX. Within the Assessment

area, most of the county-level emission estimates are

low or below average in comparison to other counties in

the United States. Figure 1.6 does not include emission

estimates of nitrogen oxides from natural sources. The

1995 emissions from natural sources were low or below

average throughout most of the Assessment area

(fig. 1.7).

Figure 1.5—Emissions of nitrogen oxide (NOx) by source categories, based on

1994 data (U.S. EPA 1995).

6
Figure 1.6—Estimates of human-caused nitrogen

oxide (NOx) emissions in Assessment area and

surrounding counties in 1994 (U.S. EPA 1995).

Figure 1.7—Estimates of natural nitrogen oxide (NOx) emissions in

Assessment area counties in 1995.

7
Volatile Organic Compounds

Volatile organic compounds represent a wide range of

organic chemicals that are emitted into the atmosphere.

Combined with nitrogen dioxide, these chemicals contribute to the formation of ground-level ozone. Trees are the

primary source of naturally produced volatile organic

compounds (Placet and others 1991), and much of the

Assessment area is forested and has high natural emissions (see fig. 1.8) compared to other counties in the

United States. As in the rest of the Nation, motor vehicle

use is the main human (anthropogenic) source of volatile

organic compounds, accounting for 37 percent of the total

emissions (U.S. EPA 1995) (fig. 1.9). Solvent utilization

(27 percent) and processes at petroleum and related

industries (14 percent) also release volatile organic

compounds within 120 mi of the receptors. Figure 1.10

shows a wide range of anthropogenic emissions from

counties within 120 mi of the receptors; the highest

emissions of volatile organic compounds occur near cities

such as Dallas, TX; St. Louis, MO; and Memphis, TN.

Vehicles

37%

Solvent utilization

27%

Petroleum and related 

industries

14%

Miscellaneous

12%

Fuel combustion and other 

industries

10%

8

Figure 1.8—Estimates of natural volatile organic compound

emissions in Assessment area counties in 1995.

Figure 1.9—Sources of volatile organic compound emissions in 1994 (U.S.

EPA 1995).
Figure 1.10—Estimates of human-caused volatile organic compound emissions in Assessment area and surrounding counties in 1994 (U.S. EPA 1995).

9
Sulfur Dioxide

Sulfur dioxide is a gas transformed in the atmosphere

into secondary pollutants called sulfates, the main contributors to visibility reduction and acid deposition. Electrical utilities are the largest source of sulfur dioxide

affecting the Highlands (72 percent) (fig. 1.11), which is

consistent with the national pattern (U.S. EPA 1995).

There are very few emissions of sulfur dioxide from

natural sources in the Assessment area. As with nitrogen

oxides or volatile organic compounds, the counties with

the largest emissions are near the largest cities (fig. 1.12).

Vehicles

1%

Fuel combustion, 

electrical utilities

72%

Fuel combustion, 

industrial

15%

Other industries and 

miscellaneous

11%

Fuel combustion, other

1%

10

Figure 1.11—Sources of sulfur dioxide (SO2) emissions in

1994 (U.S. EPA 1995).

Figure 1.12—Estimates of human-caused sulfur dioxide (SO2) emissions in Assessment area and surrounding counties in

1994 (U.S. EPA 1995).

Fuel combustion,

electrical utilities

72%

Vehicles

1%

Other industries and

miscellaneous

11%

Fuel combustion, other

1%

Fuel combustion,

industrial

15%
Pollution Exposure Index Results

The Atmospheric Team used two approaches to

identify which areas receive the greatest exposure to

pollutants: (1) ambient monitoring data and (2) a statistical

approach. The team found that there were few ambient

monitoring sites across the Assessment area, which is the

case in rural areas such as the Ozark-Ouachita Highlands. With little data, it is difficult to interpret pollution

patterns, so the team chose an additional approach. They

considered using an atmospheric dispersion model to

predict the transformation of primary pollutants (such as

sulfur dioxide) and where the secondary pollutants (such

as sulfates) are likely to be the greatest. There are

numerous complex atmospheric models available, but the

cost was prohibitive. Therefore, the Atmospheric Team

chose to use the statistical PEI model (Miller 1997),

described earlier, to identify areas with the greatest

pollution exposure.

The PEI values are calculated based upon: (1) the

annual emission of the specific pollutant, (2) the distance

between the source and receptor, (3) the frequency of

winds blowing from the source toward the receptor

(assuming a straight-line windflow), and (4) the degree to

which hills or mountains impede the windflow. The results

from the PEI model do not predict pollutant deposition of

secondary pollutants (such as sulfates or ozone). Therefore, the model results should not be used to say that

impacts are occurring from a specific pollutant. Furthermore, the PEI uses the direction frequency of ground

level winds, which is usually not reliable at locations far

from a measurement site. Atmospheric dispersion models

use the results of complex meteorology modeling to

estimate wind directions and speeds at ground level and

various heights in the atmosphere. The PEI model does

not take into account local topographic effects on wind

fields; therefore, a person must be cautious when making

any strong statements about a specific spot in the Assessment area. The model is useful because it predicts

which portions of the region are most likely to have the

greatest pollution exposure from anthropogenic sources,

and it may be useful in locating where further monitoring

should be conducted.

As has been noted, the greatest source of PM10

emissions is fugitive dust (fig. 1.4) in the western and

northern counties within the Assessment area (fig. 1.3).

Therefore, the PEI model predicts that PM10 exposures

are likely to be greatest along the western portions of the

Assessment area (fig. 1.13). PM10 exposures in the air

are likely to be less in the central portions of the Assessment area because particles transported eastward by

predominately westerly winds are likely to deposit from

the atmosphere near their sources (fig. 1.13).

The team did not have emission estimates from natural

sources of volatile organic compounds and nitrogen

oxides within 120 mi of the receptors (fig. 1.2). Therefore, only anthropogenic emission estimates for volatile

organic compounds and nitrogen oxides were used as

input into the PEI model. Assessing where these pollutants have the potential for the greatest impact is difficult

since the PEI model does not include transformation of

these emissions to ground-level ozone or nitrate deposition. The PEI results for nitrogen oxides and volatile

organic compounds are shown in figures 1.14 and 1.15,

respectively. The model simulations suggest that anthropogenic exposure to nitrogen oxides and volatile organic

compounds is lowest in the interior and highest near the

perimeter of the Assessment area—particularly near the

east-central, west-central, and northeastern perimeters.

Figure 1.13—Pollution Exposure Index modeling results

using 1994 particulate matter (PM10) emissions data.

11
Chameides and Cowling (1995) suggest that ozone

formation is likely to be greater downwind of nitrogen

oxide sources because volatile organic compounds

typically are not a limiting factor in rural areas of the

Southern United States. The PEI results for nitrogen

oxides (fig. 1.14) suggest ozone exposures are likely to

be greatest in the Ozark-Ouachita Highlands downwind

of St. Louis, MO; Little Rock, AR; Dallas, TX; and

Tulsa, OK.

The Atmospheric Team decided the PEI results should

not be used to predict where sulfate and nitrate deposition

would be the greatest because the total deposition in a

given area is the sum of the amount in the rainfall, dry fall

(usually seen as haze), and cloud water. Furthermore, fine

particles (2.5 microns or smaller) of sulfates and nitrates

travel very long distances before settling to the ground.

The PEI results in figures 1.14 and 1.16 show where

nitrogen and sulfur (such as nitrogen oxide and sulfur

dioxide) exposures may be the greatest. As mentioned

previously, exposures to nitrogen oxide emissions may be

the greatest downwind of several large cities within or

near the Assessment area (fig. 1.14). Exposure to sulfur

compounds is likely to be greatest in the northeastern

portion of the Assessment area near St. Louis, MO.

One consistent pattern from the PEI model results is

evident (figs. 1.13 to 1.16). The modeling results of the

emissions from the four primary pollutants of interest

indicate a pattern throughout a large portion of the

Assessment area that suggests that pollution exposures

should be less there, whereas they appear to be higher

close to the boundary.

Emission Trends

The Clean Air Act (CAA) is the primary means by

which the American public’s health and welfare (as they

relate to air pollution) are protected. Implementation of

the CAA and its amendments since 1977 has resulted in

significant reductions for several primary pollutants (fig.

1.17). Nationally, particulate matter emissions from

stationary sources decreased significantly between 1940

and 1994 (fig. 1.17), but total PM10 emissions have not

decreased because a large portion is from fugitive dust

sources (U.S. EPA 1995). Therefore, total PM10 emissions are predicted to remain constant in the future

because there are no initiatives that would significantly

reduce fugitive dust emission.

Figure 1.14—Pollution Exposure Index modeling results

using 1994 human-caused nitrogen oxide (NOx)

emissions data.

Figure 1.15—Pollution Exposure Index modeling results

using 1994 human-caused volatile organic compound

emissions data.

12
Figure 1.16—Pollution Exposure Index modeling results

using 1994 sulfur dioxide (SO2) emissions data.

Figure 1.17—Trends in national emissions of nitrogen oxides (NOx),

volatile organic compounds (VOC), sulfur dioxide (SO2), and particulate matter (PM10, nonfugitive dust sources) from 1940 through 1994

(U.S. EPA 1995).

Nationally, emissions of nitrogen oxides rose from 1940

through 1994 (fig. 1.17). Most of this growth is attributed

to an increase in the number of vehicle miles traveled

annually as well as increases from electrical utilities (U.S.

EPA 1995). Compliance with Title IV of the 1990 CAA

amendments will reduce nitrogen oxide emissions from

electrical utility boilers by 2 million tons from the 1980

level (U.S. EPA 1996), but there is no Federal law to

keep overall nitrogen oxide emissions at or below 1980

levels. Recently, the EPA notified State and local air

pollution control agencies in 22 Eastern States that further

nitrogen oxide reductions are necessary for urban areas

to reach the national standards for ground-level ozone

concentrations. Therefore, several States (Illinois, Kentucky, Missouri, and Tennessee) near and within the

Ozark-Ouachita Highlands are likely to reduce nitrogen

oxide emissions by an additional 35 to 43 percent. There

is a high likelihood these reductions will be achieved by

the electrical utilities, but each State will determine which

nitrogen oxides sources will make future reductions.

Nationally, emissions of volatile organic compounds

increased between 1940 and 1970; subsequently, they

have decreased (fig. 1.17). The team is uncertain about

the direction of future emission levels of volatile organic

compounds because they are difficult to predict. Emissions could decrease as a result of (1) compliance with

the CAA amendments of 1990, (2) further decreases in

the use of solvents (used to clean equipment), and (3) an

increase in the amount of water-based compounds (such

as paints) that have low amounts of volatile organic

compounds. These reductions have the potential to be

negated if the number of vehicle miles traveled continues

to increase in the future.

Sulfur dioxide emissions increased nationally between

1940 and 1970; since then, they have steadily decreased

and are now approximately at 1940 levels (fig. 1.17).

Compliance with the 1990 CAA amendments will reduce

sulfur dioxide emissions by 10 million tons below the 1980

level (U.S. EPA 1996), and there will be a cap on

emissions from utility and industrial sources.

Implications and Opportunities

To discuss air quality within the Assessment area, the

Atmospheric Team needed to understand where pollution

releases are the greatest and what types of sources are

13
emitting specific pollutants. The team compiled countylevel data that can be used by land managers and others

to see how a proposed action (e.g., a community in the

Assessment area is looking for a new industry or prescribed fire is needed to reduce undergrowth) may

influence emissions from a specific county. Emissions

estimates are critical for such determinations. Future

assessments should gather emission estimates for the

primary pollutants considered in this report, as well as

particulate matter 2.5 microns and smaller (PM2.5) in size.

