United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical

Report NRS-34

Ozone Bioindicators and Forest 

Health: A Guide to the Evaluation, 

Analysis, and Interpretation of the 

Ozone Injury Data in the Forest 

Inventory and Analysis Program

Gretchen C. Smith

John W. Coulston

Barbara M. O’Connell
Abstract

In 1994, the Forest Inventory and Analysis (FIA) and Forest Health Monitoring programs 

of the U.S. Forest Service implemented a national ozone (O3) biomonitoring program 

designed to address specific questions about the area and percent of forest land 

subject to levels of O3 pollution that may negatively affect the forest ecosystem. This is 

the first and only nationally consistent effort to monitor O3 stress on the forests of the 

United States. This report provides background information on O3 and its effects on 

trees and ecosystems, and describes the rationale behind using sensitive bioindicator 

plants to detect O3 stress and assess the risk of probable O3 impact. Also included are 

a description of field methods, analytic techniques, estimation procedures, and how to 

access, use and interpret the ozone bioindicator attributes and data outputs such as the 

national ozone risk map.

The Authors

GRETCHEN C. SMITH, National Ozone Field Advisor, University of Massachusetts, 

Department of Natural Resources Conservation, Amherst, MA. gcsmith@nrc.umass.edu

JOHN W. COULSTON, National Ozone Advisor and Supervisory Research Forester, U.S. 

Forest Service, Southern Research Station, Knoxville, TN. jcoulston@fs.fed.us

BARBARA M. O’CONNELL, Forester, U.S. Forest Service, Northern Research Station, 

Newtown Square, PA. boconnell@fs.fed.us

Acknowledgments

The ozone biomonitoring program began in the early 1990s and has succeeded due to 

the persistent efforts of numerous investigators, regional trainers, and state forest health 

coordinators. The authors wish to extend special thanks to the following individuals: Ed 

Jepsen, Wisconsin Department of Natural Resources; Bill Smith, Dan Stratton, and John 

Simpson, U.S. Forest Service, Southern Research Station; Melissa Patterson and Joel 

Thompson, U.S. Forest Service, Pacific Northwest Research Station; Vicki Sanderford 

and Jeff Herrick, U.S. Environmental Protection Agency, Research Triangle Park, NC; and 

Patrick Temple, Ecological Systems Group, Riverside, CA. We also thank the regional 

field crews and quality assurance staff for their efforts and commitment to data quality, 

and the many private landowners who generously allow us on their land. Without them the 

national ozone biomonitoring program would not be possible. We thank Sally Campbell, 

Don Davis, Ed Jepsen, and Will McWilliams for reviewing the manuscript; and Chris 

Bergweiler, Dan Duriscoe, Ron Kelley, Bob Kohut, Bill Manning, Howie Neufeld, Jim 

Renfro, and Pat Temple for providing excellent photographs of ozone injury. 

Cover Photos

Front top right courtesy of Pat Temple, U.S. Forest Service; all others courtesy of 

Gretchen Smith, University of Massachusetts.

Front

Top left: ozone injury assessment along a field edge at a biosite in New Hampshire.

Top right: ozone chlorotic mottle on ponderosa pine in California.

Bottom left: field evaluation of black cherry at a biosite in Vermont.

Bottom right: leaves with obvious injury collected in the field for quality assurance 

purposes.

Back

Left: plant press used in the field to preserve leaf samples for later examination by 

regional experts.

Right: hand lens used in the field for close examination of leaves for ozone injury.

Manuscript received for publication 10 April 2008
Contents

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

Program History...............................................................................................1

Ozone the Air Pollutant.....................................................................................2

Ozone Formation, Sources, and Transport......................................................2

Ambient Concentrations and Exposure Patterns.............................................3

Ozone Uptake and Plant Response................................................................5

Evidence that Ozone Injures Trees..................................................................7

Foliar Injury......................................................................................................8

Growth and Vigor.............................................................................................9

. . Western Pines and Associated Species....................................................10

. . Eastern White Pine....................................................................................11

. . Trembling Aspen and Aspen Clones.........................................................11

. . Black Cherry..............................................................................................12

. . Other Eastern Species..............................................................................12

Ecosystem Effects.........................................................................................13

Ozone Biomonitoring and Forest Health Assessment................................15

The Conceptual Model...................................................................................16

Assessment Questions..................................................................................18

Issues of Uncertainty.....................................................................................18

Forest Health Assessment at the State and Regional Levels........................19

Data Collection and Estimation......................................................................20

National Protocol...........................................................................................20

Field Methods................................................................................................22

Quality Assurance..........................................................................................24

. . Performance Evaluations and MQOs........................................................25

Estimation Methods.......................................................................................26

Database Structure and Documentation.......................................................27

Field Data......................................................................................................27

Standard Summary Tables and Derived Ozone Data....................................28

. . Ozone Biosite Summary Table..................................................................28

. . Ozone Plot Summary Table.......................................................................28

. . Ozone Species Summary Table................................................................29

Population-level Core Tables and Maps........................................................30

Validation Data Table.....................................................................................30

	 Crosswalks and Site Identification Data........................................................31
Data Summaries..............................................................................................31

National Program Summary..........................................................................31

. . Regional Summaries and State Reports...................................................37

Analytical Procedures and Risk Assessment...............................................46

Population Estimates.....................................................................................46

The National Ozone Risk Map.......................................................................47

Link to Evaluation Monitoring.........................................................................50

Link to Air Quality Standards.........................................................................51

Advantages and Disadvantages of the Data ................................................53

Summary and Recommendations.................................................................55

Summary Comments.....................................................................................55

Recommendations.........................................................................................56

References.......................................................................................................58

Appendix I.—Key Resources and Web Sites................................................73

Air Quality Data and the 2008 Revised Ozone Standard..............................73

Air Quality Indices..........................................................................................73

Research Studies and Educational Material..................................................73

Peer-reviewed Journals.................................................................................74

Air Pollution Workshop...................................................................................74

Climate Change Issues and Links.................................................................75

Appendix 2.—National Maps of Ozone Air Quality.......................................76

Appendix 3.—Field Images of Ozone Injury on Eastern and Western 

Bioindicator Species...................................................................................78

Appendix 4.—Ozone Sensitivity of Tree and Shrub Species......................83

Appendix 5.—Field Data Sheets and Data Flow Chart for the Ozone 

Bioindicator..................................................................................................86

Appendix 6.—Ozone Summary Tables..........................................................92

Appendix 7.—Core Tables for State and Regional Reports........................95


INTRODUCTION

The Montreal Process was formed in 1994 to develop an internationally agreed upon set of 

criteria and indicators for the conservation and sustainable management of temperate and boreal 

forests (Anonymous 1995). In response, the Forest Inventory and Analysis (FIA) and Forest 

Health Monitoring (FHM) programs of the U.S. Forest Service implemented a national ozone 

(O3) biomonitoring program to address specific questions about the area and percent of forest 

land subject to levels of O3 pollution that may negatively affect the forest ecosystem. This is the 

first and only nationally consistent effort to monitor O3 stress on the forests of the United States. 

This program provides critical baseline information on the current status of O3 air quality and 

the potential effects of O3 on forest health and productivity.

This report provides background information on O3 formation, sources, and transport, and a 

comprehensive review of O3 impacts on trees. The rationale for O3 biomonitoring is discussed 

along with the documentation of the field and laboratory procedures for data collection and 

quality control. Emphasis is placed on describing generalized approaches for summarizing, 

analyzing, and interpreting the O3 bioindicator attributes and discussing the strengths and 

limitations of the data. Detailed information on the sampling and estimation techniques 

described in this report has been published elsewhere (Smith et al. 2007). This report is intended 

to educate and inform FIA/FHM analysts and other researchers interested in ozone and forest 

health, and provide guidance on ways to incorporate ozone bioindicator data into reports and 

research studies.

Program History

Beginning in the 1990s, FHM developed, tested, and implemented a suite of forest health 

indicators to respond to emerging demands for an assessment of the current condition and longterm sustainability of the Nation’s forests (Lewis and Conkling 1994, Smith 2002). The ozone 

indicator or ozone biomonitoring program, as it is referred to in this report, was part of this new, 

large-scale, health-based monitoring effort along with crown condition, lichen abundance and 

diversity, vegetation structure and diversity, down woody materials, and soil properties. Within 

the FHM program, the ground surveys designed to collect baseline information on the forest 

health indicators and detect changes from those baselines over time are referred to as Detection 

Monitoring (http://fhm.fs.fed.us/fact/03/dm.pdf ). When a potential forest health concern has 

been identified on Detection Monitoring ground plots, Evaluation Monitoring studies are used 

to examine the extent and severity of the problem and to investigate probable cause.

In 2001, the FHM health-based indicators were integrated into FIA, which has served as the 

primary source of information on the forest resources of the United States for more than 70 years 

(McRoberts et al. 2004). The design of the joint FHM and FIA monitoring programs has been 

described elsewhere (http://fia.fs.fed.us/library). Aspects of the FHM/FIA merger that are relevant 

to the ozone biomonitoring program are the integrated forest inventory and forest health plots 

known as the Phase 2 (125,000 plots nationally) and Phase 3 plots, respectively. In Phase 2 (P2), 

field crews collect data on more than 300 variables related to land ownership, tree species, tree 

size, and tree condition. Phase 3 (P3) plots, a subset of P2 plots, are measured for the broad suite 

of forest health indicators referred to in the previous paragraph. The ground survey portion of the 

FHM Detection Monitoring program is synonymous with P3 in FIA. The data collected on P3 

plots provide estimates of health and condition for the P2 sample (Bechtold and Patterson 2005).


OZONE THE AIR POLLUTANT

Ozone, an extremely reactive colorless gas, is a natural component of the atmosphere that is 

always present at low concentrations. In the upper atmosphere (stratosphere), the beneficial O3

layer screens out high energy radiation from the sun. In the lower atmosphere (troposphere), 

O3 occurs largely because of human activities and is considered an air pollutant. It is a major 

constituent of photochemical smog and part of the mix of greenhouse gases that contribute to 

global climate warming (Krupa and Legge 1995). Tropospheric O3 is toxic to human beings, 

plants, and many other life forms. Before the industrial age, the lower atmosphere was relatively 

free of O3. Today, this toxic contaminant is found across all geographic and political boundaries 

and in areas previously believed to be pristine. Plant scientists consider ground-level O3 the 

most pervasive air pollutant worldwide and a threat to world food, fiber, and timber production 

and conservation of natural plant communities (Percy et al. 2003a). Trends in surface O3

concentrations indicate an increase in background O3 concentrations over much of the world 

and a huge increase in the extent of forest areas at risk of O3 exposure

1

.

Ozone Formation, Sources, and Transport

Ground-level O3 is a secondary pollutant, formed from the reactions of precursor pollutants, 

primarily nitrogen oxides (NOx ) and volatile organic compounds (VOCs) in the presence of 

sunlight. 

 VOC + NOx

 + Sunlight = O3

The formation of O3 can be simplified to a reversible reaction between oxides of nitrogen and 

elemental oxygen driven by the energy of sunlight. The reversible reaction results in no net 

gain of O3 due to the instability of the O3 molecule. However, when VOCs are present, they 

interfere with the reverse reaction resulting in a buildup of O3 in ambient air. This is known as 

the photochemical oxidation cycle. The complex sequence of reactions that define this cycle and 

promulgate surface O3 concentrations is described by Krupa and Manning (1988) and Percy et 

al. (2003a). The simplified reaction described above provides enough information to characterize 

O3 sources and sinks and their relation to forest health.

Sources of ground-level O3 are both natural and human-made. Natural sources that contribute 

to background O3 concentrations include lightning during thunderstorms and downward 

intrusions of O3 from the upper atmosphere. Human-made sources of O3 precursor molecules 

include highway vehicle exhaust (the single most important source), emissions from industrial 

facilities, combustion from electric utilities, gasoline vapors, and chemical solvents. Biogenic 

emissions from plants and trees (e.g., isoprene and monoterpenes) can be an important source 

of VOCs on hot, bright days in heavily vegetated areas. However, natural emissions rarely result 

in harmful O3 levels such as those associated with the combined emissions of VOCs and NOx 

from anthropogenic sources (Percy et al. 2003a). Many urban areas are characterized by peak 

O3 levels, but even rural areas with relatively low emission sources experience high O3 levels as 

polluted air masses are transported hundreds of miles downwind of population centers (U.S. 

EPA 2004). The average lifetime of O3 is about 16 hours, allowing time for air movement from 

urban sources to forest sinks. Long-range transport of O3 and O3 precursors from urban to rural 

areas contributes to the regional character of O3 pollution, making it difficult to contain or 

localize either the sources or the effects.

1

Key resources and Web sites on O3 pollution and effects are listed in Appendix 1.


In a typical O3 season, monthly average O3 concentrations increase from January to June, 

stabilize at relatively high levels during the summer, and then decrease again from October 

through the year’s end. This pattern is governed primarily by seasonal changes in sunlight 

intensity and air temperature, rather than seasonal changes in the amount of precursor 

emissions. Thus, periods of high O3 concentration coincide with the growing season when 

plants are actively growing and most vulnerable to injury. During the warm summer months, 

stagnant air masses, varying in duration from one to several days, can prevent the upward 

dispersion of pollutants causing O3 buildup and high surface O3 concentrations on a regional 

scale. From May through September, forests are exposed from a few hours to several days of 

relatively high surface O3 concentrations interspersed with longer periods of relatively low O3.

In a typical urban atmosphere, O3 concentrations over the course of the day show a typical 

diurnal pattern. O3 concentrations increase rapidly in the early morning rush hour between 

0800 and 1200, reach peak levels between 1200 and 1500 when the intensity of solar radiation 

is at a maximum, and then decrease into the evening hours with the loss of sunlight and 

precursor emissions. Rural areas throughout the urbanized corridors of the U.S. also experience 

this diurnal fluctuation in O3 concentrations. However, in some upper elevation forests removed 

from the effects of fluctuating emission sources, O3 concentrations can remain relatively steady 

and moderately high through the day and night.

Ambient Concentrations and Exposure Patterns

The U.S. Environmental Protection Agency (EPA) has designated O3 one of six criteria air 

pollutants that must be regulated to reduce the risk of harmful effects to human beings, 

agricultural crops, forest ecosystems, and other resources. Regulation generally takes the form 

of emission control strategies for precursor pollutants. EPA has established a national ambient 

air quality standard (NAAQS) for O3 that is intended to bring O3 air pollution under control. 

However, as of December 2007, 347 counties across the U.S. were out of compliance with the 

8-hr NAAQS for O3 (http://www.asl-associates.com/).

Ozone concentrations in ambient air are measured in parts per million (ppm) but reported in parts 

per billion (ppb) so that values can be expressed as whole numbers. Air quality monitoring stations 

operated by EPA are located across the U.S., mainly near population centers where air quality 

effects on human health are paramount. Routine O3 monitoring is summarized as hourly average 

O3 concentrations. When forecasters report a smog alert on a summer day, they are indicating 

that average hourly O3 concentrations are expected to exceed 84 ppb over an 8-hr period. A 1-hr 

ozone standard has been in place since 1979, and an 8-hr ozone standard has been in place since 

1997. The 1-hr standard is met when the number of days per calendar year with maximum hourly 

average concentrations above 120 ppb is equal to or less than 1. The 8-hr standard is met when 

the 3-year average of the annual fourth highest daily maximum 8-hr average concentration is less 

than 80 ppb2. During some years, more than half of the states in the U.S. exceed the 8-hr standard 

more than 5 days (some states more than 20 days) during the growing season (http://airnow.gov/).

2

On March 12, 2008, EPA announced a revised 8-hr O3 standard of 0.075 ppm. Details on the 2008 

revised O3 standard are provided in Appendix 1. The 1997 standard remains in place for implementation 

purposes as EPA undertakes rulemaking to address the transition from the 1997 O3 standard to the 

2008 O3 standard. For this reason, the text of this document was not modified to reflect the March 12 

announcement.


In the U.S., trends in ground-level O3 concentrations indicate that peak values, or maximum 

1-hr O3 concentrations, have been reduced in those areas where O3 precursor control programs 

are in place. However, control programs have had little impact on moderate O3 concentrations, 

and background O3 levels are increasing. A typical average background concentration of 30 

to 40 ppb O3 is found essentially everywhere in the world today and is expected to increase 

by between 0.3 and 1.0 percent per year for the next 50 years. Historically, the highest O3

concentrations are found at urban sites and range from 80 ppb in the interior U.S. to 200 ppb 

in the West and Northeast. However, since 1996 (Percy et al. 2003a), 1-hr O3 concentrations 

in rural areas have been greater than the corresponding values at urban sites. Between 1992 and 

2001 average 8-hr O3 levels measured in national parks increased by nearly 4 percent. This shift 

in greater O3 concentrations from urban to rural areas increases the regional expanse of plantdamaging O3

 concentrations and puts more forest areas at risk.

All hourly average O3 concentrations have the potential for impacting vegetation, yet the 

higher levels (>80 ppb) are often considered a greater threat to forest health than mid (60-80 

ppb) to low (<60 ppb) levels. This is reflected in three cumulative O3 exposure indices that are 

often used to characterize ambient O3 exposure data: SUM06, W126, and N100. The SUM06 

exposure index is defined as the sum of all hourly concentrations greater than or equal to 0.06 

ppm O3, which implies an injury exposure threshold of 60 ppb, a specific 1-hr O3 value above 

which injury is expected. The W126 statistic does not use an exposure threshold but rather sums 

all hourly average O3 concentrations: both the lower, less biologically effective concentrations, 

and the higher values (>40 ppb), which are given greater weight. The N100 statistic represents 

the number of hours with O3 concentrations at or above 100 ppb. The Federal Land Manager 

Air Quality Related Values Workgroup (FLAG) recommends the combined use of W126 and 

N100 values to provide a biologically meaningful summation of hourly O3 data (USDI 2000). 

Summary exposure indices can be integrated over a 24-hr period or over the 12-hr daylight 

period, and over an entire year or over the length of the growing season. When integrated over 

the daylight period of the growing season at the scale used in this document, the W126 and 

SUM06 indices are essentially interchangeable3.

A typical O3 season for the continental U.S. (see Appendix 2), whether viewed as a map of 

county-level exceedance data, or an interpolated cumulative exposure surface, shows many parts 

of the country and much of the forest land subject to ambient O3 concentrations that exceed 

known injury thresholds for sensitive plants. The EPA Clean Air Scientific Advisory Committee 

(CASAC), the Northeast States for Coordinated Air Use Management (NESCAUM), and many 

state air quality agencies report that the current NAAQS for O3 provides a poor measure of 

seasonal ground-level O3 exposures, correlates poorly with plant stress, and is generally inadequate 

to protect natural and agronomic ecosystems (http://www.dnr.wi.gov/air/, www.nescaum.org). 

The consensus recommendation for a new ozone standard, based on a large body of scientific 

evidence, is that EPA should adopt the cumulative W126 secondary ozone standard of 7-21 ppmhr for the 3 highest consecutive months during the growing season of one calendar year.

On March 12, 2008, EPA announced a revision of the primary (human health-related) and 

secondary (vegetation effects) ozone standards. EPA decided to adopt an 8-hr O3 standard 

3

Injury thresholds for the SUM06, W126, and N100 indices of exposure are listed in Appendix 1. 


of 0.075 ppm rather than the W126 cumulative O3 exposure index described above. This 

represents a strengthening of the O3 standard and recognition that the 1997 standard (0.08 

ppm) did not adequately protect vegetation from O3 exposure even though the form and level 

of the newly revised standard did not meet the consensus recommendations published by EPA’s 

National Center for Environmental Assessment (U.S. EPA February 2006a,b,c) and EPA’s 

Office of Air Quality Planning & Standards (U.S. EPA July 2007). Information on the O3

standard setting process and links to scientific review documents are available online (http://

www.epa.gov/groundlevelozone/).

Ozone Uptake and Plant Response

Open stomata provide the pathway for O3 entry into the leaf. Once inside the leaf, O3

immediately forms toxic derivatives that react with many components of the leaf cells. 

Many studies of foliar response to O3 show that the cell membranes suffer the most injury 

characterized by changes in permeability and leakiness to important ions such as potassium 

(Fuhrer and Booker 2003, Krupa et al. 1998, Pell et al. 1997). Internal membranes are less 

affected because the toxic oxidants derived from O3 are diluted and absorbed from the outside 

toward the inside of the cells. The first reaction to injury by oxidants is loss of chlorophyll, 

increased fluorescence, and changes in energy levels. As injury progresses and antioxidants come 

into play, carbon fixation is reduced, foliar and root respiration is increased, and there is a shift 

in the partitioning of carbon into different chemical forms and allocation patterns. At the most 

basic cellular level, a plant injured by O3 is not the same as a plant without injury.

Plant response to O3 is understood as an interaction among a triad of factors involving the 

susceptibility of the host plant, the concentration and duration of exposure to O3, and the 

external cultural environment in which the host plant is growing (Fig. 1). These interacting 

factors compose a modified disease triangle as defined by Houston (1981) to describe 

the development of stress-induced disease in individual trees and tree populations, and as 

applied by Kohut (2005 and 2007) to ozone injury assessment. In this context, O3 at certain 

concentrations is understood as the primary pathogen or causal agent capable of causing 

injury or disease under favorable conditions defined by the susceptibility of the host plant and 

predisposing cultural conditions. Ozone susceptibility varies with the genotype, genus, species, 

cultivar, or variety of the host plant. Age of the plant and phenological state of development 

also affect susceptibility. Cultural conditions that influence plant-pollutant interactions include 

nutrition (primarily nitrogen), moisture levels (primarily atmospheric humidity and soil 

moisture), solar radiation, temperature, and day length. Soil moisture status is often considered 

the most critical influencing factor because stomatal closure during periods of drought or low 

soil moisture can severely limit gas exchange (O3 uptake). Plant response to O3 is greatest and 

O3 injury conditions optimal when a sensitive genotype, at a susceptible stage of development, 

is exposed to elevated O3 concentrations in a predisposing environment for the minimum time 

period required for physiological dysfunction to occur. The result is an ozone-induced disease 

condition (Hepting 1968), which may or may not lead to significant impacts on the forest 

ecosystem (Smith 1974).

Some plants are ozone sensitive, while others show resistance to O3 pollution or are able to 

tolerate it (Davis and Wilhour 1979, Dowsett et al. 1992, Neufeld et al. 2000, Temple 1989). 

Variation in response to O3 is both interspecific and intraspecific, indicating that ozone 


sensitivity has a strong genetic component. Ozone-sensitive plants are divided into two groups: 

those that show a visible foliar response to O3, and those that sustain injury or growth loss in 

the absence of visible symptoms (Reich and Amundson 1985). External growth conditions that 

cause stomates to close will prevent O3 uptake and injury (Mansfield 1998, Schaub et al. 2003). 

Stomatal conductance is one of two factors regulating exposure-response relationships; the other 

factor is atmospheric conditions (e.g., air temperature and wind conditions) that regulate the 

rate of O3 deposition onto the plant canopy.

Genetic variation in response to O3 implies that O3 pollution is capable of influencing plant 

populations through natural selection. Although challenging to prove with long-lived tree 

species, the selection pressure of ozone has been demonstrated with herbaceous plant material 

(Heagle et al. 1991, Lyons et al. 1997). In common plantain (Plantago major) and white clover 

(Trifolium repens), O3 exposures under controlled conditions caused changes that increased 

the level of resistance to O3 by reducing the genetic diversity of the plant populations (Kohut 

Figure 1.—Ozone and the disease triangle concept: A sensitive genotype, at a susceptible

developmental stage, is exposed to elevated O3 concentrations in a predisposing environment for the

minimum time required for physiological dysfunction to occur. The result is an ozone-induced disease

condition that may or may not lead to significant impacts on the forest ecosystem. Each component of

the triangle displays considerable variability, and as one component changes, it affects the degree of

disease severity within an individual plant and within a plant population.

Duration 

Frequency 

Concentration 

Soil moisture

Air temperature 

Light intensity 

Nutrient availability 

Species

Genotype

Leaf age 

Leaf position in the canopy

AMOUNT of O3 -INDUCED

DISEASE

Disease severity within an individual plant or 

within a population of plants. 


2003). A similar effect has been postulated for white pine4 in which the most ozone-sensitive 

genotypes (chlorotic dwarfs) appear to have been eliminated, leaving the residual population 

more tolerant of O3 stress. The selective force of O3 on aspen populations has also been 

demonstrated (Berrang et al. 1986, Karnosky et al. 2003).

Ozone exposure, ozone dose, and ozone flux are different terms used to describe plant/pollutant 

interactions. Ozone exposure refers to the concentration of O3 present in ambient air, which 

cannot be assumed to represent the concentration available at the plant canopy for uptake and 

injury. Ozone dose refers to the amount of O3 taken up into plant tissue, and ozone flux refers 

to the rate at which plant surfaces absorb O3. Ambient ozone exposure may be relatively high, 

but if the stomates are closed, the ozone dose will be insignificant.

EVIDENCE THAT OZONE INJURES TREES

Scientists use various methods to study O3 impacts on trees. Observational field studies 

documenting the visible foliar response of trees to ambient ozone concentrations are most 

common. Researchers also test for physiological sensitivity to O3 and use exposure chambers 

to examine growth effects. Experimental studies are generally conducted on tree seedlings in 

a noncompetitive environment, and results are not necessarily transferable to mature trees 

growing in the forest. Recent developments using chamberless free air exposure systems provide 

an opportunity for longer term studies on larger plant material growing in stand-like conditions. 

The following observations have been made about the effects of ambient ozone concentrations 

on tree species: 

• More than 50 tree species from eastern and western forest types have been identified 

as ozone sensitive based on foliar injury symptoms. Symptoms are prevalent both on 

young trees and in the canopies of mature trees.

• Ozone can affect the genetic base of sensitive species through the elimination of 

sensitive clones and hypersensitive genotypes in high O3 areas.

• Ozone can reduce carbon fixation (photosynthesis), increase foliar and root respiration, 

shift the partitioning of carbon into different chemical forms, and disrupt carbon and 

nutrient allocation patterns.

• Ozone can suppress the movement of carbon to the roots, causing reductions in root 

biomass and effects on root function including reduced ability to form mycorrhizal 

associations and use water and nutrients efficiently.

• Ozone can alter tree physiology so that sensitive species are more susceptible to secondary 

stresses including insects, diseases, and weather extremes such as cold or drought.

• Ozone can alter the survivability of sensitive species in mixed forest stands so that the 

structure and function of the ecosystem are permanently changed.

4

The scientific names of tree and shrub species referred to in this report are listed in Appendix 4. 


These statements are elaborated on in this report, starting with foliar injury effects and ending 

with documented effects on whole plant communities. Studies on individual tree species that 

have received most of the attention in the published literature are emphasized. The challenges 

of working with natural systems under ambient conditions of ozone exposure to assess and 

predict ozone impacts are discussed. For additional detail and a more comprehensive list 

of citations, see the 2006 EPA ozone criteria document available online at http://www.

epa.gov/epahome/index.html. This document evaluates all the latest peer-reviewed data on 

ambient ozone environmental effects on vegetation and ecosystems, and it is a good source of 

information for researchers interested in stand-level and ecosystem-level modeling. There are 

also published reviews by Chappelka and Samuelson (1998), McLaughlin (1985), Miller et al. 

(1996), Percy et al. (2003a), and Karnosky et al. (2007). Chappelka and Samuelson (1998), 

who focus on the eastern U.S., and Percy (2003a), who looks at a more global perspective, 

provide an excellent summary of sources of error and uncertainty inherent in field assessment 

studies. Similar analytical concerns related to ozone injury assessment in western pine forests 

are addressed by Miller et al. (1996). Karnosky et al. (2007) focus ahead on ecosystems and 

future research needs.

