### Disclaimer

This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

I-ii

### Preface

In 1971, the U.S. Environmental Protection Agency (EPA) promulgated National Ambient Air Quality Standards (NAAQS) to protect the public health and welfare from adverse effects of photochemical oxidants. In 1979, the chemical designation of the standards was changed from photochemical oxidants to ozone (O 3 ). This document focuses primarily on the scientific air quality criteria for O 3 and, to a lesser extent, on those for other photochemical oxidants such as hydrogen peroxide and the peroxyacyl nitrates. The EPA promulgates the NAAQS on the basis of scientific information contained in air quality criteria issued under Section 108 of the Clean Air Act. The previous O 3 criteria document, Air Quality Criteria for Ozone and Other Photochemical Oxidants , was released in August 1986 and a supplement, Summary of Selected New Information on Effects of Ozone on Health and Vegetation , was released in January 1992. These documents were the basis for a March 1993 decision by EPA that revision of the existing 1-h NAAQS for O 3 was not appropriate at that time. That decision, however, did not take into account some of the newer scientific data that became available after completion of the 1986 criteria document. The purpose of this revised air quality criteria document for O 3 and related photochemical oxidants is to critically evaluate and assess the latest scientific data associated with exposure to the concentrations of these pollutants found in ambient air. Emphasis is placed on the presentation of health and environmental effects data; however, other scientific data are presented and evaluated in order to provide a better understanding of the nature, sources, distribution, measurement, and concentrations of O 3 and related photochemical oxidants and their precursors in the environment. Although the document is not intended to be an exhaustive literature review, it is intended to cover all pertinent literature available through
1995. This document was prepared and peer reviewed by experts from various state and Federal governmental offices, academia, and private industry and reviewed in several public meetings by the Clean Air Scientific Advisory Committee. The National Center for Environmental Assessment (formerly the Environmental Criteria and Assessment Office) of EPA’s Office of Research and Development acknowledges with appreciation the contributions provided by these authors and reviewers as well as the diligence of its staff and contractors in the preparation of this document at the request of the Office of Air Quality Planning and
Standards.

I-iii

### Air Quality Criteria for Ozone and Related Photochemical Oxidants

Table of Contents

Volume I

1. Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

3. Tropospheric Ozone and Its Precursors . . . . . . . . . . . . . . . . . . . . . . . . 3-1

4. Environmental Concentrations, Patterns, and Exposure Estimates . . . . . . 4-1

Appendix A: Abbreviations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . A-1

Volume II

5. Environmental Effects of Ozone and Related Photochemical
Oxidants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

Appendix A: Abbreviations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B: Colloquial and Latin Names . . . . . . . . . . . . . . . . . . . . . . . . . B-1

Volume III

6. Toxicological Effects of Ozone and Related Photochemical Oxidants . . . 6-1

7. Human Health Effects of Ozone and Related Photochemical Oxidants . . . 7-1

8. Extrapolation of Animal Toxicological Data to Humans . . . . . . . . . . . . 8-1

9. Integrative Summary of Ozone Health Effects . . . . . . . . . . . . . . . . . . . 9-1

Appendix A: Abbreviations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . A-1

I-v

### Table of Contents

Page

List of Tables

............................................. I-xiii List of Figures

............................................. I-xviii Authors, Contributors, and Reviewers

............................. I-xxv U.S. Environmental Protection Agency Science Advisory Board,

Clean Air Scientific Advisory Committee

......................... I-xxxi U.S. Environmental Protection Agency Project Team for Development

of Air Quality Criteria for Ozone and Related Photochemical Oxidants

.... I-xxxiii

1. EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1
1.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1
1.2 LEGISLATIVE AND REGULATORY BACKGROUND . . . . . . 1-1
1.3 TROPOSPHERIC OZONE AND ITS PRECURSORS . . . . . . . . . 1-2 1.4 ENVIRONMENTAL CONCENTRATIONS, PATTERNS,
AND EXPOSURE ESTIMATES . . . . . . . . . . . . . . . . . . . . . . . 1-9
1.5 ENVIRONMENTAL EFFECTS OF OZONE AND
RELATED PHOTOCHEMICAL OXIDANTS

.............. 1-12
1.6 TOXICOLOGICAL EFFECTS OF OZONE AND
RELATED PHOTOCHEMICAL OXIDANTS

.............. 1-18
1.7 HUMAN HEALTH EFFECTS OF OZONE AND
RELATED PHOTOCHEMICAL OXIDANTS

.............. 1-22
1.8 EXTRAPOLATION OF ANIMAL TOXICOLOGICAL
DATA TO HUMANS

............................... 1-27
1.9 INTEGRATIVE SUMMARY OF OZONE HEALTH
EFFECTS

....................................... 1-28

2. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1
2.1 LEGISLATIVE BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . 2-2
2.2 REGULATORY BACKGROUND . . . . . . . . . . . . . . . . . . . . . . 2-2
2.3 SUMMARY OF MAJOR SCIENTIFIC TOPICS
PRESENTED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 2.3.1 Air Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 2.3.2 Air Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5
2.3.3 Environmental Effects . . . . . . . . . . . . . . . . . . . . . . . . 2-5
2.3.4 Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6
2.4 ORGANIZATION AND CONTENT OF THE DOCUMENT . . . . 2-6
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

3. TROPOSPHERIC OZONE AND ITS PRECURSORS . . . . . . . . . . . . . . 3-1
3.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

I-vii

### Table of Contents (cont’d)

Page

3.2 TROPOSPHERIC OZONE CHEMISTRY . . . . . . . . . . . . . . . . . 3-2 3.2.1 Background Information . . . . . . . . . . . . . . . . . . . . . . . 3-2 3.2.2 Structure of the Atmosphere . . . . . . . . . . . . . . . . . . . . 3-3 3.2.2.1 Vertical and Horizontal Mixing in the Atmosphere . . . . . . . . . . . . . . . . . . . . . . . 3-4 3.2.2.2 Formation of Stratospheric Ozone . . . . . . . . . . 3-4 3.2.3 Background Ozone in the Troposphere . . . . . . . . . . . . . 3-6 3.2.3.1 Tropospheric Hydroxyl Radicals . . . . . . . . . . . 3-7 3.2.3.2 Tropospheric Nitrogen Oxides Chemistry . . . . . 3-8 3.2.3.3 The Methane Oxidation Cycle