Also, further work after this Assessment, or a future

assessment, could use one or more complex atmospheric

dispersion models to gain a more complete understanding

of how the emissions from an area will impact a downwind region.

14
Chapter 2: Particulate Matter (PM10) in the Air

Question 2: What is the status of particulate

matter in the Ozark-Ouachita Highlands?

The previous chapter provided a description of some

of the relevant pollutant emissions characteristics within

the Assessment area, including particulate matter. This

chapter provides a more indepth analysis of the typical

seasonal particulate matter concentration patterns in the

atmosphere over the Assessment area that result from

the emissions patterns described in Chapter 1.

Particulate matter (PM10) as an air pollutant consists

of those particles suspended in the atmosphere that are

10 microns or smaller in diameter. The most important

constituents of particulate matter are particles 2.5

microns in diameter (PM2.5) or smaller. These tiny

particles can be breathed into human lungs and create

serious health problems (e.g., respiratory ailments and

asthma). White (1995) of the American Lung Association maintains that there is no tolerance level below

which particulates do not affect human health. Stated

another way—any increase in particulate concentration

can cause an increase in human health problems. These

problems have been triggered at concentrations well

below current National Ambient Air Quality Standards

(NAAQS) (White 1995).

Particulates come from many sources: industry,

electrical power production, internal combustion engine

exhaust, dust from natural and artificial sources, smoke

from agricultural and forestry burning, and wildland fires.

The U.S. Environmental Protection Agency (EPA)

categorizes sources of particulate matter emissions that

are 10 microns or smaller in diameter (PM10) as point

sources (smokestack emissions) or fugitive process

sources (e.g., dust, leaks, uncontrolled vents). Fugitive

dust is also generated by wind erosion, agricultural tilling,

mining, construction, and both paved and unimproved

roads (U.S. EPA 1996).

Even though industrial production has increased

nationally during recent decades, pollution control equipment has dramatically reduced PM10 emissions from

industrial processes (fig. 1.17). Figure 2.1 illustrates that

in 1970, smokestacks generated over 12 million tons of

PM10 particles, while in 1995, they produced only about

2.5 million tons (U.S. EPA 1996). The Clean Air Act

Figure 2.1—National smokestack and miscellaneous particulate matter (PM10) emissions

(in million short tons) from 1970 through 1995 (U.S. EPA 1996).

15
(1970) and its amendments (1977 and 1990) encouraged

this abatement.

According to the guidelines of the NAAQS, PM10

concentrations at any location are not to exceed 150

micrograms per cubic meter (μg m-3) during a 24-hour

period. The NAAQS also limit the average annual

exposure of PM10 at any location to 50 μg m-3. In July

1997, the EPA implemented a new ambient air quality

standard based on particulate matter 2.5 microns or

smaller in diameter (PM2.5). This new standard states

that the average annual and 24-hour concentrations are

not to exceed 15 μg m-3 and 65 μg m-3, respectively, at

monitoring sites that represent a large-scale area and are

not related to a specific source.

In addition to its potential effect on human health,

particulate matter reduces visibility (discussed in the next

chapter). Within the Assessment area, all areas presently

meet the PM10 NAAQS. There are no chronic particulate matter (PM) problems on or near national forest

lands in Arkansas or Oklahoma. In southern Missouri,

however, the charcoal industry has created a locality with

reoccurring days of high PM concentrations (Braun

1996). The State is looking into this situation.

Key Findings

 1. Particulate matter concentrations (PM10) show a

definite seasonal trend over the Assessment area.

The highest concentrations between 1991 and 1995

were during the summer months, with an average

period concentration of 33.05 micrograms per cubic

meter (μg m-3); the average winter concentration

was 19.84 μg m-3.

 2. Rural areas have lower PM10 concentrations than

urban areas.

 3. There is a spatial distribution of PM10 across the

Assessment area, with the lowest annual average

PM10 concentrations occurring in western Arkansas.

 4. The Assessment area is well within the National

Ambient Air Quality Standards (NAAQS) for PM10.

Implementation of the new PM2.5 regulations may

create a challenge to prescribed burning programs of

farmers and land management agencies such as the

USDA Forest Service.

Data Sources and Methods of Analysis

Most data analyzed for this Assessment are from the

EPA’s Aerometric Information Retrieval System (AIRS).

In addition, the team used data from the Interagency

Monitoring of Protected Visual Environment (IMPROVE)

monitoring network.

The AIRS data base is the national data storage

system for all Criteria Pollutants. Data were obtained

from AIRS sites both inside and within 100 miles (mi) of

the Assessment area boundary. The EPA sets rigid data

collection standards and assures the quality of all data

entered into the system. Because the EPA changed PM

standards in 1987 from measurement of total suspended

particles (TSP) to PM10, the team decided to avoid using

TSP data and instead chose PM10 data from 1991 to

1995 because all sites were monitoring with PM10

equipment by 1991. The point data were displayed using

the Geographic Information System (GIS) software

called ArcInfo®. These data were analyzed across the

Assessment area using the “inverse distance weighting

method” (Burrough 1988) and then displayed in a grid

format. Each grid was assigned a value. These grid

values represent estimated rather than measured values.

This limitation needs to be considered when making

assertions or recommendations using these or similar

data.

16
Patterns and Trends

Table 2.1 shows the seasonal, annual, and period

means of PM10 concentrations over the entire Assessment area based on observational data from the AIRS

network (mainly urban areas). The numbers in table 2.1

represent averages of all the PM10 monitors inside the

Assessment area from 1991 through 1995. The seasonality of PM10 is clearly evident in table 2.1, with the

average PM10 concentrations at AIRS monitoring sites

over the entire Assessment area typically increasing from

wintertime minimum values to summertime maximum

values. Figure 2.2 shows the typical, large-scale spatial

distributions of PM10 concentrations for each season over

the Assessment area based solely on AIRS network

data. Because most observation sites in the AIRS

network are in urban areas where PM10 concentrations

tend to be higher than in rural settings, the interpolated

spatial patterns of PM10 concentrations across the

Assessment area most likely overestimate nonurban

PM10 concentrations. Nevertheless, the spatial patterns

shown in figure 2.2 provide a general indication of the

impact of urban PM10 emission sites on PM10 concentrations in the Assessment area.

The interpolated mean winter PM10 data from the

AIRS network shown in figure 2.2 indicate that most

PM10 concentrations in the Assessment area are less

than 22.5 μg m-3 (based on 1991 to 1995 data). Concentrations tend to increase during the spring months over

parts of the Assessment area, particularly over the

western and eastern sections of the Assessment area as

well as in southern Missouri (fig. 2.2). The interpolated

Table 2.1—Average PM10 concentrations (in μg m-3) in the

Assessment area by season, year, and 5-year period

1991–

Season 1991 1992 1993 1994 1995 1995

Winter 21.68 21.04 19.47 19.74 18.33 19.84

Spring 23.75 25.05 22.32 25.53 24.69 24.08

Summer 34.04 31.29 34.12 25.47 32.81 33.05

Fall 26.74 23.52 21.61 23.59 28.09 24.77

Annual 26.35 25.20 24.29 25.43 25.93 25.47

PM10 = particulate matter 10 microns or smaller; μg m-3 = micrograms

per cubic meter.

AIRS PM10 data indicate average spring concentrations

in these regions are about 22.51 to 27.5 μg m-3, although

actual mean concentrations in some of the more rural

locations in these regions are probably less. In central

Arkansas, average springtime concentrations are generally lower. The highest particulate matter concentrations

throughout the Assessment area are usually found during

the summer months (fig. 2.2). The interpolated AIRS

data suggest summertime PM10 concentrations in the

Assessment area often exceed 27.5 μg m-3 (especially in

urban areas). Particulate matter concentrations tend to

decrease during the fall months, although they are still

relatively high compared to the wintertime minimum

concentrations (fig. 2.2). The far northern sections of the

Assessment area experience the most dramatic decrease

in PM10 concentrations from the summer to fall seasons.

Based solely on AIRS network data, the annual mean

PM10 concentrations for the entire period over most of

the Assessment area range from 22.51 to 27.5 μg m-3

(fig. 2.3), well within the present NAAQS of 50 μg m-3.

The seasonality of PM10 is partly due to increased dust

production in the spring and summer months compared to

the winter months—especially during dry years—as well

as increased power production for air conditioning. Also,

emissions from automobiles and other internal combustion

engines increase during the summer. Another source of

particulate matter is the natural increase in atmospheric

moisture (water vapor) during the summer. Certain kinds

of particles, especially sulfates, are hygroscopic (meaning

they attract water), which increases their weight. The

AIRS data also appear to indicate the effects of agricultural tillage. For example, in March, higher PM10 values

show up in southwestern Missouri during tillage; in April

and May, these higher concentrations are in the agricultural areas in Arkansas (tillage occurs later because the

area retains wetness). (Monthly maps are available upon

request from the Forest Service in Arkansas—see

information inside the front cover of this report.)

Figure 2.4 illustrates the average PM10 mass concentrations in μg m-3 from 1992 to 1995 at sites in the

IMPROVE network. These are Class I wilderness

areas—wildernesses that are larger than 5,000 acres and

national parks larger than 6,000 acres in existence on or

before August 7, 1977. Class I areas are defined by the

Clean Air Act (CAA) amendments of 1977 as having

“special protection” from effects of air pollution because

17
Figure 2.2—Average PM10 values (μg m-3) during winter, spring, summer, and fall from 1991 through 1995.

18
Figure 2.3—Average PM10 values (μg m-3) for the 1991 through 1995 period.

of their “air-quality related values (AQRV’s),” i.e., water

quality, native vegetation, ecosystem integrity, and

visibility. Figure 2.4 indicates that the annual mean PM10

concentrations in the Class I areas within the Assessment

area are between 15 and 18 μg m-3

 (average concentration at Deer, AR—a nonurban area—is 17.4 μg m-3

).

Comparing these values with the annual mean PM10

concentration in urban areas (AIRS network data) within

the Assessment area (25.47 μg m-3

 from table 2.1)

suggests that the rural forested regions in the Assessment

area have about 30 to 40 percent less particulate matter

than the urban areas when averaged over an entire year.

Prescribed burning is currently a minor source of

particulate emissions in the Highlands area on a yearly

basis. The 1995 level of managed burning reported by the

EPA produced 538 short tons of emissions, accounting

for 1.3 percent of the national total PM10 emissions (U.S.

EPA 1996). These emissions include silvicultural and

agricultural burning. On shorter time scales, however,

prescribed burning can result in significant local emissions

of particulate matter. Two studies have been reported in

the Southern States where portable PM10 monitors were

set up adjacent to prescribed fires for 2 to 12 hours. The

Florida Department of Environmental Protection and the

19
Figure 2.4—Average PM10 mass concentration (μg m-3) for each site in the IMPROVE network for the 1992 through 1995 period (CIRA 1997).

Apalachicola National Forest conducted one study (FL

DEP 1993), and the Texas Forest Service and the

National Forests in Texas conducted the second (Hunt

and others 1994).

In 90 percent of the cases from these prescribed fire

studies, particulate-matter concentrations were less than

150 μg m-3 when measured more than 1 mi from the

control line of the prescribed fire. The standard was

usually maintained as close as 1 mi from the control line.

However, the likelihood of exceeding the 24-hour NAAQS

for particulate matter of 150 PM10 μg m-3 increased as fire

control lines were approached, with only a few cases

noted where the 24-hour standard was not exceeded at

the control line. Both studies showed that prior to burning,

the PM10 concentrations in the air mass were 15 to 30 μg

m-3

, well below both the annual and 24-hour standards.