Foliar Injury

Ozone cannot be identified as a causal agent of stress or disease for a given tree species unless 

the cause and effect relationship between the host species and O3 has been established under 

controlled conditions of growth and exposure. Controlled fumigation studies were used 

early on to identify a large number of tree species as ozone sensitive based on foliar response. 

Visible symptoms on broadleaf plants and tree species are characterized primarily by changes 

in pigmentation that appear under magnification as small red, purple, or black stipples on 

the upper leaf surface. On conifers, the two most common symptoms are chlorotic mottle 

and tipburn. For both broadleaf and coniferous trees, injured leaves and needles may drop 

prematurely. Appendix 3 provides images of classic ozone injury on a variety of tree species and 

bioindicator plants, and Appendix 4 contains a comprehensive list of tree and shrub species 

identified as O3 sensitive based on foliar injury response.

Visible foliar injury is categorized as either acute or chronic. Acute injury is normally associated 

with short exposures (hours) to relatively high ozone concentrations (>80 ppb) and usually 

appears 24 hr after exposure. Chronic injury, whether mild or severe, is associated with 

long-term or intermittent exposures to relatively low ozone concentrations (<60 ppb) over 

the entire growth cycle or lifespan of the plant. Acute injury symptoms normally involve 

significant pigment changes and cell death, and the injury may be bifacial or involve tipdieback on conifers. Chronic injury symptoms develop slowly within days or weeks following 

exposure and are characterized by a general upper leaf surface pigmentation and premature 

leaf senescence. On conifers, chronic injury develops as mottled chlorosis of the needle tip, 

progressing basipetally until the entire needle is affected (Stolte 1996). Most forests in the U.S. 

are subject to chronic ozone exposure, although forest land downwind of major metropolitan 

areas also sustain occasional spikes (hours) or episodes (days) of acute exposures associated with 

temperature inversions. Chronic foliar injury symptoms are the norm under conditions of 

ambient ozone exposure and they become increasingly visible on sensitive plants over the course 

of the growing season.


Field surveys conducted in the Great Smoky Mountains National Park from 1987 to 1992 

identified 33 tree species as sensitive to ambient ozone concentrations (Neufeld et al. 1992). An 

observational study conducted in Shenandoah National Park reported similar findings (Skelly et 

al. 1983). The species exhibiting the most severe and consistent injury response at both locations 

included black cherry, sassafras, yellow-poplar, and white ash. A field survey in Ohio and 

Indiana over a 2-year period demonstrated that drought conditions have a controlling influence 

on the development of visible injury symptoms on sensitive plants (Showman 1991). Although 

ambient ozone concentrations were very high in 1988, little injury was observed on sensitive 

tree species. In 1989 ozone concentrations were lower, but injury was much greater. Rainfall was 

much less in 1988 than in 1989 with drought conditions existing across the study area.

Visible foliar injury to ozone-sensitive trees indicates lost photosynthetic area, and it is 

reasonable to suggest that sustained foliar loss will have a negative impact on growth. Still, 

the preponderance of evidence indicates no consistent relationship between foliar injury and 

growth reductions except in some hypersensitive genotypes. Absence of a growth effect despite 

significant foliar injury suggests the presence of compensatory processes (Pye 1988). For 

example, photosynthetic rates in uninjured portions of the leaf or tree canopy may increase to 

compensate for reduced rates of photosynthesis in canopy leaf tissues damaged by O3. For many 

shade-tolerant species and at least one shade-intolerant species, greater leaf sensitivity to O3 has 

been observed in shaded vs. sunlit environments (Samuelson 1994, Tjoelker et al. 1993, Wei 

et al. 2004). This difference is related to the ability of shade-tolerant species to maintain high 

stomatal conductance and O3 uptake in low light.

There is a large information base on the development and recognition of ozone-induced foliar 

injury symptoms on trees and forest vegetation: some in published texts (Krupa et al. 1998, 

Skelly et al. 1987) and some on agency Web sites5. Not all researchers agree about which 

species are ozone sensitive, especially at ambient O3 concentrations, which is not surprising 

given that ozone sensitivity and the development of visible injury symptoms are affected by 

genotype, tree developmental stage, foliar position, leaf age, leaf position in the sun or shade, 

and a combination of edaphic and climatic factors that are not fully understood (Heck 1968, 

McCool 1998, Zierl 2002). There is agreement that ozone-induced foliar injury symptoms 

occur routinely on a variety of eastern and western forest tree species during the growing season. 

Further, the detection of foliar injury in natural areas is diagnostic for the presence of phytotoxic 

ozone concentrations in U.S. forests.

Growth and Vigor

Researchers studying O3 effects on growth and growth-related processes work with tree seedlings 

because they are easy to manipulate and affordable. Experimental conditions can be controlled 

and hypotheses tested without the confounding influences of the natural environment. In 

general, the findings from controlled environment studies on tree seedlings demonstrate 

unequivocally that O3 has the potential to cause a wide range of negative effects on growth 

and tree vigor (U.S. EPA 1996b, U.S. EPA February 2006b,c). Except in a few cases, it is not 

certain that these documented effects can be used to estimate or predict O3 effects on saplings or 

mature trees in the forest.

5

Key references and Web sites on ozone injury symptoms are provided in Appendix 3.
10

Western Pines and Associated Species

Miller and his colleagues were responsible for much of the early work on ponderosa and 

Jeffrey pines in the San Bernardino Mountains downwind of the smog-laden Los Angeles Air 

basin (Miller et al. 1963, Miller and Elderman 1977, Miller et al. 1982, Miller et al. 1989, 

Williams 1980). Fumigation studies with tree seedlings revealed O3 as the main cause of foliar 

injury manifested as chlorotic mottle and premature needle senescence. Current-year needles 

of ponderosa and Jeffrey pines become symptomatic when summer ozone exposure levels are 

high and soil moisture levels are favorable for ozone uptake through open stomata. Injury 

begins as the walls of the cell layer just below the epidermis degrade, causing the loss of cellular 

contents and a degradation of chlorophyll within the cells (Stolte 1996). The loss in chlorophyll 

beneath the epidermis appears on the needle surfaces as chlorotic blotches with diffuse borders 

that occur in no regular pattern, giving a yellow mottled appearance: hence the term chlorotic 

mottle. On ponderosa and Jeffrey pines, this foliar symptom indicates ozone pollution (Grulke 

and Lee 1997, Olson et al. 1992).

When ozone injury is severe, the disruption in biological processes in the needles eventually 

leads to accelerated needle loss, which leads in turn to a reduction in crown vigor, leaving 

the damaged pines more susceptible to secondary stress organisms. Researchers examined the 

relationships between ozone pollution and bark beetle attacks on ponderosa pines and found 

that trees with severe ozone injury suffered changes to the structure and chemistry of the 

phloem tissues, reducing the natural resistance of the trees to bark beetle attack (Cobb et al. 

1968, Cobb and Stark 1970). Ozone does not kill trees, but it does weaken the host plants 

and leaves them vulnerable to aggressive infestation by bark beetles that do cause tree decline 

and mortality. Even in the absence of bark beetle attack, growth losses attributable to ozone 

pollution have been documented in both symptomatic and asymptomatic ponderosa and Jeffrey 

pines (Williams 1980).

The most ozone-sensitive ponderosa and Jeffrey pines have disappeared from the mixed conifer 

forest type of southern California, which is the only documented case where a tree-level effect 

induced by O3 on a particular forest type has resulted in a significant disturbance to the 

structure and function of the ecosystem. Over a 14-year study period, Miller and his colleagues 

were able to demonstrate that ozone-sensitive ponderosa and Jeffrey pines lost basal area in 

relation to other species more tolerant of ozone. The elimination of ozone-sensitive pines and 

the accumulation of ozone-tolerant species in the understory present a fuel ladder that puts the 

remaining overstory trees in increased danger in the event of a catastrophic fire. Further, the 

ozone-tolerant species are more susceptible to fire damage because of thinner bark and branches 

closer to the ground.

More recent studies on the sensitive pine forests in the San Bernardino Mountains have 

confirmed that average ambient ozone concentrations of 50-60 ppb are sufficient to cause 

foliar injury, reduced carbon fixation, and significant growth declines in bigcone Douglas-fir, 

as well as ponderosa and Jeffrey pines (Bytnerowicz and Grulke 1992, Peterson et al. 1991). 

The combined effects of prolonged drought and high ozone (Arbaugh et al. 1999) and O3 and 

high nitrogen deposition (Grulke and Balduman 1999) are contributing to growth disturbances 

in the forest that researchers believe may lead to the eventual replacement of pines by more 

nitrogen-tolerant and ozone-tolerant tree species (Arbaugh et al. 2003, Panek 2004) in the not 
11

too distant future. Other western species that have been tested for ozone sensitivity include 

western white pine and California black oak (foliar sensitivity), western hemlock (dry weight 

reduction), western redcedar (no effect), and giant sequoia (no effect).

Eastern White Pine

In the 1960s, researchers started reporting foliar symptoms on white pine thought to be caused 

by ozone pollution (Berry and Hepting 1964, Dochinger and Seliskar 1970). Symptomatic white 

pine were described as having foliage with needle blight, emergence tipburn, semimature needle 

blight, and chlorotic dwarf. Fumigation studies established that these symptoms were caused 

by O3, although some reports suggested that foliar pathogens were contributing factors, and that 

semimature needle blight was a physiological disorder (Linzon 1967). Symptoms that are visible 

on young developing needles in early summer to midsummer involve collapse of the palisade cells 

next to the stomata, causing light flecking and chlorosis close to stomata openings. As injury 

and chlorophyll loss progress, chlorotic flecks become more visible and trees develop a yellowishgreen color characteristic of ozone-induced symptoms on white pine. Affected needles may further 

exhibit brown needle necrosis and tipburn and eventually fall off. Many affected trees are found 

growing side by side with non-affected (ozone-tolerant) trees, indicating genotypic differences in 

susceptibility to O3. Ozone-sensitive genotypes of white pine hold their needles for only 1 to 2 

growing seasons compared to 3 to 5 growing seasons for ozone-tolerant white pine.

The most sensitive white pine genotypes have short stunted needles, poor needle retention, and 

only minimal height and diameter growth each year, resulting in a condition known as chlorotic 

dwarf (Dochinger 1968, Karnosky et al. 2007). These hypersensitive individuals appear to have 

been eliminated from the gene pool because they are no longer found in the forest. Reports of 

ozone-sensitive trees with the chlorotic fleck symptom are common throughout the range of 

eastern white pine. However, much of the resource remains unsampled (Bennett et al. 1994), 

leading some to suggest the sensitivity of the species has been exaggerated. Although there is 

evidence of a relationship between chlorotic fleck on ozone-sensitive white pine and reduced 

annual growth increment (Benoit et al. 1982), there is no indication that ozone stress has caused 

widespread mortality in the white pine growing stock. 

Trembling Aspen and Aspen Clones

Aspen is another important and widely distributed tree species that is ozone sensitive (Karnosky 

1976, 1989). Foliar injury symptoms on trembling aspen are visible as moderate to large, 

black, necrotic areas that extend across the leaf veins and are bifacial. Ozone exposure leads 

to accelerated physiological maturity and leaf senescence, decreased photosynthesis and 

chlorophyll, and adverse effects on growth such as root growth. Under controlled conditions in 

the greenhouse, researchers have found evidence that adverse effects of O3 on aspen root growth 

can have indirect carryover effects on growth in the following growing season (Woodbury et al. 

1994). Under natural conditions, an ozone-induced reduction in root growth might be expected 

to make trees more susceptible to drought and nutrient deficiency (Greitner et al. 1994). Trees 

stressed by O3 are more susceptible to leaf rust fungi and a number of other foliar pathogens. 

Ozone predisposes aspen to attacks by leaf rust fungi by altering the leaf surface waxes so that 

leaves are more wettable, thereby creating a microenvironment on the leaf surfaces that is 

favorable for fungal spore germination and subsequent infection by fungi (Karnosky et al. 2002, 

Percy et al. 2003b).
12

Because there is much genetic variation in aspen response to high ambient ozone 

concentrations, forest geneticists postulate that O3 has had an impact on aspen populations 

through natural selection in areas with relatively high ozone exposure. Evidence for this is 

provided by two fumigation studies conducted with aspen clones collected from natural 

populations growing in polluted and nonpolluted areas. Aspen collected from areas with high 

ambient ozone concentrations sustained less visible injury than clones collected from areas with 

low ambient ozone concentrations (Berrang et al. 1991). The authors inferred that the selective 

force of O3 had already eliminated the more sensitive aspen genotypes in the more polluted 

areas. Sensitive aspen genotypes are not killed directly by O3 but are eliminated through 

intraspecific competition for light, nutrients, and water with their ozone-resistant neighbors. 

Evidence from open-air exposure systems (e.g., FACE6) demonstrates that longer term exposure 

(5 to 10 years) to lower level ozone concentrations has a consistently negative effect on aspen, 

significantly reducing height and diameter growth, and increasing foliar susceptibility to 

herbivorous insects (Isebrands et al. 2001, Percy et al. 2003b).

Black Cherry

Black cherry is highly variable in its response to O3 ranging from sensitive to intermediate to 

tolerant with respect to visible foliar symptoms (Lee et al. 1999). Ozone-induced symptoms 

on black cherry show up in the latter half of the growing season following repeated exposure to 

high ambient ozone concentrations. The symptoms are known as upper-leaf-surface stipple and 

appear as a reddish-purple or black pigmentation between the major veins of the leaf (Davis 

et al. 1982). Severely damaged leaves will drop prematurely. Season-long exposures to ambient 

O3 have been shown to have many adverse effects on black cherry seedling growth, but ozoneinduced symptoms on mature trees in the field leading to significant growth declines have not 

been demonstrated (Karnosky et al. 2007, Neufeld et al. 1995, Rebbeck 1996).

Chevone (2002) identified ozone-tolerant and ozone-sensitive genotypes of black cherry in the 

field based on foliar response and then measured photosynthetic activity and leaf gas exchange 

rates on these same plants. Because these two processes control carbon assimilation of the tree 

canopy, any adverse effects of ambient ozone concentrations on these processes will eventually 

cause a corresponding negative effect on biomass accumulation, growth, and reproductive 

capacity. Ambient ozone had a significant negative effect on the photosynthetic function of 

sensitive black cherry trees. As leaf symptoms increased, chlorophyll content decreased in a 

linear fashion. Measurements of maximum net photosynthetic rates followed a similar pattern. 

However, sampling was confined to a single leaf position on a small subsample of the canopy. 

Many more measurements of different aged leaves that have been exposed to O3 for different 

lengths of time are needed to come up with an estimation of O3 effects at the canopy level. 

Impaired canopy-level photosynthesis would be evidence of a direct link between ozoneinduced visible foliar injury and biomass loss.

Other Eastern Species

Duchelle et al. (1982) used charcoal-filtered and ambient air open-top chambers to study O3

effects on height growth of seven forest tree species native to the eastern U.S. After 2 years of 

exposure under natural field conditions, tulip-poplar and green ash seedlings exhibited growth 

6

FACE = Free-Air Carbon Dioxide Enrichment. For more information, go to: http://aspenface.mtu.edu/.
13

reductions of 44 and 77 percent respectively, while black locust, Virginia pine, eastern white 

pine, table mountain pine, and eastern hemlock seedlings showed a range in height growth 

loss of 13 to 23 percent. Similar adverse effects of O3 on tree growth have been reported for 

multi-year open-top field chamber studies with loblolly pine seedlings. Ozone-induced growth 

reductions of loblolly may occur with no visible evidence of needle injury or effects on pigment 

concentrations (Kress and Skelly 1982, Shafer and Heagle 1989).

Researchers differ in their assessment of possible O3 effects on some of the commercially 

important southern pines like loblolly pine. Seasonal O3 exposures have been shown to reduce 

needle net photosynthesis and growth in loblolly and shortleaf pine seedlings (Chappelka 

and Samuelson 1998), but a longer 3-year study indicated little effect of ambient ozone on 

loblolly (Kelly et al. 1993). Other southern pines where adverse growth effects have been 

reported include slash pine, Virginia pine, and table mountain pine. Species where experimental 

fumigations or field studies showed no adverse effect of O3 on physiological processes or growth 

include red spruce and Fraser fir (Chappelka and Samuelson 1998).

Simini et al. (1992) examined ambient ozone effects on black cherry, yellow-poplar, northern 

red oak, and red maple seedlings and observed no effects on growth after 2 years even though 

both black cherry and yellow-poplar exhibited visible foliar injury and premature leaf drop 

in both years of the study. In a similar study, Rebbeck (1996) exposed black cherry, yellowpoplar, and sugar maple seedlings for two growing seasons to ambient and twice-ambient ozone 

concentrations and reported a significant decrease in aboveground biomass in black cherry 

but no effect of O3 on the growth of yellow-poplar and sugar maple. Others have reported 

that yellow-poplar shows no negative effect of O3 on net photosynthesis or growth despite 

this species being considered highly sensitive to O3 based on foliar response (Chappelka and 

Samuelson 1998, Jensen 1973, Kress and Skelly 1982). After one growing season, significant 

growth reductions were observed in hybrid poplar (Populus masimowiczii x trichocarpa, NE 

388), although not in eastern cottonwood or black locust exposed to either ambient (nonchambered) or nonfiltered (ambient air) chambers compared with plants grown at sub-ambient 

ozone levels (Wang et al. 1986).

Ecosystem Effects

Little work has been done with ozone effects on forest communities or ecosystems apart from 

that already mentioned for the mixed conifer forest type of southern California (Miller et al. 

1996). In the early 1970s, Treshow and Stewart (1973) used portable field chambers to expose 

a community of 70 plant species to above-ambient (>0.15 ppm) ozone concentrations in the 

grassland, oak, aspen, and conifer communities in Utah. The fumigations caused visible injury 

to more than half the perennial forbs and woody species in the study area. This result led the 

authors to postulate that lower, ambient concentrations of O3 over an extended exposure period 

might well be expected to impair growth and affect community vigor and stability. These effects 

were never validated in the field.

Barbo et al. (1998) in Alabama strengthened the argument that O3 is having an impact on the 

eastern forest community. Open-top field chambers, with and without O3, were placed in a 

pine forest that had been recently cleared, to study ozone effects on the regeneration of native 

plant species. After 2 years, the developing plant community in the carbon-filtered air (no O3) 
14

showed significantly greater canopy cover, more layers of foliage, and greater species diversity 

than the community of plants in the ozone chamber. The authors concluded that elevated 

O3 can simplify plant community structure, presumably by altering competitive interactions 

between plants and eliminating some species.

Researchers have used scaling techniques and statistical approaches to develop risk assessment 

models that can simulate exposure/response scenarios and predict forest response to O3

(Chappelka and Samuelson 1998, Karnosky et al. 2007). Models tested so far indicate that 

ozone is likely affecting forest growth in both eastern and western forest types (Hogsett et al. 

1995, 2004; U.S. EPA February 2006b). Predicted growth losses have been estimated to be 

greater than 20 percent for black cherry and aspen, 5 to 12 percent for yellow-poplar, loblolly 

pine, sugar maple, and white pine, and zero percent for red maple and Virginia pine. Similarly, 

estimates of increasing growth loss for ponderosa pine correlate well with increasing ozone 

exposures (Tingey et al. 2001, 2004), results that appear to be substantiated by field studies 

along a natural ozone gradient (Grulke and Balduman 1999). The largest uncertainty factor in 

these models is their dependence on published seedling data to estimate mature forest response. 

Moreover, the models are not yet developed enough to take into account the relative influences 

of insect pests, biotic pathogens, and abiotic stressors (other than O3) in contributing to the 

measured (modeled) changes in forest health and productivity (Chappelka and Samuelson 

1998). An exhaustive discussion of modeling, scaling, and other techniques designed to 

translate known effects of O3 on seedlings to potential impacts on mature trees, forest stands, 

and ecosystems is presented in the EPA air quality criteria document for O3 and related 

photochemical oxidants (U.S. EPA February 2006c).

For forest health monitoring, it is reasonable to suggest that an ecosystem exposed year after 

year to increasing levels of ambient ozone, is not the same as an unexposed ecosystem even if we 

are unable to discern any immediate effects of O3 due to the inherent complexity and variability 

of natural systems (Anderson and Grulke 2001, Teskey 1995). Bennett et al. (2006) were able to 

measure relationships between plant response and O3 in field-grown black cherry and milkweed 

by using a growth attribute directly responsive to current-year ozone exposures (growing branch 

tips) and a very high sample count (thousands of plants). The authors suggest that increasing 

sample size allows relatively small effects occurring in real-world field conditions to be both 

measurable and statistically significant. Chappelka and Samuelson (1998) agree that increasing 

experimental replication and identifying the most ozone-responsive whole-tree measurement 

attribute will improve the accuracy of risk assessment models.

Modeled forest responses to O3 depend on improved data from mature trees in natural settings 

such as that provided by McLaughlin et al. (2007a). The authors combined intensive field 

measurements with climatic data to test the hypothesis that ambient ozone concentrations 

increase water stress in forested ecosystems. Over a 3-year period, they examined relationships 

among stem growth, sap flow, and soil moisture measurements in a mixed deciduous forest 

in Tennessee and found compelling evidence that periods of peak ozone exposure throughout 

the growing season were followed by measurable increases in sap flow and tree-stem water 

loss that led to substantial losses in seasonal stem growth. The cumulative negative effects of 

O3 on stem increment growth were evident in 9 of the 10 tree species tested and exceeded 30 
15

percent in an average ozone year and 50 percent in a high ozone year. The most sensitive species 

(pitch pine and red oak) also suffered a pronounced inability to recover from periods of high 

moisture demand that frequently accompanied peak ozone exposures. In a companion study 

(McLaughlin et al. 2007b), the authors reported that ozone-induced increases in water use by 

forest trees were accompanied by significant reductions in soil moisture in the rooting zone of 

sample trees and contributed significantly to reduced late-season streamflow in the associated 

watersheds.

Clearly, the potential for harmful effects of O3 on forest health has been demonstrated and 

the risk is rightly assumed to be great. The ongoing FIA ozone biomonitoring program 

maps geographical incidence and patterns of ozone stress to gain insight into plant/pollutant 

interactions. Each ozone season is unique, a result of the relationship between causal agent and 

host plant as influenced by each season’s exposure environment and the ever variable interplay 

between predisposing and compensatory factors. The effort to understand the risk of O3 to 

forest health demands a long-term commitment to observation, data collection, and study.

OZONE BIOMONITORING AND 

FOREST HEALTH ASSESSMENT

Ozone is the only regional gaseous air pollutant that has been measured at known phytotoxic 

levels at both remote and more urbanized forest locations across the continental United States 

(Heck and Cowling 1997, Lefohn and Pinkerton 1988). As such, O3 is of primary concern for 

United States forests (Coulston et al. 2004). Ozone can be monitored using physical air quality 

monitors including UV photometric analyzers that continuously measure the amount (ppm) 

of O3 in a sample of ambient air, and passive sampling devices that depend on air diffusing 

across a filter to provide an estimate of weekly or seasonal average ozone concentrations. UV 

photometric analyzers require expensive calibrations, shelter, electricity, and computer hookups 

and are used primarily in populated areas to assess the risk of unhealthy ozone concentrations 

on human health. Passive sampling devices are a less expensive alternative that have been 

used successfully for ozone sampling in national parks and other less populated areas (Ray 

and Flores 1994), but significant costs are still associated with setup, data collection, and lab 

analyses. Ozone can also be monitored by visually evaluating the amount and severity of ozoneinduced foliar injury to sensitive bioindicator plants, an approach known as biomonitoring 

(Chappelka et al. 1997, Fedder 1978, Kohut et al. 1997, Krupa et al. 2001, Manning 2003). 

Biomonitoring provides information on ozone effects rather than ozone concentrations in 

ambient air, although dose-response models are available for some plant species. It is a low cost 

method that can be applied anywhere ozone-sensitive species are growing whether in urban, 

rural, or remote forested areas, and the findings are directly relevant to an assessment of O3

stress on our Nation’s forests.

The FIA ozone indicator is designed to determine the presence or absence of ozone injury 

conditions on the FIA Detection Monitoring plots also know as Phase 3 (P3) forest health 

plots. Ozone injury conditions are characterized by the prolonged exposure of sensitive plants 

to elevated ozone concentrations in a predisposing environment. Under these conditions, O3

causes direct foliar injury to many native plant species. This visible injury response is used 

to detect and monitor ozone stress in the forest environment. The ozone-sensitive plants 
16

(individuals within a species) that exhibit typical foliar injury symptoms when exposed to 

O3 are called bioindicators or, more specifically, ozone detectors (Krupa et al. 1998). Ozone 

bioindicators detect phytotoxic levels of O3 when edaphic and atmospheric conditions are 

conducive to ozone uptake through the stomates. Detection of ozone-induced foliar injury 

on bioindicator plants tells us not only that ambient ozone concentrations were high for a 

particular time and place, but also that other necessary conditions that allow ozone uptake 

and injury (e.g., adequate light, nutrition, and soil moisture) were also present. This concept 

of necessary conditions for injury defines the biological relevance (i.e., value) of the FIA 

biomonitoring data. A physical, air sampling monitor (active or passive) may provide absolute 

concentrations of O3, but it cannot indicate how much of the O3 is taken up into plant tissues. 

In contrast, the bioindicator plant provides a visible record of the O3 dose that has immediate 

biological relevance. Ambient O3 levels may be high, but if trees do not “see” this ozone because 

the stomates are closed, then the ozone stress is insignificant. By the same token, moderate 

ozone concentrations may generate significant injury response if external conditions are such 

that ozone uptake is enhanced.

The Conceptual Model

The conceptual model for the ozone indicator (Fig. 2) illustrates how foliar injury can be used as 

an indicator of environmental stress (i.e., ozone air quality 7) and forest condition (i.e., potential 

impacts of ozone on tree-level and ecosystem-level components). When O3 contaminates 

the environment, the bioindicator plant shows a visible response. Other components of 

the ecosystem may show a physiological response, but the visible response is lacking. Some 

plants are resistant to ozone stress and do not respond. In Detection Monitoring, we are most 

concerned with the sensitive ozone bioindicator plants. This sensitive component of the system 

will allow us to detect any early change in forest condition that may be associated with poor 

air quality (i.e., O3 pollution). Stress-induced changes in the structure and function of an 

ecosystem begin with the response of the most sensitive individuals of a population (Laurence 

and Anderson 2003, Reich 1987). As time passes, and the stress continues or is magnified, 

effects may be transmitted from tree level to ecosystem level. Examples of ozone-induced foliar 

injury, tree-level alterations in growth and vigor, and ecosystem-level changes in structure and 

function were outlined in the previous section of this report. 

In the conceptual model, the bioindicator plant provides the visible link between ozone 

exposure and effects. When bioindicator species are also an important component of the forest 

community (e.g., Prunus and Fraxinus spp. in the East and Pinus and Populus spp. in the 

West), they provide an even more compelling record of environmental impact. In Detection 

Monitoring, the ozone indicator provides estimates of environmental stress and probable 

impact (risk) and detects changes in these two estimates over time. If and when changes are 

detected that are outside normal bounds, Evaluation Monitoring studies are warranted to more 

closely examine the causes and consequences of the change in health or condition of the forest 

ecosystem.

7

Ozone air quality is a commonly used phrase that refers to the amount of O3 in the air. Ozone air quality 

may be good (low O3), moderate (intermediate O3), or poor (high O3) with respect to potential effects or 

stress on biological systems.
17

Figure 2.—Conceptual model for the ozone indicator. See text (page 18) for associated forest health assessment questions.