............ 3-10
3.2.3.4 Cloud Processes in the Methane-Dominated Troposphere

......................... 3-15 3.2.4 Photochemistry of the Polluted Atmosphere

......... 3-16 3.2.4.1 Tropospheric Loss Processes of Volatile Organic Compounds

............. 3-17
3.2.4.2 Chemical Formation of Ozone in Polluted
Air . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30 3.2.4.3 Hydrocarbon Reactivity with Respect to
Ozone Formation

..................... 3-34
3.2.5 Photochemical Production of Aerosols

............. 3-38 3.2.5.1 Phase Distributions of Organic Compounds . . . 3-38 3.2.5.2 Acid Deposition

...................... 3-40
3.3 METEOROLOGICAL PROCESSES INFLUENCING OZONE
FORMATION AND TRANSPORT

..................... 3-42 3.3.1 Meteorological Processes

...................... 3-42 3.3.1.1 Surface Energy Budgets

................. 3-42 3.3.1.2 Planetary Boundary Layer

............... 3-43 3.3.1.3 Cloud Venting

....................... 3-46 3.3.1.4 Stratospheric-Tropospheric Ozone Exchange

........................... 3-47 3.3.2 Meteorological Parameters

..................... 3-48 3.3.2.1 Sunlight

............................ 3-48 3.3.2.2 Temperature

......................... 3-49 3.3.2.3 Wind Speed

......................... 3-54
3.3.2.4 Air Mass Characteristics

................ 3-56
3.3.3 Normalization of Trends

....................... 3-58
3.4 PRECURSORS OF OZONE AND OTHER OXIDANTS

....... 3-59
3.4.1 Sources and Emissions of Precursors

.............. 3-59
3.4.1.1 Introduction

......................... 3-59 3.4.1.2 Nitrogen Oxides

...................... 3-60 3.4.1.3 Volatile Organic Compounds

............. 3-70

I-viii

### Table of Contents (cont’d)

Page

3.4.1.4 Relationship of Summertime Precursor
Emissions and Ozone Production

.......... 3-79
3.4.2 Concentrations of Precursor Substances in Ambient
Air . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-80 3.4.2.1 Nonmethane Organic Compounds

.......... 3-81 3.4.2.2 Nitrogen Oxides

...................... 3-84
3.4.2.3 Ratios of Concentrations of Nonmethane Organic Compounds and Nitrogen Oxides

.... 3-85 3.4.3 Source Apportionment and Reconciliation

........... 3-86 3.4.3.1 Source Apportionment

.................. 3-86
3.4.3.2 Source Reconciliation

.................. 3-89
3.5 ANALYTICAL METHODS FOR OXIDANTS AND
THEIR PRECURSORS

.............................. 3-90 3.5.1 Sampling and Analysis of Ozone and Other
Oxidants

.................................. 3-90
3.5.1.1 Ozone

............................. 3-90 3.5.1.2 Peroxyacetyl Nitrate and Its Homologues

..... 3-101 3.5.1.3 Gaseous Hydrogen Peroxide

.............. 3-105 3.5.2 Sampling and Analysis of Volatile Organic Compounds

................................ 3-107
3.5.2.1 Introduction

......................... 3-107 3.5.2.2 Nonmethane Hydrocarbons

............... 3-108 3.5.2.3 Carbonyl Species

..................... 3-114 3.5.2.4 Polar Volatile Organic Compounds

......... 3-116 3.5.3 Sampling and Analysis of Nitrogen Oxides

......... 3-117
3.5.3.1 Introduction

......................... 3-117
3.5.3.2 Measurement of Nitric Oxide

............. 3-118 3.5.3.3 Measurements for Nitrogen Dioxide

........ 3-120
3.5.3.4 Calibration Methods

................... 3-126 3.6 OZONE AIR QUALITY MODELS

..................... 3-127 3.6.1 Definitions, Description, and Uses

................ 3-128
3.6.1.1 Grid-Based Models

.................... 3-129 3.6.1.2 Trajectory Models

..................... 3-131 3.6.2 Model Components

.......................... 3-133 3.6.2.1 Emissions Inventory

................... 3-133 3.6.2.2 Meteorological Input to Air Quality
Models

............................ 3-135
3.6.2.3 Chemical Mechanisms

.................. 3-139 3.6.2.4 Deposition Processes

................... 3-140 3.6.2.5 Boundary and Initial Conditions

........... 3-143
3.6.2.6 Numerical Methods

.................... 3-143

I-ix

### Table of Contents (cont’d)

Page

3.6.3 Urban and Regional Ozone Air Quality Models

....... 3-144
3.6.3.1 The Urban Airshed Model

............... 3-148 3.6.3.2 The Regional Oxidant Model

............. 3-151 3.6.3.3 The Regional Acid Deposition Model

....... 3-154
3.6.4 Evaluation of Model Performance

................ 3-156
3.6.4.1 Model Performance Evaluation
Procedures

.......................... 3-157
3.6.4.2 Performance Evaluation of Ozone Air Quality Models

....................... 3-159
3.6.4.3 Database Limitations

................... 3-160 3.6.5 Use of Ozone Air Quality Models for Evaluating Control Strategies

........................... 3-162
3.6.6 Conclusions

............................... 3-163
3.7 SUMMARY AND CONCLUSIONS

..................... 3-165 3.7.1 Tropospheric Ozone Chemistry

.................. 3-165 3.7.1.1 Ozone in the Unpolluted Atmosphere

....... 3-165
3.7.1.2 Ozone Formation in the Polluted Troposphere

......................... 3-166 3.7.2 Meteorological Processes Influencing Ozone Formation and Transport

...................... 3-168 3.7.2.1 Meteorological Processes

................ 3-168 3.7.2.2 Meteorological Parameters

............... 3-168
3.7.2.3 Normalization of Trends

................ 3-169
3.7.3 Precursors

................................. 3-169 3.7.3.1 Volatile Organic Compound Emissions

...... 3-169 3.7.3.2 Nitrogen Oxides Emissions

............... 3-169 3.7.3.3 Concentrations of Volatile Organic Compounds in Ambient Air

............. 3-170 3.7.3.4 Concentrations of Nitrogen Oxides in
Ambient Air

......................... 3-170
3.7.3.5 Ratios of Concentrations of Nonmethane Organic Compounds to Nitrogen Oxides