The PM10 concentrations associated with prescribed

burns are dependent on weather, type and amount of fuel,

fuel moisture, and the intensity and duration of the burn.

These conditions are variable in time and space over the

area of the burn and downwind of the burn.

Implications and Opportunities

Any future trends in particulate matter concentrations

over the Assessment area will likely be influenced by the

new, stricter NAAQS for PM2.5. The smaller particle is

considered a health hazard since it can be breathed

deeply into the lungs. The smaller particle also is more

efficient at scattering light, so reductions for health

concerns will also improve visibility. Any improvements in

the exhaust systems of internal combustion engines, both

gasoline and diesel, could result in emission reductions of

20
particulate matter. Also, dust abatement on gravel roads

may become necessary in some areas. Until the PM2.5

monitoring system mandated by the new NAAQS

provides the data, the impact of the stricter standards on

the use of prescribed fire will remain conjecture. It will be

approximately 5 years before these data will be available.

How will the new PM2.5 regulations impact the use of

prescribed fire? Haddow (1990) found that approximately 70 percent of the particulate matter produced by

wildland fuels is within the PM2.5 size class. The proposed regulations call for the 24-hour standard to be less

than 65 μg m-3 and the annual average to be less than

15 μg m-3. Figure 2.5 illustrates that the 1992 to 1995

annual average concentration of fine mass particles

(diameter of 2.5 microns or smaller) was between 9 and

11 μg m-3 over the more rural areas of the Assessment

area based on interpolated IMPROVE network data.

These concentrations represent between 60 and 73

percent of the proposed standard annual average of

15 μg m-3 and 18 to 22 percent of the current annual

standard of 50 μg m-3. Thus, even with the implementation of the new PM2.5 standards, the more rural sections

of the Assessment area should still be in compliance if

current PM2.5 concentration averages continue to

characterize the region.

According to Forest Service records, most prescribed

burning occurs during March in the Assessment area.

Average PM10 concentrations in the Assessment area

during March (from 1991 through 1995)—when prescribed burning is common—ranged from a minimum

of 10 to 20 μg m-3 to a maximum of 30 to 40 μg m-3

(fig. 2.6) with a mean of 22.7 μg m-3 (based on

Figure 2.5—Average fine particle mass (PM2.5) concentrations (μg m-3) for each site in the IMPROVE network for the 1992 through 1995 period

(CIRA 1997).

21
interpolated AIRS data). If prescribed fire becomes a

more widely used land management tool in the Assessment area during the normal prescribed fire season, total

PM10 and PM2.5 emissions and concentrations in the

Figure 2.6—Average PM10 concentrations (μg m-3) during March for the 1991 through 1995 period.

atmosphere will likely increase during the springtime.

These increases would lead to a more dramatic degradation of air quality from the winter to spring seasons

than what is currently observed (fig. 2.2).

22
Chapter 3: Visibility

Question 3: How good is visibility in the

Assessment area; how does air pollution affect

visibility?

The Ozark-Ouachita Highlands are blessed with

scenic beauty. Visitors and residents want to be able to

enjoy the natural sights surrounding them. In fact, one of

the reasons most often given for living near or visiting the

Highlands is to enjoy the scenery. The clarity of air—or

visibility—affects the ability to see the pine and oak

covered mountains, rocky bluffs, and scenic streams of

the Highlands. In addition to being an important component of the quality of life in the region and a major factor

in recreation and tourism, visibility is protected by Federal

law. The Clean Air Act (CAA) amendments of 1977

declared “ . . . as a national goal the prevention of any

future, and the remedying of any existing, impairment of

visibility in mandatory Class I Federal areas . . .” when

such an impairment results from air pollution caused by

human activities. Within the Highlands, there are three

Class I areas: Caney Creek Wilderness on the Ouachita

National Forest, Upper Buffalo Wilderness on the OzarkSt. Francis National Forests, and Hercules Glade Wilderness on the Mark Twain National Forest. (Mingo

Wilderness, a Class I area managed by the U.S. Fish and

Wildlife Service, lies near the eastern border of the

Missouri Ozarks.)

Visibility impairment is most simply described as the

haze that obscures clarity, color, texture, and form.

Several components interact to determine visibility

conditions: (1) the object being viewed, (2) atmospheric

conditions influencing the sight path, (3) lighting conditions, and (4) the viewer. Visibility impairment is caused

by aerosols (solid or liquid particles dispersed in the air)

or gases in the atmosphere that scatter or absorb light.

Knowledge of the chemical and physical properties of the

aerosols responsible for visibility impairment can provide

insight into the causes of visibility problems. Scattering

efficiency for visible light is greatest for particles and

aerosols with diameters in the 0.1 to 1.0 micron range.

Fine particles with diameters of 2.5 microns or smaller

(PM2.5) contribute greatly to the scattering and absorption

of light, the sum of which is called light extinction. The

significant chemical components in fine aerosols are

sulfates, nitrates, organic carbon, soot (light-absorbing

carbon), and soil dust.

A wide variety of pollutants may result from daily

activities that include driving cars, generating electricity,

and producing consumer goods. Depending on location,

time of year, and atmospheric conditions, these pollutants

can significantly reduce visibility.

Once pollutants enter the atmosphere, their fate is

largely determined by meteorological conditions, especially winds, relative humidity, and solar radiation.

According to the U.S. Environmental Protection Agency

(EPA) (1995), most visibility impairment results from the

transport by winds of emitted aerosols, often over great

distances (typically hundreds of miles). Consequently,

visibility impairment is usually a regional problem rather

than a local one. Regional haze is caused by the combined effects of emissions from many sources distributed

over a large area, rather than from individual plumes

caused by a few sources at specific sites. Stable atmospheric conditions produce stagnation areas that inhibit

movement of pollutants, sometimes leading to severe

haze episodes (Holzworth and Fisher 1979).

Relative humidity is another weather parameter that

affects visibility. In a humid atmosphere, sulfate particles

combine with water and grow to sizes that make them

more efficient light scatterers. Thus, for a given level of

pollution, an atmosphere with higher relative humidity will

have more haze than one with lower relative humidity

(Sisler and others 1993).

23
Key Findings

 1. A definite seasonal pattern exists. The best visibility

occurs during the fall, and the worst visibility occurs

during the summer. Summer is also the time of

highest PM2.5 concentrations.

 2. The Upper Buffalo Wilderness on the Ozark-St.

Francis National Forests has the best visibility of the

three Class I wilderness areas on national forests

within the Assessment area.

 3. Sulfates are the primary aerosols responsible for

visibility impairment within the Assessment area.

 4. Visibility impairment in the form of regional haze

exists within the Assessment area, but the team

found that there are not enough data to identify

trends.

 5. Compliance with the Clean Air Act amendments of

1990 should reduce sulfates and improve visibility.

Data Sources and Methods of Analysis

Scientists and resource managers use several different

types of equipment to measure visibility conditions, each

of which differs in terms of cost, siting restrictions, ease

of operation, and usefulness of data. The most common

types of optical visibility-monitoring equipment include the

transmissometer and the nephelometer. These tools

directly measure the light-extinction coefficient and

scattering coefficient, respectively. Scenic monitoring

uses interpretation of 35-mm photographic slides. Aerosol

monitors measure the particles in the atmosphere that

affect visibility. Combinations of these types of equipment

are used to describe and define visibility.

Three different parameters are used to express

visibility: standard visual range (SVR), light extinction

coefficient (Bext), and the deciview (dv). The Forest

Service commonly uses the SVR derived from photographs to measure visibility, although this method has a

fair amount of uncertainty associated with it because of

the subjective nature of estimates from photographs. The

SVR, usually expressed in kilometers, is the greatest

distance at which an observer can barely see a black

object the size of a mountain viewed against the horizon

sky. The higher the SVR value, the better the visibility.

Another common measure of visibility is the Bext,

which represents the ability of the atmosphere to absorb

and scatter light. As the light-extinction coefficient

increases, visibility decreases. Direct relationships exist

between concentrations of particles in the air and their

contribution to the extinction coefficient. These relationships are often presented in an annual extinction-budget

plot showing the percentage of light extinction attributed

to each particle type. The extinction budget (discussed

further later) is an important method for assessing the

causes of visibility impairment.

Neither the SVR nor the Bext has a consistent direct

relationship to perceived visual changes caused by uniform

haze. Depending on baseline visibility conditions, a specific

change in the SVR or the Bext can result in a visual

change that is either obvious or imperceptible relative to

the total SVR. For example, an improvement of 10 miles

(mi) in SVR may be quite perceptible at an eastern

location with an annual average visibility of 40 mi, but a

10-mi change in SVR may not be perceptible at a western location with an annual average visibility of 150 mi.

The dv is another commonly used measure of visibility

(Pitchford and Malm 1994). The dv is designed to be

perceptually linear (similar to the decibel scale for sound),

meaning that a change of any given dv should appear to

have approximately the same magnitude of visual change

on any scene regardless of baseline visibility conditions.

(The dv is designed to describe changes in visibility

perception across locations with all types of baseline

conditions.) A change of 1 dv is about a 10 percent

change in the Bext—a small but perceptible change in

visibility. The dv value increases as haze increases, so it

is known as a haziness index.

The Forest Service has collected visibility data with

cameras at the three Class I areas within the Assessment

area, and Air Resource Specialists, Inc., of Fort Collins,

CO, has analyzed the findings. Fine mass data (PM2.5)

were collected as part of the IMPROVE network (Sisler

and others 1996). These data were then used to determine extinction budgets showing the percentage of each

different aerosol pollutant that causes visibility degradation measured in terms of the SVR.

24
Patterns and Trends

Table 3.1 gives the average annual camera-based

SVR in miles and kilometers as well as the haze factor in

dv’s for the Class I wilderness areas of the Assessment

area from 1988 to 1995. Estimates in this table indicate

that Upper Buffalo has the best visibility—perhaps

because of the remoteness of this wilderness and prevailing precipitation patterns. Caney Creek normally receives

more precipitation than Upper Buffalo and Hercules

Glade. The resultant greater number of foggy days and

higher relative humidity at Caney Creek would account

for the reduced visibility. Photographs of the visibility

conditions at the three Class I wildernesses can be seen

in figures 3.1 to 3.3.

Figures 3.4 and 3.5 represent the average “clear day,”

“median day,” and “hazy day” fine particle (PM2.5)

budgets at the Upper Buffalo IMPROVE site for the four

seasons from 1992 through 1995. The “fine mass budget”

pie charts show the actual percentages of the particle

components, based on weight. The “extinction budget”

pie charts show by what percentage each constituent

reduces visibility.

The top half of figure 3.4 shows that during the winter

months over this time period, PM2.5 concentrations at the

Upper Buffalo site averaged 3.5 milligrams per cubic

meter (μg m-3) on “clear days,” 6.7 μg m-3 on “median

days,” and 13.5 μg m-3 on “hazy days.” On average, the

percentage contribution of nitrate to the fine mass budget

(28 percent) equaled the sulfate contribution on “clear

days” during the winter months. Increases in sulfate

concentration percentages from 28 percent (“clear

days”) to 40 percent (“hazy days”) were the main cause

of visibility reductions during the winter months over this

period. The extinction budgets shown in the top half of

figure 3.4 indicate that sulfate contributed an average of

18 percent to the reduction of the SVR (average of 113

kilometers (km)) during “clear days” and 33 percent

during “hazy days” (SVR average of 38 km). The

reduction in the SVR due to Rayleigh scattering (scattering of light by gas molecules, e.g., nitrogen and oxygen

molecules) typically becomes less important relative to

the effects of increased sulfate concentrations in the

atmosphere when comparing “hazy days” to “clear

days.”