OZONE BIOINDICATOR 

PLANTS

Injured Foliage 

------------------------------------- 

Susceptible Host - Visible Injury 

Susceptible Host – Invisible injury 

Resistant Host – No injury 

FOREST CONDITION 

Tree-level ĺEcosystem-level Dysfunction 

Visible damage to leaves and needles 

Increased leaf senescence 

Altered carbon allocation 

Reduction in growth increment 

Changes in crown condition 

Predisposition to other stresses 

Changes in species composition 

Altered water use and nutrient cycling 

ENVIRONMENTAL STRESSOR (O3)

Elevated ozone concentrations coincident with other necessary 

conditions for injury result in direct and indirect (or delayed) 

effects on bioindicator plants and other sensitive components of 

the forest ecosystem. 

Environmental Value: low air contamination 

Environmental Values: scenic beauty and ecological integrity 

Indicates

Indicates

Cause and effect 

Cause and effect 
18

Assessment Questions

The ozone indicator has been part of Detection Monitoring activities in FHM since 1990 when 

the first forest health ground plots were established in New England. The early implementation 

of this indicator underscores its importance in current forest health issues and societal values 

(Montreal Process 1995). The specific environmental values that the ozone indicator addresses 

include both consumptive (e.g., growth increment) and nonconsumptive (e.g., scenic beauty) 

use of the forest resource. The following forest health assessment questions derive from the 

conceptual model (Fig. 2) and provide the essential rationale for implementing the ozone 

indicator in FIA.

Are phytotoxic concentrations of ozone present in the forest ecosystem?

Is regional air quality (e.g., ozone pollution) changing over time?

If so, is it improving or deteriorating?

In what percentage of a region or forest type is there an indication of ozone air quality 

impacts on scenic beauty as defined by measurable changes in leaf color, leaf size, or leaf 

number?

In what percentage of a region or forest type is there an indication of ozone air quality 

impacts on ecological integrity as defined by measurable changes in biodiversity, growth 

increment, crown condition, or damage?

Answers to questions 1, 2, and 3 provide a direct assessment of air quality (i.e., O3 stress) across 

the landscape (environmental value = low air contamination). Answers to questions 4 and 5 

address impact (environmental values = scenic beauty and ecological integrity). The link between 

the biological ozone data and issues of air quality is direct and of immediate interpretive value. 

The links to impacts on scenic beauty or growth are less direct and involve evaluating correlations 

between the foliar response data and other FIA indicators. Risk of impact is examined by mapping 

the overlay between areas of high biological response (i.e., moderate to severe foliar injury) and 

the natural range of certain ozone-sensitive species (Coulston et al. 2004). The use of the 

bioindicator data for forest health risk assessment is described later in this report.

Issues of Uncertainty

Despite the wealth of scientific studies documenting ozone injury on sensitive plants and the 

long history of bioindicators in air quality research, we lack a complete scientific understanding 

of the relationship between foliar injury and plant growth. Many studies have linked foliar 

damage to immediate or delayed impacts on a variety of growth processes. However, most 

studies conducted under artificially controlled conditions in the growth chamber have found 

little evidence of a direct relationship between foliar injury and growth. Further, adverse 

effects noted in the growth chamber have been indiscernible in the field. The only undisputed 

exception to these observations comes from the scientific record of ozone impact on ponderosa 

pine in California. Studies with other tree species where a direct relationship between foliar 

injury and growth increment has been indicated do not fully account for the influence of other 

biotic and abiotic factors on the results. Even in the sensitive pine forests of California, new 

1.

2.

3.

4.

5.
19

evidence suggests drought and nitrogen are as important as ozone to the stress-driven changes in 

this ecosystem. Scientific uncertainty lies in whether significant changes are occurring in other 

forest types, at the ecosystem level, in response to chronic ozone exposure. The biomonitoring 

effort in FIA provides one way to address this uncertainty. The extensive biological network, 

represented by the FIA ozone biomonitoring program, provides detailed information on 

ozone exposure and plant response relationships under natural conditions and in the complex 

environment of the forest. As biomonitoring data from the FIA program become more widely 

available and are used more often for ecological assessments and forest health models, we expect 

to gain greater understanding of plant/pollutant interactions and reduce uncertainty.

Forest Health Assessment at the State and Regional Levels

The issue of O3 and forest health is of interest and importance to policymakers, forest managers, 

university researchers, and the general public. The ozone indicator data are easily summarized 

in tables and maps to provide detection-level information on where ozone stress is occurring 

across a state, region, or ecological region. Reporting on the ozone indicator provides an 

opportunity to educate and inform the public about ground-level ozone, ozone effects on trees, 

and biomonitoring. Although the data should not be used to make definitive statements about 

forest health, it does provide easily interpreted information on the presence and absence of a 

specific environmental stress (i.e., ozone pollution) that clearly relates to the maintenance of 

forest health and vitality (Montreal Process 1995).

In relatively clean air regions of the U.S. (e.g., the Northern Plains), the data serve as a baseline 

of ozone stress and a documentation of the presence or absence of ozone stress in areas where 

historical air quality data are completely absent. In regions with relatively poor air quality (e.g., 

the mid-Atlantic), the emphasis should be on mapping the extent and severity of the stress 

across the landscape, any change in extent and severity of the stress over time, and an assessment 

of probable impacts with respect to the distribution of ozone-sensitive tree species in the region. 

Core data for the ozone indicator include tabular data describing plot-level counts and injury 

indices, and two national maps of interpolated ozone injury data and air quality.

Using the ozone summary tables and maps, analysts responsible for state and regional forest 

health assessment reports address the following questions:

How many biosites are evaluated/injured?

How many plants are evaluated/injured? 

What species are used for biomonitoring? 

Do the injury data indicate that phytotoxic concentrations of ozone are present in the 

forests? 

Do the injury data indicate that ozone air quality is changing over time? 

If so, is it improving or deteriorating? 

Where is the injury most severe, or frequent? 

1.

2.

3.

4.

5.

6.

7.
20

What is the relationship between ambient ozone concentrations and the injury data?

What amount of forest land is subject to levels of ozone pollution that may cause 

negative impacts?

 What volume of ozone-sensitive species is at risk, and where is it?

If time and resources allow, the data should be further analyzed to identify localized areas of 

moderate to high risk where evaluation studies are warranted and to determine if there is a 

relationship between areas of moderate to high risk and other FIA indicators of tree health 

and condition. Results may be summarized by state and county but are more appropriately 

summarized and reported by region or ecological region.

DATA COLLECTION AND ESTIMATION

The primary objective of the field procedures for the ozone indicator is to establish a detectionlevel ozone biomonitoring site within each polygon on the FIA ozone grid (Fig. 1 in Smith et 

al. 2007). These sites are used to detect and monitor trends in ozone air pollution injury on 

sensitive species. The scope of the program is national, but procedures are amended regionally to 

reflect differences in ozone exposures, growing season, topography, and forest type. This section 

provides an overview of the sampling grid and sampling rules for the ozone indicator. Quality 

assurance procedures are reviewed and data quality results are summarized to demonstrate crew 

performance and the reliability of the data. Estimation procedures used to generate the national 

ozone risk map are briefly described.

National Protocol

Ozone sampling occurs on a unique national grid based on the Environmental Monitoring 

and Assessment (EMAP) design (White et al. 1992). The ozone grid consists of a single panel 

of ozone biomonitoring plots that are measured annually (Fig. 3). The grid design generates 

differing sampling intensities across the landscape based on the best available information on air 

quality regimes and potential loss due to ozone damage. The strata are defined as follows:

• Stratum 0 = one ozone biosite for every 5 million acres in areas less than 7.5 percent 

forest.

• Stratum 1 = one ozone biosite for every million acres of forest land in areas with 

relatively low ozone exposure (SUM06 <10 ppm-hr) and/or where tolerant genotypes 

exist (pinyon-juniper forest type).

• Stratum 2 = one ozone biosite for every 500,000 acres of forest land in areas with 

moderate ozone exposure (SUM06 10-25 ppm-hr).

• Stratum 3 = one ozone biosite for every 250,000 acres of forest land in areas with 

relatively high ozone exposure (SUM06 >25 ppm-hr).

Additional criteria are used to adjust and finalize the ozone sample (e.g., no state has less than 

five ozone biosites) to achieve a national total of 1,239 ozone biomonitoring sites, comparable 

8.

9.

10.
21

in number to the P3 plot sample for other forest health indicators (Smith et al. 2001). 

Throughout this document, the ground locations used for biomonitoring are referred to as sites 

or biosites rather than plots to distinguish them from the forest inventory plots that make up 

the traditional P2 and P3 plot samples.

The ozone grid is purposive both at the grid level and at the biosite. Biosite locations on the 

ground are deliberately chosen first for ease of access and secondly for optimal size, species, 

and plant counts. The ozone biomonitoring sites vary in size and do not have set boundaries. 

They are defined by the presence of ozone-sensitive bioindicator species indigenous to each 

FIA region. One biosite is required per polygon on the national ozone grid. Some states use an 

intensified ozone grid (e.g., Vermont and Wisconsin) with the result that two or more biosites 

may be located in each polygon on the base grid. Biosite locations are mapped, geographic 

coordinates are recorded, and the same sites are evaluated every year.

Ozone injury and our ability to detect injury increase over the course of the field season. For 

this reason, the sampling window for the ozone indicator is limited. In the East, to eliminate 

problems with seasonal variability in ozone response, all foliar evaluations are conducted during 

a 3-week period from late July to mid-August within which the indicator is considered stable 

(Smith 1995). This minimizes variability and the error associated with the data collection 

Figure 3.—FIA national ozone biomonitoring grid developed from the Environmental Monitoring

and Assessment Program (EMAP) base grid (White et al. 1992). The grid has four sampling

intensities based on sensitive species and ambient ozone concentrations (see: Smith et al. 2001).
22

system. In the West, due to differences in growing season, topography, target species, and 

other regional factors that influence plant response to O3, it is difficult to identify an optimum 

evaluation window for this indicator. Nevertheless, to maintain national consistency and 

improve crew logistics, the western regions use a defined window for foliar injury evaluations 

that extends from mid-July through mid-August.

Data collected at each site are entered into an electronic file that is part of the FIA National 

Information Management System (NIMS). Basic field equipment includes a 10x hand lens for 

close examination of plant leaves for ozone injury, a forester-grade plant press for the voucher 

leaf samples, and the field data sheets for mapping and quality control.

Field Methods

Basic procedures for biomonitoring are standardized nationally and updated annually in a 

detailed peer-reviewed field manual (U.S. Forest Service 2000). The most recent version of the 

manual is available online8. The manual details training and quality assurance requirements 

for field crews, criteria for selection of biomonitoring sites and plant species, injury evaluation 

procedures, and guidelines for proper collection and handling of the leaf vouchers.

The national list of ozone bioindicator species selected for use in the biomonitoring program 

(Tables 1 and 2) was gleaned from a variety of sources including the peer-reviewed scientific 

literature, interagency reports, and communications with Federal and university researchers 

experienced in ozone biomonitoring. Species selected are relatively common across a variety of 

forest types, are relatively easy to identify and distinguish from similar species, and are ozone 

sensitive based on a combination of field evidence and causative fumigation experiments. The 

eastern bioindicator species have a long history of application in ozone field studies (Krupa et 

al. 1998, Skelly et al. 1987, Skelly 2000). Some of the western bioindicator species are not as 

well tested under natural conditions of ozone exposure, but they have received enough testing 

to justify inclusion in the program (Brace et al. 1999, Campbell et al. 2000, Duriscoe and 

Temple 1996, Mavity et al. 1995, Temple 2000) and were all recently identified as good ozone 

bioindicators by a panel of experts (USDI 2003).

The sampling rules for the ozone biosites are as follows. Biosites must be wide-open areas, 

at least one acre in size, within or alongside forested areas. Each site must contain at least 30 

individual plants of at least two bioindicator species. If 30 plants of two bioindicator species 

are not available at one location, two nearby open areas, within 3 miles of each other, may 

be combined to maximize plant counts. The 3-mile rule reflects the limit at which one can 

reasonably expect ozone levels for a given locale to be similar (Mohnen 1988). Access to the 

biosite locations must be easy, and they must be free of significant soil compaction and other 

human-made disturbance.

The characteristics of each site are described in terms of the size of the open area, elevation, 

terrain position, aspect, soil drainage (East) or plot wetness (West), soil depth, and site 

8

To download a copy of the Phase 3 Field Guide for Ozone Bioindicator Plants (Eastern U.S. and 

Western U.S.) go to: http://www.fia.fs.fed.us/ and click on FIA Library – Field Guides, Methods, and 

Procedures and scroll down to FIA Field Methods for Phase 3 Measurements.
23

disturbance using a standardized coding system. If characteristics vary significantly across 

the biosite, then the area where most of the bioindicator species are growing is described and 

variations are recorded on the site map and notes. When two nearby open areas are used, each 

location is described separately. Only a small percentage (<1 percent) of the biosites are split 

between two locations.

Table 1.—Eastern bioindicator species

Scientific name Common name

Asclepias spp. common and tall milkweed

Prunus serotina black cherry

Rubus allegheniensis blackberry

Apocynum androsaemifolium spreading dogbane

Fraxinus americana1 white ash

Sassafras albidum sassafras

Liriodendron tulipifera yellow-poplar 

Aster macrophyllus2 bigleaf aster

Liquidambar styraciflua sweetgum

Prunus pensylvanica pin cherry

1

Fraxinus pennsylvanica (green ash) is sampled occasionally in states

where white ash is not available. 2

Synonym for Eurybia macrophylla.

Table 2.—Western bioindicator species1

Scientific name Common name

Symphoricarpos oreaphilus2 mountain snowberry

Populus tremuloides quaking aspen

Pinus ponderosa3 ponderosa pine

Salix scouleriana Scouler’s willow

Physocarpus malvaceus ninebark

Vaccinium membranaceum huckleberry

Sambucus racemosa red elderberry

Alnus rubra red alder

Sambucus mexicana4 blue elderberry

Pinus jeffreyi Jeffrey pine

Artemesia ludoviciana western wormwood

Artemesia douglasiana mugwort

Physocarpus capitatus Pacific ninebark

Rhus trilobata skunkbush

Asclepias spp. milkweed

Oenothera elata evening primrose

1

Spreading dogbane (Apocynum androsaemifolium) is sampled in western

regions as of 2008. 2

Symphoricarpos spp. also included. 3

Pinus ponderosa var. scopulorum: Rocky Mountain States; P. ponderosa

var.ponderosa: West Coast States. 4

Synonym for S. cerulea.
24

Up to 30 plants of each species are randomly selected for injury evaluation. Plants less than 12 

inches in height, suppressed, shaded, or with more than half the crown out of sight or reach 

are not evaluated. The approximate locations of the plants used for evaluations are drawn on 

the site map so that the same population of plants is evaluated on return visits to the biosite. 

The entire open area is sampled until 30 plants of two (ideally, three or more) species have 

been evaluated. The eastern and western FIA regions average 3.5 and 2.5 species per biosite, 

respectively (data not shown).

At each ozone biosite, 30 individual plants of two bioindicator species and between 10 and 30 

individual plants of additional bioindicator species are evaluated for ozone injury. Each plant 

is rated for the proportion of leaves with ozone injury (injury amount) and the mean severity 

of symptoms (injury severity) using a Horsfall-Barratt (HB) scale with breakpoints at 0, 6, 25, 

50, 75, and 100 percent (Horsfall and Cowling 1978). This scale uses class breakpoints that 

correspond to the ability of the human eye to distinguish gradations of healthy and unhealthy 

leaf tissue. The recognition of ozone injury symptoms in the field is not an exact science, and 

mimicking symptoms can make field diagnosis difficult. For this reason, crews are trained to 

record amount and severity ratings for injury they are unsure of, as well as the more obvious and 

classic injury symptoms. Voucher leaf samples (three leaves of each injured species evaluated at 

each location) are collected to provide the necessary validation of the ozone injury symptom 

observed in the field by the field crews.

The voucher leaf samples are a critical aspect of the data collection procedures. Field crews 

collect a minimum of three injured leaves (broadleaved plants) or two branch samples (pine 

species) from a random sample of individual plants that show obvious ozone injury, and then 

they mail pressed leaf samples to a regional expert for review. The experts are trained plant 

pathologists with much experience in ozone injury diagnosis. One expert reviews vouchers for 

eastern bioindicator species, and a second expert reviews western bioindicator species. Following 

microscopic examination, they validate injury for all samples that show a characteristic color 

and injury pattern for O3, and that are otherwise free of confounding signs and symptoms of 

other mimicking stress agents (e.g., insects, disease, mites, or weather). If the symptoms are not 

typical of ozone-induced injury, then the field data associated with the invalidated leaf voucher 

are corrected to zero values9. If a leaf voucher is missing and unable to be validated, then the 

field data associated with the missing voucher are flagged so that they cannot be used in data 

summaries or analyses.

Quality Assurance

The ozone indicator is included in the FIA National QA Plan (U.S. Forest Service 2004). Just 

before the sampling window, ozone training and certification sessions are held in each region. 

All crews receive training in identifying bioindicator species, selecting sites, evaluating ozone 

injury, and handling vouchers. Crews with less than 2 years of experience collecting ozone 

data are audited in the first week of the sampling window. A training package is available 

that includes a detailed, well-illustrated presentation of procedures, data sheets and training 

9

Raw data collected by the field crews are maintained in each FIA region. Field data associated with an 

invalidated leaf voucher are corrected to zero values and maintained along with other edited files used for 

data summaries and analysis. 
25

handouts, biosite-level measurement codes, definitions, and measurement quality objectives, 

and color slides of ozone injury and mimicking symptoms. Experienced crews in good standing 

with 3 or more years of field experience with the ozone indicator may use the training package 

for certification.

Experienced and certified QA staff conduct field revisits and audits on 10 percent of the biosites 

in each FIA region. Measurement quality objectives (MQOs) are defined for identification 

of bioindicator species, numbers of stems evaluated, numbers of stems injured, amount and 

severity of injury, and recognition of the ozone injury symptom (Pollard and Smith 2001). 

Endpoint data quality goals are also set for the calculated biosite index value (BI) and the leaf 

voucher data (Pollard 2004). Quality assurance procedures dictate that the O3 injury symptom 

must be verified for each injured species on each site regardless of whether a regular crew or QA 

crew is evaluating the biosite. With respect to presence or absence of O3 injury and core reports 

on numbers and distribution of biosites where O3 injury is present, the voucher system virtually 

guarantees the accuracy of the plot value.

Field data are collected electronically on a portable data recorder (PDR) and are subject to 

computerized editing and validation. Paper data sheets are used to map the biosite and track 

the leaf vouchers and for backup in the event of a PDR failure. Examples of field data sheets 

and a chart detailing the flow of data from the field to the validated regional data archives are 

presented in Appendix 5.

Performance Evaluations and MQOs

The crew level of difficulty for the ozone indicator is low and no specialized equipment is 

needed. Field protocols have remained essentially unchanged since 1994. The sampling 

window is abbreviated within each region, minimizing index-period variability. The selected 

bioindicator species are relatively common, easy to identify, and characteristically ozone 

sensitive, showing foliar symptoms that are easy to diagnose. Results from field revisits and 

audits demonstrate that crews are effectively trained to detect ozone injury and discriminate 

against mimicking symptoms. Analysis of QA data from 1997 to 2002 and from 2004 

to 2005 established the following: (1) For all measured biosites and based on actual crew 

performance, two independent crews will come up with a site-level injury index that varies by 

2 percent or less 90 percent of the time; and (2) For all measured biosites and based on actual 

crew performance, two independent crews will come up with the same species count, plus or 

minus one species, 80 percent of the time. The voucher system was tested by asking crews to 

mark a subset of leaf vouchers as 100 percent certain or fairly certain for ozone injury. All the 

leaf vouchers submitted as 100 percent certain were confirmed by an expert as ozone injury 

while 78 percent of the vouchers submitted as fairly certain were confirmed as ozone injury. 

Normally, crews are trained to collect data and submit leaf vouchers whether they are 100 

percent certain of their diagnosis or not. Leaf vouchers are reviewed, and the injury symptom 

validated by an expert for every injured species on every biosite. Ozone injury data are zeroed 

out if the leaf vouchers associated with the injury data are determined by an expert to be 

free of ozone-induced injury symptoms, or if ozone injury cannot be distinguished due to a 

preponderance of mimicking symptoms.
26

Estimation Methods

The validated foliar injury data are used to calculate a biosite index (BI) for each site and 

year. Each ozone season is unique, influenced by variable ozone levels, weather, wind flow, 

and precipitation patterns. Therefore, 5-year averages of the BI are recommended to generate 

more representative estimates of ozone injury. Whether reported by year or as 5-year moving 

averages, the BI values are classified into four categories of risk (Table 3) based on groupings 

proposed by Smith (1995). The risk assigned to each category represents a relative measure 

of probable impacts from ambient ozone exposure (i.e., ozone risk). The same categories are 

used to describe relative ozone air quality.

Because the biomonitoring grid is independent of the FIA base grid, spatial interpolation 

is used to predict potential risk of ozone injury on the P2 ground plots. The interpolated 

surface of foliar injury response is referred to as the national ozone risk map. A new map 

is generated every year based on moving 5-year averages of the BI. The ozone risk map is 

intersected with P2 and P3 plot locations to provide an interpolated BI value (IBI) and other 

bioindicator attributes for all FIA ground plots. The IBI for each forest inventory (P2) and 

forest health (P3) plot can then be merged with other tree and plot attributes to generate 

population estimates for the ozone indicator as discussed in the Analytical Procedures and 

Risk Assessment section of this report. Additional details on the formulation of the BI, the 

spatial interpolation techniques used to develop IBI values for all P2 and P3 plots, and the 

methods used to estimate status and change in forested areas with respect to ozone risk are 

provided in the Ozone Bioindicator Sampling and Estimation report (General Technical 

Report NRS-20, Smith et al. 2007).

Table 3.—Classification scheme for the FIA biosite index

Biosite index1 Bioindicator response2 Assumption of risk Relative air quality3 Probable impact

0 to < 5 Little or no foliar injury None Good Visible injury to highly sensitive

species, e.g. black cherry

5 to < 15 Light to moderate foliar

injury

Low Moderate Visible injury to moderately

sensitive species, e.g. tulip-poplar

15 to < 25 Moderate to severe foliar

injury

Moderate Unhealthy for

sensitive species

Visible and invisible injury.

Tree-level response.4

≥ 25 Severe foliar injury High Unhealthy Visible and invisible injury.

Ecosystem-level response.4

1

The categorizations of the biosite index are subjective and based solely on expert opinion.

2

Based on amount and severity of ozone-induced foliar injury symptoms as described in FIA Field Methods for Phase 3 Measurements (U.S.

Forest Service 2006).

3

Relative ozone air quality for plant receptors (e.g., trees, woody shrubs, and nonwoody herb species), not human receptors.

4

According to EPA’s final Guidelines for Ecological Risk Assessment EPA/630/R095/002F (U.S. EPA 1998).
27

DATABASE STRUCTURE AND DOCUMENTATION

FIA uses the National Information Management System (NIMS) to process and store the 

ozone indicator data. Data files collected by the field crews and voucher validation files are 

loaded into five NIMS OracleTM tables: OZONE_PLOT_TBL, OZONE_VISIT, OZONE_

SPECIES, OZONE_PLOT_NOTES, and OZONE_VALIDATION. Further processing 

computes indices and creates three standard summary tables: OZONE_BIOSITE_SUMMARY, 

OZONE_PLOT_SUMMARY, and OZONE_SPECIES_SUMMARY. The data from these 

ozone tables (except for OZONE_PLOT_NOTES) are reformatted for public use and 

presented in the FIADB 10 (Forest Inventory and Analysis Database).

The FIADB is available for download through the FIA DataMart Web page11. The download 

data format is comma-separated-value (CSV), which can be opened in Microsoft Excel or other 

programs that import text files. To access ozone data from 1994 to the present, go to: http://fia.

fs.fed.us/ and click on FIA Data and Tools – Download Inventory Data, and review various 

options for ozone reference data. The FIA Data and Tools Web page also includes a data tool 

called Forest Inventory Data Online (FIDO), which creates tables and maps from the FIADB 

Phase 2 plot data. FIDO applications are being developed for the ozone data.

This section provides an overview of measurement attributes, data tables, and map products 

associated with the ozone indicator. It describes field data attributes, biosite-level attributes 

derived from the field data, population-level attributes and maps, and leaf voucher attributes. 

Information on ozone crosswalk tables is also provided. Examples of standard tables and maps 

for the ozone indicator are provided in Appendix 2 and Appendix 7. Data storage tables as they 

appear in NIMS and the FIABD are introduced in Appendix 6. For detailed information on the 

ozone attribute codes and definitions, and guidance on interpreting summarized attributes for 

internal or external reports, all users should refer to the FIADB Database Description and Users 

Guide version 3.0 Phase 3 (Forest Health Indicators) available online12 at the FIA Data Mart 

Web page.

Field Data

Field data attributes listed in the ozone visit table (OZONE_VISIT) are described in the 

ozone indicator field manual (http://www.fia.fs.fed.us/ - click on FIA Library—Field Guides, 

Methods, and Procedures - scroll down to FIA Field Methods for Phase 3 Measurements and 

click on Ozone Bioindicator Plants) and in the FIADB Users Guide (v3.0 P3 cited above). 

The field crew uses a standardized coding system to summarize identifying characteristics 

of the biosite with respect to site conditions and measurement status on the national ozone 

grid. Coded site attributes include plot size, elevation, aspect, terrain position, soil depth, soil 

drainage, plot wetness, and plot disturbance. Additional attributes identify the crew type and 

QA status of the data and whether or not the recorded values are intended for quality assurance 

purposes. The injury check attribute allows the field crew to document the presence or absence 

12http://www.fia.fs.fed.us/tools-data/docs/pdfs/FIADB_user%20guide%203-0_P3_6_01_07.pdf

11http://fiatools.fs.fed.us/fiadb-downloads/fiadb3.html

10To protect landowner privacy, true biosite locations are obscured in the FIADB. For more information, 

or to obtain access to biosite coordinates, contact the National Ozone Advisor, John Coulston. 
28

of ozone injury on nontallied13 plants or species. The plot notes table (OZONE_PLOT_

NOTES) provides additional descriptive information on site characteristics, plant species, 

and injury patterns that may influence the results. Biosite notes are not available in the public 

domain because they may contain information that compromises landowner privacy.

Standard Summary Tables and Derived Ozone Data

Every year, the results of the field crew injury surveys and expert review of the leaf vouchers are 

summarized at the regional level in three standard summary tables. Core data from the three 

tables are used to describe field crew activities and report on status and trends. The biosite 

summary table (OZONE_BIOSITE_SUMMARY) provides computed indices and summary 

statistics for each ozone biosite. The plot summary table (OZONE_PLOT_SUMMARY) 

contains summary statistics for each ground location. Ground location differs from ozone 

biosite when the data from two nearby locations are combined to meet the site selection 

requirements for the ozone indicator. The species summary table (OZONE_SPECIES_

SUMMARY) provides computed indices and summary statistics by species. For each table, the 

attribute codes, computation specifications, and definitions are fully described in the FIADB 

Users Guide v3.0 P3. The most useful attributes for FIA regional reports are discussed below. If 

there are anomalies in the derived ozone data, the analyst should review the raw data files and 

notes for an explanation. Annual summary statistics may be summarized by species or by biosite 

and reported by county, state, FIA region, or ecological region.