..... 3-171 3.7.3.6 Source Apportionment and Reconciliation

.... 3-171 3.7.4 Analytical Methods for Oxidants and Their
Precursors

................................. 3-172
3.7.4.1 Oxidants

........................... 3-172 3.7.4.2 Volatile Organic Compounds

............. 3-173 3.7.4.3 Oxides of Nitrogen

.................... 3-174 3.7.5 Ozone Air Quality Models

..................... 3-174 3.7.5.1 Definitions, Descriptions, and Uses

......... 3-174 3.7.5.2 Model Components

.................... 3-175
3.7.5.3 Evaluation of Model Performance

.......... 3-176

I-x

### Table of Contents (cont’d)

Page

3.7.5.4 Use of Ozone Air Quality Model for Evaluating Control Strategies

............. 3-176
3.7.5.5 Conclusions

......................... 3-176
REFERENCES

......................................... 3-177

4. ENVIRONMENTAL CONCENTRATIONS, PATTERNS, AND
EXPOSURE ESTIMATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1
4.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1.1 Characterizing Ambient Ozone Concentrations . . . . . . . 4-2 4.1.2 The Identification and Use of Existing Ambient
Ozone Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4
4.2 TRENDS IN AMBIENT OZONE CONCENTRATIONS . . . . . . . 4-6
4.3 SURFACE OZONE CONCENTRATIONS

................ 4-14
4.3.1 Introduction

............................... 4-14
4.3.2 Urban Area Concentrations

..................... 4-15
4.3.3 Nonurban Area Concentrations

.................. 4-27 4.3.3.1 Sites That Experience Low Maximum Hourly Average Concentrations

................. 4-27
4.3.3.2 Urban-Influenced Nonurban Areas

......... 4-36
4.4 DIURNAL VARIATIONS IN OZONE CONCENTRATIONS . . . 4-46
4.4.1 Introduction

............................... 4-46
4.4.2 Urban Area Diurnal Patterns

.................... 4-47
4.4.3 Nonurban Area Diurnal Patterns

................. 4-51
4.5 SEASONAL PATTERNS IN OZONE CONCENTRATIONS . . . 4-55
4.5.1 Urban Area Seasonal Patterns

................... 4-55
4.5.2 Nonurban Area Seasonal Patterns

................ 4-57 4.5.3 Seasonal Pattern Comparisons with Sites Experiencing Low Exposures

................... 4-61
4.6 SPATIAL VARIATIONS IN OZONE CONCENTRATIONS

.... 4-62
4.6.1 Urban-Nonurban Area Concentration Differences

..... 4-62 4.6.2 Concentrations Experienced at High-Elevation Sites

.... 4-62 4.6.3 Other Spatial Variations in Ozone Concentrations

..... 4-65
4.7 INDOOR OZONE CONCENTRATIONS

................. 4-72
4.8 ESTIMATING EXPOSURE TO OZONE

................. 4-73
4.8.1 Introduction

............................... 4-73 4.8.2 Fixed-Site Monitoring Information Used To Estimate Population and Vegetation Exposure

.............. 4-76
4.8.3 Personal Monitors

........................... 4-77 4.8.4 Population Exposure Models

.................... 4-78 4.8.5 Concentration and Exposures Used in Research Experiments

............................... 4-80

I-xi

### Table of Contents (cont’d)

Page

4.9 CONCENTRATIONS OF PEROXYACETYL NITRATES IN
AMBIENT ATMOSPHERES

.......................... 4-81
4.9.1 Introduction

............................... 4-81 4.9.2 Urban Area Peroxyacetyl Nitrate Concentrations

...... 4-82 4.9.3 Concentration of Peroxyacetyl Nitrate and Peroxypropionyl Nitrate in Rural Areas

............ 4-83
4.10 CONCENTRATION AND PATTERNS OF HYDROGEN
PEROXIDE IN THE AMBIENT ATMOSPHERE

........... 4-86
4.11 CO-OCCURRENCE OF OZONE

....................... 4-88
4.11.1 Introduction

............................... 4-88 4.11.2 Nitrogen Oxides

............................ 4-89
4.11.3 Sulfur Dioxide

............................. 4-89
4.11.4 Acidic Sulfate Aerosols

....................... 4-90 4.11.5 Acid Precipitation

........................... 4-91
4.11.6 Acid Cloudwater

............................ 4-93
4.12 SUMMARY

...................................... 4-94
REFERENCES

......................................... 4-102

APPENDIX A: ABBREVIATIONS AND ACRONYMS . . . . . . . . . . . . . . . A-1

I-xii

### List of Tables

Number Page

2-1 National Ambient Air Quality Standards for Ozone . . . . . . . . . . . 2-3

3-1 Estimated Emissions of Methane, Nonmethane Organic Compounds, Nitrous Oxide, and Nitrogen Oxides into the Earth’s Atmosphere from Biogenic and Anthropogenic
Sources

.......................................... 3-16

3-2 Calculated Tropospheric Lifetimes of Selected Volatile Nonmethane Organic Compounds Due to Photolysis and Reaction with Hydroxyl and Nitrate Radicals and with
Ozone

........................................... 3-19

3-3 Calculated Incremental Reactivities of Selected Volatile Organic Compounds as a Function of the Volatile Organic Compound/Nitrogen Oxide Ratio for an Eight-Component Volatile Organic Compound Mixture and Low-Dilution
Conditions

........................................ 3-37

3-4 Rates of Increase of Peak Ozone with Diurnal Maximum Temperature for Temperature Less Than 300 K and Temperature Greater Than 300 K, Based on Measurements for April 1 to September 30, 1988

....................... 3-52

3-5 Recent Studies Examining Trends in Ozone Data After Removal of Variability Associated with Meteorological Factors

............ 3-60

3-6 Source Categories Used To Inventory Nitrogen Oxides
Emissions

........................................ 3-62

3-7 1991 Emission Estimates for Manmade Sources of Nitrogen
Oxides in the United States

............................ 3-63

3-8 Recent Trends in Nitrogen Oxides Emissions for Major Manmade Source Categories

........................... 3-66

3-9 Comparison of Estimates of Nitrogen Oxides Emissions
from Manmade Sources in the United States

................ 3-68

3-10 Annual Nitrogen Oxides Emissions from Soils by U.S. Environmental Protection Agency Region

.............. 3-69

I-xiii

### List of Tables (cont’d)

Number Page

3-11 Estimated 1991 Emissions of Volatile Organic Compounds
from Manmade Sources in the United States

................ 3-71

3-12 Recent Trends in Emissions of Volatile Organic Compounds from Major Categories of Manmade Sources

................ 3-73

3-13 Annual Biogenic Hydrocarbon Emission Inventory for the
United States

...................................... 3-77

3-14 Annual Biogenic Hydrocarbon Emission Inventory by Month and by U.S. Environmental Protection Agency Region for
United States Emissions