Table 3.1—Visibility in Class I wilderness areas represented by camera-based estimates of the

average annual standard visual range and haze, 1988 to 1995

 Clearesta Medianb Haziestc

Class I wilderness SVR Haze SVR Haze SVR Haze

mi km dv mi km dv mi km dv

Caney Creek, AR 64 102 13.4 29 47 21.2 13 21 29.2

Hercules Glades, MO 78 125 11.4 46 73 16.8 24 39 23.1

Upper Buffalo, AR 86 137 10.5 47 75 16.5 19 30 25.7

SVR = standard visual range; mi = miles; km = kilometers; dv = deciviews.

a “Clearest” represents the best visibility conditions. Of all the observations, only 10 percent were better and 90

percent were worse than these conditions.

b “Median” represents median visibility conditions. Of all the observations, 50 percent were better and 50 percent

were worse than the median conditions.

c “Haziest” represents the worst visibility conditions. Of all the observations, 90 percent were better and 10 percent

were worse than these conditions.

25
Figure 3.1—Visibility conditions at the Caney Creek Wilderness on the Ouachita National Forest in Arkansas (photos: USDA Forest Service, Ouac

Hot Springs, AR).

26
Figure 3.2—Visibility conditions at the Hercules Glades Wilderness on the Mark Twain National Forest in Missouri (photos: USDA Forest Service

27Forest, Rolla, MO).
Figure 3.3—Visibility conditions at the Upper Buffalo Wilderness on the Ozark-St. Francis National Forests in Arkansas (photos: USDA Forest Se

Francis National Forests, Russellville, AR).

28
Figure 3.4—The fine particle (PM2.5) budget at the Upper Buffalo Wilderness IMPROVE site for winter (top

half) and spring (lower half) from 1992 through 1995. The “fine mass budget” pie charts show the actual

percentages of particle components, based on weight. The “extinction budgets” show by what percentage

each constituent reduces the standard visual range (SVR).

 Clear Day Extinction Budget

 Clear Day Fine Mass Budget Median Day Fine Mass Budget Hazy Day Fine Mass Budget

 Median Day Extinction Budget Hazy Day Extinction Budget

Soot

5%

Soil

4%

Nitrate

27%

Sulfate

34%

Organics

30%

Organics

26%

Soil

2%

Soot

5%

Nitrate

27%

Sulfate

40%

Coarse & 

soil

10%

Rayleigh

29%

Nitrate

18%

Sulfate

18%

Organics

12% Soot

13%

Sulfate

25%

Nitrate

21%

Soot

15%

Rayleigh

18%

Organics

14%

Coarse & 

soil

7%

Sulfate

33%

Organics

14%

Coarse & 

soil

4%

Soot

16%

Rayleigh

10%

Nitrate

23%

6.7 μg/m3 13.5 μg/m3

SVR = 113km SVR = 69km SVR = 38km

Sulfate

28%

Soot

7%

Soil

6%

Organics

31%

Nitrate

28%

3.5 μg/m3

 Hazy Day Fine Mass Budget

 Hazy Day Extinction Budget

 Clear Day Fine Mass Budget

 Clear Day Extinction Budget Median Day Extinction Budget

 Median Day Fine Mass Budget

Soot

5% Sulfate

42%

Soil

9%

Organics

27%

Nitrate

17%

Organics

26%

Nitrate

15%

Sulfate

49%

Soot

4%

Soil

6%

Organics

26%

Nitrate

6%

Soil

Soot 6% 4%

Sulfate

58%

Sulfate

31%

Nitrate

12%

Organics

12%

Soot

13%

Coarse & 

soil

9%

Rayleigh

23%

Sulfate

40%

Nitrate

12%

Soot

14%

Coarse & 

soil

6%

Rayleigh

15%

Organics

13%

Sulfate

50%

Organics

14%

Nitrate

5%

Rayleigh

9%

Soot

16%

Coarse & 

soil

6%

4.9 μg/m3 8.4 μg/m3 15.4 μg/m3

SVR = 88km SVR = 58km SVR = 34km

29
Figure 3.5—The fine particle (PM2.5) budget at the Upper Buffalo Wilderness IMPROVE site for summer

(top half) and fall (lower half) from 1992 through 1995. The “fine mass budget” pie charts show the actual

percentages of particle components, based on weight. The “extinction budgets” show by what percentage

each constituent reduces the standard visual range (SVR).

 Clear Day Extinction Budget Median Day Extinction Budget Hazy Day Extinction Budget

 Clear Day Fine Mass Budget Median Day Fine Mass Budget Hazy Day Fine Mass Budget

Organics

33%

Soil

11%

Nitrate

5%

Sulfate

47%

Soot

4%

Sulfate

51%

Organics

27%

Soot

3%

Soil

15%

Nitrate

4%

Organics

21%

Soot

2%

Soil

23%

Nitrate

2%

Sulfate

52%

Organics

15%

Soot

13%

Rayleigh

19%

Coarse & 

soil

7%

Nitrate

5%

Sulfate

41%

Organics

14%

Nitrate

4%

Sulfate

51%

Coarse & 

soil

7%

Soot

14%

Rayleigh

10%

Nitrate

3%

Organics

11%

Soot

17%

Sulfate

56%

Rayleigh

Coarse & 5%

soil

8%

6.0 μg/m3 12.7 μg/m3 26.1 μg/m3

SVR = 73km SVR = 39km SVR = 21km

 Hazy Day Extinction Budget

 Clear Day Fine Mass Budget Median Day Fine Mass Budget Hazy Day Fine Mass Budget

 Clear Day Extinction Budget Median Day Extinction Budget

Sulfate

39%

Soil

8%

Nitrate

12%

Organics

34%

Soot

7%

Organics

32%

Soot

5%

Soil

5%

Sulfate

40%

Nitrate

18%

Organics

32%

Soot

4%

Soil

6%

Nitrate

3%

Sulfate

55%

Sulfate

24%

Nitrate

7%

Organics

13%

Soot

14%

Rayleigh

32%

Coarse &

soil

10%

Sulfate

32%

Nitrate

14% Organics

16%

Coarse & 

soil

6%

Soot

16%

Rayleigh

16%

Nitrate

3%

Organics

18%

Soot

17%

Sulfate

48%

Coarse & 

soil

5%

Rayleigh

9%

3.2 μg/m3 7.7 μg/m3 16.0 μg/m3

SVR = 122km SVR = 63km SVR = 34km

30
As average PM2.5 concentrations increase from the

winter to spring months, visibility typically decreases. The

fine mass budgets shown in the lower half of figure 3.4

indicate that average PM2.5 concentrations at the Upper

Buffalo Wilderness site ranged from 4.9 μg m-3 to 15.4

μg m-3 for “clear” and “hazy” days, respectively, from

1992 through 1995. As in the winter months, increases in

sulfate concentrations were responsible for the reductions

in the SVR (88 km during “clear days,” 58 km during

“median days,” and 34 km during “hazy days”). Sulfate

accounted for 42 percent of the PM2.5 mass budget on

“clear days” and 58 percent of the PM2.5 mass budget on

“hazy days.” Nitrate, on the other hand, decreased from

17 percent of the PM2.5 mass budget on “clear days” to 6

percent on “hazy days.”

The poorest visibility at the Upper Buffalo Wilderness

site typically occurs during the summer months, as shown

in the top half of figure 3.5. The average SVR on “clear

days” from 1992 through 1995 was 73 km and decreased

to 21 km during “hazy days.” Changes in the PM2.5 mass

budget associated with “clear” and “hazy” days included

a slight increase in the sulfate percentage (47 to 52

percent), an increase in the soil percentage (11 to 23

percent), and a decrease in the organics percentage (33

to 21 percent). These constituent changes in the budget

resulted in average PM2.5 concentrations of 6.0 μg m-3 on

“clear days” and 26.1 μg m-3 on “hazy days.” Although

the contribution of soil particles to the increase in PM2.5

concentrations on “hazy days” was significant, the

percentage contribution of soil particles to the SVR

extinction budget did not change substantially from “clear

days” to “hazy days” at the site from 1992 through 1995

(7 to 8 percent). In contrast to the soil particles, the slight

increase in the sulfate percentage of the fine mass budget

(47 to 52 percent) from “clear” to “hazy” days resulted in

a 41 to 56 percent change in the contribution that sulfate

made to the SVR extinction budget.

The lower half of figure 3.5 shows the fall fine mass

budgets and extinction budgets for the Upper Buffalo

Wilderness site from 1992 through 1995. The average

PM2.5 concentration during fall “clear days” over this

period was similar to the average concentrations observed

during winter “clear days” (3.2 μg m-3 vs. 3.5 μg m-3), and

average concentrations on fall “median days” (7.7 μg m-3)

and “hazy days” (16 μg m-3) were similar to the observed

springtime concentrations (8.4 μg m-3 and 15.4 μg m-3,

respectively). The percentages of the all the fine mass

particle components that make up the fall fine mass

budget did not change significantly from “clear days” to

“hazy days” over this period, except for the sulfate and

nitrate percentages. The nitrate percentage decreased

from 12 percent of the total fine mass budget to 3 percent

on “clear days” vs. “hazy days,” while the sulfate percentage increased from 39 to 55 percent. This increase in

the contribution of sulfate to the total fine mass budget

from “clear days” to “hazy days” resulted in a 24 to 48

percent change in the contribution of sulfate to the

reduction in the average SVR (122 km on “clear days”

and 34 km on “hazy days”).

Implications and Opportunities

To address the “remedying of any existing impairment” portion of the 1977 CAA amendments, the EPA

published a “Notice of Proposed [Regional Haze Regulations]” in the July 31, 1997, Federal Register, Vol. 62, No.

147. These regulations proposed (1) that a “reasonable

progress target” be set to reduce visibility impairment due

to regional haze and (2) that visibility on the best 20

percent of days not be degraded more than 0.1 dv. In

addition, on the 20 percent of most impaired days,

visibility must be improved at either the rate of 1 dv per

10 years or 1 dv per 15 years (U.S. EPA 1997).

Title IV (Acid Deposition Control) of the CAA

amendments of 1990 specifies that sulfur dioxide (SO2)

emissions will be reduced by 10 million tons and nitrogen

oxide (NOx) emissions by 2 million tons from 1980

emission levels (U.S. EPA 1996). When these reductions

are fully implemented by the year 2000, visibility should

be improved. As figures 3.4 and 3.5 show, sulfates are

the major factor in visibility reduction, especially during

the summer when visibility is poorest. Newly proposed

PM2.5 and ozone regulations, while targeted to improve

human health, should have the added benefit of improving

visibility through anticipated reductions in atmospheric

sulfate concentrations.

31

Chapter 4: Ground-Level Ozone

Question 4: What impact does ground-level ozone

have on forests?

Ozone, a naturally occurring chemical in the upper

atmosphere and at ground level, is potentially the most

significant pollutant affecting forests in North America

(Barnard and others 1991). Hourly average concentration

levels of ozone near the ground are usually less than

0.04 parts per million (ppm) in undeveloped areas of the

world with little anthropogenic pollution (Lefohn and

others 1990). Hourly average concentrations above

0.05 ppm are frequently recorded at monitoring stations

in the Eastern United States (Lefohn and Jones 1986).