Ozone Biosite Summary Table

The measurement variables in this table summarize ozone injury data by ozone plot number 

(O3PLOT) and inventory year (INVYR). The ozone plot number in the ozone summary tables 

provides a unique identifier that can be used in combination with inventory year and, for clarity, 

state and county codes to identify the ozone biomonitoring site. For each biosite, summary values 

are tabulated for the total number of species evaluated, the total number of plants evaluated, the 

total number of plants injured, the ratio of injured to evaluated plants, the percent of sampled 

plants in each HB injury severity class, and the biosite-level ozone injury index (BIOSITE_

INDEX). These summary statistics may be used in an annual report to list how many biosites 

were visited, how many plants were evaluated, and how many (or what percent) of the total sites 

and plants sustained ozone injury. Over time, these summary statistics can be used to report on 

regional trends in ozone stress in terms of significant changes in the number and distribution of 

biomonitoring sites with ozone injury, changes in injury severity classifications, and increases or 

decreases in the ozone injury index.

Ozone Plot Summary Table

The measurement variables in this table summarize ozone injury, species counts, and site 

characteristics for each ground location visited by the field crews. Ground location differs from 

ozone biosite because there are a small number of ozone biosites that consist of two ground 

locations. Ozone biosites that consist of two locations are referred to as split plots14. Two 

13Nontallied refers to extra plants or species that are evaluated after the crew has tallied 30 injury amount 

and severity ratings for 5 different species. Nontallied plants are not part of the tally that is used to 

compute the site-level injury index, but they may be used to record the presence of ozone injury at a 

given location if the nontallied leaf voucher is validated by an expert. See FIADB Users Guide v3.0 P3 for 

details. 
29

locations are used to increase species and plant counts for a single ozone biosite. If two locations 

are used, they are within 3 miles of each other. When a biosite is split, the ozone plot number 

(O3PLOT) is the same for both locations except for the last digit (1 or 2) and the two locations 

have different split plot identification numbers (SPLIT_PLOTID). The ozone plot number 

provides a unique identifier that can be used in combination with inventory year (INVYR) 

and, for clarity, state and county codes to uniquely identify each ground location. Coded site 

characteristics for each ground location include plot size, elevation, aspect, terrain position, soil 

depth, soil drainage, plot wetness, and plot disturbance.

The location-specific attributes in this table provide the opportunity to examine certain 

site characteristics (e.g., elevation, plot size) more closely. For example, ambient ozone 

concentrations vary with elevation due to the influences of global radiation, air temperature, 

and wind on ozone exposure. However, there are no detailed field studies on how changes in 

elevation that are typical of the ozone sample might affect plant response to ozone (Skelly et al. 

2003). Plants at higher elevations may be stressed by poor site conditions and be less responsive 

to ozone exposure than plants at lower elevations. Ozone exposures also vary with the size of the 

opening used for biomonitoring. Open areas that are more than 3 acres in size are ideal because 

they optimize ozone air mixture, but not all biomonitoring sites are this open.

Although a calculated biosite index is provided in this table for each ground location, the 

preferred summary statistic for Detection Monitoring reports is the biosite-level injury index 

presented in the biosite summary table.

Ozone Species Summary Table

The measurement variables in this table summarize ozone injury, plant counts, and site 

characteristics for each bioindicator species (BIOSPCD) evaluated at each ground location. At 

each location, every plant evaluated by the field crews is rated for amount and severity of ozone 

injury. The minimum, maximum, and mean values for these two indices are summarized by 

species. Injury amount is an estimate of the percent injured leaves on each plant. Injury severity 

is an estimate of the mean severity of symptoms on injured foliage. Both attributes should be 

considered. Some plants may have slight to moderate injury on all leaves; others may have 

severe injury on a small number of leaves. The injury pattern may be species specific or may 

relate to the stage of development at the time of ozone exposure. Injury may also depend on site 

characteristics that have a greater or lesser influence on amount and severity of injury depending 

on the species. Site characteristics presented by species at each ground location include plot size, 

elevation, aspect, terrain position, soil depth, soil drainage, plot wetness, and plot disturbance.

A species-level ozone injury index (BIOSPCD_INDEX) is also presented. This index provides 

an opportunity to make comparisons among sites using indices derived from the same species. A 

species-specific analysis may be appropriate for certain Evaluation Monitoring studies. However, 

for Detection Monitoring reports, the preferred summary statistic is the biosite-level ozone 

injury index presented in the biosite summary table.

14In these cases, there are two data records for a biosite in the OZONE_PLOT_SUMMARY table and 

only one record in the OZONE_BIOSITE_SUMMARY table because the data from both locations are 

combined in the biosite summary attributes.
30

Population-Level Core Tables and Maps

In addition to the standard summary tables described in the previous section, two core ozone 

maps are available for download from the FIADB DataMart15 along with a core list of ozonesensitive tree and shrub species (Appendix 4). The map products are derived from weighted data 

that are not readily available, and true biosite locations that are not in the public domain16. The 

first map product is the national ozone risk map, which is based on the 5-year rolling average of 

the biosite index (BIOSITE_INDEX), and provides an interpolated surface of probable ozone 

injury across the landscape. The second map product is an interpolated surface of ambient 

ozone concentrations (e.g., SUM06 data). Data users select their area of interest (e.g., state, FIA 

region, or ecological region) from these two map products and use the procedures outlined in 

the Ozone Bioindicator Sampling and Estimation report (Smith et al. 2007) to calculate and 

interpret population estimates for the ozone indicator. Core products for the ozone indicator 

include the ozone risk map and tables or graphs that document status and trends in ozone 

population estimates for a state, FIA region, or ecological region.

The national ozone risk map is used to generate an estimated BI value for forested ground 

plots on the FIA P2 grid. This interpolated biosite index (IBI) and accompanying plant count 

attribute are part of the larger P2 table of forest plot attributes in the FIADB. With the map 

surface and IBI, any user can examine relationships between the ozone indicator and other FIA 

indicators of tree growth, forest health, and forest condition. Similarly, the national ozone risk 

map can be used in conjunction with the interpolated surface of ambient ozone concentrations 

to help interpret FIA findings. Overlays are also possible with other external databases or map 

surfaces such as climate or seasonal drought.

Validation Data Table

Coded attributes in the ozone validation table (OZONE_VALIDATION) are used by the FIA 

data processor in each region to edit the field data before they are loaded into the three standard 

summary tables described previously. The biosite summary statistics do not load properly unless 

the validation table is complete and in accord with the data files entered by the field crews. The 

following steps describe the process. The field crew collects a leaf voucher for every bioindicator 

species at every location where ozone injury is recorded. These are mailed to an expert ozone 

diagnostician who reviews the leaf vouchers, generates the ozone validation file, and returns 

the validation table to each region for data processing. The validation table provides a record 

of whether the ozone injury rated by the field crews was validated or not for every species at 

every ground location. If the injury is not validated, or if the voucher is missing, the crew data 

file is modified to reflect this fact. Occasionally, the field crew submits leaf vouchers for plants 

or species that are not included in the injury data file. These are considered nontallied leaf 

vouchers as defined by the injury check attribute (see Field Data section, page 27). If injury 

on nontallied plants or species is validated, this is reflected in the validation file. Injury to 

nontallied plants or species can be used only to tabulate or map presence and absence of ozone 

15http://nrs.fs.fed.us/fia/topics/ozone/pubs/pdfs/Nationalpercent20Ozonepercent20Riskpercent20Map.

pdf http://nrs.fs.fed.us/fia/topics/ozone/pubs/pdfs/Nationalpercent20SUM06percent20Surface.pdf

16Contact John Coulston, the National Ozone Advisor, if data links presented in this document are no 

longer active or for data not readily available in the public domain: jcoulston@fs.fed.us
31

injury. Only validated data from tallied plants and species are used in the computation of ozone 

summary statistics for core tables and maps.

Crosswalks and Site Identification Data

Data stored in the ozone crosswalk table (OZONE_CROSSWALK) are used to track changes 

to the ozone sample over time. The ozone indicator was part of the FHM grid sample from 

1994 through 2001. When FIA assumed administration of the P3 forest health indicators 

in 2002, the ozone indicator moved to a new sample grid constructed to meet the unique 

needs of the ozone indicator. The new grid allowed greater flexibility to select optimal sites for 

biomonitoring (i.e., large, undisturbed open areas with adequate soil moisture, and high plant 

and species counts). The concept of split plots was introduced to maximize plant counts in 

more challenging areas. The 2002 grid was adjusted in 2007 to address Detection Monitoring 

concerns in the Interior West. These changes to the O3 sample have resulted in changes to O3

site identification numbers even though ground locations have been stable. The ozone crosswalk 

table allows data collected at site number A1 in 1994 on the FHM grid to be linked to data 

collected at the same ground location that is identified as site number A2 on the O3GRID_

2002, and site number A3 on the O3GRID_2007. Further, starting with the 2002 ozone 

sample grid, ozone biomonitoring sites were assigned to one of four possible strata and the data 

must be weighted accordingly for interpretive analyses. Strata weights change as the sample grid 

changes, as it did in 2007. Weights, calculated annually, are available from John Coulston, the 

FIA National Ozone Advisor.

A second set of crosswalk tables track changes to the ozone plot coordinates (GPS_LAT and 

GPS_LON) and provide links to the fuzzed coordinates (LAT and LON) that are generated for 

public use. Field crews are trained to replace sites that become overgrown or disturbed, although 

the site identification number (FIELD_ID) stays with the relocated plot. Changes in ground 

location that exceed 3 miles are documented in these base plot tables using the ground location 

attribute (GROUND_LOC_CD). The crosswalk tables are not part of the FIADB DataMart, 

nor are they readily accessible to FIA analysts. However, analysts should be aware that these files 

exist so that they make full use of older (1994 to 2002) and more current (2003 to current year) 

ozone data files.

DATA SUMMARIES

Core summary statistics for the ozone indicator are discussed in this section by state, FIA 

region, and year. The intent is to provide information on the scope of the national ozone 

biomonitoring program and gain appreciation for regional differences (e.g., East versus West) 

that may influence results and reporting. All the data summarized here are available in the 

FIA databases (NIMS and FIADB) beginning with the first year the ozone indicator was 

implemented in a given state up to the most recent field season. The sample graphics and 

accompanying text can be modified to fit the particular concerns of the FIA reporting unit.

National Program Summary

Currently, there are 1,130 ozone biomonitoring sites in 45 states. Three pilot sites in Alaska 

are not included in this count. Only New Mexico, Mississippi, Oklahoma, and Hawaii are not 

yet involved in the program. Numbers of sites vary by state depending on the availability of 
32

bioindicator species in certain forest types and the intensity of the sampling grid for O3. For 

example, there are relatively few sites in northern Maine due to a scarcity of bioindicator species 

in the dominant spruce-fir forests of that region, while the numbers of ozone sites are relatively 

high in Pennsylvania due to increased sampling activity on the part of the state cooperator. The 

number of biosites evaluated increased over the years as new states were added to the program, 

stabilizing in 2002 when the FIA ozone sampling grid was finalized (Table 4). More biosites 

with injury and more injured plants are detected in eastern regions then in the West because 

most of the eastern United States experiences high ambient ozone concentrations across the 

landscape during the growing season (Cleveland and Graedel 1979, Lefohn et al. 1997, Skelly 

2000). In contrast, except for the Los Angeles basin area, and limited air sheds around Las 

Vegas, Phoenix, and Salt Lake City, ozone air quality is relatively good across wide areas of the 

West (U.S. EPA 1996a, Lee and Hogsett 2000, Lee et al. 2003, Miller et al. 1996).

From 1994 to 2006, ozone injury was detected at least once, often every year, in every state in 

the eastern FIA regions except Nebraska, North Dakota, and South Dakota (Table 5). Some 

of the Southern States with low injury counts (e.g., Florida) have trouble finding undisturbed, 

open areas with bioindicator species. The low count in Alabama is not easily explained and may 

reflect a sampling problem given the routine detection of ozone injury in the neighboring states 

of Georgia and Tennessee. However, the absence of injury in the three Plains States (Nebraska, 

North Dakota, and South Dakota) is not surprising especially because the neighboring states 

in the Rocky Mountain region are also without injury. In fact, no injury has been detected 

across the Interior West States in 7 years of biomonitoring except in Utah, downwind of Salt 

Lake City in a state park planted with ponderosa pine not indigenous to the area.17 On the 

West Coast, ozone injury has not been detected in Oregon but has been detected repeatedly 

in the same irrigated location in Washington and routinely in numerous locations throughout 

California. Regardless of whether injury is detected every year in a given state or region, or 

only infrequently, the percent of sampled plants that sustain injury is relatively small ranging 

from less than 1 percent to 26 percent (Table 4). Most ozone-sensitive bioindicator plants 

remain free of ozone injury symptoms in all regions and all years. This finding agrees with other 

ozone surveys that have demonstrated that a relatively low percentage of any given population 

of ozone-sensitive plants will show a visible injury response to elevated ozone concentrations 

under natural conditions of ambient exposure (Davis and Orendovici 2006, Skelly et al. 

1987, Treshow and Stewart 1973). It is noteworthy when ozone injury is detected in a state 

or region previously thought to be free of ozone stress, even if the injury occurs on a single 

plant. Bioindicator plants with injury have been detected for the first time by FIA crews in 

Washington and the northernmost portions of states in the Northeast and North Central U.S., 

thus providing important baseline data for the ozone indicator in these areas. In those states 

where ozone injury is detected every year (Table 5: KY, NC, SC, and TN in the Southeast; 

CT, MD, MA, NY, OH, PA, RI, VT, and WV in the Northeast; IL, IN, and WI in the North 

Central), the results underscore the fact that a large area and percent of forest land in this 

country is subject to levels of ozone pollution that may negatively affect the forest ecosystem.

17Ozone injury was observed at Rocky Mountain National Park in Colorado on cutleaf coneflower 

(Rudbeckia laaciniata v. ampla) over a period of 3 years ending in 2008 (R. Kohut, pers. comm.). 

Coneflower is currently on the FIA list of trial bioindicator species, but at the time when FIA crews were 

evaluating biosites in the Rocky Mountain region (1997 to 2004) not enough was known about the 

ozone sensitivity of this species to include it on the official bioindicator list.
33

Table 4.—Number of biosites evaluated and injured, number of plants evaluated and 

injured, and percent sampled plants uninjured and injured by region and state for the 

years 1997 to 2006 

Region

and year

Number of biosites Number of plants

Percent of sampled

plants

Evaluated Injured Evaluated Injured Uninjured Injured

Southern States

1997 19 8 697 101 86 14

1998 22 12 1,419 260 82 18

1999 90 33 4,495 405 91 9

2000 178 62 9,070 535 94 6

2001 248 76 14,623 660 96 4

2002 314 62 26,836 628 98 2

2003 319 96 29,026 877 97 3

2004 351 61 30,890 364 99 1

2005 359 43 29,267 333 99 1

2006 335 27 29,085 161 99 1

Northeast States

1994 86 56 3,590 923 74 26

1995 147 56 6,049 578 90 10

1996 126 58 5,468 731 87 13

1997 151 48 7,156 445 94 6

1998 269 158 11,915 1976 84 16

1999 372 82 19,467 627 97 3

2000 269 109 17,371 1146 93 7

2001 341 106 29,399 1028 97 3

2002 230 98 22,893 1067 95 5

2003 229 82 22,793 660 97 3

2004 227 80 22,376 891 96 4

2005 232 79 23,360 502 98 2

2006 233 103 23,239 700 97 3

North Central States

1994 32 10 1,293 58 96 4

1995 137 17 5,245 133 97 3

1996 103 24 4,469 260 94 6

1997 123 25 5,764 296 95 5

1998 196 67 10,592 722 93 7

1999 188 91 14,237 645 95 5

2000 290 157 21,089 1096 95 5

2001 233 100 17,445 592 97 3

2002 260 64 23,253 247 99 1

2003 269 55 23,628 240 99 1

2004 267 71 24,392 271 99 1

2005 240 52 23,130 174 99 1

2006 237 58 23,391 258 99 1

Rocky Mountain States1

1998 79 0 3,068 0 100 0

1999 84 0 3,481 0 100 0

2000 113 0 5,271 0 100 0

2001 129 0 6,043 0 100 0

2002 72 0 5,298 0 100 0

2003 116 0 9,269 0 100 0

2004 161 0 13,698 0 100 0

continued
34

Table 5.—Number of years of biomonitoring, number of years with ozone injury, and year 

biomonitoring was started by region and state for the years 1994 to 2006

Region and year

Number of years

Start year1 Biomonitoring Ozone injury detected

Southern States

Alabama 7 1 1994

Arkansas 6 3 2001

Florida 5 1 2002

Georgia 10 9 1994

Kentucky 7 7 2001

Louisiana 5 1 2001

North Carolina 8 8 1998

South Carolina 8 8 1998

Tennessee 7 7 1998

Texas 5 2 20012

Virginia 10 9 1994

Northeast States

Connecticut 13 13 1994

Delaware 11 10 1995

Maine 13 6 1994

Maryland 13 13 1994

Massachusetts 13 13 1994

New Hampshire 13 11 1994

New Jersey 13 11 1994

New York 8 8 1999

Ohio 10 10 1997

Pennsylvania 10 10 1995

Rhode Island 13 13 1994

Vermont 13 13 1994

West Virginia 12 12 1995

North Central States

Illinois 10 10 1997

Indiana 11 11 1996

Iowa 7 3 2000

Kansas 5 2 2002

Michigan 13 12 1994

Minnesota 13 4 1994

West Coast States

1998 67 0 3,691 0 100 0

1999 90 1 3,576 2 100 <0.05

2000 64 7 3,323 105 97 3

2001 67 12 3,728 120 97 3

2002 118 19 8,621 177 98 2

2003 129 17 9,847 123 99 1

2004 126 22 9,626 139 99 1

2005 134 23 9,445 259 97 3

2006 134 24 9,915 185 98 2

1

There are no data available for the Rocky Mountain States after 2004.

Table 4.—continued

Region

and year

Number of biosites Number of plants

Percent of sampled

plants

Evaluated Injured Evaluated Injured Uninjured Injured

continued
35

The most common species found on eastern biomonitoring sites are blackberry, milkweed, 

black cherry, and white ash (Table 6). In addition, regional differences in the natural range of 

certain species affect usage. For example, sweetgum, sassafras, and yellow-poplar are common 

bioindicator species in the Southern States, much less so in the Northeast and North Central 

States; and several bioindicator species used in the westernmost states of the North Central 

region are not found at all in the South and Northeast. Species selection is prioritized in the 

field procedures to increase the likelihood that most sites in the East will contain similar species. 

Even so, gathering foliar injury data from a range of herbaceous and woody plant species 

common to the forest environment increases the responsiveness of the ozone injury index (BI) 

for a given locale (W.D. Smith, pers. comm.). Over the years, we have learned that certain 

species are more useful as ozone detectors in cool, wet years (e.g., milkweed), while others 

(e.g., black cherry) are more useful in hot, dry years. Biosites that have both these species are 

more likely to provide a consistent, uncompromised response to O3 across years with variable 

temperature and precipitation patterns.

Quaking aspen in the Rocky Mountain States and ponderosa pine in the West Coast States 

are the most common tree species on western biomonitoring sites (Table 7). Associated shrub 

species that are widely sampled in both the interior and coastal states include snowberry, 

blue and red elderberry, and skunkbush. In many parts of the West, the forested landscape 

is characterized by large natural openings populated by a single overstory species (e.g., aspen 

or ponderosa pine). It can be challenging to find nearby locations that include one or more 

of the understory bioindicator species. For this reason, western crews average 2.5 species per 

site compared to 3.5 species per site for the eastern crews. All the eastern and western species 

used for biomonitoring are relatively common across a variety of forest types, relatively easy 

to identify and distinguish from similar species, and ozone sensitive based on a combination 

Missouri 7 6 2000

Nebraska 5 0 2002

North Dakota 5 0 2002

South Dakota 5 0 2002

Wisconsin 13 13 1994

Rocky Mountain States3

Arizona 3 0 2002

Colorado 7 0 1997

Idaho 5 0 1997

Montana 2 0 2003

Nevada 3 0 2000

Utah 5 0 2000

Wyoming 4 0 1997

West Coast States

California 9 8 1998

Oregon 9 0 1998

Washington 9 6 1998

1

Some states are missing interim years between start date and current year. 2

West Texas started in 2004. 3

There are no data for Rocky Mountain States after 2004 and for Idaho and Wyoming after 2001.

Table 5.—continued

Region and year

Number of years

Start year1 Biomonitoring Ozone injury detected
36

Table 7.—List of bioindicator species and numbers of evaluated plants by species for 

the 2004 field season in the Rocky Mountain and West Coast States 

Number of plants evaluated (20041)

Bioindicator species

Rocky Mountain

States

West Coast

States2 Totals

Mountain snowberry 3,851 1,793 5,644

Ponderosa pine3 2,718 2,129 4,847

Quaking aspen 3,654 932 4,586

Scoulers’s willow 259 990 1,249

Blue elderberry 469 569 1,038

Skunk bush 584 328 912

Western wormwood 50 30 880

Red elderberry 417 400 817

Red alder 0 734 734

Ninebark 612 111 723

Mugwort 02 602 704

Jeffrey pine 30 652 682

Huckleberry 42 334 376

Pacific ninebark 60 22 82

Milkweed 50 - 50

1

The year 2004 was selected as representative for all regions. Data are available by year (1994 to

current year) and by species in the FIA national database. 2

In the West Coast States, California black oak, chokecherry, and thimbleberry were sampled in 1998-

2000 and then dropped from the bioindicator species list. 3

Pinus ponderosa var. scopulorum in Rocky Mountain States; var. ponderosa in West Coast States.

Table 6.—List of bioindicator species and numbers of evaluated plants by species for the 

2004 field season in the Southern, Northeast, and North Central States

Number of plants evaluated (20041)

Bioindicator species Southern States Northeast States

North Central

States2 Totals

Blackberry 9,552 4,428 2,206 16,186

Milkweed 1,464 4,453 5,439 11,356

Black cherry 4,312 3,335 3,287 10,934

White ash 1,171 3,450 4,260 8,881

Spreading dogbane 403 3,336 4,335 8,074

Sweetgum 6,996 469 252 7,717

Sassafras 3,307 1,242 1,175 5,724

Yellow-poplar 3,343 973 298 4,614

Pin cherry 352 630 395 1,377

Bigleaf aster 30 60 1,103 1,193

Mountain snowberry - - 673 673

Western wormwood - - 554 554

Skunkbush - - 280 280

Ponderosa pine - - 135 135

1

The year 2004 was selected as representative for all regions. Data are available by year (1994 to current year)

and by species in the FIA national database.

2

In the North Central States, evening primrose, green ash, and quaking aspen are sampled occasionally.
37

of field evidence and causative fumigation experiments. They were all recently identified as 

excellent ozone detectors by a panel of experts (USDI 2003, http://www2.nature.nps.gov/air/).

Having to use different bioindicator species in each region presents some concern for national 

reports. The classification scheme for the biosite index (Table 3) is based on knowledge and 

experience gained largely in eastern forest types. It is not clear, for example, how a biosite 

value derived from eastern species (e.g., black cherry) compares to a biosite value derived from 

western species (e.g., ponderosa pine). The index appears to work well in all regions based on a 

comparison of the two map products for the ozone indicator as long as the data summarization 

period covers multiple years. When a national map derived from the 5-year rolling average 

of the BI data is compared to a national map of interpolated SUM06 data for the same time 

period, it is apparent that low, moderate, and high risk zones for probable ozone impact (IBI 

data) generally match low, moderate, and high risk zones for ozone exposure (SUM06 data) 

regardless of whether the zones are in California or the mid-Atlantic region. The exception to 

this observation is the Interior West (Colorado, Utah, and Arizona in particular) where low 

moisture conditions during the growing season and resistant genotypes of ozone-sensitive plant 

species appear to limit the biological response of plant receptors to ozone pollution (Duriscoe 

and Temple 1996).

Regional Summaries and State Reports

Earlier sections of this report answer questions about what ozone pollution is, how it injures trees, 

and what types of health assessment questions are addressed by the FIA ozone biomonitoring 

program. These topics should be worked into any issue-driven report on the ozone indicator. As 

suggested in the national program summary, it is useful to summarize the data for the years and 

area of concern by mapping the distribution of biosites across a state or region and reporting 

on the numbers of biosites evaluated and injured, the numbers of species and plants evaluated 

and injured, the severity of injury as reflected in the field assessed injury scores (i.e., HB rating), 

and the categorized risk of probable impact as described by the biosite index (BI). Additional 

interpretive information is gained by examining injury patterns across the state or region relative 

to spatial and temporal variations in ambient ozone concentrations, precipitation patterns, and 

soil moisture indices. Examples of this approach are presented here using data from the states in 

FIA-North and the sampling period 1997 through 2006. Additional examples of state reports that 

serve as models for FIA analysts include the 5-year summary reports for South Carolina (Conner 

et al. 2004) and Indiana (Woodall et al. 2005) and the two published general technical reports by 

Campbell et al. (2000 and 2007) that include data from Washington, Oregon, and California. A 

general technical report for the southern FIA region is in progress (Rose and Coulston, In press).

An initial step in assessing the interpretive value of ozone biomonitoring data is to examine the 

relationship between the FIA plant injury data (e.g., calculated BI values) and the more traditional 

ozone exposure data (e.g., SUM06: sum of all hourly average ozone concentrations ≥0.06 ppm) 

derived from air quality monitoring stations operated by EPA. Using this approach, analysts 

working with Indiana data (1997-2002) reported the following: (1) Indiana’s ozone exposures 

(SUM06 values) are the highest in the North Central region; (2) essentially all the forested acreage 

in the state is exposed to elevated ozone concentrations; (3) Indiana has the highest amount and 

severity of foliar injury to ozone-sensitive species among the North Central States; and (4) the 

highest injury index scores coincided with the highest ozone exposure levels in south-central 
38

Indiana (Woodall et al. 2005). The summarized BI data, together with information on the 

distribution of ozone exposures and tree species in Indiana, led analysts to suggest that forests 

across the state are at risk of ozone-induced negative effects. In contrast, a similar comparison of 

BI and air quality data in South Carolina (1999-2001) allowed state analysts to report that even 

though much of the northern half of the state is subject to moderate to high ozone exposures, 

low BI scores taken together with several years of below average rainfall suggest the probability 

of ozone impacts on forests in South Carolina is low to none (Conner et al. 2004).

The relationship between plant injury and ozone exposure may also be examined at the 

regional level. For example, along the northeast corridor from Maine to Virginia, there is an 

obvious gradient of increasing ozone exposure from the relatively clean air states of northern 

New England, across the moderate ozone states of southern New England to the mid-Atlantic 

States (Table 8)18. Similarly, air quality in the Northern Plains States is clearly different from 

that found in the industrialized east North Central States. An examination of the plant injury 

summary statistics for the Northeast and North Central regions indicates that the average biosite 

index (BI) and percent sampled plants with higher HB severity ratings were highest in the states 

characterized by high ozone exposure, intermediate in the states characterized by moderate ozone 

exposure, and lowest in the relatively clean air states of northern New England and the Northern 

Plains (Table 9). In moderate and high ozone exposure groupings, the average BI value was highest 

in 1998, showing a sharp drop in value for all exposure groupings in 1999. According to data 

Table 8.—Regional differences in maximum and mean ozone exposure data for 1994-2005

Region1

Range of maximum ozone exposure

values (SUM06)2

1994-2005

Mean value

1994-2005

Ozone exposure

category3

Northern New England 8.25 - 29.20 6.22 Clean

Southern New England 14.94 - 34.73 18.03 Moderate

Mid-Atlantic States 15.58 - 110.25 26.27 Unhealthy

Northern Plains 1.50 - 39.14 7.53 Clean

East North Central 8.61 - 54.34 18.20 Moderate

South 13.01 - 120.68 21.62 Moderate

Northwest 5.11 - 50.41 8.26 Clean

Interior West 19.58 - 85.97 20.15 Moderate

Southwest 76.80 - 117.34 28.73 Unhealthy

1

Regions are defined as follows. Northern New England = ME, NH, VT; Southern New England = MA, CT, RI; Mid-Atlantic = DE,

MD, NJ, PA, WV; Northern Plains = IA, KS, MN, NE, ND, SD; North Central = IL, IN, MI, MO, OH, WI; South = AL, AR, GA, KY,

LA, NC, SC, TN, VA; Northwest = ID, MT, OR, WA, WY; Interior West = AZ, CO, NV, UT; and Southwest = CA.