.............................. 3-78

3-15 Performance Specifications for Automated Methods of Ozone Analysis

......................................... 3-92

3-16 Reference and Equivalent Methods for Ozone Designated by the U.S. Environmental Protection Agency

............... 3-93

3-17 List of Designated Reference and Equivalent Methods for
Ozone

........................................... 3-94

3-18 Performance Specifications for Nitrogen Dioxide Automated
Methods

......................................... 3-121

3-19 Comparability Test Specifications for Nitrogen Dioxide

........ 3-121

3-20 Reference and Equivalent Methods for Nitrogen Dioxide Designated by the U.S. Environmental Protection Agency

....... 3-122

3-21 Grid-Based Urban and Regional Air Pollution Models: Overview of Three-Dimensional Air Quality Models

.................. 3-145

3-22 Grid-Based Urban and Regional Air Pollution Models: Treatment of Emissions and Spatial Resolution

...................... 3-146

3-23 Grid-Based Urban and Regional Air Pollution Models: Treatment of Meteorological Fields, Transport, and Dispersion

........... 3-147

3-24 Grid-Based Urban and Regional Air Pollution Models: Treatment
of Chemical Processes

............................... 3-149

I-xiv

### List of Tables (cont’d)

Number Page

3-25 Grid-Based Urban and Regional Air Pollution Models: Treatment of Cloud and Deposition Processes

....................... 3-150

3-26 Regional Oxidant Model Geographical Domains

............. 3-152

3-27 Applications of Photochemical Air Quality Models to Evaluating
Ozone

........................................... 3-164

4-1 Ozone Monitoring Season by State . . . . . . . . . . . . . . . . . . . . . . . 4-5

4-2 Summary by Forestry and Agricultural Regions for Ozone Trends Using the W126 Exposure Parameter Accumulated on a Seasonal
Basis

............................................ 4-13

4-3 The Highest Second Daily Maximum One-Hour Ozone Concentration by Metropolitan Statistical Area for the Years
1989 to 1991

...................................... 4-17

4-4 Summary of Percentiles of Hourly Average Concentrations for the April-to-October Period

............................ 4-20

4-5 The Highest Second Daily Maximum Eight-Hour Average Ozone Concentration by Metropolitan Statistical Area for the Years
1989 to 1991

...................................... 4-22

4-6 Seasonal (April to October) Percentile Distribution of Hourly Ozone Concentrations, Number of Hourly Mean Ozone Occurrences Greater Than or Equal to 0.08 and Greater Than or Equal to 0.10, Seasonal Seven-Hour Average Concentrations, W126, and SUM06 Values for Sites Experiencing Low Hourly Average Concentrations with Data Capture Greater Than or Equal to 75%

.................... 4-30

4-7 Seasonal (April to October) Percentile Distribution of Hourly Ozone Concentrations, Number of Hourly Mean Ozone Occurrences Greater Than or Equal to 0.08 and Greater Than or Equal to 0.10, Seasonal Seven-Hour Average Concentrations, and W126 Values for Three "Clean" National Forest Sites with Data Capture Greater Than or Equal to 75%

.......................... 4-33

I-xv

### List of Tables (cont’d)

Number Page

4-8 The Value of the W126 Sigmoidal Exposure Parameter
Calculated Over the Annual Period

....................... 4-34

4-9 The Value of the Ozone Season (Seven-Month) Average of the Daily Seven-Hour (0900 to 1559 Hours)
Concentration

...................................... 4-35

4-10 Summary of Percentiles, Number of Hourly Occurrences Greater Than or Equal to 0.10 ppm, and Three-Month SUM06 Values for Selected Rural Ozone Monitoring Sites in 1989 (April to October)

......................... 4-37

4-11 Summary of Percentiles of Hourly Average Concentrations for Electric Power Research Institute Sulfate Regional Experiment Program Sites/Eastern Regional Air Quality Study Ozone Monitoring Sites

.............................. 4-38

4-12 Seven-Hour Growing Season Mean, W126 Values, and Number of Hourly Ozone Concentrations Greater Than or Equal to 80 ppb for Selected Eastern National Dry Deposition
Network Sites

..................................... 4-40

4-13 Summary of Percentiles for National Dry Deposition Network Monitoring Sites

............................. 4-41

4-14 Description of Mountain Cloud Chemistry Program
Sites

............................................ 4-63

4-15 Seasonal (April to October) Percentiles, SUM06, SUM08, and W126 Values for the Mountain Cloud Chemistry Program Sites

..................................... 4-64

4-16 Summary Statistics for 11 Integrated Forest Study Sites

........ 4-67

4-17 Quarterly Maximum One-Hour Ozone Values at Sites in and Around New Haven, Connecticut, 1976

............... 4-69

4-18 Summary of Reported Indoor-Outdoor Ozone Ratios

.......... 4-74

4-19 Summary of Measurements of Peroxyacetyl Nitrate and Peroxypropionyl Nitrate in Urban Areas

................... 4-84

I-xvi

### List of Tables (cont’d)

Number Page

4-20 Summary of Measurements of Peroxyacetyl Nitrate and Peroxypropionyl Nitrate in Rural Areas

.................... 4-87

I-xvii

### List of Figures

Number Page

3-1 The cyclic reactions of tropospheric nitrogen oxides

........... 3-10

3-2 Atmospheric reactions in the complete oxidation
of methane

....................................... 3-13

3-3 Cyclic reactions of methane oxidation to formaldehyde, conversion of nitric oxide to nitrogen dioxide, and concomitant formation of ozone in the atmosphere

..................... 3-15

3-4 Major steps in production of ozone in ambient air

............ 3-31

3-5 Time-concentration profiles for selected species during irradiations of a nitrogen oxide-propene-air mixture in an indoor chamber with constant light intensity

................ 3-32

3-6 Time-concentration profiles for selected species during irradiations of a nitrogen oxide-propene-air mixture in an outdoor chamber with diurnally varying light intensity

......... 3-32

3-7 Surface radiation budget for short- and long-wave radiation

...... 3-43

3-8 The number of reports of ozone concentrations greater than or equal to 120 ppb at the 17 cities studied in Samson and Shi (1988)

............................... 3-50

3-9 A scatter plot of maximum daily ozone concentration in Atlanta, Georgia, and New York, New York, versus maximum daily temperature

....................................... 3-51

3-10 A scatter plot of maximum daily ozone concentration in Detroit, Michigan, and Phoenix, Arizona, versus maximum daily temperature