Ozone is formed by the reaction of nitrogen oxides

with volatile organic compounds on hot sunny days (NRC

1991). Chameides and Cowling (1995) suggest that

ground-level ozone formation is likely to be limited in the

Southern United States by the amount of nitrogen oxides

in the atmosphere. High amounts of nitrogen oxides are

emitted in counties with large cities within and outside of

the Ozark-Ouachita Highlands (fig. 1.6). The release of

nitrogen oxides, in combination with natural and anthropogenic sources of volatile organic compounds, has the

potential to result in ozone concentrations that can

damage or injure forest vegetation.

Ozone enters a leaf through openings called stomata.

Several factors affect the uptake of ozone by a plant, but

primarily, a plant’s genetic makeup influences its response. Plant species vary in sensitivity to ozone; even

within a species, variations can occur. For instance,

during field surveys, researchers have found one plant

with severe ozone symptoms growing adjacent to an

individual of the same species with no ozone symptoms.

In an example outside the Assessment area, a portion of

the most ozone-sensitive eastern white pines appears to

have been removed from the population due to ozone

exposures (U.S. EPA 1986). Other factors, such as light,

temperature, relative humidity, soil nutrients, and soil

moisture also influence the uptake of ozone. The U.S.

Environmental Protection Agency (EPA) (1986) has

presented evidence that indicates that drought stress may

reduce the impact of ozone on plants (because the closed

stomata prevent ozone entry into the leaves). The

vegetation could suffer growth losses, of course, due to

the lack of moisture.

Monitored concentrations in the atmosphere are

recorded as hourly average values in parts per million in

the data base. These concentrations represent “exposure,” defined as the amount of ozone that contacts the

outside of the leaf. As used here, the term “dose” is the

amount of ozone that actually enters the leaf. Estimates

of dose are difficult to make without detailed modeling of

the relationship between exposure and dose. Exposure is

used as a surrogate for dose; researchers and policymakers use exposures to assess the possible effects of

ozone on vegetation.

Ozone inside the leaf is either destroyed by biochemical processes or the ozone kills the cells found just below

the upper leaf surface and between the veins of the leaf.

Cells that are killed lose their green pigmentation and

usually turn reddish or black in a process called stippling.

The symptoms of ozone injury cannot be observed until a

large number of cells are dead. People who conduct field

surveys have a checklist of characteristics to identify

plants with ozone symptoms. The visible symptoms

(stippling) indicate that the plant has had a physiological

response to the ozone dose, resulting in injury. Damage

results when the ozone dose was large enough to reduce

the intended human use or the value of the plant or

ecosystem (Tingey and others 1991).

Several surveys of ozone symptoms have been

conducted within and near the Ozark-Ouachita Highlands. Between 1991 and 1995, the incidence of ozone

symptoms at the Caney Creek Wilderness and Upper

Buffalo Wilderness was low. The percentage of blackberry plants with ozone symptoms was highest in 1991,

with 14 percent showing symptoms at Caney Creek and

24 percent at Upper Buffalo. In 1995, there were no

ozone symptoms on any of the blackberry plants (Kertz

and others 1995).

33
This chapter will focus on the possible extent and

frequency of damage from ozone to trees in forest

settings. The Atmospheric Team uses the term “damage”

to mean that ozone exposure has likely been sufficient to

cause reduced tree growth (Dowsett and others 1992).

Key Findings

 1. Using available ozone monitoring data, it appears

that ground-level ozone had a minimal impact on

forest tree growth between 1990 and 1995.

 2. There are few ozone monitors within the Assessment area. Consequently, there could be localized

areas where growth losses occurred to trees that are

highly sensitive to ozone.

 3. Future ozone exposure may be less as Federal,

State, and local air pollution control agencies implement strategies to reduce pollution, especially

nitrogen oxide (which will, in turn, reduce the

formation of ground-level ozone).

Data Sources and Methods of Analysis

Ozone formation is strongly influenced by meteorological conditions and the amounts of ozone precursors

present in the atmosphere (NRC 1991). For example,

1988 was a hot and dry year during which large sections

of the Eastern United States had high ozone exposures.

In comparison, 1989 was cool and moist, and the ozone

exposures were low (Lefohn and others 1997). The

range in yearly ozone exposures required that more than

1 year be examined to describe the current condition for

the study area. Therefore the team selected 1990 through

1995 as the period for analysis.

For this Assessment, data in the EPA’s Aerometric

Information Retrieval System (AIRS) data base and from

the National Dry Deposition Network program were

used. The monitoring sites included those in the States of

Arkansas, Illinois, Kansas, Kentucky, Louisiana, Mississippi, Missouri, Oklahoma, Tennessee, and Texas. The

monitoring sites selected from these States had 75

percent or greater data capture for each particular year.

Numerous statistics were calculated using the 24-hour

period over the growing season, defined as the months of

April through October. Initially, many researchers used

long-term average concentrations to describe ozone

exposures when assessing vegetation effects (Heck and

others 1982). The EPA (1989) examined the peerreviewed literature and concluded that long-term averages are not adequate indicators for relating ozone

exposure to plant response. Furthermore, the EPA (1986

and 1992) concluded that greater effects to vegetation

occur when the exposures include short-term, high

concentrations rather than long-term, low concentrations.

The team chose to use the mathematical index W126, as

described by Lefohn and Runeckles (1987), to evaluate

whether trees within the Assessment area may be

suffering from growth losses (damage). All of the

available hourly ozone estimates were used to calculate

the seasonal W126. Results obtained from the W126

calculations (or any other cumulative exposure index) can

have high values, but concentrations equal to or above

0.10 ppm may not occur. Consequently, the Atmospheric

Team followed the recommendation of Lefohn and Foley

(1992) and included the number of hours greater than or

equal to 0.10 ppm in the analysis.

Numerous studies have been conducted to examine

the relationship between ozone exposures and tree

response. Lefohn and others (1997) reviewed the

controlled exposure studies and recommended a technique that divided the landscape into four categories,

based on the W126 statistic and the number of hours the

ozone concentration was greater than or equal to 0.10

ppm. To accomplish this task, it was necessary to

extrapolate the ozone monitoring data beyond the monitoring sites. Extrapolations of the W126 values across the

Assessment area were accomplished using a statistical

technique described in a report by Lefohn and others

(1995). The W126 estimates were made for the center of

grid cells of 0.5 degrees latitude by 0.5 degrees longitude

across much of the region (fig. 4.1). Estimates were not

available for the northern portion of the Assessment

area. The add-on software to ArcView® called Spatial

Analyst® was used to interpolate the W126 values

between the points and for the northern portion of the

Assessment area. A spline technique was used for the

interpolations to a 6.2-mile (mi) by 6.2-mi grid. Twelve

points were used for the spline interpolation, but estimates only were made for the area within the Assessment boundary.

34
The team then categorized the interpolated W126

values (grids) as follows:

• Minimal—Ozone exposure is so low that little or no

growth loss is predicted to occur in any species.

Ozone symptoms may have been present even though

the exposures were low.

• Level 1—Ozone exposure is high enough to cause

growth reductions in species considered highly sensitive to ozone, such as black cherry.

• Level 2—Ozone exposure is high enough to cause

growth reductions in species with moderate sensitivity

to ozone, such as tulip poplar, in addition to those

species that are included in Level 1.

• Level 3—Ozone exposure at this level is high enough

to cause growth loss in many species—even those

normally considered resistant to ozone exposures such

as red oak—in addition to those species in Level 1 and

Level 2.

Figure 4.1—Locations of W126 estimates.

The Atmospheric Team used a subjective analysis to

classify the amount of area within a tree response

category (table 4.1). The criteria listed in table 4.1

required consistency with both the W126 and the number

of hours with concentrations greater than or equal to 0.10

ppm. Because the number of hours greater than or equal

to 0.10 ppm was not statistically extrapolated across the

study area, it was necessary to finish the classification by

visually examining the monitored values for the number of

hours greater than or equal to 0.10 ppm. Grids that had

one or more ozone monitors present were classified using

the results from the ozone monitors, but cells that did not

have an ozone monitor were classified by examining the

pattern from ozone monitors surrounding the cell to be

classified. Grids that met the W126 criteria for a particular category and not the number of hours greater than or

equal to 0.10 ppm for the same category were assigned

the category that matched the number of occurrences

greater than or equal to 0.10 ppm.

The experimental studies summarized by Lefohn and

others (1997) used plants grown under optimum conditions of adequate soil moisture and nutrients. Before the

team applied the criteria in table 4.1, they gathered

Table 4.1—Forest tree response categories based on W126

values and exposure hours required for various levels of forest

tree damage

W126 Exposure

Forest tree response category values hours

 ppm >0.10

 hours ppm

Minimal > 0 and > 0

Level 1 (only highly sensitive

species affected, e.g., black

cherry) > 5.9 and > 6

Level 2 (moderately sensitive

species affected, e.g., yellowpoplar) > 23.8 and > 51

Level 3 (resistant species

affected, e.g., red oak) > 66.6 and 135

W126 = ozone exposure index; ppm = parts per million; exposure hours

= number of hours the hourly average ozone concentrations is > 0.10

ppm.

Source: Lefohn and others (1997).

35
Figure 4.2—Palmer Hydrologic Index results for April through July

1990.

information on whether the environmental conditions

were favorable for ozone to enter the leaf. Of the

environmental conditions that need to be considered, soil

moisture has been identified as an important variable that

influences the uptake of ozone by a plant (U.S. EPA

1986). Showman (1991) and Jackson and others (1992)

have observed fewer ozone symptoms on sensitive

species during periods of drought than in seasons with

adequate rainfall, even though ozone exposures were

higher during the drought. Thus, the Atmospheric Team

chose the Palmer Hydrologic Index (Palmer 1965) as a

surrogate measurement of soil moisture. The index is a

monthly value computed for specific climate divisions

(regions within a State with similar climatic and hydrologic characteristics). Also, the index indicates the

severity of a wet or dry spell. A Palmer Hydrologic

Index of less than -2 (minus two) was considered to

indicate low soil-moisture conditions, that is, a drought

(Briffa and others 1994). The team hypothesized that

ozone exposure would not damage the plants during a

drought. Values above -2 indicated adequate soil moisture, when ozone could potentially penetrate leaves and

damage plants. The average Palmer Hydrologic Index

for April through July was calculated for each climate

division.

Combining the results from the Palmer Hydrologic

Index (fig. 4.2) and ozone exposure (fig. 4.3) provides an

indication that (1) soil moisture was sufficient for plants

to keep their stomata open enough for ozone to penetrate

the leaves and (2) ozone exposures were severe enough

to cause growth losses. Areas classified as experiencing

a drought (dry) were assigned the growth response

category of “minimal” effect from ozone; otherwise the

sensitivity category value remained the same after

applying the criteria in table 4.1.

Patterns and Trends

The average April to July Palmer Hydrologic Index

values from 1990 to 1995 indicate that normal to wet

conditions were prevalent over the Assessment area (fig.

4.2 shows the average April to July Palmer Hydrologic

Index values for 1990 only). Consequently, the team

hypothesized that the stomata were open at times during

the growing season, and ozone could have penetrated into

the leaves. The results from statistical estimates placed

almost all of the Assessment area into W126 values of

5.9 to 23.8 ppm hours (Level 1) for the years 1991

through 1993 (figs. 4.4, 4.5, and 4.6). Whereas, in 1990,

1994, and 1995 (figs. 4.3, 4.7, and 4.8), most of the

Assessment area had W126 values between 23.8 and

66.5 ppm hours (Level 2), no areas were classified as

having less than 5.9 ppm hours (minimal). Using only the

W126 values would indicate that Level 1 and/or Level 2

tree species may have had growth losses for the 6 years.

However, hourly average ozone concentrations were

seldom greater than or equal to 0.10 between 1990 and

1995.