2

SUM06 = Sum of hourly ozone concentrations ≥0.06 ppm. Maximum and mean values are calculated by state and year and then

averaged for each region. Identical mean values for 1994 and 1995 in Kansas, Nebraska, and Missouri were considered outliers

and dropped from the analysis.

3

Descriptive ozone exposure categories are based on mean values. Clean = SUM06 <10 ppm-hr; Moderate = SUM06 10-25 ppmhr; Unhealthy = SUM06 >25 ppm-hr.

18New York is not included in the regional summaries presented in Tables 8, 9, and 10 due to differences 

in sampling period and size. Data collection began in 1999 as compared to 1994 and 1997 for other 

states in the Northeast. Conditions vary considerably across the state so New York is not easily grouped 

with either New England or the mid-Atlantic States. State averages for injury (BI=2.38) and O3 level 

(SUM06=14.1 ppm-hr) do not accurately reflect conditions of probable O3 impact in the southeastern 

portion of New York that borders the mid-Atlantic States. 
39

Table 9.—Number of biomonitoring sites evaluated for ozone-induced foliar injury, number of biosites with 

injury, average biosite index, and percent of sampled plants in each HB injury severity category for groups of 

states with low, moderate, and high ozone exposure regimes across the FIA-North region

State group1

and year

Number

of biosites

evaluated

Number of

biosites with

injury

Average

biosite

index 2

HB injury severity categories3

0 1 2 3 4 5

Northern New England (ME, NH, VT) Percent sampled plants3

1997 63 10 6.76 95 1 2 1 <1 <1

1998 70 26 6.56 94 2 2 1 <1 <1

1999 68 11 0.46 98 1 <1 <1 <1 -

2000 61 7 0.99 97 1 1 <1 <1 -

2001 59 7 0.69 99 <1 <1 <1 - -

2002 36 5 0.41 99 <1 <1 <1 <1 -

2003 39 5 0.30 99 <1 <1 <1 - -

2004 40 5 2.05 99 - <1 <1 <1 <1

2005 39 11 1.44 99 <1 <1 - - -

2006 35 10 0.60 99 <1 <1 - - -

Southern New England (MA, CT, RI)

1997 28 20 6.85 87 6 5 1 <1 -

1998 28 24 19.81 82 7 7 3 <1 <1

1999 28 15 8.38 93 2 3 1 <1 <1

2000 26 19 3.97 91 4 3 1 - -

2001 18 11 8.54 89 5 4 1 <1 -

2002 20 13 4.47 93 3 3 <1 <1 -

2003 20 13 1.77 96 2 1 <1 - -

2004 17 9 3.06 93 4 2 <1 <1 -

2005 21 12 2.33 95 2 2 <1 - -

2006 18 18 7.50 87 5 6 2 - -

Mid-Atlantic States (DE, MD, NJ, PA, WV, VA)

1997 50 11 12.30 92 1 2 1 1 <1

1998 168 103 34.82 76 5 8 6 3 2

1999 199 46 13.40 96 1 1 1 <1 <1

2000 179 85 4.87 91 3 3 1 1 <1

2001 203 77 5.08 96 1 1 1 <1 <1

2002 128 58 7.06 94 1 2 1 1 <1

2003 133 52 2.89 96 1 2 <1 <1 <1

2004 140 45 5.10 96 2 1 <1 <1 -

2005 143 41 6.68 98 1 1 <1 - -

2006 149 51 4.24 98 1 1 <1 - -

Northern Plains States (IA, KS, MN, NE, ND, SD)

1997 40 1 0.08 99 - - <1 - -

1998 59 1 0.09 99 <1 <1 - - -

1999 46 0 <0.01 100 - - - - -

2000 49 1 <0.01 99 <1 - - - -

2001 33 1 0.15 99 - - - <1 -

2002 73 0 <0.01 100 - - - - -

2003 73 5 0.09 99 <1 <1 <1 - <1

2004 74 1 <0.01 99 <1 - - - -

2005 74 1 0.05 99 - <1 - - -

2006 71 4 0.35 99 <1 <1 <1 - -

East North Central States (IL, IN, MI, MO, OH, WI)

1997 102 34 14.28 94 1 2 2 1 -

1998 156 78 20.11 91 3 3 2 1 -

1999 160 95 4.34 95 3 2 <1 - -

continued
40

obtained from the regional climate center at Cornell University (http://met-www.cit.cornell.edu/), 

1998 was a comparatively wet year and 1999 was very dry for much of the Northeast. Percent 

sampled plants with no injury (HB severity class = 0) was lowest in 1998 regardless of the ozone 

exposure grouping.

From 1997 to 2006, average BI values ranged from <1.0 in northern New England and the 

Northern Plains to 19.81 in southern New England, 20.11 in the east North Central States, and 

34.82 in the mid-Atlantic States. These results suggest that reporting a single regionwide injury 

index for FIA-North would clearly mask state-level gradations in air quality and plant response, 

making it difficult to assess changes in the ozone indicator over time. Even using seemingly 

logical state groupings may cause some important state-level information to be lost. For 

example, Vermont stands out in the northern New England States as having a greater number 

of biosites with injury and more severe ozone injury across greater forested acreage than either 

New Hampshire or Maine (state-level data not shown), which becomes visually apparent when 

the site-level biosite data are interpolated across the landscape (Fig. 4). A simpler map showing 

the distribution of biosites with and without ozone injury, or weighted by the number of years 

with and without injury over a 5- to 10-year period, is just as effective as a reporting tool. 

Outside of FIA-North, many of the larger states to the west and south have sharply different 

ozone exposure zones in areas with different topography or population densities, making it 

entirely inappropriate to calculate mean values by state or by some geographical grouping of 

states as described above. Air quality in northern California, for example, is relatively clean and 

more similar to Oregon and Washington than central and southern portions of the state, which 

sustain some of the highest ozone concentrations in the country.

A second example of state-level sampling statistics using the same state groupings presents mean 

growing season BI alongside corresponding data on ozone air quality, precipitation norms, and 

soil moisture indices (Table 10). The classification schemes for the various indices are derived 

from the literature and expert opinion (Table 11). The classification scheme for the biosite index 

has already been described (Table 3). The ozone season is characterized by ozone exposure level 

based on categorizations of SUM06 data and 8-hr exceedance days. Seasonal mean SUM06 

values range from zero to more than 30 ppm-hr reflecting an increasing number of hours during 

the growing season when the ambient ozone concentrations exceeded a threshold value (0.06 

2000 260 161 5.60 95 3 2 <1 - -

2001 234 107 3.85 96 1 1 <1 <1 <1

2002 221 72 1.21 99 <1 1 - - -

2003 230 59 1.24 99 <1 <1 - - -

2004 227 82 1.52 98 1 <1 - - -

2005 200 61 1.15 99 <1 <1 - - -

2006 200 61 1.27 99 <1 <1 - - -

1

States are grouped by ozone air quality characteristics. Northern New England States and Northern Plains States have relatively

clean air: SUM06 <10 ppm-hr; Southern New England States and East North Central States have moderate air quality: SUM06 10-25

ppm-hr; Mid-Atlantic States have relatively poor air quality: SUM06 >25 ppm-hr. 2

Average biosite index values were calculated for each state grouping. 3

Injury severity is an estimate of the mean severity of symptoms on injured foliage (0 = no injury; 1 = 1- 6%; 2 = 7-25%;

3 = 26-50%; 4 = 51-75%; 5 = >75%). Calculated percents are rounded to the nearest whole number.

Table 9.—continued.

State group1

and year

Number

of biosites

evaluated

Number of

biosites with

injury

Average

biosite

index2

HB injury severity categories3

0 1 2 3 4 5
41

Figure 4.—Example of the intersection of interpolated biosite index (IBI) values

and ecoregions in the Northeast showing areas of increasing risk with respect to

probable O3 impact. Green areas indicate low risk of impact (BI = 0 to <5), yellow

areas indicate moderate risk (BI = 5 to <25), and pink areas indicate high risk of

ozone impact (BI >=25).

ppm O3) above which ozone is considered phytotoxic. Similarly, mean 8-hr exceedance days 

range from zero to more than 20, reflecting an increasing number of days when peak ozone 

concentrations exceed the 8-hr NAAQS (0.084 ppm O3) set to protect plants from ozone 

injury. The SUM06 statistic serves as a good indicator of chronic ozone stress, whereas the 8-hr 

exceedance statistic provides an indication of peak ozone concentrations, or maximum stress. The 

soil moisture indices (PDSI and Palmer Z) and precipitation measurements (percent normal) were 

categorized using generally accepted terms to describe a progression from dry to near normal to 

wet conditions19. The Palmer Z index measures short-term drought while the Palmer Drought 

Severity Index (PDSI) provides a measure of long-term cumulative drought. Seasonal mean 

values for all measurement variables were calculated by state and by year and then averaged within 

previously described state groupings after it was determined that precipitation indices within 

each group varied by no more than one class 100 percent of the time.

19National Climate Data Center, U.S. Palmer Drought Indices: http://www.ncdc.noaa.gov/oa/ncdc.html.
42

Table 10.—Seasonal averages of the biosite index, air quality, soil moisture, and precipitation indices by year 

(1997 to 2005) for groups of states with varying air quality

State group1

and year Biosite index2

Seasonal ozone

SUM06

(ppm-hr)

Seasonal ozone

8 hr exceedance

days

Seasonal soil

moisture

(PDSI)

Seasonal

precipitation

(% normal)

Seasonal soil

moisture

(Z Index)

Northern New England (ME, NH, VT)

1997 6.76 6.9 4.3 1.90 100 0.57

1998 6.56 5.2 3.7 2.46 141 2.82

1999 0.46 8.5 5.3 -2.15 77 -1.50

2000 0.99 2.6 1.7 2.64 94 0.59

2001 0.69 6.9 4.3 -2.03 79 -1.16

2002 0.41 8.9 7.7 -0.33 90 -0.15

2003 0.30 5.5 1.0 -0.41 98 0.15

2004 2.05 3.7 2.0 1.76 118 1.86

2005 1.44 4.6 2.0 2.72 106 0.47

Mean values 2.18 5.9 3.6 0.73 100 0.41

Southern New England (CT, MA, RI)

1997 6.85 19.4 15.3 -0.73 86 -0.46

1998 19.81 16.2 8.0 1.03 132 1.50

1999 8.38 21.1 13.3 -2.29 52 -2.30

2000 3.97 12.8 5.0 1.89 125 1.71

2001 8.54 20.0 14.7 0.64 116 0.89

2002 4.47 25.4 16.3 -0.57 80 -0.62

2003 1.77 16.9 8.3 2.00 125 1.27

2004 3.06 11.8 3.7 1.03 103 0.45

2005 2.33 16.3 8.0 -0.88 75 -1.22

Mean values 6.57 17.8 10.3 0.24 99 0.14

Mid-Atlantic States (DE, MD, NJ, PA, WV)

1997 12.30 30.5 18.2 0.86 89 -0.44

1998 34.82 31.4 28.4 0.14 88 -0.53

1999 13.40 35.0 22.4 -3.55 71 -2.19

2000 4.87 18.5 10.8 1.25 120 1.28

2001 5.08 26.4 15.8 0.21 110 0.57

2002 7.16 34.9 24.4 -2.11 79 -1.38

2003 2.89 20.4 6.8 3.50 144 2.39

2004 5.10 12.8 4.6 3.30 122 1.31

2005 6.68 19.7 9.2 -0.38 93 -0.78

Mean values 10.25 25.5 15.6 0.36 102 0.03

Northern Plains States (IA, KS, MN, NE, ND, SD)

1997 0.08 6.1 0 3.11 102 1.09

1998 0.09 2.3 1.0 2.79 117 0.56

1999 <0.01 3.9 0 4.59 115 1.82

2000 <0.01 3.5 0.5 1.04 94 0.97

2001 0.15 6.2 1.5 1.80 93 -0.68

2002 <0.01 9.3 0.8 -1.45 101 -0.54

2003 0.09 10.1 0.8 -0.74 78 -0.96

2004 <0.01 3.3 0 0.94 96 0.57

2005 0.05 9.5 0.8 1.97 114 1.40

Mean values 0.09 6.0 0.6 1.56 101 0.47

East North Central States (IL, IN, MI, MO, OH, WI)

1997 14.28 17.6 9.4 1.50 101 0.70

1998 20.11 18.4 16.2 0.82 110 0.71

1999 4.34 19.8 17.8 -0.62 96 -0.05

2000 5.60 12.8 5.3 1.25 115 1.44

2001 3.85 19.0 11.3 0.67 101 0.34

continued
43

2002 1.21 23.5 22.0 -0.11 88 -0.71

2003 1.24 16.9 7.5 0.77 100 0.54

2004 1.52 6.2 1.5 2.50 109 1.33

2005 1.15 19.5 10.8 -1.74 92 -0.80

Mean values 5.92 17.1 11.3 0.56 101 0.39

1

State groups are allowed based on the similarity of precipitation values and air quality data. All the combined states held statelevel mean Z values that agreed within one category 100 percent of the time.

2

Classification schemes for the biosite index, seasonal ozone, seasonal soil moisture, and seasonal precipitation data are

described in Table 11 of this report.

Table 10.—continued

State group1

and year Biosite index2

Seasonal ozone

SUM06

(ppm-hr)

Seasonal ozone

8 hr exceedance

days

Seasonal soil

moisture

(PDSI)

Seasonal

precipitation

(% normal)

Seasonal soil

moisture

(Z Index)

The results suggest that BI values are highest within each state grouping when seasonal soil 

moisture and precipitation indices indicate near normal to wet conditions. When seasonal soil 

moisture and precipitation indices indicate dry to drought conditions, the BI value tends to 

be relatively low even when seasonal ozone exposure indices are relatively high. In southern 

New England, for example, mean values for the 1997 to 2005 study period were in the low to 

moderate foliar injury class for BI and in the low to moderate class for ozone exposure. In 1997, 

precipitation and soil moisture conditions were near normal and the ozone exposure and injury 

indices stayed close to the mean. In 1998, ozone exposure values were slightly lower than the 

1997 values, but the BI value almost tripled from 6.85 (low to moderate injury class) to 19.81 

(moderate to severe injury class) presumably caused by wet conditions favoring ozone flux. In 

1999, the seasonal precipitation index plummeted to 52 percent of normal, and even though 

the ozone exposure value was relatively high at 21.1 ppm-hr (moderate O3 exposure class), 

the BI value dropped back down to 8.38 suggesting the plant stomates were closed. Seasonal 

ozone exposures values were highest in 2002, both in terms of SUM06 data (25.4 ppm-hr) 

and number of 8-hr exceedance days (16.3), but the BI statistic was relatively low at 4.47 (little 

or no foliar injury class). Seasonal precipitation was 80 percent of normal with soil moisture 

indices on the dry side of near normal conditions. Additional or more site-specific information 

on the exposure environment of the sample plants is needed to know what contributed to the 

below average BI value in 2002.

Trends in ozone injury and exposure are examined for FIA-North by looking at changes in 

average ozone injury and exposure indices for the consecutive 5-year periods, 1997 to 2001 

and 2002 to 2006 (Table 12). Along the northeast ozone gradient, the percent injured biosites 

has not changed significantly from one 5-year period to the next, but in areas with moderate 

to high ozone exposures (including the east North Central States) the percent injured plants 

has decreased considerably as has the average biosite index. Both periods are characterized by 

fluctuating precipitation and soil moisture indices (refer back to Table 10), suggesting that 

weather alone does not appear to be the controlling factor in the regionwide trend toward 

less injury. The average ozone exposure indices suggest no significant change in chronic ozone 

exposure as reflected in the SUM06 index. Average number of ozone exceedance days dropped 

slightly, suggesting a reduction in peak ozone exposure levels. In the high ozone subregion 

of the mid-Atlantic, for example, the average number of exceedance days was greater than 

15 in 4 of the 5 years from 1997 to 2001 and less than 10 in 4 of the 5 years from 2002 to 
44

Table 11.—Classification schemes for the seasonal indices1 of foliar injury (BI), ozone exposure 

(SUM06 and 8-hr exceedance days), soil moisture (Palmer Drought Severity Index and Palmer 

Z), and precipitation (percent normal)

Foliar Injury Response

Biosite-level injury index values (BI)2

0 to 5 = little or no foliar injury; no risk of ozone impacts

>5 to 15 = low to moderate foliar injury; low risk

>15 to 25 = moderate to severe foliar injury; moderate risk

>25 = severe foliar injury; high risk

Seasonal Ozone Exposure

SUM06 values (sum of all hourly average ozone concentrations ≥0.06 ppm O3)

3

0 to 10 ppm-hr = clean (no O3)

>10 to ≤20 ppm-hr = low O3

>20 to ≤30 ppm-hr = moderate O3

>30 ppm-hr = high O3

8-hr exceedance days (number of days with an exceedance of the 8-hr O3 standard)3

0 to 10 days = low O3

>10 to 20 days = moderate O3

>20 days = high O3

Seasonal Soil Moisture

PDSI (long-term drought) and Palmer Z (short-term drought) values4

≥3.00 = very wet

2.00 to 2.99 = moderately wet

1.00 to 1.99 = slightly wet

-0.99 to 0.99 = near normal

-1.0 to -1.99 = mild drought

-2.0 to -2.99 = moderate drought

≥-3.00 = severe drought

Seasonal Precipitation

% Normal

>134 = very wet

110 to 134 = wet

85 to 109 = near normal

60 to 84 = dry

< 60 = very dry

1

All seasonal mean values were calculated by state and by year and then averaged within a state grouping.

2

The BI classification scheme describes the degree of foliar injury response and assumption of probable ozone

injury ranging from little or no foliar injury and no risk of impact to low, moderate, and severe foliar injury and low,

moderate, and high risk of ozone impact to the forest ecosystem.

3

The ozone exposure classifications describe a progression from clean to moderate to unhealthy air quality.

Seasonal mean SUM06 values were categorized as clean (0-10 ppm-hr), low (>10-20 ppm-hr), moderate (>20-30

ppm-hr), and high (≥30 ppm-hr). Mean 8-hr exceedance days were categorized as low (0-10), moderate (>10-20),

and high (>20).

4

The soil moisture and precipitation measurement variables describe a progression from dry to normal to wet

conditions. The Palmer Drought Severity Index shows how long-term cumulative moisture conditions have changed

over the past 12 months. The Palmer Z Index shows how monthly (short-term) moisture conditions depart from

normal.
45

2006. This finding is in accord with national trends in O3 air quality that show that air quality 

control strategies have reduced maximum 1-hr O3 concentrations but have had little impact on 

moderate O3 concentrations.

Trend data for the Northeast suggest that changes in ozone exposure have had less of an effect 

on the numbers of injured biosites than on the BI index. Injury is detected on about the same 

number of sites every year, often the same sites every year, but the BI value that associates injury 

amount, injury severity, and incidence of injury at each site tends to fluctuate in response to 

changes in ozone level and the ozone exposure environment. This result is indicative of the 

biological responsiveness of the BI index and underscores the need for analysts to include 

ancillary data when presenting and interpreting results. It is generally true for states in FIANorth that ozone stress is causing visible foliar injury to ozone-sensitive species on less than 

20 percent of the biomonitoring sites in years or states with relatively clean air, and often 

on more than 30 percent of the biomonitoring sites in years or states with moderate to high 

ozone exposure. The year 1998, characterized by relatively high O3 and adequate growing 

season moisture, is the only year when foliar injury and the presumed risk of ozone stress 

were moderate to severe across large portions of FIA-North. The remaining years during the 

1997 to 2006 sampling period were relatively free of ozone injury from a regional perspective. 

However, there are localized areas and significant portions of ecological regions where BI values 

are relatively high year after year (Fig. 4), and these high risk zones for probable ozone impact 

should be investigated further.

Table 12.—Differences in calculated mean values for percent injured biosites, percent injured plants, 

average biosite index, and ozone exposure indices for consecutive 5-year sampling periods from 1997 

to 2001 and from 2002 to 2006

State group1

and year

Percent injured

biosites

Percent injured

plants

Average biosite

index2

Average SUM06

value

Average

exceedance days

Northern New England (ME, NH, VT)

1997-2001 18.4 3.0 3.09 6.0 3.9

2002-2006 19.0 0.8 0.96 5.7 3.2

Southern New England (MA, CT, RI)

1997-2001 68.8 11.5 9.51 17.9 11.3

2002-2006 68.0 6.9 3.83 17.6 9.1

Mid-Atlantic States (DE, MD, NJ, PA, WV, VA)

1997-2001 38.2 7.8 14.14 28.4 19.1

2002-2006 35.8 4.3 5.19 21.9 11.3

Northern Plains States (IA, KS, MN, NE, ND, SD)

1997-2001 1.8 <0.1 0.11 4.4 0.5

2002-2006 3.0 <0.1 0.16 8.1 0.6

East North Central States (IL, IN, MI, MO, OH, WI)

1997-2001 52.1 5.4 9.64 17.5 12.0

2002-2006 31.1 1.4 1.28 16.5 10.5

1

State and air quality groupings are defined as follows: Northern New England = ME, NH, VT (SUM06 <10 ppm-hr); southern

New England = MA, CT, RI (SUM06 10-25 ppm-hr); mid-Atlantic = NJ, PA, MD, DE, WV, VA (SUM06 >25 ppm-hr); Northern

Plains = IA, KS, MN, NE, ND, SD (SUM06 <10 ppm-hr); east North Central = IL, IN, MI, MO, OH, WI (SUM06 10-25 ppm-hr) 2

Average biosite index values were calculated for each state grouping.
46

ANALYTICAL PROCEDURES AND RISK ASSESSMENT

In Detection Monitoring, the biomonitoring data are used to identify forested areas that may 

be at risk from ozone stress. Foliar injury to a bioindicator plant is a function of the interaction 

between ozone level and the exposure environment of the plant. For injury to occur, ozone 

must be present above some threshold concentration and the exposure environment must 

be favorable for gas exchange and the overall responsiveness of the plant. For Detection 

Monitoring, bioindicators detect “phytotoxic” ozone conditions, i.e., the combination of 

ozone level and exposure environment that leads to uptake and injury. Quantifying the 

amount and severity of foliar injury to bioindicator plants with the site-level biosite index 

(BI) provides a comparative measure of ozone injury conditions on the Detection Monitoring 

plots. Given what we know about the adverse affects of O3 on trees and understory species, 

we can infer that as the injury index increases, so does the risk of ozone impacts. Although 

the injury index developed for this biomonitoring program cannot measure ozone impact, it 

can be used to provide a reliable estimate of the risk of probable ozone impact to the more 

ozone-sensitive trees and forests. Risk analysis has been defined as the process of quantifying, 

either qualitatively or quantitatively, the probability and potential impacts of some risk (Vose 

2000). Detection Monitoring analyses using the ozone biomonitoring data are both qualitative 

(presence or absence) and quantitative (injury index) and fall under the category of risk 

analysis.

Population Estimates

Plot-level attributes required for population estimates are developed by spatial interpolation 

of the biosite data. Additional details on the estimation procedures are provided in the Ozone 

Bioindicator Sampling and Estimation report (Smith et al. 2007). Spatial interpolation 

techniques are widely used in the analysis of air pollution, environmental, and ecological data. 

For example, EPA provides interpolated maps of ozone air quality that cover the landscape 

even though air quality monitoring stations are often limited to population centers. In a similar 

fashion, the measurements of ozone injury on the FIA biomonitoring plots are used to create 

an interpolated bioindicator response surface across the U.S.

Bioindicator attributes are estimated yearly for all P2 and P3 plots by intersecting the map 

of interpolated values with P2 and P3 plot locations (Fig. 5a,b). As a result, each P2 plot 

(and tree) is assigned an interpolated biosite index value (IBI) and accompanying plant 

count attribute and then placed in a defined category of ozone risk and relative air quality as 

previously described (Table 3). These bioindicator attributes are merged with other tree plot 

attributes in the FIA database to generate population estimates using nationally standardized 

procedures (Bechtold and Patterson 2005, Johnson et al. 2003). Population estimates for 

the ozone bioindicator include, but are not limited to, (1) proportion of forest land in each 

biosite index category by region, ecoregion, and state; (2) acres of forest land in each biosite 

index category by region, ecoregion, and state; and (3) volume of ozone-susceptible species in 

each biosite index category by region, ecoregion, and state. Analysts may also use the map of 

interpolated values to examine relationships between the IBI and other FIA indicators of tree 

growth, forest health, and forest condition.
47

The National Ozone Risk Map

The interpolated bioindicator response surface is referred to as the national ozone risk map 

(Fig. 6). A new map is created every year for status and trend analyses. Analysts are expected to 

extract areas of interest (e.g., states or ecoregions) from the national map for regional reports. 

An example of this approach is provided by Campbell et al. (2007) for the Pacific Northwest 

(PNW). Analysts from the PNW Research Station extracted Washington, Oregon, and 

California from the national map (average 2000 to 2005 BI) to examine risks of ozone impact 

to forests in West Coast States. Using 6-year averages of the biosite index, they estimated acres 

of forest land and volume of ozone-susceptible tree species in each ozone risk category. In 

Oregon and Washington, all the forest land and all susceptible species are classified in the lowest 

biosite index category with no risk of impact. In contrast, the ozone risk map identified areas 

of low, moderate, and high risk in California. Areas of greatest risk are in the southern Sierra 

Nevada Mountains, portions of the central coast, and much of the area east of Los Angeles (Fig. 

2 in Campbell et al. 2007). Most of the ozone-sensitive tree resource in California is not at risk. 

Seventy-four percent of the forest land with 88 percent of the tree volume of ozone-sensitive 

tree species is classified in the lowest risk category. However, more than 8.7 million acres with 

4.7 billion cubic feet (11 percent) of ozone-sensitive tree species are at low, moderate, or high 

risk of ozone injury. Species-specific analyses indicate that about 18 percent of ponderosa pine 

volume and 29 percent of Jeffrey pine volume fall into the low to moderate risk category. The 

authors of the PNW report suggest that the ozone indicator map of ozone risk for California 

Figure 5.—Biosite index values are estimated for all P2 and P3 plots by intersecting the map of interpolated values

with P2 and P3 plot locations (a). This results in a biosite index value estimate for each P2 and P3 plot (b). For

additional detail on the interpolation method, see Smith et al. (2007).

Plot number 

Biosite 

index

Injured 

plants (%)

27120110311029 13.8 15.8

27120110311156 16.1 18.0

27120110319064 0.1 1.1

27120110319251 20.8 40.8

27120110319361 19.9 25.0

27120110319385 9.7 10.4

27120110712093 1.7 3.8

27120110712438 8.4 8.0

27120110712720 6.1 7.9

27120110712907 24.5 30.8

27120110713096 10.6 10.0

27120110713107 20.9 14.5

27120110713459 14.0 12.9

27120110759099 13.6 6.8

27120110759237 2.8 5.0

(b) Interpolated biosite index values and plant 

(a) Block kriging and intersecting FIA P3 plot locations. count attribute. 

EMAP Hex Centers

< 5

>= 5 and < 15

>=15 and < 25

>= 25
48

National Ozone Risk Map 

Figure 6.—Spatial interpolation of the mean biosite index for the 2000 to 2004 sampling period. Interpolated biosite index estimates are used to categorize therisk of probable ozone impact to forest ecosystems. Dark green indicates areas of relatively low risk, light green areas of moderate risk, and purple indicatesareas of relatively high risk. Areas in white were not sampled.
49

is generally consistent with other models derived from nonbiological databases. Models that 

take into account environmental factors that influence ozone flux such as air temperature, 

precipitation, elevation, and wind patterns are more spatially and temporally explicit.