....................................... 3-51

3-11 A scatter plot of maximum ozone concentration versus maximum daily temperature for four nonurban sites

.................. 3-52

I-xviii

### List of Figures (cont’d)

Number Page

3-12 The frequency of 24-hour trajectory transport distance en route to city when ozone was greater than or equal to 120 ppb in four Southern U.S. cities, compared with the percent frequency distribution for all 17 cities of a nationwide study,
1983 to 1985

...................................... 3-55

3-13 The frequency of 24-hour trajectory transport distance en route to city when ozone was greater than or equal to 120 ppb in four New England cities, compared with the percent frequency distribution for all 17 cities of a nationwide study,
1983 to 1985

...................................... 3-55

3-14 The root-mean-square-difference between CLASS observations and profiler observations as a function of height above ground level

.................................. 3-56

3-15 The root-mean-square-difference between CLASS observations and lidar observations as a function of height above ground level

.................................. 3-57

3-16 Model of ozone levels using regression techniques

............ 3-58

3-17 Simulated versus observed ozone levels using regression techniques on an independent data set obtained in the summer of 1992 in Atlanta, Georgia

.............................. 3-59

3-18 The 50 largest sources of nitrogen oxides (power plants) in the
United States

...................................... 3-63

3-19 Nitrogen oxides emissions from manmade sources in the 10 U.S. Environmental Protection Agency regions of the United States, 1991

.................................. 3-64

3-20 Changes in nitrogen oxides emissions from manmade sources in the United States, 10-year intervals, 1940 through 1990

........ 3-65

3-21 Changes in nitrogen oxides emissions from stationary source fuel combustion and transportation from 1940 through
1990

............................................ 3-66

I-xix

### List of Figures (cont’d)

Number Page

3-22 Changes in emissions of volatile organic compounds from major manmade sources in the United States, 10-year intervals, 1940 through 1990

...................................... 3-72

3-23 Changes in emissions of volatile organic compounds from major manmade sources, 1940 through 1990

..................... 3-73

3-24 Estimated biogenic emissions of volatile organic compounds in
the United States as a function of season

................... 3-80

3-25 Example of Empirical Kinetic Modeling Approach diagram for high-oxidant urban area

............................ 3-132

3-26 Regional oxidant model superdomain with modeling domains

.... 3-153

4-1 National trend in the composite average of the second highest
maximum one-hour ozone concentration at both National Air Monitoring Stations and all sites with 95% confidence intervals,
1983 to 1992 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

4-2 The annually averaged composite diurnal curves for the following sites that changed from nonattainment to attainment status: Montgomery County, Alabama; Concord, California; Louisville, Kentucky; and Dade County, Florida; for the period 1987 to 1990

................................. 4-10

4-3 A summary of the seasonal (January to December) averaged composite ozone diurnal curve and integrated exposure W126 index for the Los Angeles, California, site for the period 1980
to 1991

.......................................... 4-11

4-4 United States map of the highest second daily maximum one-hour average ozone concentration by Metropolitan Statistical Area,
1991

............................................ 4-16

4-5 The relationship between the second highest daily maximumhourly average ozone concentration and the maximum threemonth SUM06 value and the second highest daily maximumeight-hour average ozone concentration and the maximum threemonth SUM06 value for specific site years at rural agricultural sites for the 1980-to-1991 period

................ 4-24

I-xx

### List of Figures (cont’d)

Number Page

4-6 The relationship between the second highest daily maximumhourly average ozone concentration and the maximum threemonth SUM06 value and the second highest daily maximumeight-hour average ozone concentration and the maximum threemonth SUM06 value for specific site years at rural forested sites for the 1980-to-1991 period

........................ 4-25

4-7 The location of National Dry Deposition Network monitoring sites
as of December 1990

................................ 4-39

4-8 The kriged 1985 to 1986 maximum seven-hour and 12-hour average concentrations of ozone across the United States

....... 4-44

4-9 The kriged estimates of the W126 integrated ozone exposure index
for the eastern United States for 1988 and 1989

.............. 4-45

4-10 The comparison of the seasonal diurnal patterns using 1988 data for Jefferson County, Kentucky, and Oliver County, North
Dakota

.......................................... 4-48

4-11 Diurnal behavior of ozone at rural sites in the United States in July

.......................................... 4-49

4-12 Percent of time hourly average concentrations greater than or equal to 0.1 ppm occurred between 0900 and 1559 hours in comparison to 24-hour period for all rural agricultural and forested sites with three-month SUM06 greater than or equal to 26.4 ppm per hour . . 4-50

4-13 Percent of time hourly average concentrations greater than or equal to 0.1 ppm occured between 0900 and 1559 hours in comparison to 24-hour period for all non-California rural agricultural and forested sites with three-month SUM06 greater than or equal to 26.4 ppm per hour

.................................. 4-50

4-14 Diurnal pattern of one-hour ozone concentrations on July 13, 1979, Philadelphia, Pennsylvania

............................. 4-51

4-15 Diurnal and one-month composite diurnal variations in ozone concentrations, Washington, District of Columbia, July 1981

..... 4-52

I-xxi

### List of Figures (cont’d)

Number Page

4-16 Diurnal and one-month composite diurnal variations in ozone concentrations, St. Louis County, Missouri, September 1981

..... 4-53

4-17 Diurnal and one-month composite diurnal variations in ozone concentrations, Alton, Illinois, October 1981 (fourth quarter)

..... 4-54

4-18 Composite diurnal patterns of ozone concentrations by quarter, Alton, Illinois, 1981

................................. 4-55

4-19 Quarterly composite diurnal patterns of ozone concentrations at selected sites representing potential for exposure of major crops, 1981

....................................... 4-56

4-20 Composite diurnal ozone pattern at a rural National Crop Loss Assessment Network site in Argonne, Illinois, August 6 through September 30, 1980

................................. 4-57

4-21 Composite diurnal ozone pattern at selected National Dry Deposition Network sites

.............................. 4-58

4-22 Composite diurnal pattern at Whiteface Mountain, New York, and the Mountain Cloud Chemistry Program Shenandoah National Park site for May to September 1987

..................... 4-59

4-23 Seasonal variations in ozone concentrations as indicated by monthly averages and the one-hour maximum in each month at selected sites, 1981

.................................. 4-60

4-24 Seven- and 12-hour means at Whiteface Mountain and Shenandoah National Park for May to September 1987 and integrated exposures at Whiteface Mountain and Shenandoah National Park for May to September 1987