Most of the ozone monitors within and near the

Assessment area indicated less than 6 hours during the

growing season when the average hourly concentrations

were greater than or equal to 0.10 ppm (figs. 4.3 to 4.8).

Therefore, by following the recommendation of Lefohn

and Foley (1992) and the procedure of Lefohn and others

(1997), the team decided it is likely that ground-level

ozone exposures had a minimal impact to forest trees and

that no growth losses resulted from ozone exposures. The

lack of ozone symptoms reported by Kertz and others

36
Figure 4.3—Ozone estimates for 1990 showing sites classified according to tree response categories (see table 4.1)

and W126 values.

(1995) provides additional evidence that ozone likely had

a minimal impact on forest tree growth.

The team believes caution should be used in interpreting these findings since there were less than 10 ozone

monitors collecting data within the Assessment area

between 1990 and 1995. There could be localized areas

within the Ozark-Ouachita Highlands with more than 5

hours of hourly averaged ozone concentrations greater

than or equal to 0.10 ppm. If such events did occur, the

potential for growth losses to the most sensitive species

(Level 1) may have been realized in localized areas,

possibly near St. Louis, MO, and Tulsa, OK. Conversely,

it is important to note that the W126 exposure index

values for this study were accumulated for the growing

season—April through October. Most of the experimental

data used in this Assessment were collected for 3 to 4

months (Lefohn and others 1997). Thus, using a 7-month

period to accumulate the W126 value may overestimate

the likelihood of predicted growth losses.

37
Figure 4.4—Ozone estimates for 1991 showing sites classified according to tree response categories (see table 4.1) and

W126 values.

Implications and Opportunities

Current emission strategies implemented by local,

State, and Federal air pollution agencies may reduce

ozone exposures in rural forests in the future. For example, there could be a lowering of ozone exposures in

the Ozark-Ouachita Highlands as ozone nonattainment

areas outside the study area (such as Dallas) implement

control strategies that bring the urban area back into

compliance with Federal law. Furthermore, the revision of

the National Ambient Air Quality Secondary (NAAQS)

standard to an 8-hour average of .085 ppm is likely to

benefit forests. To lessen the risk of tree growth loss, air

pollution control agencies will have to implement emission

reduction strategies in areas that violate the new primary

standard and decrease the number of hours of average

ozone concentrations greater than or equal to 0.10 ppm.

Ozone exposures in the study area result from the

chemical reaction of nitrogen oxides and volatile organic

compounds. The volatile organic compounds are known

to be so abundant (figs. 1.8 and 1.10) that it appears

nitrogen oxides may be the limiting factor in ozone

38
Figure 4.5—Ozone estimates for 1992 showing sites classified according to tree response categories (see table 4.1) and

W126 values.

formation (Chameides and Cowling 1995). Implementation of and compliance with the Clean Air Act Amendments of 1990 should reduce nitrogen oxide emissions

nationally by 2 million tons and may reduce ozone exposures further within the Assessment area. Other strategies that reduce nitrogen oxides may also result in lower

ozone exposures for the Ozark-Ouachita Highlands area.

Recently, the EPA notified State and local air pollution

control agencies in 22 Eastern States that further reductions in nitrogen oxides are needed for some urban areas

to satisfy the NAAQS for ground-level ozone. Of the

States included in this analysis, the EPA requested that

Illinois, Kentucky, Missouri, and Tennessee reduce

between 35 and 43 percent of emissions of nitrogen

oxides. Implementation of nitrogen oxide reductions of

this magnitude likely will reduce the amount of groundlevel ozone in the Assessment area.

39
Figure 4.6—Ozone estimates for 1993 showing sites classified according to tree response categories (see table 4.1)

and W126 values.

40
Figure 4.7—Ozone estimates for 1994 showing sites classified according to tree response categories (see table 4.1) and

W126 values.

41
Figure 4.8—Ozone estimates for 1995 showing sites classified according to tree response categories (see table 4.1) and

W126 values.

42
Chapter 5: Acid Deposition

Question 5: To what extent are resources in the

Highlands being affected by acid deposition?

Sulfur dioxide (SO2) and nitrogen oxides (NOx)

present in the atmosphere can react with water, oxygen,

and oxidants (such as ozone) to form mild solutions of

sulfuric acid and nitric acid (U.S. EPA 1998a). These

acidic solutions are often found in rainwater, snow, fog,

and other forms of precipitation. When precipitation

containing these acidic compounds falls to the earth, the

acidic compounds are deposited on the earth’s surface

(acid deposition or wet deposition), and the precipitation

carrying the acidic compounds is referred to as “acid

rain.” Scientists have discovered that air pollution from

the burning of fossil fuels is the major cause of acid rain.

Acidic compounds in the atmosphere and the deposition

of acidic compounds on the earth’s surface can disrupt

forest and aquatic ecosystem functions, impair visibility,

and create human health problems.

Chapter 1 provides a regional perspective on sources

and amounts of nitrogen (fig. 1.6) and sulfur compounds

(fig. 1.12) emitted into the atmosphere. It is worth

restating that 28 percent of nitrogen oxide (NOx) emissions in the Assessment area and nearby are from

industrial fuel combustion sources, 22 percent are from

vehicles (internal combustion engines), and 20 percent

are from electric utilities (fig. 1.5). As figure 1.11 shows,

sulfur dioxide (SO2) is primarily produced from electric

power generation (72 percent), with all other industry

sources producing 15 percent (U.S. EPA 1996).

Since the mid- to late 1970’s, emissions of SO2 and

NOx have decreased and leveled off, respectively, in

contrast to the upward trends in emissions of these gases

experienced from the early 1950’s to the mid-1970’s (fig.

1.17). Vehicle miles traveled have doubled since 1970, but

exhaust emissions have only slightly increased (U.S. EPA

1996) because of better fuel economy and more efficient

engines. Since 1996, this same strategy has been applied

to selected nonroad categories (e.g., small engines such

as lawn mowers and outboard motors). The Environmental Protection Agency (EPA) (1996) expects significant

emission reductions after the year 2000. Sulfur emissions

have also been reduced per unit of production. From 1970

to 1995, SO2 emissions from electric utilities decreased

about 31 percent (U.S. EPA 1996). Implementation of

the Clean Air Act amendments of 1990 should reduce

SO2 emissions by 10 million tons and NOx by 2 million

tons by the year 2000 (U.S. EPA 1996).

The impact of acid deposition on forest and aquatic

ecosystems has been the subject of much research and

debate. The 1980 to 1990 National Acid Precipitation

Assessment Program (NAPAP) provided indepth

research on these impacts (see Baker and others

1991). As NAPAP and others have reported, the

potential harmful effects of acidic deposition include:

(1) leaching of nutrients from plant foliage and the soil by

hydrogen, sulfate, or nitrate ions (Lee and Webber 1982);

(2) alteration of beneficial micro-organisms in the forest

floor and upper soil horizons, including damage to symbiotic nitrogen-fixing organisms (Francis 1982); (3) acidinduced mobility and toxicity of aluminum in the soil

solution leading to toxic levels of aluminum in streamflow

and water bodies (Steiner and others 1980; Ulrich and

others 1980; Johnson and others 1981); (4) increased

susceptibility of trees and plants to environmental

stresses, including drought (Johnson and others 1981; Lee

and Webber 1982); (5) delayed frost hardiness leading to

vegetation damage (DeHayes 1992); and (6) acidification

of aquatic ecosystems (Herlihy and others 1996). Impacts may thus affect entire ecosystems including

vegetation, soil, and the receiving watercourse.

43
Key Findings

 1. Atmospheric wet acid loadings are less than the

loadings observed in the Southern Appalachian

region. Nitrate and sulfate loadings are expected to

decrease in the future based on expected decreases

in emissions of sulfur dioxide and nitrogen oxides

(see Chapter 1).

 2. Most surface waters within the Assessment area do

not appear to be adversely impacted by the previous

and present rate of acid deposition.

 3. The low acid neutralizing capacity headwater areas

of the Ouachita Mountains make them most at risk

while the limestone areas of the Ozark Plateau are

least at risk.

Data Sources and Methods of Analysis

Because nitrate and sulfate depositions over a region

are related to precipitation patterns, it was necessary to

assess the spatial variations in precipitation over the

Assessment area at a sufficient resolution to account for

major elevation changes. The deposition model of Lynch

and others (1997), used in this Assessment for generating

nitrate and sulfate deposition maps, incorporates precipitation patterns that account for the changes in topographic

variations observed in the Assessment area. The coordinates, elevations, and monthly precipitation records from

the National Oceanic and Atmospheric Administration’s

(NOAA) precipitation monitoring sites in the States within

and adjacent to the Assessment area constitute the

precipitation-volume data set used to develop the deposition model. Lynch and others (1997) obtained precipitation

concentration data for sulfates and nitrates from weekly

rainfall samples collected at monitoring sites within and

adjacent to the Assessment area.

The deposition model uses a statistical method that

includes: (1) the precipitation observed at monitoring sites,

(2) elevation, and (3) a set of variables representing both

slope and aspect. The derived regression equations from

each 0.1-degree block are then applied to corresponding

digital elevation data to produce a grid of precipitation

estimates. The current model compares the predicted and

observed quarterly and annual precipitation volumes at

approximately 1,500 validation sites scattered over the

Assessment area. The average annual estimation error is

consistently near 3.0 inches (in.) for each year from 1991

through 1993 (Lynch and others 1997).

The NOAA data set comprises the only precipitation

values that cover the Assessment area at a site density

sufficient for deposition modeling. A major limitation on

the accuracy of the model’s precipitation calculations is

the imprecise coordinates of the NOAA precipitation

sites. NOAA coordinates for the location of a rain gauge

are reported at a resolution no finer than 1 minute of a

degree of latitude or longitude. This level of uncertainty

impedes the modeling of localized—but important—

geographic influences on precipitation. The modifications

that Lynch and others (1997) made to the model overcame the imprecision of the coordinates and estimated

more precise coordinates of each NOAA precipitation

monitoring station.

Because precipitation varies year to year, the amount

of deposition also varies. Deposition rates, therefore, are

modeled values, not measured values; they should be

considered relative rather than absolute values and used

with caution. Actual values determined at the site of

concern will be needed to document any influence on the

local ecosystem.

Patterns and Trends

Figures 5.1 and 5.2 indicate that the southwestern part

of the Assessment area has the highest sulfate and

nitrate deposition. Figure 5.2 shows that sulfate deposition

exceeds 20 pounds per acre (lbs/ac) in the high elevation

and high precipitation areas of the southwest portion of

the Ouachita range. An EPA finding suggests this pattern

could be a result of sulfur dioxide emissions from coalburning industries in Louisiana and Texas (OK WRB

1990). The other area receiving high sulfates but not high

nitrate deposition is the northeastern portion of the

Assessment area, including the Potosi-Fredericktown

District and the eastern half of the Salem District of the

Mark Twain National Forest.

Figure 5.3 shows the average modeled sulfate and

nitrate deposition within the Assessment area from 1983

to 1995. Compared to the Southern Appalachians, the

annual sulfate deposition in the Ozark-Ouachita Highlands is moderate—15 lbs/ac in the Highlands compared

to 20 to 25 lbs/ac for the Southern Appalachians. The

44
average annual nitrate deposition in the Assessment area

is 10 lbs/ac compared to 15 to 20 lbs/ac in the Southern

Appalachians (SAMAB 1996).

During the NAPAP era (1980 to 1990), there was an

effort to look for evidence that acidic deposition was

affecting surface waters within the Assessment area.