Air quality data from South Carolina indicate that this state has some of the highest ozone 

concentrations in the Southeast. Peak ozone levels are often in excess of the national 8-hr 

standard of 0.085 ppm, and cumulative growing season exposures (e.g., SUM06 data) rival 

those of southern California, suggesting that forest land in South Carolina is at high risk of 

ozone injury. Population estimates derived from spatial interpolation of the biosite data differ 

(Conner et al. 2004). In South Carolina, about 16.7 billion cubic feet of tree volume is in the 

lower ozone risk categories, slightly more than half of which is made up of ozone-sensitive tree 

species (Table 13). Thirty-eight percent of the total volume is in the no risk ozone category, 60 

percent in the low risk category, 2 percent in the moderate ozone risk category, and zero percent 

in the high ozone risk category. Ozone injury is detected in the state on sensitive bioindicator 

plants almost every year, and more injury occurs in those parts of the state with higher ambient 

ozone concentrations. Nevertheless, the particular physiographic conditions of South Carolina 

and genotypic characteristics of the indigenous ozone-sensitive species indicate that most of 

the forest land in the state is not at any significant risk of ozone impact. Similar findings were 

reported for Virginia (Rose 2007). The sampling period discussed here (1998-2002) covers 

a period of below average rainfall during the growing season20 that undoubtedly influenced 

the results. This situation could change dramatically if predisposing factors favorable for gas 

exchange and physiological responsiveness shift toward conditions that favor ozone injury such 

as might happen if rainfall amounts and distribution during the growing season shift from 

below normal (drought conditions) to a near normal or wet precipitation pattern for South 

Carolina and the entire Southeast. Other researchers working in the southeast reported that 

drought conditions during the 1988 to 1999 time period overrode any potentially adverse 

effects of ozone on growth in the central Appalachian Mountains (Edwards et al. 2004).

As with other detection-level activities, there is a high noise to signal ratio with the application 

of the national ozone risk map (Smith et al. 2007). For this reason, Evaluation Monitoring 

is an essential part of the risk assessment process. The categories of risk defined by the ozone 

indicator (Table 3) are broad enough that analysts should be able to easily determine if the 

risk of probable ozone injury in their area of interest differs from the rest of the state, region, 

or ecological region, or if it changes significantly over time. If differences are detected, analysts 

need to examine explanatory variables and pursue appropriate correlative analyses. The 

appropriate analytical technique and explanatory variables depend greatly on the spatial scale of 

interest. For large-scale analyses, climate information and ambient ozone levels are important 

covariates in analysis (Smith et al. 2001). For smaller scale analyses, site factors such as terrain 

position, elevation, aspect, and soil drainage may also be important. Certain explanatory 

variables (e.g., site characteristics and ambient ozone statistics) are part of the FIA database 

for the ozone indicator. Climate variables may be more readily available from sources outside 

the FIADB, such as the National Climatic Data Center (http://www.ncdc.noaa.gov/oa/ncdc.

html). Analysts are strongly encouraged to examine both internal and external databases related 

to ozone exposure characteristics, plant properties, and external growth conditions when 

20National Climate Data Center: http://www.ncdc.noaa.gov/oa/ncdc.html
50

presenting and interpreting results for the ozone indicator. The national ozone risk map provides 

a detection-level assessment of ozone stress across the landscape, but it should not be used to 

make location-specific statements of cause and effect about O3 and forest health.

Link to Evaluation Monitoring

When regional or national analyses of detection-level data indicate areas of potential impact on 

forest productivity and sustainability, these areas are evaluated through additional studies on an 

intensified grid. For example, the biomonitoring data may indicate a band of high ozone stress 

across a state or region, which may prompt the regional analyst to ask if the finding is real or 

some artifact of data collection. The next logical step is an evaluation study to take a closer look 

at the area of concern. For the ozone indicator, an evaluation study should include an intensified 

sampling grid and an analysis of air quality and environmental data that influence plant 

response to O3. It may also be important to examine species distribution maps available from 

the FIA forest inventory database. An example of ozone Detection Monitoring identifying a 

potential problem area and Evaluation Monitoring verifying the problem is summarized below.

In a published report (Coulston et al. 2003), FHM researchers examined the bioindicator 

data from the Northeast and mid-Atlantic for the sampling period 1994 through 1999. With 

the techniques described in the ozone estimation document (Smith et al. 2007), the FIA 

biomonitoring data were used to generate a spatial distribution of probable ozone injury to 

plants that was then related to the spatial distribution of forest tree species in the study area. The 

objective was to identify forest tree species likely to exhibit regional-scale ozone impacts. One 

of the findings of this study indicated that large portions of western Pennsylvania experienced 

conditions where plant injury from O3 was expected. Black cherry, an ozone-sensitive 

bioindicator species, was a major component of the forests in this area. This finding prompted 

FHM researchers to wonder if the biosite data were unduly influenced by the distribution 

pattern of black cherry in Pennsylvania.

To address this question, an intensified biomonitoring grid was designed for the area of concern 

in southwestern Pennsylvania (Skelly et al. 2003). Ten plots were identified and evaluated 

for ozone exposures and foliar injury symptoms on a variety of ozone-sensitive species (trees, 

shrubs, and herbaceous material) weekly from June through early September. The study also 

Table 13.—Acres of forest land, total tree volume, tree volume of ozone-sensitive tree species, and 

sensitive volume as a percent of total volume by ozone risk category for South Carolina (2002 data)

Population estimates for

South Carolina1

Biosite index and risk estimation2

Totals for

each estimate

0 to 5

No risk

>5 to 15

Low risk

>15 to 25

Moderate risk

>25

High risk

Forest land area

(thousand acres) 4,780.4 7,404.0 270.4 0.0 12,455

Total tree volume

(million cu. ft.) 6,316.7 10,047.4 321.1 0.0 16,685

Volume of ozone-sensitive

species (million cu. ft.) 3,275.0 5,381.9 143.9 0.0 8,801

Sensitive volume as a percent

of total volume (%) 51.8 53.6 44.8 - 53

1

According to Conner et al. 2004. 2

Categories of biosite index and risk estimation as are described in Table 3 of this report.
51

examined weather (precipitation amounts, departure from normal) and soil-site conditions 

(gravimetric soil moisture) that might have a controlling influence on the plant injury data. The 

results of this investigation established that the area of concern was in fact characterized by high 

ozone concentrations and that ozone-induced injury symptoms were evident on a variety of 

bioindicator species on the 10 study plots. Typical symptoms of ozone injury were prominent 

not just on black cherry, but also on blackberry, milkweed, white ash, and tulip-poplar. This 

conclusion confirmed the findings of the original report authored by Coulston et al. (2003). 

Questions raised by Detection Monitoring were investigated through an intensified sampling 

design and answered in such a way as to support and strengthen the findings and analytical 

approach of the detection-level biomonitoring program.

Other questions of regional ozone impact raised by Coulston et al. (2003) await further study. 

Four ozone-sensitive tree species (black cherry, loblolly pine, sweetgum, and serviceberry) and 

two tree species of unknown ozone sensitivity (southern red oak and sweet birch) were identified 

as at risk on a regional scale from ambient levels of ozone pollution. A wealth of information 

from the P2 ground plots could be used to support an indepth study of actual impacts on 

growth increment, species composition, or some other measurable indicator of ecosystem health 

and stability. This type of intensified analysis is needed to provide more informed responses to 

forest health assessment questions posed by the FIA ozone indicator (page 18).

A second evaluation monitoring project conducted by Bennett et al. (2006) attempted to 

measure and compare ozone-induced growth effects on milkweed and black cherry growing 

in two areas west and east of Lake Michigan that were similar except for the ambient ozone 

environment. The study plots west of Lake Michigan were subject to relatively low seasonal 

ozone exposures (low risk of impact) while the field plots to the west were subject to relatively 

high ozone exposures (high risk of impact). As expected, foliar injury was detected on both 

milkweed and black cherry over the 3-year study period, and the severity of foliar injury 

symptoms and quantitative effects of O3 were greater in the high risk area. Although findings 

were complicated by fluctuating environmental factors and natural variability in sample 

populations, the statistical analyses indicated that branch elongation in black cherry and 

stem height and pod production in milkweed were significantly reduced by ambient ozone 

concentrations. This evaluation study was informative because the authors had selected growth 

attributes directly responsive to current-year ozone exposures (i.e., growing branch tips in black 

cherry) and very high sample counts (thousands of plants). The authors suggested increasing 

sample size allows relatively small effects occurring in real-world field conditions to be both 

measurable and statistically significant. In agreement with this point, Chappelka and Samuelson 

(1998) stated that increasing experimental replication and identifying the most ozone-responsive 

whole-tree measurement endpoint will improve the accuracy of risk assessment models.

Link to Air Quality Standards

EPA operates a network of more than 1,100 active ozone monitors generally sited near 

population centers that continuously measure ozone concentrations in ambient air (http://www.

epa.gov/air/data/index.html). The data are summarized as hourly average ozone concentrations 

that can be used to identify counties that are out of compliance with the NAAQS for O3 (http://

www.asl-associates.com/). Point-level ozone data from the network of EPA monitoring stations 

are used by analysts to interpolate a uniform surface of ozone air quality across the landscape, 
52

thereby allowing researchers to estimate O3 concentrations anywhere on the ground (Appendix 

2, Fig. 1). In a similar fashion, the measurements of ozone injury on the FIA biomonitoring 

plots are used to generate the national ozone risk map (Fig. 6). The mapped EPA data provide 

an indication of what is in the air, whereas the mapped FIA data provide an indication of 

what is getting into the plants, or where site conditions are generally conducive to O3 uptake 

and injury. One of the goals of the FIA biomonitoring program is to support national policy 

on plant health protection. The biological information provided by both the biomonitoring 

plot data and the national ozone risk map can be used to help inform and influence the 

establishment of meaningful air quality standards to protect plants from O3 damage.

The 1970 Clean Air Act requires EPA to periodically review its air quality standards to ensure 

continued protection of human health (primary standard) and the environment (secondary 

standard) and to update the standards if necessary. EPA last updated the standards for O3 in 

199721. At that time there was considerable discussion in the plant science community about 

the need for a more stringent secondary standard (Heck et al. 1998). Heck and Cowling (1997) 

highlighted the following key points and recommendations: (1) plants are more sensitive to O3

than humans and thus require a more restrictive standard; (2) unlike its effect on humans, the 

effect of ozone on plants is both cumulative and longterm (therefore, the secondary standard 

should be both cumulative and long term); and (3) the SUM06 summed over a running 

90-day maximum (June, July, August), using values from a 12-hr (0800-1959) daily window 

should be accepted as the biologically relevant form of the ozone standard. Further, the report 

recommended a SUM06 range of values from 8 to 15 ppm-hr as sufficient to protect the 

more sensitive endpoints (e.g., foliar injury) for natural ecosystems. EPA did not adopt these 

recommendations in 1997 and for the past 10 years the secondary standard has retained the 

form of a 1-hr (0.120 ppm) and 8-hr (0.084 ppm) average value.

The year 2007 marks the year of a new scientific review of the national ambient air quality 

standards for ozone (U.S. EPA 2007), and this time the recommendations of the scientific 

community may prevail. In July of 2007, EPA published a final staff paper on ozone that 

recommends a standard that is a cumulative, weighted total of daily 12-hr exposures over a 

3-month period within the growing season. For the first time, FIA biomonitoring data were 

included in the review, demonstrating the importance of the program to the establishment of 

meaningful air quality standards. Peer-reviewed publications of the ozone program by Smith 

et al. (2003), and Coulston et al. (2003, 2004), as well as national technical reports from 

FHM (Coulston et al. 2005) were cited in EPA’s 2007 policy assessment of scientific and 

technical information on the ozone standard (U.S. EPA 2007) and in EPA’s 2007 Report on the 

Environment (http//www.epa.gov/indicators/roe).

A collaborative study conducted by EPA and FIA staff demonstrates the usefulness of the 

FIA data to the standard setting process (U.S. EPA July 2007). The study was designed to 

assess how meeting various O3 standard levels (e.g., the current standard and alternative levels 

under consideration) affected the incidence of visible foliar injury on the biomonitoring 

plots (Table 14). Between 235 and 286 counties had both EPA ozone monitoring stations 

21On March 12, 2008, after considerable scientific review (U.S. EPA 2007) EPA announced a revised 8-

hr O3 standard of 0.075 ppm. Details on the 2008 revised O3 standard are provided in Appendix 1.
53

and FIA biomonitoring plots during 2001 to 2004. These matched data were compared for 

number of counties with and without visible foliar injury to discern the degree of protection 

provided by the current form and level of the secondary ozone standard. The data demonstrate 

unequivocally that visible foliar injury to ozone-sensitive bioindicator plants is occurring in 

counties that are meeting the current 8-hr average O3 standard (0.084 ppm) and the alternative 

secondary standard option of SUM06 ≤ 25 ppm-hr proposed in 1996. By comparison, the 

two lower air quality alternatives (0.074 ppm 8-hr average and SUM06 ≤ 15 ppm-hr) provide 

more protection from visible foliar injury across all years (2001-2004) than either of the 

aforementioned standards. Even with these more stringent alternatives to the current standard, 

however, the percent of counties showing injury ranged from 12 to 35 percent. The principal 

authors suggest that if protection from foliar injury is an agreed upon objective for natural 

systems and forest stands, then a 3-month 12-hr SUM06 value of 8 to 12 ppm-hr should be 

recommended as first proposed in the 1996 consensus workshop held on the secondary O3

standard (Heck and Cowling 1997).

Evidence that the current NAAQS is inadequate to protect natural systems from adverse 

ozone effects is summarized in the conclusions and recommendations of the EPA staff report 

(U.S. EPA July 2007). First, the FIA biomonitoring sites show widespread evidence of ozoneinduced foliar injury in forested ecosystems. Second, studies conducted at the Aspen FACE 

site in Wisconsin on quaking aspen (Percy and Karnosky 2007), along an urban-to-rural ozone 

gradient in New York on cottonwood (Gregg et al. 2003), and in the mixed deciduous forests 

of eastern Tennessee on mature trees of different species (McLaughlin et al. 2007a) confirm 

the detrimental effect of ambient ozone exposures on tree growth. In the report, EPA staff 

propose a more stringent secondary ozone standard derived from differentially weighted peak 

concentrations and cumulative seasonal exposures. The W126 statistic is recommended over 

the SUM06 statistic because it has no minimum ozone concentration threshold and only 

lightly weights the lower ozone concentrations. The form of the 12-hr W126 is defined as the 

sigmoidally weighted 3-month sum of all ozone concentrations observed during the daily 12-hr 

period between 0800 and 2000. The 3 months are the maximum consecutive 3 months during 

the ozone season. The recommended upper and lower air quality levels of 12-hr W126 are 21 

and 13 ppm-hr, respectively.

Table 14.—Number of counties with FIA biomonitoring sites and EPA air quality sampling stations and the percent of 

the total number of counties with visible foliar injury to bioindicator plants at various air quality standard levels for the 

years 2001 to 2004

Year

Number of counties with

EPA O3 monitoring and

FIA biomonitoring sites

Ozone standard levels

O3 ≤ 0.084 ppm1

(current)

SUM06 ≤ 25 ppm-hr2

(1996 proposal)

O3 ≤ 0.074 ppm1

(proposed)

SUM06 ≤ 15 ppm-hr2

(proposed)

Percent counties with foliar injury

2001 235 39 49 25 23

2002 270 21 26 12 12

2003 285 28 34 11 25

2004 286 35 37 30 35

1

These standard levels represent the annual fourth highest 8-hr maximum average. 2

These standard levels represent the sum of the number of hours greater than 0.06 ppm O3.
54

Advantages and Disadvantages of the Data

The ozone indicator provides a simple analytical tool to discuss the very real and naturally 

complex relationship between ozone pollution and forest health. The key cautionary note 

for analysts working with the biomonitoring data is to keep it simple. The summarized data 

provide an opportunity to educate and inform the public on forest health risks associated 

with ozone exposure. However, the presentation and discussion of summarized field data 

and population estimates should stay within the bounds of Detection Monitoring. The 

ozone indicator documents the amount and severity of ozone-induced foliar injury to ozonesensitive bioindicator plants on a national grid of ozone biomonitoring sites. Unlike most P3 

(forest health) indicators, the ozone indicator documents and attempts to quantify the effects 

(foliar injury) of a specific environmental stress (ozone). Independently, the information 

gathered at biomonitoring sites identifies whether conditions exist for plant injury to occur. 

This information alone can be used to report status and trends with respect to the ozone 

indicator (Smith et al. 2001). For example, if field crews detect symptoms of ozone injury 

on bioindicator plants in northern Michigan, then we know something more about forest 

health in Michigan than we did without this information. We know that ambient ozone 

levels are capable of reaching phytotoxic concentrations in an area of the state that may have 

previously been considered risk free with respect to ozone exposure. What we do not know is 

whether the detected presence of phytotoxic ozone concentrations in northern Michigan is 

having any measurable impact on the health of individual trees or species in the forests there. 

Answering questions about impact requires additional information, an investment in Evaluation 

Monitoring or other research-oriented programs.

Written summaries of the ozone indicator data should include information on the number 

and distribution of plus ozone sites across a state or region, as well as the relative severity of 

that injury as quantified by the site-level injury index (BI). Five consecutive years of detectionlevel findings should be reviewed to assess the presence or absence of significant ozone stress 

in a given state or region. We need to know, for example, if field crews detect relatively low, 

moderate, or severe injury in northern Michigan, and whether this happens every year or only 

once in a 5-year period. Changes in the number and distribution of plus ozone plots, as well 

as increases or decreases in the BI from one 5-year period to the next, provide a simple trend 

analysis for the ozone indicator.

Analysts working with ozone indicator data obtained from overlaying the national ozone risk 

map and the P2 plots should present their findings in a risk analysis framework, keeping in 

mind that the purpose of the overlay and the resulting calculations of population estimates is, 

once again, Detection Monitoring. Continuing with the Michigan example, we may learn that 

the risk of probable impact to ozone-sensitive tree species in northern Michigan is relatively 

low, meaning that the interpolated BI values in northern Michigan are relatively low compared 

to the interpolated BI values from other areas in Michigan and the region. A risk assessment 

approach provides different information from that obtained from the summarization of 

sampling statistics (i.e., we can talk about populations rather than plot counts), but we are no 

closer to answering questions about impact with any certainty. This is an expected limitation of 

Detection Monitoring. The intent is for the regional FIA analyst to highlight areas of concern 

where the overlay of biomonitoring data and the P2 sample indicate a large number of ozone-
55

sensitive tree species in a zone of elevated ozone risk. Detecting such a finding should trigger 

additional correlative analyses to help explain the results or a field-based Evaluation Monitoring 

study. Significant changes to the national ozone risk map provide the basis for an analysis of 

long-term trends in population estimates for the ozone indicator.

The national ozone risk map provides a nationally uniform template for extracting and 

reporting regional population estimates for the ozone indicator. A weakness of the system is that 

the population of interest is forest trees while the bioindicator data are collected on bioindicator 

plants on biomonitoring sites. Information collected from one population (bioindicator plants) 

is used to indicate probable conditions in the population of interest (forest trees). However, 

there is a long history of research using bioindicator plants to detect and monitor for ozone 

injury conditions in the surrounding forest. There is no other way to get biological response 

data under ambient conditions of ozone exposure in the natural environment of the forest. 

Most of the available data are from artificial systems (e.g., open-top chambers and exposure 

rings), or small-scale survey projects limited to a particular national park, national wildlife 

refuge, or Class I Wilderness Area (Davis and Orendovici 2006, Hildebrand et al. 1996, Kohut 

2007, Manning et al. 1996, Neufeld et al. 1992, Pronos and Vogler 1981). This is the first time 

a highly standardized biomonitoring program has been implemented on a national scale. To its 

advantage, the crew level of difficulty is low, no specialized personnel or equipment is required, 

and there have been no significant changes to the field protocols since 1994. Further, the ozone 

injury symptoms identified by crews in the field are verified by an expert in the laboratory, 

virtually guaranteeing the accuracy of the site-level injury value. We know a lot about the effects 

of O3 on tree physiology and growth, and scientists agree that ground-level O3 is a significant 

threat to forest health. Knowledge gained from empirical studies conducted under controlled 

conditions of ozone exposure is not easily translated to the complex environment of the forest. 

Researchers and forest health modelers are eager for ozone injury data from natural systems such 

as that provided by the FIA biomonitoring program.

SUMMARY AND RECOMMENDATIONS

The FIA ozone biomonitoring data are the only source of information available that documents 

plant injury from air pollution using consistent protocols. The ozone bioindicator provides 

a biological index of ozone stress on a nationwide system of biomonitoring sites. Ozone 

biomonitoring is part of the FIA P3 sample and is based on the documentation of visible 

foliar injury to known ozone-sensitive plant species under conditions of ambient exposure. 

The field methods, site variables, and site-level biosite index were developed with support from 

the scientific research community (Smith 1995), and the sampling procedures and analytical 

techniques have been peer reviewed (Coulston et al. 2003, Smith et al. 2003). The following 

comments and recommendations summarize the key points in this report with respect to data 

collection, data analysis, and reporting.

Summary Comments

Ozone is considered the most pervasive air pollutant in the United States and a worldwide 

threat to sustainable forest management. There is ample evidence that O3 injures trees and 

has the potential to have negative impacts on ecosystem structure and function. Due to the 

complexity of plant/pollutant interactions, it is almost impossible to determine dose/response 
56

relationships in natural forest systems. Researchers who have developed risk assessment models 

for O3 and forest growth using the best available information from controlled exposure studies 

report growth loss percentages at ambient ozone concentrations of zero to 26 percent. The FIA 

biomonitoring data should help to strengthen the reliability of these models.

Data illustrate that ozone-induced foliar injury on FIA biomonitoring plots is widespread, 

affecting all 13 states in the Northeast region, 8 of 11 states in the North Central region, all 11 

states in the Southern region, and 2 of 3 states in the West Coast region. No injury has been 

detected on regular biomonitoring plots in the Rocky Mountain region or in Alaska. Severity of 

injury has generally been greater in states and years with higher ambient ozone concentrations, 

although localized conditions cause much site-level variation. Conditions of drought or other 

significant disturbance during the growing season inhibit the response of sensitive plants to O3, 

thus lowering the risk of impact. Ozone injury occurs routinely in areas currently identified 

as meeting the NAAQS, suggesting that the form and level of the ozone standard need to be 

updated. Although there is no obvious evidence linking ozone stress to a specific tree health 

problem or regional decline, little or no analytical work has been done to address this possibility. 

The national ozone risk map clearly identifies millions of acres of ozone-sensitive tree species 

in both eastern and western regions at varying levels of risk of ozone injury. However, only 

California, Indiana, and South Carolina have tried to quantify this phenomenon. This situation 

should improve as FIA analysts gain experience working with forest health (P3) indicators. 

Planned advances in P3 information management will ensure public access to the core products 

for the ozone indicator including site-level summary statistics and the national ozone risk map.

The usefulness of the biomonitoring data to national air quality policy was demonstrated 

when EPA scientists included the data in the most recent scientific review of the secondary 

ozone standard. Four years of results from the FIA ozone sample were analyzed and discussed 

within the framework of tree risk assessments using foliar injury as an assessment endpoint. 

The analysis led EPA staff to conclude that foliar injury would be reduced and forest protection 

enhanced with a more stringent secondary ozone standard. This finding agreed with all other 

scientific evidence put forth by the EPA Office of Air Quality Planning and Standards for this 

most recent mandated review.

The biomonitoring program will remain useful to both internal and external users as long as the 

strengths and weaknesses of the data are understood and respected. As stated in the EPA staff 

paper, visible injury is a valuable indicator of the presence of phytotoxic concentrations of O3

in ambient air, but it is not always a reliable indicator of damage, or other injury endpoints. 

When evaluating ozone risk, analysts need to be aware that for some tree species, ozone-induced 

foliar injury correlates with biomass loss or some other adverse effect, but for a larger group of 

trees, no correlation between foliar symptoms and biomass loss has been demonstrated. The 

combined total of tree species that can be categorized in either of these groups as sensitive, 

resistant, or tolerant of ozone stress is rather small. We know even less about nontree species or 

about the sensitive vegetative component of forested systems that suffers nonvisible, indirect, or 

delayed effects from ozone exposure.

There is a need for increased use of computer models and simulations to help suggest or predict 

outcomes of the many complex interactions of O3 and various combinations of environmental 
57

factors (e.g., insects, disease, competing plants, mineral nutrients, increased atmospheric CO2). These 

models are only as reliable as the input data used for parameterization. The FIA biomonitoring 

program provides a biologically relevant database (e.g., identified gradients of ozone risk across 

a forest type or ecological region) that can be used to test assumptions of ecosystem response to 

ambient ozone exposure and provide a focus for basic and applied research studies on plant/pollutant 

interactions in natural systems, thereby generating information that will improve the reliability of air 

quality, forest health, and climate change models.

Recommendations

The biomonitoring program underscores the commitment of the Forest Service to sustainable 

forest management and forest productivity loss protection as defined by the Montreal 

Process (1995). The program makes a significant contribution to forest health professionals 

and the scientific research community by collecting ozone effects data from a wide range 

of forest environments at a high level of standardization, quality, and scale. The following 

recommendations are directed at maintaining the quality of the program while expanding its 

usefulness and application to forest health issues.

The training and quality assurance procedures for the field crews and QA staff must be 

maintained in every FIA region. New training programs in data analysis and reporting should 

be developed. Access to summarized data and map products must be easy and quick so that 

both internal and external users can start to incorporate the results into their own research and 

modeling projects which will increase the visibility of the ozone indicator and facilitate linkage 

to interdisciplinary and international projects such as has already been initiated with the FACTS 

II (Aspen FACE) project near Rhinelander, Wisconsin (http://aspenface.mtu.edu/).

The data record from the biomonitoring network helps to confirm and strengthen what is 

known about common bioindicator species. FIA crews also look for and identify ozonelike injury symptoms on forest trees, shrubs, and herbs that have never been evaluated for 

ozone sensitivity under controlled conditions. This effort provides immediate benefit to the 

research community as the challenge of identifying and field testing new bioindicator species is 

advanced. Once fumigation trials are complete, these additional species offer the possibility of 

expanding the biomonitoring sample in areas where traditional bioindicator species are lacking.

This report provides an introduction to analysis and interpretation of the ozone injury index 

(BI) alongside seasonal measurements of soil moisture, rainfall, and ozone levels. Much more 

ancillary data can be reviewed and analyzed to increase the interpretability of the ozone 

indicator. The FIA database includes additional information on soil/site conditions (e.g., aspect 

and elevation) at each biomonitoring site. Additional climate attributes (e.g., air temperature 

and humidity) and environmental factors (e.g., CO2 concentration) should be investigated. 

Similarly, analysts must begin to examine relationships among the biosite summary data or 

population estimates and other indicators of forest health and condition including growth 

increment, crown condition, soil nutrient status, vegetation diversity, and lichen communities.