......... 4-65

4-25 Integrated exposures for three non-Mountain Cloud Chemistry Program Shenandoah National Park sites, 1983 to 1987

........ 4-66

4-26 Number or days in 1991 for which the maximum hourly average ozone concentration was greater than 0.1 ppm at Chicago,
Illinois

.......................................... 4-70

I-xxii

### List of Figures (cont’d)

Number Page

4-27 Maximum one-hour ozone concentrations and average 0800 to 2000 hours strong acid concentrations for each day that pulmonary function data were collected at Fairview Lake camp in 1988

...................................... 4-78

4-28 Maximal one-hour ozone concentrations at Fairview Lake during the study period

.................................... 4-79

4-29 The number of occurrences for each of the seven categories
described in text

.................................... 4-82

4-30 The co-occurrence pattern of ozone and sulfuric acid for July 25, 1986, at a summer camp on the north shore of Lake Erie, Ontario, Canada

.................................... 4-91

4-31 Sulfate, hydrogen ion, and ozone measured at Breadalbane Street (Site 3) in Toronto during July and August, 1986, 1987, and
1988

............................................ 4-92

I-xxiii

### Authors, Contributors, and Reviewers

Chapter 1. Executive Summary

Principal Authors

Mr. James A. Raub—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. William G. Ewald—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. J.H.B. Garner—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Judith A. Graham—National Exposure Research Laboratory (MD-75), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Beverly E. Tilton—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711



### Chapter 2. Introduction

Principal Author

Mr. James A. Raub—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711



### Chapter 3. Tropospheric Ozone and Its Precursors

Principal Authors

Dr. A. Paul Altshuller—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Roger Atkinson—Statewide Air Pollution Research Center, University of California, 900 Watkins Avenue, Riverside, CA 92521

Mr. Michael W. Holdren—Battelle, 505 King Avenue, Columbus, OH 43201

Dr. Thomas J. Kelly—Battelle, 505 King Avenue, Columbus, OH 43201-2693

I-xxv

### Authors, Contributors, and Reviewers (cont’d)

Dr. Charles W. Lewis—National Exposure Research Laboratory (MD-47) U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Perry J. Samson—Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, 2455 Hayward Street, Ann Arbor, MI 48109

Dr. John H. Seinfeld—Division of Engineering and Applied Science, California Institute of Technology, 391 South Holliston Avenue, Pasadena, CA 91125

Dr. Joseph Sickles II—National Exposure Research Laboratory (MD-75), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Beverly E. Tilton—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Halvor (Hal) Westberg—Department of Civil and Environmental Engineering, Washington State University, Pullman, WA 99164

Reviewers

Dr. A. Paul Altshuller—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Robert R. Arnts—National Exposure Research Laboratory (MD-84) U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Frank M. Black—National Exposure Research Laboratory (MD-46), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Joseph J. Bufalini—National Exposure Research Laboratory (MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Daewon Byun—National Exposure Research Laboratory (MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Jason K. S. Ching—National Exposure Research Laboratory (MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Kenneth L. Demerjian—Atmospheric Sciences Research Center (SUNY-Albany), 100 Fuller Road, Albany NY 12205

I-xxvi

### Authors, Contributors, and Reviewers (cont’d)

Dr. Robin L. Dennis—National Exposure Research Laboratory (MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Basil Dimitriades—National Exposure Research Laboratory (MD-75), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Marcia C. Dodge—National Exposure Research Laboratory (MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Chris D. Geron—National Risk Management Laboratory (MD-62), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Michael W. Gery—Atmospheric Research Associates, 160 North Washington Street, Boston, MA 02114

Dr. James M. Godowitch—National Exposure Research Laboratory (MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Jimmie W. Hodgeson—National Exposure Research Laboratory (MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Harvey E. Jeffries—Department of Environmental Sciences and Engineering, School of Public Health, CB #7400, University of North Carolina, Chapel Hill, North Carolina
27599-7400

Dr. Douglas R. Lawson—Energy and Environmental Engineering Center, Desert Research Institute, Reno, NV 89506

Dr. Charles W. Lewis—National Exposure Research Laboratory (MD-47), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. William A. Lonneman—National Exposure Research Laboratory (MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. William A. McClenny—National Exposure Research Laboratory (MD-44), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Frank F. McElroy—National Exposure Research Laboratory (MD-77), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Thomas B. McMullen—Office of Air Quality Planning and Standards (MD-14), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

I-xxvii

### Authors, Contributors, and Reviewers (cont’d)

Dr. Edwin L. Meyer—Office of Air Quality Planning and Standards (MD-14), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. David C. Misenheimer—Office of Air Quality Planning and Standards (MD-14), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. J. David Mobley—Office of Air Quality Planning and Standards (MD-14), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Will Ollison—American Petroleum Institute, 1220 L Street NW, Washington, DC 20005

Dr. Kenneth Olszyna—Tennessee Valley Authority, CEB 2A, Muscle Shoals, AL 35660

Mr. Thomas E. Pierce—National Exposure Research Laboratory (MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Larry J. Purdue—National Exposure Research Laboratory (MD-56), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Kenneth A. Rehme—National Exposure Research Laboratory (MD-77), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Harold G. Richter—Private consultant, 8601 Little Creek Farm Road, Chapel Hill, NC
27516

Mr. Shawn J. Roselle—National Exposure Research Laboratory (MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Kenneth L. Schere—National Exposure Research Laboratory (MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Jack H. Shreffler—National Exposure Research Laboratory (MD-75), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Joseph Sickles II—National Exposure Research Laboratory (MD-75), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Robert L. Seila—National Exposure Research Laboratory (MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Beverly E. Tilton—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

I-xxviii

### Authors, Contributors, and Reviewers (cont’d)

Dr. Fred Vukovich—Private consultant, 7820 Harps Mill Road, Raleigh, NC 27615

Mr. Richard A. Wayland—Office of Air Quality Planning and Standards (MD-14), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711



### Chapter 4. Environmental Concentrations, Patterns, and Exposure Estimates

Principal Authors

Dr. Allen S. Lefohn—A.S.L. & Associates, 111 Last Chance Gulch, Suite 4A, Helena, MT 59601

Dr. A. Paul Altshuller—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Reviewers

Dr. Thomas C. Curran—Office of Air Quality Planning and Standards (MD-12), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Gary F. Evans—National Exposure Research Laboratory (MD-56), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. William G. Ewald—National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Warren P. Freas—Office of Air Quality Planning and Standards (MD-14), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Jon Heuss—General Motors Environmental and Energy Staff, 3044 West Grand Blvd., Detroit, MI 48202