Between 1984 and 1986, the EPA conducted one of the

largest limnological reconnaissance projects ever undertaken—the National Surface Water Survey (NSWS)—

the results of which are available from the EPA. The

NAPAP and NSWS used the acid neutralizing capacity

(ANC) to rate streams and lakes. This survey found that

a relatively high percentage of streams with ANC equal

to or greater than 200 existed in the interior Southeast

region, which includes the Assessment area, while no

lakes or streams in this region had ANC values less than

or equal to zero (Baker and others 1991). However, this

same report indicated that the Ozark-Ouachita area had

19 percent of the streams with ANC greater than zero

and less than or equal to 50 (Baker and others 1991).

These poorly buffered systems can be considered at risk

for acidification.

Within the Assessment area, the headwater sections

of the Ouachita Mountains in Arkansas contain very little

limestone bedrock, while the Ozark Plateau area in

Missouri contains large areas of limestone and karst

topography (irregular limestone with sinks, underground

streams, and caverns). These geological differences

suggest that the Ouachita Mountain headwaters are likely

to be more at risk for acidification than streams in the

Ozark Plateau.

Figure 5.1—The period average for nitrate deposition (lbs/ac) in the Assessment area from 1983 through

1995 (Lynch and others 1997).

45
8

10

12

14

16

18

20

83 84 85 86 87 88 89 90 91 92 93 94 95

Year

lbs/acre

Sulfates

Nitrates

46

Figure 5.2—The period average for sulfate deposition (lbs/ac) in the Assessment area from

1983 through 1995 (Lynch and others 1997).

Figure 5.3—Variation in modeled mean sulfate and nitrate deposition within the Assessment area from 1983

through 1995 (Lynch and others 1997).
Another test of acidification is the amount of nitrates

and sulfates moving through a system. The consensus of

NAPAP is that the streams in the Ozark-Ouachita

Highlands are at equilibrium for sulfates and that 99

percent of the nitrates are being retained. Sulfate enrichment is occurring in some waters in the Ozark-Ouachita

Highlands (Baker and others 1991).

Nix and Thornton (1987) confirmed the overall

NAPAP and NSWS assessment that the higher elevation

streams in the Ouachitas are vulnerable to acid deposition. Their major findings were that: (1) during storm

events, ionic aluminum is released into surface waters;

(2) 90 percent of nitrogen is being retained; (3) sulfates

are near equilibrium; and (4) base cations are being

exported at twice the rate of precipitation input. They

concluded that headwater streams that have low ANC

are potentially at risk with respect to acidic deposition.

Precipitation patterns strongly influence acid deposition. As explained in the “Data Sources and Methods”

section of this chapter, elevation changes can impact

precipitation amounts. The Ouachita Mountains in the

southwestern part of the Assessment area can produce

orographic lifting (the lifting of air when it flows over

higher elevation terrain) of moist air moving northward

from the Gulf of Mexico across the Gulf Coastal Plain.

The Atmospheric Team expects this area will receive the

greatest amount of deposition. Kress and others (1988)

found that an average 400-foot increase in elevation from

the Gulf Coastal Plain to the Ouachita Mountains in

southeastern Oklahoma resulted in a 12-percent increase

in average annual precipitation (60.2 to 67.6 in. in 1984 to

1985). They also found the average pH decreased with

increased elevation of the station (4.67 at the Coastal

Plain to 4.53 at the Ouachita Mountains). Total nitrate

deposition increased from 3.2 to 3.5 lbs/ac, and total

sulfate deposition increased from 28.3 to 32.7 lbs/ac as

elevation increased.

Implications and Opportunities

Acid deposition can pose a threat to forest and aquatic

ecosystems—especially on poorly buffered, higher

elevation watersheds. Acid deposition patterns in the

Assessment area as a result of sulfate and nitrate in the

atmosphere are affected by the emissions of SO2 and

NOx and the patterns of precipitation over the region. As

noted in Chapter 1, emissions of SO2 from electrical

utilities in the Assessment area are expected to decrease

as the Clean Air Act Amendments of 1990 are fully

implemented. Furthermore, the emissions of nitrogen

oxides from fuel combustion at industrial sources in the

Assessment area will likely be reduced in response to

current efforts by the EPA. Future reductions in the

emissions of sulfur dioxide and nitrogen oxides should

lead to reduced atmospheric sulfate and nitrate concentrations, thereby reducing the potential for acid deposition

episodes. However, future changes in precipitation

patterns as a result of changes in regional climate and

climate variability may also impact the amount of acid

deposition over the Assessment area.

A comprehensive assessment of how acid deposition

patterns might change over the Assessment area as a

result of changing emissions of SO2 and NOx and a

changing climate (including precipitation patterns) will

require the use of coupled, high-resolution atmospheric

mesoscale and chemistry models that can generate

plausible scenarios of acid deposition, e.g., the U.S. EPA

(1998b) Regional Acid Deposition Model. The development of these future scenarios of acid deposition must

take into account the complex atmospheric dynamics

associated with the emission, transport, and diffusion of

the chemical species involved in the formation of acid

rain; the dynamics of cloud formation and precipitation

occurrence over the region; surface-atmosphere interactions that influence the hydrologic cycle in the region; and

the overriding chemical reactions that lead to the formation of sulfuric and nitric acid in the atmosphere. The acid

deposition scenarios generated by coupled atmospheric

mesoscale and chemistry models can provide vital

information to natural resource managers as they develop

management strategies for specific watersheds in the

Assessment area known to be sensitive to potential acid

deposition events.

47

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51
Glossary of Terms

acid deposition: rain, snow, or particulate matter containing

high concentrations of sulfuric acid, nitric acid, or hydrochloric acid, usually produced by atmospheric transformation of the by-products of fossil fuel combustion. Precipitation with a pH lower than 5.0 is generally considered to be

acidic.

anthropogenic gases: chemical gases released into the

atmosphere from human activities such as electrical generation, vehicle operation, and manufacturing.

aerosol: a suspension of colloidal particles in a gas.

Class I areas: those areas defined by the Clean Air Act

amendments of 1977 as having “special protection” from

effects of air pollution because of their “air-quality related

values (AQRV’s)” (i.e., water quality, native vegetation,

ecosystem integrity, and visibility). Class I wilderness areas

are wildernesses larger than 5,000 acres and national parks

larger than 6,000 acres that were in existence on or before

August 7, 1977.

Criteria Pollutants: pollutants for which National Ambient Air

Quality Standards have been implemented to protect human

health and welfare. They include: particulate matter, sulfur

oxides, nitrogen oxides, carbon monoxide, ozone, and lead.

deciview (dv): the metric unit based on light extinction used for

an atmospheric haze index, such that uniform changes in

haziness correspond to the same metric increment across

the entire range from pristine to highly impaired haze

conditions. Deciview values are calculated by multiplying

by 10 the natural logarithm of one tenth of the atmospheric

light extinction coefficient expressed in units of inverse

megameters. As used in this report, it is also a visibility

index designed to describe changes in visibility perception

across locations with all types of baseline conditions.

dose: the amount of ozone that enters a leaf.

emissions: the amount of a specific pollutant released into the

atmosphere.

exposure: the amount of ozone that is found in the groundlevel atmosphere or that contacts the outside of the leaf.

fine mass particle: particle with a diameter equal to or smaller

than 2.5 microns. Fine particles are responsible for most

atmospheric particle-induced light extinction.

fugitive dust: dust generated from geologic wind erosion,

agricultural tilling, mining, construction, paved and improved roads, and fugitive process sources (e.g., dust,

leaks, uncontrolled vents) at process sites. Fugitive dust is

the greatest emission source (71 percent) of PM10 from

within and outside the Assessment area.

fugitive emissions: air pollution from an unducted source, i.e.,

not emanating from an exhaust pipe or stack. Roadway dust

and emissions from refinery valves are examples.

fugitive process sources: an EPA category used to discuss

PM10. Some fugitive process sources are leaks and uncontrolled vents. Fugitive dust is also generated from geologic

wind erosion, agricultural tilling, mining, construction,

paved and improved roads, wildlife, prescribed fires, and

wildfires.

hygroscopic: having the ability to attract water.

microgram: 0.000001 gram.

micron: 0.000001 meter.

National Ambient Air Quality Standards (NAAQS): air quality

standards established and maintained by the Environmental

Protection Agency under the authority of the Clean Air Act.

The standards are intended to protect human health and

welfare.

National Acid Precipitation Assessment Program (NAPAP):

the 10-year (1980 to 1990) interagency research program

designed to investigate acid deposition and its effects

nationwide. The products of this program are the series of

State of Science and Technology documents that summarize

what is known about the severity of acid deposition and the

resources it affects.

nitric oxide: a gas formed under high temperature and/or high

pressure combustion in furnaces and internal combustion

engines. Nitric oxide is converted to nitrogen dioxide in the

presence of oxygen.

nitrogen oxides: a designation of all oxides of nitrogen that

includes nitrogen dioxide, nitric oxide, and nitrous oxide, all

of which are precursors of atmospheric ozone or nitrogen

deposition.

ozone: an atmospheric gas for which a standard is maintained

within the NAAQS. This report deals with ozone concentrations near the earth’s surface that may cause injury to plants

and animals.

52
ozone damage: in the context of this report, ozone exposure

that is large enough to cause a growth loss in a species

known to be sensitive to ozone.

ozone injury: a physiological response to ozone that has

entered into a plant’s leaf.

ozone symptoms: a discoloration, called stippling, of the upper

leaf surface of a plant species known to be sensitive to

ozone exposures under controlled conditions. The ozone

symptoms are used during field surveys to identify species

that are likely to exhibit a physiological response to ozone

exposures.

particulate matter: any substance, except pure water, that

exists as a liquid or solid in the atmosphere under normal

conditions and is of microscopic or submicroscopic size but

larger than molecular dimensions (e.g., dust and smoke).

Standards for particulate matter are maintained within the

NAAQS.

parts per million (ppm): a unit of measure used for describing

ozone exposure.

pH: the negative logarithm of hydrogen ion activity. The pH

scale goes from 1 (most acidic) to 14 (most alkaline). The

difference of 1 pH unit indicates a tenfold change in

hydrogen ion activity.

point source: includes any identifiable conveyance from which

pollutants might enter the air of the United States (e.g.,

smokestack).

Pollution Exposure Index (PEI): a statistical modeling technique used to assess where pollution exposures may be the

greatest.

prescribed burn or fire: a fire burning within the parameters of

a written statement that defines objectives to be attained

and conditions under which the fire will be allowed to burn

(e.g., wind direction and speed, fuel moisture content).

Rayleigh extinction: the natural reduction in visibility caused

by nitrogen and oxygen molecules (clean air).

reasonable progress target (visibility): an improvement in the

average of the 20 percent most impaired days each year,

equivalent to an improvement (decrease) of (Option A) 1.0

deciview (dv) per 10 years or (Option B) 1.0 dv per 15 years

and no degradation (less than 0.1 dv increase) in the average

of the 20 percent least impaired days each year.

receptors: spatial locations.

silviculture: the theory and practice of controlling forest

establishment, composition, structure, and growth.

standard visual range (SVR): the SVR is the greatest distance

at which an observer can barely see a black object viewed

against the horizon sky. The higher the SVR value—usually

expressed in kilometers—the better the visibility conditions.

sulfur dioxide (SO2): a colorless gas produced by industrial

processes, especially the burning of fossil fuels such as coal

and oil. Most sulfur dioxide emissions come from large

power plants, refineries, and smelters. This gas is transformed in the atmosphere to sulfate particles and sulfuric

acid, which can be transported to surface waters and soils in

wetfall or dryfall. SO2 is the form of sulfur that provides the

basis of emission mass calculations for NAAQS.

visibility impairment: the extent to which haze obscures clarity,

color, texture, and form.

volatile organic compounds (VOC): numerous carbon compounds that readily vaporize. Many such compounds are

precursors in the formation of atmospheric ozone.