In conclusion, the purpose of this report is to provide FIA analysts and other interested parties 

with a comprehensive understanding of the ozone biomonitoring program and its importance 

to forest health assessment. Background material on ozone is provided along with evidence that 
58

ozone injures trees, assessment questions to guide analysis and reporting, detailed information 

on field procedures and database structure, examples of biosite-level summary statistics, a 

description of spatial interpolation, and guidance on how to interpret status and change for 

the ozone indicator. Also included are examples of output for site-level data and examples 

of output for ozone risk assessment. The appendices include a current list of tree and shrub 

species susceptible to ozone, summarized plot data from each FIA region, and several additional 

documents to assist FIA analysts with ozone data access and management. The tree sensitivity 

lists will be updated as more information becomes available. The interpolation techniques will 

be improved over time, other methods of estimating change (e.g., spatio-temporal kriging) will 

be investigated, and periodic recommendations will be made to analysts as QA results become 

available. FIA analysts are encouraged to refer to the companion Ozone Bioindicator Sampling 

and Estimation report (GTR NRS-20, Smith et al. 2007) and to review recommended 

reference materials and Web sites to stay informed on the issues of ozone air quality and forest 

health protection.

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Appendix 1.—Key resources and Web Sites

Air Quality Data and the 2008 Revised Ozone Standard

EPA’s air data Web site gives you access to air pollution data for the entire United States. Find 

out what the highest ozone level measured in your state was last year, where air pollution 

monitoring sites are located, and whether there are sources of air pollution in your area. EPA 

produces reports and maps of air pollution data based on criteria you specify. http://www.epa.

gov/air/data/index.html

A.S.L. & Associates is a private company that has developed extensive experience and resources 

for assessing the potential impacts of air pollution on the environment. http://www.aslassociates.com/

AIRNow is a government-backed, cross-agency program that provides information and data on 

air quality forecasts, air quality conditions, and historical data. Use this Web site to generate an 

air quality report for your county or state. http://www.airnow.gov/

On March 12, 2008, EPA announced its decision to adopt a new 8-hr ozone standard of 0.075 

ppm rather than the W126 cumulative ozone exposure index described on page 4 of this report. 

The new 8-hour standard is met when the 3-year average of the annual fourth highest daily 

maximum 8-hr average concentration is less than 75 ppb O3. Summer smog alerts are reported 

when average hourly O3 concentrations are expected to exceed 75 ppb over an 8-hr period. For 

more information on the standard setting process, go to: http://www.epa.gov/groundlevelozone/

Air Quality Indices

Foliar injury thresholds for the SUM06, W126, and N100 indices of ozone exposure:

SUM06 is defined as the running 90-day maximum sum of the 0800 to 2000 hourly 

concentrations of O3 equal to or greater than 0.06 ppm (Heck and Cowling 1997).

Class of vegetation SUM06 injury index

Natural ecosystems 8-12 ppm-hr (foliar injury)

Tree seedlings 10-16 ppm-hr (10-12% reduction in growth)

Agricultural crops 15-20 ppm-hr (10% reduction in 25-35% of crops)

W126 is defined as the weighted sum of the twenty-four 1-hr O3 concentrations daily from 

April through October plus N100 which is defined as the number of hours of exposure greater 

than or equal to 100 ppb during that same period (Lefohn et al. 1997). 

Ozone sensitivity class W126 injury index N100

Highly sensitive species 5.9 ppm-hr 6

Moderately sensitive species 23.8 ppm-hr 51

Low sensitivity 66.6 ppm-hr 135

Research Studies and Educational Material

The Montréal Process is the Working Group on Criteria and Indicators for the Conservation 

and Sustainable Management of Temperate and Boreal Forests. It was formed in Geneva, 

Switzerland, in June 1994 to develop and implement internationally agreed criteria and 
74

indicators for the conservation and sustainable management of temperate and boreal forests. 

http://www.mpci.org

The National Park Service Air Resources Division provides links to many resources on ozone air 

quality and the impacts of ozone on ecological systems. http://www2.nature.nps.gov/

IUFRO is a nonprofit, nongovernmental international network of forest scientists that promotes 

global cooperation in forest-related research and enhances the understanding of the ecological, 

economic, and social aspects of forests and trees. http://www.iufro.org/iufro/.

The Aspen FACE (Free-Air Carbon Dioxide Enrichment) Experiment is a multidisciplinary 

study to assess the effects of increasing tropospheric ozone and carbon dioxide levels on the 

structure and function of northern forest ecosystems. http://aspenface.mtu.edu/.

The Agricultural Research Service (ARS) is the U.S. Department of Agriculture’s chief scientific 

research agency. Follow links to its air quality program. http://www.ars.usda.gov/

The U.S. Forest Service Forest Health Monitoring Program produces a national technical report 

every year that presents results from forest health data analyses including air pollution (sulfates, 

nitrates, and ozone) and the relationship of air pollution data to other forest health indicators. 

http://www.treesearch.fs.fed.us/

Peer-reviewed Journals

Many fine journals publish articles on air quality and forest health, but the journal most often 

used by researchers in the United States is Environmental Pollution. 

http://www.elsevier.com/locate/envpol

The following special issues and articles from Environmental Pollution are available online: 

• Volume 50 Nos. 1& 2 (1988) ISSN 0269-7491 Toxic Substances in the Environment

• Volume 115 (2001) Impacts of Air pollution on Forest Ecosystems

• Volume 147 (2007) Air Pollution and Climate Change: A Global Overview of Effects 

on Forest Vegetation

• Volume 149 (2007) Air Pollution and Vegetation Effects Research in National Parks 

and Natural Areas: Implications for Science, Policy and Management

Refer to the reference section of this document for additional peer-reviewed journals that 

publish articles on air quality issues and ozone effects research. 

Air Pollution Workshop

The 40th Air Pollution Workshop was held in Raleigh, NC, April 7-10, 2008. Since 1969, plant 

scientists interested in air pollution effects on all forms of vegetation and natural ecosystems 

have met each year at various locations around the U.S., Canada, and Mexico. Participants 

are from research programs within universities, agricultural experiment stations, and Federal 

agencies such as EPA, USDA, and National Park Service. In addition, many state and local 
75

officials have attended meetings when held near their offices. A strong contingent of European 

colleagues interested in air pollution and vegetation effects attended the most recent workshops. 

For more information, go to: http://www.apworkshop.org/index.htm

Additional helpful links can be found at: http://www.apworkshop.org/links.htm

Climate Change Issues and Links

Ground-level O3 is a major constituent of photochemical smog and part of the mix 

of greenhouse gases that contribute to global climate warming. Data from the ozone 

biomonitoring program described in this report help assess the risk of climate change on forest 

health in the U.S. For more information about Forest Service initiatives on climate change, go 

to: http://www.fs.fed.us/research/fsgc/climate-change/

For comprehensive information on the issue of climate change and U.S. climate policy, visit the 

EPA climate change Web site at: http://epa.gov/climatechange/index.html

The Intergovernmental Panel on Climate Change (IPCC) was established to give decision 

makers and others interested in climate change an objective source of information about climate 

change. The IPCC is a scientific intergovernmental body set up by the World Meteorological 

Organization (WMO) and by the United Nations Environment Program (UNEP). Go to: 

http://www.ipcc.ch/

Visit the FIA ozone biomonitoring program Web site for links to the field manual, data, 

publications, regional contacts and other information.

http://nrs.fs.fed.us/fia/topics/ozone/default.asp/
76

Appendix 2.—National Maps of Ozone Air Quality

Figure 1 is a cumulative O3 exposure surface (SUM06) for the continental United States. 

Interpolated SUM06 values (sum of all hourly O3 concentrations ≥0.06 ppm) were averaged 

over the growing season (June, July, August) for the 2000 to 2004 sampling period. Cumulative 

O3 exposures are generally characterized as relatively clean (<10 ppm-hr), low (10 to ≤20 ppmhr), moderate (>20 to ≤30 ppm-hr), and high (>30 ppm-hr). 

Map source: http://www4.ncsu.edu/~jwcouls1/dep/

Data source: U.S. EPA

Figure 2 identifies the U.S. counties that are in violation of the revised national ambient air 

quality standard for O3. Additional counties may be listed when EPA issues final designations 

of attainment and nonattainment (target date = March 2009).

Map source: A.S.L. & Associates©2008, asl-associates.com/map.htm

Data source: US EPA
77

Mean 3 mo sum06 2000−2004

ppm−hrs / yr

0 − 4.7

4.8 − 9.4

9.5 − 14.1

14.2 − 18.1

18.2 − 21.4

21.5 − 24.8

24.9 − 29.1

29.2 − 35.1

35.2 − 41.5

41.6 − 47.9

48.0 − 54.6

54.7 − 61.3

61.4 − 68.6

68.7 − 76.7

76.8 − 85.7

Figure 2.—U.S. counties with O3 air quality monitors in violation of the revised O3 standard of 0.075 ppm for 

2004 to 2006. 

Figure 1.—Spatial interpolation of mean 3-month cumulative ozone concentrations (SUM06) for the 2000 to 2004 

sampling period.

Source: A.S.L. & Associates©2008, asl-associates.com/map.htm

Mean 3 mo sum06 2000-2004

ppm-hrs/yr
78

Appendix 3.—Field images of ozone injury on 

eastern and western bioindicator species

Under natural conditions of O3 exposure, there is considerable variation in visible foliar injury 

between and among species, and many biotic and abiotic stresses cause mimicking symptoms. 

Ozone-induced injury may be confounded by the presence of fungal fruiting structures, insect 

feeding damage, sun scorch, and water stress. Because classic injury symptoms are not always 

encountered in the field, leaves and needles evaluated for O3 injury by FIA field crews are 

collected and mailed to a regional expert for review and validation of the O3 injury symptom. 

Field images of bioindicator species with O3 injury from eastern and western biomonitoring 

sites, along with a list of reference books and Web sites that provide images of O3 injury on 

various species, are provided below.

http://nrs.fs.fed.us/fia/topics/ozone/default.asp - click on: ‘ozone sensitive species’

http://www.ozone.wsl.ch/index-en.ehtml

A Guide to Ozone Injury in Vascular Plants of the Pacific Northwest

Available from: Pacific Northwest Research Station, 333 S.W. First Avenue, Portland, OR 

97208-3890. (Brace et al. 1999)

Diagnosing Injury to Eastern Forest Trees: a manual for identifying damage caused by air 

pollution, pathogens, insects, and abiotic stresses

Available from: Publications Distribution Center, The Pennsylvania State University, 112 

Agricultural Administration Building, University Park, PA 16802. (Skelly et al. 1997)

Evaluating Ozone Air Pollution Effects on Pines in the Western Unites States

Available from: http://www.psfs.gov. (Miller et al. 1996)

Ozone and Broadleaved Species: A guide to the identification of ozone-induced foliar 

injury

Available from: http://www.wsl.ch/publikationen/books/4295_EN?redir=1& (Innes et al. 2001)

Recognition of Air Pollution Injury to Vegetation: A Pictorial Atlas 2nd Edition

Available from: Publications Order Department, Air & Waste Management Association, P.O. 

Box 1020, Sewickley, PA 15143-1020. (Flagler 1998)
79

 

Blackberry

Photos courtesy of Gretchen Smith, University of Massachusetts (top and bottom left), and Ron Kelley, VT Department of Forests, Parks & Recreation

Common Milkweed

Photos courtesy of Bill Manning, University of Massachusetts, (left and top right), and Gretchen Smith, University of Massachusetts (bottom right)
80

Black cherry

Photo courtesy of Bill Manning, University of Massachusetts

Photo courtesy of Ron Kelly, VT Department of Forests, Parks & Recreation

Pin cherry

Photo courtesy of Ron Kelly,

VT Department of Forests, Parks & Recreation

Big leaf aster

Spreading dogbane

Photo courtesy of Chris Bergweiler,

University of Massachusetts Photo courtesy of Ron Kelly, VT Department of Forests, Parks & Recreation
81

White ash

Photo courtesy of Ed Jepsen, WI Department of Natural Resources

Sassafras

Photo courtesy of Jim Renfro, National Park Service

Photo courtesy of Gretchen Smith, University of Massachusetts

Sweetgum

Yellow-poplar

Photo courtesy of Gretchen Smith,

University of Massachusetts

Photo courtesy of Ron Kelly, VT Department of Forests, Parks & Recreation

Photo courtesy of Ron Kelly,

VT Department of Forests, Parks & Recreation
82

Eastern biomonitoring sites

Photos courtesy of Gretchen Smith, University of Massachusetts

Western biomonitoring site

Photo courtesy of Dan Duriscoe, National Park Service
83

Ponderosa pine

Quaking aspen

Blue elderberry

Above photos courtesy of Dan Duriscoe, National Park Service

Jeffrey pine

Evening primrose Photo courtesy of Bob Kohut, BTI at Cornell University

Photo courtesy of Howie Neufeld, Appalachian State University

Left: Ozone injury on cutleaf coneflower (Rudbeckia laciniata v. ampla) at Rocky Mountain National Park

where the plant is found along watercourses and on moist sites. The injury is markedly different from the

bronzing found on R. laciniata v. laciniata in the Smokies (right).

Cutleaf coneflower Cutleaf coneflower
84

Appendix 4.—Ozone Sensitivity of Tree and Shrub Species

The abbreviations used to assign sensitivity in the following tables are as follows: 

Sen = ozone sensitive, ModSen = moderately sensitive, InSen = ozone insensitive, Unk = unknown ozone 

sensitivity because of conflicting evidence from different observers. Regional analysts should review both 

tables because species listed as eastern may be found in limited areas in Western states and vice versa. 

Additional ozone sensitivity lists for plants common to the forest environment can be found at: 

http://www2.nature.nps.gov/air/Pubs/pdf/BaltFinalReport1.pdf.

Table 1.—List of eastern tree and shrub species and their ozone sensitivity

Eastern species Sensitivity Citation

balsam fir Abies balsamea InSen1 Smith 1981

Fraser fir Abies fraseri InSen Renfro 1987-1992

boxelder Acer negundo ModSen1 Smith 1981

striped maple Acer pensylvanicum Unk

red maple Acer rubrum Sen Eckert et al. 1999

silver maple Acer saccharinum Unk USDI 2003

sugar maple Acer saccharum InSen Renfro 1987-1992

mountain maple Acer spicatum Unk

Ohio buckeye Aesculus glabra Unk USDI 2003

yellow buckeye Aesculus octandra Sen2 USDI 2003

tree-of-heaven Ailanthus altissima Sen2 USDI 2003

speckled alder Alnus rugosa Sen2 USDI 2003

serviceberry Amelanchier arborea Sen Renfro 1987-1992

Allegheny seviceberry Amelanchier laevis Unk USDI 2003

pawpaw Asimina triloba Unk

yellow birch Betula alleghaniensis Sen Renfro 1987-1992

sweet birch Betula lenta Unk Dowsett 1992

paper birch Betula papyrifera ModSen Eckert et al. 1999

gray birch Betula populifolia ModSen Eckert et al. 1999

bitternut hickory Carya cordiformis Unk

pignut hickory Carya glabra InSen Dowsett 1992

shagbark hickory Carya ovata InSen Dowsett 1992

hickory sp. Carya sp. Unk

mockernut hickory Carya tomentosa InSen Dowsett 1992

hackberry Celtis occidentalis InSen Dowsett 1992

common buttonbush Cephalanthus occidentalis Unk USDI 2003

eastern redbud Cercis canadensis ModSen, Sen2 Renfro 1987-1992, USDI 2003

yellowwood Cladrastis lutea Unk USDI 2003

Virgin’s bower Clematis virginiana Sen2 USDI 2003

flowering dogwood Cornus florida ModSen Renfro 1987-1992

American hazelnut Corylus americana Sen2 USDI 2003

hawthorn Crataegus sp. Sen3 Krupa et al. 1998

common persimmon Diospyros virginiana Unk

American beech Fagus grandifolia InSen Dowsett 1992

white ash Fraxinus americana Sen Skelly 2000

black ash Fraxinus nigra Sen3 Krupa et al. 1998

green ash Fraxinus pennsylvanica Sen Krupa and Manning 1988

black huckleberry Gaylussacia baccata Sen2 USDI 2003

witch-hazel Hamamelis virginiana Unk USDI 2003

American holly Ilex opaca InSen1 Smith 1981

black walnut Juglans nigra InSen Dowsett 1992, Smith 1981

eastern redcedar Juniperus virginiana Unk

continued
85

tamarack (native) Larix laricina Unk

sweetgum Liquidambar stryraciflua Sen Krupa et al. 1998

spicebush Lindera benzoin Unk USDI 2003

yellow-poplar Liriodendron tulipifera Sen Krupa and Manning 1988

maleberry Lyonia ligustrina Sen2 USDI 2003

cucumbertree Magnolia acuminata Unk InSen Dowsett 1992

apple sp. Malus sp. Unk

blackgum Nyssa sylvatica ModSen Renfro 1987-1992

sourwood Oxydendrum arboreum ModSen Renfro 1987-1992

Virginia creeper Parthenocissus quinquefolia Sen2 USDI 2003

sweet mock orange Philadelphus coronarius Sen2 USDI 2003

Norway spruce Picea abies InSen1 Smith 1981

white spruce Picea glauca InSen1 Smith 1981

black spruce Picea mariana Unk

red spruce Picea rubens InSen Eckert et al. 1999

jack pine Pinus banksiana Sen2 USDI 2003

shortleaf pine Pinus echinata ModSen1 Smith 1981

table mountain pine Pinus pungens Sen Renfro 1987-1992

red pine Pinus resinosa InSen1 Smith 1981

pitch pine Pinus rigida InSen, Sen2 Eckert et al. 1999, USDI 2003

eastern white pine Pinus strobus Sen Krupa and Manning 1988

Scotch pine Pinus sylvestris ModSen1 Smith 1981

loblolly pine Pinus taeda Sen Taylor 1994

Virginia pine Pinus virginiana ModSen, Sen2 Renfro 1987-1992, USDI 2003

American sycamore Platanus occidentalis Sen Krupa and Manning 1988

balsam poplar Populus balsamifera Sen3 Krupa et al. 1998

eastern cottonwood Populus deltoides Sen3 Krupa et al. 1998

bigtooth aspen Populus grandidentata Sen3 Krupa et al. 1998

quaking aspen Populus tremuloides Sen Krupa and Manning 1988

wild plum Prunus americana Unk USDI 2003

pin cherry Prunus pensylvanica ModSen Renfro 1987-1992

black cherry Prunus serotina Sen Krupa and Manning 1988

choke cherry Prunus virginiana ModSen Renfro 1987-1992

white oak Quercus alba InSen Renfro 1987-1992

scarlet oak Quercus coccinea ModSen1 Smith 1981

northern pin oak Quercus ellipsoidalis ModSen1 Smith 1981

southern red oak Quercus falcata Unk

shingle oak Quercus imbricaria InSen1 Smith 1981

bur oak Quercus macrocarpa InSen1 Smith 1981

pin oak Quercus palustris ModSen1 Smith 1981

willow oak Quercus phellos Unk Dowsett 1992

chestnut oak Quercus prinus Unk Dowsett 1992

northern red oak Quercus rubra InSen Eckert et al. 1999

post oak Quercus stellata Unk

black oak Quercus velutina ModSen1 Smith 1981

winged sumac Rhus copallina Sen2 USDI 2003

black locust Robina pseudoacacia ModSen, Sen2 Renfro 1987-1992, USDI 2003

Allegheny blackberry Rubus allegheniensis Sen2 USDI 2003

thornless blackberry Rubus canadensis Sen2 USDI 2003

sand blackberry Rubus cuneifolius Sen2 USDI 2003

black willow Salix nigra Unk

American elder Sambucus canadensis Sen2 USDI 2003

Table 1.—continued

Eastern species Sensitivity Citation

continued
86

sassafras Sassafras albidum Sen Krupa et al. 1998

common snowberry Symphoricarpos albus Sen2 USDI 2003

northern white-cedar Thuja occidentalis InSen Eckert et al. 1999

American basswood Tilia americana InSen1 Smith 1981

Chinese tallow Triadica sebifera Sen2 USDI 2003

eastern hemlock Tsuga canadensis InSen Renfro 1987-1992

American elm Ulmus americana Unk Dowsett 1992

slippery elm Ulmus rubra Unk

northern fox grape Vitis labrusca Sen2 USDI 2003

1

Based on relative sensitivity to acute ozone exposure. 2

Based on sensitivity to ambient ozone concentrations in the field and exposure chamber. 3

Based on relative sensitivity of genus, not species.

Table 2.—List of western tree and shrub species and their ozone sensitivity

Western Species Sensitivity Citation

red alder Alnus rubra Sen3 Brace et al. 1996

Sitka alder Alnus sinuata Sen Brace et al. 1996

western serviceberry Amelanchier alnifolia ModSen3 Brace et al. 1996

single-leaf ash Fraxinus anomala Sen4 USDI 2003

twinberry Lonicera involucrata Sen4 USDA 2003

lodgepole pine Pinus contorta1 ModSen3 Brace et al. 1996

Jeffrey pine Pinus jeffreyi Sen Miller et al. 1996

western white pine Pinus monticola Sen4 Arbaugh et al. 1999

ponderosa pine Pinus ponderosa2 Sen Smith 1981

Monterey pine Pinus radiata Sen4 USDI 2003

Pacific ninebark Physocarpus capitatus Sen3 Brace et al. 1996

mallow ninebark Physocarpus malvaceus Sen3 Brace et al. 1996

Fremont cottonwood Populus fremontii Sen4 USDI 2003

quaking aspen Populus tremuloides Sen Smith 1981

black cottonwood Populus trichocarpa ModSen3 Brace et al. 1996

big cone Douglas-fir Pseudotsuga macrocarpa Sen4 Arbaugh et al. 1999

Douglas-fir Pseudotsuga menziesii ModSen3 Brace et al. 1996

California black oak Quercus kelloggii ModSen Miller et al. 1996

skunk bush Rhus trilobata Sen Temple 2000

thimbleberry Rubus parviflorus Sen4 USDI 2003

Gooding’s willow Salix gooddingii Sen4 USDI 2003

Scouler’s willow Salix scouleriana Sen4 Brace et al. 1996

willow sp. Salix sp. ModSen5 Krupa and Manning 1988

blue elderberry Sambucus mexicana Sen Temple 2000

red elderberry Sambucus racemosa ModSen3 Brace et al. 1996

giant sequoia Sequoiadendron giganteum InSen Arbaugh et al. 1999

common snowberry Symphoricarpos albus Sen4 USDI 2003

snowberry sp. Symphoricarpos sp Sen5 Smith 1981

western redcedar Thuja plicata InSen Arbaugh et al. 1999

western hemlock Tsuga heterophylla ModSen3 Brace et al. 1996

huckleberry Vaccinium membranaceum Sen4 USDI 2003

huckleberry sp. Vaccinium sp. ModSen3 Brace et al. 1996

1

Pinus contorta var. latifolia. 2

Pinus ponderosa var. ponderosa. 3

Based on relative sensitivity to acute ozone exposure. 4

Based on sensitivity to ambient ozone concentrations in the field and exposure chamber. 5

Based on relative sensitivity of genus, not species.

Table 1.—continued

Eastern species Sensitivity Citation
87

Appendix 5.—Field Data Sheets and Data Flow

Chart for the Ozone Bioindicator

Examples of standardized field data sheets used by crews working in FIA-North are provided 

along with a sample site location map. The data sheets for other FIA regions are similar although 

different species are used for injury evaluations. Regions may modify the data sheets for ease 

of handling and interpretation as long as the content remains the same. Ozone injury data are 

collected electronically. The injury data sheets are used for backup in the event of PDR failure. 

The voucher data sheets must be filled out by hand and mailed with the leaf samples to the 

regional ozone expert for review. The site map and site characteristics data sheets are filled out by 

hand and stored in the region so that they are available to regular and QA crews every year.

A flow chart showing movement of the ozone indicator data from the field to the FIA database 

is also provided. 
88

STATE COUNTY FIELD ID (O3 Hex Num) SPLIT PLOT ID MONTH DAY CREW ID Circle QA STATUS 

 STANDARD -- QA BLIND CHK 

Code Species Amount of Injury – % of leaves injured relative to the total leaf number

915 Blackberry Severity of Injury – Average severity of symptoms on the injured leaves

762 Black Cherry 

365 Milkweed Code Scale

621 Yellow Poplar 0 No Injury 

541 White Ash 1 1-6% 

931 Sassafras 2 7-25% 

366 Spreading Dogbane 3 26-50% 

364 Big Leaf Aster 4 51-75% 

611 Sweetgum 5 >75%

761 Pin Cherry 

Species Code Species Code Species Code Species Code Species Code 

Plant Amount Severity Amount Severity Amount Severity Amount Severity Amount Severity 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

Did you collect 3 leaves that clearly show ozone stipple, for each injured species? Enter injury location and injury type codes for the sampled plants. 

¥=yes

Location = Type = Location = Type = Location = Type = Location = Type = Location = Type = 

Refer to the Ozone Bioindicator Plants section of the FIA Field Guide for codes and definitions. Mail hardcopy of injury data for supplemental species to G.Smith. 

Notes:

OZONE BIOINDICATOR FOLIAR INJURY DATA SHEET • NORTH 

Amount: 8 inj out of 8 = 100%, Code 5 

Severity: mean of 8 inj lvs = Code 4 

Amount: 4 inj out of 8 = 50%, Code 3 

Severity: mean of 4 inj lvs = Code 2 

Code 5 

Code 4

Code 3

Code 4

Code 5

Code 2 

 Code 2 

Code 3 

Code 2 

Code 3 

 Code 1 

Code 3 

Example 1 Example 2 
89

OZONE BIOINDICATOR PLANTS • BIOSITE CHARACTERISTICS • NORTH

Crew Reminders: Take this sheet to the Biosite. Complete it in the field. This sheet must be completed in the field 

only if you are not recording this same information electronically on the FIA data recorder. 

To be filled out by the FIELD CREW or Cooperator: Refer to Ozone Field Guide for code definitions. 

State County FIELD ID (O3 hex number) SPLIT PLOT ID1 Month Day Crew ID QA Status (circle one)

 Standard

QA blind check

1

Split Plot ID refers to the number of locations (1 or 2) used for each hexagon number (F_ID). A separate sheet should be used for each location. 

 Please put a check mark beside the correct information. Please complete all data fields. 

Ozone Sample Kind: [O3SK] 

Initial biosite establishment on the FIA ozone grid. (Data collection in a previously empty polygon) 

Remeasurement of a previously established biosite, or a replacement site within 3 miles of last year’s GPS location. 

Remeasurement when the replacement biosite is more than 3 miles away from last year’s GPS location. 

Biosite size (Plot Size): [SIZE] Terrain position: [TERR] 

 > 3.0 acres (1.2 hectares) Ridge top or upper slope 

1to 3 acres (0.4 – 1.2 hectares) Bench or level area along a slope 

Other: please describe Lower slope 

Flat land unrelated to slope 

 Bottom land with occasional flooding 

Aspect: 000q = no aspect; 360q = N aspect [ASP] Elevation: record estimate in feet or meters [ELEV] 

Record to nearest degree = Feet = Meters = 

Soil Drainage: [SDRA] Soil Depth: [SDEP] 

 Well-drained Bedrock not exposed 

 Wet Bedrock exposed 

 Excessively dry 

Disturbance: Disturbance on the site or in localized areas where the bioindicator plants are growing. [DSTB] 

No recent or significant disturbance; Do not count disturbance >3 years old. 

Evidence of overuse; Human activity causing obvious soil compaction or erosion. 

Evidence of natural disturbance including fire, wind, flooding, grazing, pests, etc. 

Fill in below all that apply. Check here if geographic coordinates were obtained from a topographic map: 

Latitude and longitude are recorded in degrees, minutes, and seconds. 

GPS Unit [UNIT] = GPS Datum [GPSD]= GPS Serial Number [GPS#] = 

Latitude [N] = GPS Error [ERRS]= 

Longitude [W] = Number of GPS Readings [READ] = 

Elevation [ELEV]= GPS File Name (optional) = 1

If no GPS Unit is available, please use a map and record estimated latitude, longitude, and elevation for each biosite location.

Comments: Include information on additional species in the area, safety, directions, or additional site characteristics that may be useful. 