Dr. Nelson Kelly—Environmental Sciences Department, General Motors Research and Development Center, Warren, MI 48090

Dr. Paul J. Lioy—Department of Environmental and Community Medicine, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NY 08854

Mr. Thomas R. McCurdy— National Exposure Research Laboratory (MD-56), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

I-xxix

### Authors, Contributors, and Reviewers (cont’d)

Mr. Cornelius J. Nelson—National Exposure Research Laboratory (MD-56), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. William Parkhurst—Tennessee Valley Authority, CEB 2A, Muscle Shoals, AL 35660

Mr. Harvey M. Richmond—Office of Air Quality Planning and Standards (MD-12), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

I-xxx

### U.S. Environmental Protection Agency Science Advisory Board Clean Air Scientific Advisory Committee

Ozone Review

Chairman

Dr. George T. Wolff—General Motors Corporation, Environmental and Energy Staff, General Motors Bldg., 12th Floor, 3044 West Grand Blvd., Detroit, MI 48202

Members

Dr. Stephen Ayres—Office of International Health Programs, Virginia Commonwealth University, Medical College of Virginia, Box 980565, Richmond, VA 23298

Dr. Jay S. Jacobson—Boyce Thompson Institute, Tower Road, Cornell University, Ithaca, NY
14853

Dr. Joseph Mauderly—Inhalation Toxicology Research Institute, Lovelace Biomedical and Environmental Research Institute, P.O. Box 5890, Albuquerque, NM 87185

Dr. Paulette Middleton—Science & Policy Associates, Inc., Western Office, Suite 140, 3445 Penrose Place, Boulder, CO 80301

Dr. James H. Price, Jr.—Research and Technology Section, Texas Natural Resources Conservation Commission, P.O. Box 13087, Austin, TX 78711

Invited Scientific Advisory Board Members

Dr. Morton Lippmann—Institute of Environmental Medicine, New York University Medical Center, Long Meadow Road, Tuxedo, NY 10987

Dr. Roger O. McClellan—Chemical Industry Institute of Toxicology, P.O. Box 12137, Research Triangle Park, NC 27711

Consultants

Dr. Stephen D. Colome—Integrated Environmental Services, University Tower, Suite 280, 4199 Campus Drive, Irvine, CA 92715

I-xxxi

### U.S. Environmental Protection Agency Science Advisory Board Clean Air Scientific Advisory Committee (cont’d)

Dr. A. Myrick Freeman—Department of Economics, Bowdoin College, Brunswick, ME 04011

Dr. Allan Legge—Biosphere Solutions, 1601 11th Avenue, NW, Calgary, Alberta T2N 1H1,
CANADA

Dr. William Manning—Department of Plant Pathology, University of Massachusetts, Amherst,
MA 01003

Dr. D. Warner North—Decision Focus, Inc., 650 Castro Street, Suite 300, Mountain View,
CA 94041

Dr. Frank E. Speizer—Harvard Medical School, Channing Lab, 180 Longwood Avenue, Boston, MA 02115

Dr. George E. Taylor—Department of Environmental and Resource Sciences, 130 Fleischmann Agriculture Bldg. 199, University of Nevada, Reno, NV 89557

Dr. Mark J. Utell—Pulmonary Disease Unit, Box 692, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642

Designated Federal Official

Mr. Randall C. Bond—Science Advisory Board (1400), U.S. Environmental Protection Agency, 401 M Street, SW, Washington, DC 20460

Staff Assistant

Ms. Lori Anne Gross—Science Advisory Board (1400), U.S. Environmental Protection Agency, 401 M Street, SW, Washington, DC 20460

I-xxxii

### U.S. Environmental Protection Agency Project Team for Development of Air Quality Criteria for Ozone and Related Photochemical Oxidants

Scientific Staff

Mr. James A. Raub—Health Scientist, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. A. Paul Altshuller—Physical Scientist, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. William G. Ewald—Health Scientist, National Center for Environmental Assessment (MD52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. J.H.B. Garner—Ecologist, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Judith A. Graham—Associate Director, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Ellie R. Speh—Secretary, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Beverly E. Tilton—Physical Scientist, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Technical Support Staff

Mr. Douglas B. Fennell—Technical Information Specialist, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC
27711

Mr. Allen G. Hoyt—Technical Editor and Graphic Artist, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC
27711

Ms. Diane H. Ray—Technical Information Manager (Public Comments), National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

I-xxxiii

### U.S. Environmental Protection Agency Project Team for Development of Air Quality Criteria for Ozone and Related Photochemical Oxidants (cont’d)

Mr. Richard N. Wilson—Clerk, National Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Document Production Staff

Ms. Marianne Barrier—Graphic Artist, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Mr. John R. Barton—Document Production Coordinator, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Lynette D. Cradle—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Shelia H. Elliott—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Sandra K. Eltz—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Jorja R. Followill—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Sheila R. Lassiter—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Wendy B. Lloyd—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Carolyn T. Perry—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. Cheryl B. Thomas—Word Processor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Mr. Peter J. Winz—Technical Editor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

I-xxxiv

### U.S. Environmental Protection Agency Project Team for Development of Air Quality Criteria for Ozone and Related Photochemical Oxidants (cont’d)

Technical Reference Staff

Mr. John A. Bennett—Bibliographic Editor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

Ms. S. Blythe Hatcher—Bibliographic Editor, Information Organizers, Inc., P.O. Box 14391, Research Triangle Park, NC 27709

Ms. Susan L. McDonald—Bibliographic Editor, Information Organizers, Inc., P.O. Box 14391, Research Triangle Park, NC 27709

Ms. Carol J. Rankin—Bibliographic Editor, Information Organizers, Inc., P.O. Box 14391, Research Triangle Park, NC 27709

Ms. Deborah L. Staves—Bibliographic Editor, Information Organizers, Inc., P.O. Box 14391, Research Triangle Park, NC 27709

Ms. Patricia R. Tierney—Bibliographic Editor, ManTech Environmental Technology, Inc., P.O. Box 12313, Research Triangle Park, NC 27709

I-xxxv

# 1

Executive Summary

1.1 Introduction

Air Quality Criteria for Ozone and Related Photochemical Oxidants evaluates the latest scientific information useful in deriving criteria that form the scientific basis for U.S. Environmental Protection Agency (EPA) decisions regarding the National Ambient Air Quality Standards (NAAQS) for ozone (O 3 ). This Executive Summary concisely summarizes key conclusions from the document, which comprises nine chapters. Following this Executive Summary is a brief Introduction (Chapter 2) containing information on the legislative and regulatory background for review of the O 3 NAAQS, as well as a brief discussion of the issues presented and the format for their discussion in the document. Chapter 3 provides information on the chemistry, sources, emissions, measurement, and transport of O 3 and related photochemical oxidants and their precursors, whereas Chapter 4 covers environmental concentrations, patterns, and exposure estimates of O 3 and oxidants. Chapter 5 deals with environmental effects, and Chapters 6, 7, and 8 discuss animal toxicological studies, human health effects, and extrapolation of animal toxicological data to humans, respectively. The last chapter, Chapter 9, provides an integrative, interpretative characterization of health effects associated with exposure to O 3 . The following sections conform to the chapter organization
of the criteria document.