53
Glossary of Abbreviations and Acronyms

AIRS: Aerometric Information Retrieval System (of the U.S.

EPA)

Al: aluminum

ANC: acid neutralizing capacity

AQRV’s: air-quality related values

Bext: a light-extinction coefficient that represents the ability of

the atmosphere to absorb and scatter light

CAA: Clean Air Act

dv: deciview

EPA: U.S. Environmental Protection Agency

FL DEP: Florida Department of Environmental Protection

GIS: Geographic Information System

IMPROVE: Interagency Monitoring of Protected Visual

Environments

μg m-3: microgram per cubic meter

NAAQS: National Ambient Air Quality Standards

NAPAP: National Acid Precipitation Assessment Program

NOx: nitrogen oxide

NOAA: National Oceanic and Atmospheric Administration

NRC: National Research Council

NSWS: National Surface Water Survey

OK WRB: Oklahoma Water Resources Board

PEI: Pollution Exposure Index

pH: a measure of hydrogen ion concentration or acidity

PM: particulate matter

PM10: that portion of particulate matter that has an aerodynamic diameter of 10 microns or less

PM2.5: that portion of particulate matter that has an aerodynamic diameter of 2.5 microns or less

ppm: parts per million

SAMAB: Southern Appalachian Man and the Biosphere

SO2: sodium dioxide

SVR: standard visual range

TSP: total suspended particles

USDA: United States Department of Agriculture

USDA FS: USDA Forest Service

USDI: United States Department of the Interior

U.S. EPA: U.S. Environmental Protection Agency

VOC: volatile organic compounds

W126: a mathematical index of ozone exposure that places

emphasis on high concentrations of ozone but does not

ignore potential effects that can occur at lower concentrations; a statistically weighted function to describe ozone

exposures for a predefined time

54
List of Tables

Page

Table 1.1—Main categories of area, mobile, and point source emissions in the Assessment area. . . . . . . . . . . . . 2

Table 1.2—Emissions categories described by the range of pollutants (tons per square mile) resulting from

natural sources and human activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Table 2.1—Average PM10 concentrations (in μg/m3) in the Assessment area by season, year, and

5-year period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Table 3.1—Visibility in Class I wilderness areas represented by camera-based estimates of the average annual

standard visual range and haze, 1988 to 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Table 4.1—Forest tree response categories based on W126 values and exposure hours required for various

levels of forest tree damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

55
List of Figures

Page

Figure 1.1—Counties in the Ozark-Ouachita Highlands Assessment area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

Figure 1.2—Location of receptors used for statistical modeling of pollution exposure within and near the

Assessment area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Figure 1.3—Estimates of emissions of particulate matter 10 microns or smaller (PM10) in Assessment area and

surrounding counties in 1994 (U.S. EPA 1995). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Figure 1.4—Particulate matter emissions 10 microns or smaller (PM10) by source categories in 1994 (U.S. EPA

1995). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Figure 1.5—Emissions of nitrogen oxide (NOx

) by source categories, based on 1994 data (U.S. EPA 1995). . . . . . 6

Figure 1.6—Estimates of human-caused nitrogen oxide (NOx

) emissions in Assessment area and surrounding

counties in 1994 (U.S. EPA 1995). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 1.7—Estimates of natural nitrogen oxide (NOx

) emissions in Assessment area counties in 1995. . . . . . . . . 7

Figure 1.8—Estimates of natural volatile organic compound emissions in Assessment area counties in 1995. . . . 8

Figure 1.9—Sources of volatile organic compound emissions in 1994 (U.S. EPA 1995). . . . . . . . . . . . . . . . . . . . . 8

Figure 1.10—Estimates of human-caused volatile organic compound emissions in Assessment area and

surrounding counties in 1994 (U.S. EPA 1995). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Figure 1.11—Sources of sulfur dioxide (SO2

) emissions in 1994 (U.S. EPA 1995). . . . . . . . . . . . . . . . . . . . . . . . . . 10

Figure 1.12—Estimates of human-caused sulfur dioxide (SO2) emissions in Assessment area and surrounding

counties in 1994 (U.S. EPA 1995). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Figure 1.13—Pollution Exposure Index modeling results using 1994 particulate matter (PM10) emissions data. . . 11

Figure 1.14—Pollution Exposure Index modeling results using 1994 human-caused nitrogen oxide (NOx)

emissions data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 1.15—Pollution Exposure Index modeling results using 1994 human-caused volatile organic compound

emissions data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 1.16—Pollution Exposure Index modeling results using 1994 sulfur dioxide (SO2

) emissions data. . . . . . . 13

Figure 1.17—Trends in national emissions of nitrogen oxides (NOx), volatile organic compounds (VOC), sulfur

dioxide (SO2

), and particulate matter (PM10, nonfugitive dust sources) from 1940 through 1994 (U.S. EPA 1995). 13

Figure 2.1—National smokestack and miscellaneous particulate matter (PM10) emissions (in million short tons)

from 1970 through 1995 (U.S. EPA 1996). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 2.2—Average PM10 values (μg m-3) during winter, spring, summer, and fall from 1991 through 1995. . . . . 18

Figure 2.3—Average PM10 values (μg m-3) for the 1991 through 1995 period. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 2.4—Average PM10 mass concentration (μg m-3) for each site in the IMPROVE network for the 1992

through 1995 period (CIRA 1997). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Figure 2.5—Average fine particle mass (PM2.5) concentrations (μg m-3) for each site in the IMPROVE network

for the 1992 through 1995 period (CIRA 1997). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Figure 2.6—Average PM10 concentrations (μg m-3) during March for the 1991 through 1995 period. . . . . . . . . . . 22

56
Page

Figure 3.1—Visibility conditions at the Caney Creek Wilderness on the Ouachita National Forest in Arkansas.

(Photo: USDA Forest Service, Ouachita National Forest, Hot Springs, AR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 3.2—Visibility conditions at the Hercules Glades Wilderness on the Mark Twain National Forest in

Missouri. (Photo: USDA Forest Service, Mark Twain National Forest, Rolla, MO.) . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 3.3—Visibility conditions at the Upper Buffalo Wilderness on the Ozark-St. Francis National Forests in

Arkansas. (Photo: USDA Forest Service, Ozark-St. Francis National Forests, Russellville, AR.) . . . . . . . . . . . . . . 28

Figure 3.4—The fine particle (PM2.5) budget at the Upper Buffalo Wilderness IMPROVE site for winter (top half)

and spring (lower half) from 1992 through 1995. The “fine mass budget” pie charts show the actual percentages

of particle components, based on weight. The “extinction budgets” show by what percentage each constituent

reduces the standard visual range (SVR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 3.5—The fine particle (PM2.5) budget at the Upper Buffalo Wilderness IMPROVE site for summer (top

half) and fall (lower half) from 1992 through 1995. The “fine mass budget” pie charts show the actual percentages

of particle components, based on weight. The “extinction budgets” show by what percentage each constituent

reduces the standard visual range (SVR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 4.1—Locations of W126 estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Figure 4.2—Palmer Hydrologic Index results for April through July 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Figure 4.3—Ozone estimates for 1990 showing sites classified according to tree response categories

(see table 4.1) and W126 values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 4.4—Ozone estimates for 1991 showing sites classified according to tree response categories

(see table 4.1) and W126 values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 4.5—Ozone estimates for 1992 showing sites classified according to tree response categories

(see table 4.1) and W126 values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Figure 4.6—Ozone estimates for 1993 showing sites classified according to tree response categories

(see table 4.1) and W126 values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 4.7—Ozone estimates for 1994 showing sites classified according to tree response categories

(see table 4.1) and W126 values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 4.8—Ozone estimates for 1995 showing sites classified according to tree response categories

(see table 4.1) and W126 values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 5.1—The period average for nitrate deposition (lbs/ac) in the Assessment area from 1983 through

1995 (Lynch and others 1997). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Figure 5.2—The period average for sulfate deposition (lbs/ac) in the Assessment area from 1983 through

1995 (Lynch and others 1997). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Figure 5.3—Variation in modeled mean sulfate and nitrate deposition within the Assessment area from 1983

through 1995 (Lynch and others 1997). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

57
U.S. Department of Agriculture, Forest Service. 1999. Ozark-Ouachita Highlands

Assessment: air quality. Report 2 of 5. Gen. Tech. Rep. SRS-32. Asheville, NC:

U.S. Department of Agriculture, Forest Service, Southern Research Station. 57 p.

This publication provides information about the atmospheric conditions in and near

the national forests in the Ozark-Ouachita Highlands: the Mark Twain in Missouri, the

Ouachita in Arkansas and Oklahoma, and the Ozark-St. Francis National Forests in

Arkansas. This report includes information about particulate matter, visibility, ozone

concentrations, and acid deposition in the Ozark-Ouachita Highlands Assessment area.

Keywords: Acid deposition, Clean Air Act, ozone, particulate matter, pollution,

visibility.
The Forest Service, U.S. Department of Agriculture, provides leadership in the management, protection, and use of the Nation’s forests and rangelands. The Agency takes an

ecological approach to the implementation of multiple-use management, providing

sustained yields of renewable resources such as water, forage, wildlife, wood, and recreation. The

Forest Service has embraced ecosystem management as its operating philosophy and is committed

to the preservation of wilderness, biodiversity, and landscape beauty as well as the protection of

the basic resources of soil, water, and air quality.

The Forest Service is responsible for the 191.8-million-acre National Forest System, with its 155

national forests and 20 grasslands in 44 States, Puerto Rico, and the Virgin Islands. In addition, the

Agency works with State land management organizations to help private landowners apply good

natural resource management practices on their lands. The International Program of the Forest

Service enables the Agency to share its technical expertise and managerial skills with other nations.

The Research and Development Program of the Forest Service conducts extensive research to

enhance and protect productivity on all of America’s forests and rangelands, with special attention

to long-term natural resource issues of national and international scope.

The U. S. Department of Agriculture (USDA) prohibits discrimination in all its programs and

activities on the basis of race, color, national origin, gender, religion, age, disability, political beliefs,

sexual orientation, and marital or familial status. (Not all prohibited bases apply to all programs.)

Persons with disabilities who require alternative means for communication of program information

(Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 202-720-2600 (voice

and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W,

Whitten Building, 14th and Independence Avenue, SW, Washington, DC 20250, or call 202-720-5964

(voice or TDD). USDA is an equal employment opportunity provider and employer.
This report is one of five that documents the results of the Ozark-Ouachita Highlands

Assessment. Three of the remaining reports examine Aquatic Conditions, Social and

Economic Conditions, and Terrestrial Vegetation and Wildlife, respectively, and the fourth

provides an overall summary.

Summary Report

REPORT

1

OF 5

Ozark-Ouachita

Highlands Assessment

United States

Department of

Agriculture

Forest Service

General Technical

Report SRS-31 Aquatic Conditions

REPORT

3

OF 5

Highlands Assessment

Ozark-Ouachita

General Technical

Report SRS-33

United States

Department of

Agriculture

Forest Service

Terrestrial Vegetation and Wildlife

REPORT

5

OF 5

Highlands Assessment

Ozark-Ouachita

General Technical

Report SRS-35

United States

Department of

Agriculture

Forest Service

REPORT

4

OF 5

Ozark-Ouachita

Highlands Assessment

United States

Department of

Agriculture

Forest Service

General Technical

Report SRS-34 Social and Economic Conditions