File this completed data sheet with the sheet used for mapping the Bioindicator Site Location and then store it in the 

appropriate Ozone Plot Folder for your State or Region.
90

OZONE BIOINDICATOR PLANTS 

Data Sheet for Directions and Mapping for the Bioindicator Site Location 

Use the back of this sheet to document directions (mileage and key landmarks) to the ozone biosite. 

To be filled out by the FIELD CREW or Cooperator: Refer to Field Guide for code definitions. 

STATE COUNTY OZONE HEXAGON NUMBER OZONE PLOT NUMBER1 MONTH DAY YEAR CREW ID 

xx xxx xxxxxxx x xx xx xxxx xxxx 1

O3Plot Number refers to the number of locations (1 or 2) used for each hexagon number. A separate sheet should be used for each location.

Include the following information on the map: 

1. Location of the site relative to some obvious and permanent marker. 

2. Road names and distances as needed. 

3. North arrow. 

4. Species codes and approximate location of plant groupings used for the ozone injury evaluations. 

5. Location and distance to two major roads; distance and direction to two major towns. 

6. Gazetteer reference page if available. 

Return the original of this map to the corresponding Biosite Folder so that it can be used by audit and regular crews in subsequent visits 

to the plot. Mail a copy to the National Indicator Advisor the year that the site is established. 

GPS UNIT: x GPS DATUM = cccnn GPS SERIAL NUMBER: xxxxxx 

Latitude = xx xx xxxx GPS ERROR = xxx 

Longitude = xxx xx xxxx NUMBER OF READINGS = xxx 

Elevation = xxxxxx GPS FILE NAME = xxxxxxxx.xxx 

EASTING: NORTHING: +/-Error(ft.): Grid Zone: 

Note: This biosite consists of two locations and there are 2 maps on file for this biosite. The second location is 

approximately two miles south on Highway xx in the town of SSSS. 

North Town of NNNN

Town of SSSS 

A

The biosite is an abandoned field approximately 1.8 

miles south of the intersection of Highway xx and 

Town Road xx and across from a private residence 

(number xxx). 

The open area is approximately 3.5 acres in size with 

bioindicator species scattered throughout and along 

the field edges. The foot path leads to a second 

opening which can be used as needed. 

Green house with white 

shutters (Street No. xxx) 

A to Starting Point = 1.8 miles 

NW corner of house to SP = 400ft. Azimuth = 346q

Species on site = 365, 541, 915, and 762 

Gazetteer page xx 

Starting 

Point

Highway xx 

Town Road xx 

Foot path
91

OZONE BIOINDICATOR PLANTS

Data Sheet for the Voucher Leaf Samples 

- NORTH - 

To be filled out by the FIELD CREW or Cooperator: Refer to the Ozone Field Guide for code definitions. 

State County FIELD ID (O3 Hex Number) SPLIT PLOT ID1

 Month Day Crew ID ¥ QA STATUS 

 Standard 

 Blind Chk 1

SPLIT PLOT ID refers to the number of locations (1 or 2) used for each hexagon number (F_ID). Use separate sheets for each location. 

Fill in the required codes. ONE SPECIES PER LINE. Code definitions are in the Field Guide.

Bioindicator Species Code 

or Common Name 

Injury 

Location 

Injury 

Type

Is the leaf sample injury close to 100% ozone stipple () , or is some other 

upper-leaf-surface injury also present (e.g., insect injury or fungal lesions)?

1st Close to 100% ______ Estimated percent other _______ 

2nd Close to 100% ______ Estimated percent other _______ 

3rd Close to 100% ______ Estimated percent other _______ 

4th Close to 100% ______ Estimated percent other _______ 

Species codes: Injury Location codes: 

915 Blackberry 1 = greater than 50% of the injured leaves are younger leaves. 

762 Black cherry 2 = greater than 50% of the injured leaves are mid-aged or older. 

365 Milkweed 3 = injured leaves are all ages. 

621 Yellow poplar 

541 White ash Injury type codes: 

931 Sassafras 1 = greater than 50% of the injury is upper-leaf-surface stipple. 

611 Sweetgum 2 = greater than 50% is not stipple; may be flecks, bifacial, gen. discolor. 

761 Pin cherry 3 = injury is varied or difficult to describe. 

366 Spreading dogbane 

364 Bigleaf aster. 

998 Supplemental 

(write out common name) 

999 Unknown 

Mail this sheet with the leaf samples to: 

QA/QC PERSON: To be filled out by the National Ozone Advisor or Regional Expert. 

Date checked Date rechecked Sample condition Biosite O3 Status 

GOOD

easy to read - ID obvious FAIR

POOR

samples unreadable or not 

labeled correctly 

(+ozone) (- ozone) 

Bioindicator 

Species

Positive 

for ozone 

Negative 

for ozone 

Explanation 

Gretchen Smith 

Department of Natural Resources Conservation 

160 Holdsworth Way 

University of Massachusetts 

Amherst, MA 01003 

CHECK  all that apply: Species codes with undersized leaves: 

Voucher leaves are from 1 plant: 

Voucher leaves are from multiple plants: Species codes for which normal sized leaves were 

Biosite growth conditions are poor: uninjured or unavailable: 

Biosite conditions are unsafe: 

Weather has been very dry: Species codes for voucher leaves that are from 

Weather has been very wet: NON-TALLIED plants: 

See comments on back: 

Biosite Notes: (use back of sheet as needed) 
92

Leaf Voucher Data FIA Regional Data Processor for NIMS 

Edited Voucher Data Edited O3 Data Files 

NIMS Regional Data Processor – Biosite Coordinates 

Once the O3 Data Files are reconciled with the Voucher Data, the FIA 

Regional Data Processor validates the current year biosite coordinates. 

 GPS coordinates and State and County FIPS codes must match up.

Load Edited O3 Data Files 

to Regional NIMS 

Load Validated Leaf Voucher 

Data to Regional NIMS 

Run standard edit checks 

Run P3 LAB System Report Program 

Generate the 3 standard O3 summary tables: 

NIMS Recommendation: 

Reconcile the voucher 

data with the field crew 

data files before loading 

the data into NIMS. 

NIMS Note: 

Edited QA data 

files may be 

integrated into 

NIMS or held in 

separate files.

OZONE_BASE_PLOT 

Regional NIMS 

5 standard O3 tables: 

OZONE_PLOT_TBL 

OZONE_VISIT 

OZONE_SPECIES 

OZONE_PLOT_NOTES 

OZONE_VALIDATION

At end of ozone season, FIA Field Crews submit: 

 (1) O3 Data Files and (2) Leaf Vouchers Raw field data: 

3 standard O3 summary tables: 

OZONE_BIOSITE_SUMMARY 

OZONE_PLOT_SUMMARY 

OZONE_SPECIES_SUMMARY 

Table 1 

NIMS_OZONE_BIOSITE_SUMMARY 

National Ozone 

Advisor checks 

tabular output and 

O3 map product. 

Ozone Bioindicator ANNUAL OUTPUT 

 Regional O3 Summary Tables 

 National Ozone Risk Map 

 Biosite Index attribute for all P2 plots 

 Updated crosswalk tables 

 Updated list of O3 sensitive tree species 

FIA O3 tabular data and maps 

are available for download from 

the public access database: 

National FIADB P3 WEBSITE

FIADB Data Mart 

http://fia.fs.fed.us/

click on FIA Tools and Data

NIMS Regional Data Processor contacts UNLV 

 when the three standard O3 summary tables are ready. 

The tables are captured by UNLV- National NIMS.

UNLV also accepts the national ozone map surface and 

tabular data from the FIA Spatial Analyst-Ozone Advisor, 

and updates regional crosswalk tables. 

FIA Spatial Analyst-Ozone 

Advisor provides annual 

updates of the national 

ozone risk map, ozone 

crosswalk tables, fuzzed 

coordinates for the public 

access data tables, and 

estimated BI for the P2/P3 

ground plots.

Tabular data is stripped and nulled as needed for 

public access while protecting landowner privacy.

Table 3 

NIMS_OZONE_SPECIES_SUMMARY 

Table 2 

NIMS_OZONE_PLOT_SUMMARY 
93

Appendix 6.—Ozone Summary Tables

Ozone summary tables are produced from the field data files and leaf voucher validation files 

for the FIA National Information Management System (NIMS) and the FIA database (FIADB) 

for public use. For detailed information on the ozone attribute codes and definitions and for 

guidance on interpreting summarized attributes for internal or external reports, all users should 

refer to the FIADB Database Description and Users Guide version 3.0 Phase 3 (Forest Health 

Indicators) available online at the FIA Data Mart Web page.

http://fiatools.fs.fed.us/fiadb-downloads/fiadb3.html

http://www.fia.fs.fed.us/tools-data/docs/pdfs/FIADB_user%20guide%203-0_P3_6_01_07.pdf

Brief descriptions of the tables are as follows:

OZONE_VISIT: The measurement attributes in this table summarize identifying characteristics 

of the ozone plot or biosite with respect to site conditions evaluated by the field crew and 

measurement status on the ozone grid.

OZONE_VALIDATION: The measurement attributes in this table provide a record of whether 

or not the ozone injury rated by the field crews was validated by an expert for every species at 

every biosite.

OZONE_BIOSITE_SUMMARY: The measurement attributes in this table summarize biositelevel data including species and plant counts and injury indices. When biosites are split between 

two locations, data are summarized to represent one biosite.

OZONE_PLOT_SUMMARY: The measurement attributes in this table summarize locationspecific data including species and plant counts, site characteristics, and injury indices. 

Location-specific data differ from biosite-level data for the small number of biosites that are split 

between two locations.

OZONE_SPECIES_SUMMARY: The measurement attributes in this table summarize plant 

counts, site characteristics, and injury indices for each bioindicator species evaluated at each 

ground location.
94

OZONE_VISIT

NIMS FIADB Version 3.0

Column Name Column Name

INVYR Inventory year INVYR

STATECD State code STATECD

COUNTYCD County code COUNTYCD

FIELD_ID Field identification number FIELD_ID1

SPLIT_PLOTID Split plot identification number SPLIT_PLOTID

O3PLOT Ozone plot number O3PLOT

SMPKNDCD Ozone sample kind code SMKNDCD

MEASDAY Measurement day MEASDAY

MEASMON Measurement month MEASMON

MEASYEAR Measurement year MEASYEAR

PLTSIZE Plot size PLTSIZE

ASPECT Aspect ASPECT

TERRPOS Terrain position TERRPOS

SOILDPTH Soil depth SOILDPTH

SOILDRN Soil drainage SOILDRN

PLOTWET Plot wetness PLOTWET

PLTDSTRB Plot disturbance PLTDSTRB

QASTATCD Quality assurance status code QASTATCD

CRWTYPCD Crew type code CRWTYPCD

INJCHECK Injury check INJCHECK

GRIDDEN Ozone grid density GRIDDEN

1

In the FIADB, FIELD_ID is a system generated number used to replace the sensitive field identification number

stored in NIMS. SPLIT_PLOTID is a number used to identify the distinct locations sampled for a particular biosite.

O3PLOT is a concatenation of FIELD_ID and SPLIT_PLOTID and a unique identifier used in combination with

INVYR to identify a biomonitoring site.

OZONE_VALIDATION

NIMS FIADB Version 3.0

Column Name Column Name

INVYR Inventory year INVYR

STATECD State code STATECD

COUNTYCD County code COUNTYCD

FIELD_ID Field identification number FIELD_ID1

SPLIT_PLOTID Split plot identification number SPLIT_PLOTID

O3PLOT Ozone plot number O3PLOT

BIOSPCD Bioindicator species code BIOSPCD

QASTATCD Quality assurance status code QASTATCD

CRWTYPCD Crew type code CRWTYPCD

LEAFVCHR Leaf voucher LEAFVCHR

INJVALID Injury validation INJVALID

O3_STATCD Ozone status code O3_STATCD

MEASYEAR Measurement year MEASYEAR

1

In the FIADB, FIELD_ID is a system generated number used to replace the sensitive field identification number

stored in NIMS. SPLIT_PLOTID is a number used to identify the distinct locations sampled for a particular biosite.

O3PLOT is a concatenation of FIELD_ID and SPLIT_PLOTID and a unique identifier used in combination with

INVYR to identify a biomonitoring site.
95

OZONE_BIOSITE_SUMMARY

NIMS FIADB Version 3.0

Column Name Column Name

INVYR Inventory year INVYR

STATECD State code STATECD

COUNTYCD County code COUNTYCD

FIELD_ID Field identification number FIELD_ID1

O3PLOT Ozone plot number O3PLOT

LOCATION_CNT Location count LOCATION_CNT

GROUND_LOC_CD Ground location code GROUND_LOC_CD

MEASYEAR Measurement year MEASYEAR

PLANT_INJ_CNT Plant injury count PLANT_INJ_CNT

PLANT_EVAL_CNT Plant evaluation count PLANT_EVAL_CNT

PLANT_RATIO Plant ratio PLANT_RATIO

SPECIES_EVAL_CNT Species evaluation count SPECIES_EVAL_CNT

BIOSITE_INDEX Biosite index BIOSITE_INDEX

BIOSITE_INDEX_MULTIPLIER Biosite index multiplier BIOSITE_INDEX_MULTIPLIER

SVRTY_CLASS_ZERO Severity class zero SVRTY_CLASS_ZERO

SVRTY_CLASS_ONE Severity class one SVRTY_CLASS_ONE

SVRTY_CLASS_TWO Severity class one SVRTY_CLASS_TWO

SVRTY_CLASS_THREE Severity class one SVRTY_CLASS_THREE

SVRTY_CLASS_FOUR Severity class one SVRTY_CLASS_FOUR

SVRTY_CLASS_FIVE Severity class one SVRTY_CLASS_FIVE

1

In the FIADB, FIELD_ID is a system generated number used to replace the sensitive field identification number stored in

NIMS. O3PLOT is a unique identifier used in combination with INVYR to identify a biosite. The last digit of O3PLOT = 1 when

the biosite consists of one location or 2 when the biosite consists of two locations.

OZONE_PLOT_SUMMARY

NIMS FIADB Version 3.0

Column Name Column Name

INVYR Inventory year INVYR

STATECD State code STATECD

COUNTYCD County code COUNTYCD

FIELD_ID Field identification number FIELD_ID1

SPLIT_PLOTID Split plot identification number SPLIT_PLOTID

O3PLOT Ozone plot number O3PLOT

MEASYEAR Measurement year MEASYEAR

SPECIES_EVAL_CNT Species evaluated count SPECIES_EVAL_CNT

BIOSITE_INDEX Biosite index BIOSITE_INDEX

BIOSITE_INDEX_MULTIPLIER Biosite index multiplier BIOSITE_INDEX_MULTIPLIER

ELEV Elevation ELEV

PLTSIZE Plot size PLTSIZE

ASPECT Aspect ASPECT

TERRPOS Terrain position TERRPOS

SOILDPTH Soil depth SOILDPTH

SOILDRN Soil drainage SOILDRN

PLOTWET Plot wetness PLOTWET

PLTDSTRB Plot disturbance PLTDSTRB

GPS_LAT Latitude2 LAT

GPS_LON Longitude2 LON 1

In the FIADB, FIELD_ID is a system generated number used to replace the sensitive field identification number stored

in NIMS. SPLIT_PLOTID is a number used to identify the distinct locations sampled for a particular biosite. O3PLOT is

a concatenation of FIELD_ID and SPLIT_PLOTID and a unique identifier used in combination with INVYR to identify a

biomonitoring site.

2

Ozone biosite coordinates are fuzzed in the FIADB to protect landowner privacy.
96

OZONE_SPECIES_SUMMARY

NIMS FIADB Version 3.0

Column Name Column Name

INVYR Inventory year INVYR

STATECD State code STATECD

COUNTYCD County code COUNTYCD

FIELD_ID Field identification number FIELD_ID1

SPLIT_PLOTID Split plot identification number SPLIT_PLOTID

O3PLOT Ozone plot number O3PLOT

GROUND_LOC_CD Ground location code GROUND_LOC_CD

MEASYEAR Measurement year MEASYEAR

BIOSPCD Bioindicator species code BIOSPCD

AMNT_MAX Amount maximum AMNT_MAX

AMNT_MIN Amount minimum AMNT_MIN

AMNT_MEAN Amount mean AMNT_MEAN

SVRTY_MAX Severity maximum SVRTY_MAX

SVRTY_MIN Severity minimum SVRTY_MIN

SVRTY_MEAN Severity mean SVRTY_MEAN

PLANT_INJ_CNT Plant injury count PLANT_INJ_CNT

PLANT_EVAL_CNT Plant evaluation count PLANT_EVAL_CNT

PLANT_RATIO Plant ratio PLANT_RATIO

BIOSPCD_SUM Biospecies sum BIOSPCD_SUM

BIOSPCD_INDEX Biospecies index BIOSPCD_INDEX

ELEV Elevation ELEV

PLTSIZE Plot size PLTSIZE

ASPECT Aspect ASPECT

TERRPOS Terrain position TERRPOS

SOILDPTH Soil depth SOILDPTH

SOILDRN Soil drainage SOILDRN

PLOTWET Plot wetness PLOTWET

PLTDSTRB Plot disturbance PLTDSTRB

1

In the FIADB, FIELD_ID is a system generated number used to replace the sensitive field identification number

stored in NIMS. SPLIT_PLOTID is a number used to identify the distinct locations sampled for a particular biosite.

O3PLOT is a concatenation of FIELD_ID and SPLIT_PLOTID and a unique identifier used in combination with INVYR

to identify a biomonitoring site.
97

Appendix 7.—Core Tables for State and Regional

Reports

Core summary tables for state reports provide information to address the following forest health 

assessment questions: 

How many biosites are evaluated/injured?

How many plants are evaluated/injured? 

What species are used for biomonitoring? 

Do the injury data indicate that phytotoxic concentrations of ozone are present in the 

forests? 

Do the injury data indicate that ozone air quality is changing over time? 

If so, is it improving or deteriorating? 

Where is the injury most severe, or frequent? 

What is the relationship between ambient ozone concentrations and the injury data?

What amount of forest land is subject to levels of ozone pollution that may cause 

negative impacts?

 What volume of ozone-sensitive species is at risk, and where is it? 

A template for the ozone bioindicator core summary table is provided here along with several 

examples of state-level core tables (MA, MD, WI, and VA). Refer to Campbell et al. (2007) 

for more examples from Western States. Refer to the main text of this report (pages 28-30) for 

more information on tables and map products for the ozone indicator.

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.
98

CORE TEMPLATE Summary of ozone biomonitoring statistics by state, region, and year

Parameter ABC region State1 State2 State3 State4 State5 State6 State7 State8 State9

Number of biosites evaluatedNumber of biosites with injuryAverage biosite index score1

Percent biosites with BI = 0 to 4.92Percent biosites with BI = 5 to 14.9Percent biosites with BI = 15 to 24.9Percent biosites with BI >= 25Percent forest land with BI = 0 to 4.93Percent forest land with BI = 5 to 14.9Percent forest land with BI = 15 to 24.9Percent forest land with BI >= 25Average number of species per biositePercent sample plants by HB category:40 = no injury1 = 1 to 6 %2 = 7 to 25 %3 = 26 to 50 %4 = 51 to 75 %5 = >75 %Number of plants evaluatedNumber of plants injuredNumber of plants evaluated (injured) by species:Species 1 (number injured in parentheses)Species 2Species 3, etc.

1The biosite index is based on the average injury score (amount*severity) for each species averaged across all species on the biosite. Smith et al. (2007).2Biosite categories represent a relative measure of tree-level response (least injured to most injured) to ambient ozone exposure. See Table 3 in this report.3Percent forest land is estimated after spatial interpolation of the BI values across the landscape. Estimation methods are described in Smith et al. (2007).4HB values are an estimate of the mean severity of symptoms on injured foliage. See Field Methods in this report.
99

MARYLAND

Parameter 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Number of biosites evaluated 9 9 5 4 12 10 7 27 8 8 8 8 8Number of biosites with injury 9 9 5 3 12 3 2 16 3 2 3 8 5Average biosite index score 76.50 21.62 66.83 30.59 48.06 24.78 0.40 9.54 11.03 4.63 1.07 14.77 1.15Number of plants evaluated 392 316 248 207 414 338 261 1820 1109 762 833 671 844Number of plants injured 202 79 76 37 86 29 27 143 49 31 35 94 18Number of plants by HB severity category:1

0 = no injury 190 237 172 170 328 309 234 1677 1060 731 798 577 826

1 = 1 to 6 % 33 20 19 6 28 15 24 23 2 6 24 33

2 = 7 to 25 % 53 26 20 11 29 8 2 67 7 13 10 26 12

3 = 26 to 50 % 49 28 23 11 16 3 1 43 14 8 1 29 54 = 51 to 75 % 44 3 12 6 8 2 0 8 20 3 0 5 15 = >75 % 23 2 2 3 5 1 0 2 6 1 0 1 0Maximum SUM06 value (ppm-hr)2 101.1 105.3 110.3 106.9 53.3 50.6 34.2 42.7 52.5 30.1 23.5 30.2 -Most sampled bioindicator herb/shrub spp3 915 915 915 915 366 915 915 915 915 915 915 366 915Most sampled bioindicator tree species 611 611 611 931 541 621 611 611 931 611 611 611 611

1See Field Methods in this report for details on the HB severity rating.2See Ambient Concentrations and Exposure Patterns in this report for details on the SUM06 value.3Species codes: 364 = bigleaf aster; 365 = milkweed; 366 = spreading dogbane; 541 = white ash; 611 = sweetgum; 621 = tulip poplar; 762 = black cherry; 915 = blackberry; 931 =sassafras.

MASSACHUSETTS

Parameter 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Number of biosites evaluated 14 15 13 16 18 18 14 8 8 8 8 8 8Number of biosites with injury 11 6 3 11 14 10 9 1 2 3 2 4 8Average biosite index score 7.70 2.15 2.60 3.61 3.74 3.15 2.07 2.20 0.42 0.12 0.03 0.51 4.74Number of plants evaluated 469 492 528 424 1129 942 720 451 728 822 630 927 841Number of plants injured 88 56 22 53 146 63 41 25 15 16 6 15 71Number of plants by HB severity category:1

0 = no injury 381 436 506 371 983 879 679 426 713 806 624 912 770

1 = 1 to 6 % 60 40 5 31 94 32 21 13 9 13 4 5 312 = 7 to 25 % 15 9 11 17 30 25 13 9 5 3 2 10 273 = 26 to 50 % 9 7 6 5 19 6 7 3 1 0 0 0 124 = 51 to 75 % 3 0 0 0 2 0 0 0 0 0 0 0 15 = >75 % 1 0 0 0 1 0 0 0 0 0 0 0 0Maximum SUM06 value (ppm-hr)2 28.2 27.6 21.2 29.2 24.4 32.1 18.5 23.1 34.7 27.4 15.9 22.6 -Most sampled bioindicator herb/shrub spp3 915 915 365 915 365 365 365 365 365 365 915 365 365Most sampled bioindicator tree species 762 762 541 541 762 762 762 762 762 762 762 762 762
100

VIRGINIA

Parameter 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Number of biosites evaluated - - - 9 16 26 25 30 24 32 39 39 38

Number of biosites with injury - - - 3 7 6 10 11 2 8 5 0 2

Average biosite index score - - - 10.98 22.12 1.77 20.79 3.07 0.99 4.04 0.26 0 1.47

Number of plants evaluated - - - 366 977 1268 1122 1936 1841 2436 3347 2780 3236

Number of plants injured - - - 55 174 49 97 87 4 100 11 0 9

Number of plants by HB severity category:1

0 = no injury - - - 311 803 1219 1025 1849 1837 2336 3336 2780 3227

1 = 1 to 6 % - - - 2 41 6 2 16 0 1 0 0 5

2 = 7 to 25 % - - - 29 54 24 12 36 0 42 3 0 43 = 26 to 50 % - - - 14 47 16 33 20 1 51 7 0 04 = 51 to 75 % - - - 8 23 3 22 12 2 6 1 0 05 = >75 % - - - 2 9 0 28 3 1 0 0 0 0Maximum SUM06 value (ppm-hr)2 109.3 110.1 110.3 106.9 56.1 47.7 34.2 38.4 50.0 29.2 23.5 29.2 -

Most sampled bioindicator herb/shrub spp 3 - - - 915 915 915 915 915 915 915 915 915 915

Most sampled bioindicator tree species - - - 931 621 621 611 621 762 762 621 611 621

1See Field Methods in this report details on the HB severity rating.2See Ambient Concentrations and Exposure Patterns in this report for details on the SUM06 value.3Species codes: 364 = bigleaf aster; 365 = milkweed; 366 = spreading dogbane; 541 = white ash; 761 = pin cherry; 762 = black cherry; 915 = blackberry.

WISCONSIN

Parameter 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Number of biosites evaluated 16 65 28 34 42 54 64 39 55 57 55 31 31Number of biosites with injury 8 9 14 9 25 41 49 20 25 13 22 12 6Average biosite index score 1.51 1.07 1.24 0.47 3.65 1.19 1.97 1.10 0.67 0.41 0.48 0.51 0.77Number of plants evaluated 652 3210 1997 2287 4065 5540 6616 3751 4953 5392 5473 2933 2809Number of plants injured 47 79 36 40 252 195 218 104 107 45 75 24 38Number of plants by HB severity category:1

0 = no injury 605 3131 1961 2247 3813 5345 6398 3647 4846 5347 5398 2909 2771

1 = 1 to 6 % 30 31 21 25 160 123 118 58 51 27 50 11 192 = 7 to 25 % 14 37 12 12 72 61 80 45 54 18 23 11 193 = 26 to 50 % 3 7 3 2 12 5 12 1 2 0 2 2 04 = 51 to 75 % 0 4 0 0 6 2 5 0 0 0 0 0 05 = >75 % 0 0 0 1 2 4 3 0 0 0 0 0 0Maximum SUM06 value (ppm-hr)2 18.68 39.70 19.54 25.44 27.24 28.03 15.15 31.77 32.95 22.32 8.61 25.29 -Most sampled bioindicator herb/shrub spp3 364 915 365 365 365 365 365 365 365 365 365 365 365Most sampled bioindicator tree species 762 762 762 762 762 762 762 762 541 762 541 541 762
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Published by: For additional copies:

U.S. FOREST SERVICE U.S. Forest Service 

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

October 2008 Fax: (740)368-0152

Smith, Gretchen C.; Coulston, John W.; O’Connell, Barbara M. 2008. Ozone 

bioindicators and forest health: a guide to the evaluation, analysis, and 

interpretation of the ozone injury data in the Forest Inventory and Analysis 

Program. Gen. Tech. Rep. NRS-34. Newtown Square, PA: U.S. Department of 

Agricluture, Forest Service, Northern Research Station. 100 p.

In 1994, the Forest Inventory and Analysis (FIA) and Forest Health Monitoring 

programs of the U.S. Forest Service implemented a national ozone (O3) biomonitoring 

program designed to address specific questions about the area and percent of forest 

land subject to levels of O3 pollution that may negatively affect the forest ecosystem. 

This is the first and only nationally consistent effort to monitor O3 stress on the forests 

of the United States. This report provides background information on O3 and its 

effects on trees and ecosystems, and describes the rationale behind using sensitive 

bioindicator plants to detect O3 stress and assess the risk of probable O3 impact. 

Also included are a description of field methods, analytic techniques, estimation 

procedures, and how to access, use and interpret the ozone bioindicator attributes 

and data outputs such as the national ozone risk map.

KEY WORDS: air quality, bioindicator species, biomonitoring, forest health, ozone, 

ozone sensitive, risk assessment, spatial interpolation
Capitalizing on the strengths of existing science 

capacity in the Northeast and Midwest to attain a more 

integrated, cohesive, landscape-scale research program

www.nrs.fs.fed.us