## 1.2 Legislative and Regulatory Background
The photochemical oxidants found in ambient air in the highest concentrations are
O 3 and nitrogen dioxide (NO 2 ). Other oxidants, such as hydrogen peroxide (H 2 O 2 ) and peroxyacyl nitrates, also have been observed, but in lower and less certain concentrations. In 1971, EPA promulgated NAAQS to protect the public health and welfare from adverse effects of photochemical oxidants, at that time, defined on the basis of commercially available measurement methodology. After 1971, however, O 3 -specific commercial analytical methodsbecame available, as did information on the concentrations and effects of the related nonO 3 photochemical oxidants. As a result, the chemical designation of the standards was changed in 1979 from photochemical oxidants to O 3 . The EPA is required under Sections 108 and 109 of the Clean Air Act to evaluate periodically the air quality criteria that reflect the latest scientific information relevant to
review of the O 3 NAAQS. These air quality criteria are useful for indicating the kind and extent of all identifiable effects on public health or welfare that may be expected from the presence of O 3 and related photochemical oxidants in ambient air. The last O 3 criteria

1-1document was released in 1986, and a supplement was released in 1992. These documents were the basis for a March 1993 decision by EPA that revision of the existing 1-h NAAQS
for O 3 was not appropriate at that time. That decision, however, did not take into consideration more recent scientific information that has been published since the last literature review in early 1989. The purpose of this revised criteria document, therefore, is to summarize the pertinent information contained in the previous O 3 criteria document and to critically evaluate and assess the more recent scientific data associated with exposure to
O 3 and, to a lesser extent, to H 2 O 2 and the peroxyacyl nitrates, particularly peroxyacetyl nitrate (PAN). This document will be used by EPA’s Office of Air Quality Planning and Standards to provide a staff paper assessing the most significant scientific information and presenting staff recommendations on whether revisions to the O 3 NAAQS are appropriate.



## 1.3 Tropospheric Ozone and Its Precursors

Introduction Ozone is found in the stratosphere, the "free" troposphere, and the planetary boundary layer (PBL) of the earth’s atmosphere. In the PBL, background O 3 occurs as the result of (1) the intrusions of stratospheric O 3 into the "free" troposphere and downward transport into the PBL, and (2) photochemical reactions of methane (CH 4 ), carbon monoxide (CO), and nitrogen oxides (NO

x ). These processes contribute to the background O 3 near the surface. The major source of O 3 in the PBL is the photochemical process involving anthropogenic and biogenic emissions of NO

x with the many classes of volatile organic compounds (VOCs). The topics considered in this section of the document include: tropospheric
O 3 chemistry; meteorological influences on O 3 formation and transport; precursor VOC and
NO

x emissions, ambient concentrations of VOCs and NO

x , and source apportionment and reconciliation of measured VOC ambient concentrations with emission inventories; O 3 air quality models; and analytical methods for oxidants and precursors.



### Tropospheric Ozone Chemistry Ozone occurs in the stratosphere as the result of chemical reactions initiated by short-wavelength radiation from the sun. In the "free" troposphere, O 3 occurs as the result of incursions from the stratosphere; upward venting from the PBL (the layer next to the surface of the earth) through certain cloud processes; and photochemical formation from precursors, notably CH 4 , CO, and NO

x . The photochemical production of O 3 and other oxidants found at the surface of the earth (in the PBL, troposphere, or ambient air [used interchangeably in this summary]) is the result of atmospheric physical processes and complex, nonlinear chemical processes involving two classes of precursor pollutants: (1) reactive anthropogenic and biogenic VOCs and (2) NO

x . The only significant initiator of the photochemical production of O 3 in the polluted troposphere is the photolysis of NO 2 , yielding nitric oxide (NO) and a ground-state oxygen atom that reacts with molecular oxygen to form O 3 . The O 3 thus formed reacts with NO, yielding oxygen and NO 2 . These cyclic reactions attain equilibrium in the absence of VOCs. However, in the presence of VOCs, which are abundant in polluted ambient air, the equilibrium is upset, resulting in a net increase in O 3 . Methane is the chief VOC found in the free troposphere and in most "clean" areas of the PBL. The VOCs found in polluted ambient

1-2air are much more complex and more reactive than CH 4 , but, as with CH 4 , their atmospheric oxidative degradation is initiated through attack on the VOCs by hydroxyl (OH) radicals. As
in the CH 4 oxidation cycle, the conversion of NO to NO 2 during the oxidation of VOCs is accompanied by the production of O 3 and the efficient regeneration of the OH radical. The
O 3 , PAN, and higher homologues formed in polluted atmospheres increase with the NO 2 /NO
concentration ratio. At night, in the absence of photolysis of reactants, the simultaneous presence of
O 3 and NO 2 results in the formation of the nitrate (NO 3 ) radical. Reactions with NO 3 radicals appear to constitute major sinks for alkenes, cresols, and several other compounds, although the chemistry is not well characterized. Most inorganic gas-phase processes (i.e., the nitrogen cycle and its interrelationships with O 3 production) are well understood. The chemistry of the VOCs in ambient air is not as well understood. It is well known, however, that the chemical loss processes of gas-phase VOCs include reaction with OH and NO 3 radicals and O 3 , and photolysis. Reaction with the OH radical is the only important atmospheric reaction (loss process) for alkanes, aromatic hydrocarbons, and the higher aldehydes and ketones that lack >C=C< bonds; and the only atmospheric reaction of alcohols and ethers. Photolysis is the major loss process for formaldehyde and acetone. Reactions with OH and NO 3 radicals and
with O 3 are all important loss processes for alkenes and for carbonyls containing >C=C<
bonds. Uncertainties in the atmospheric chemistry of the VOCs can affect quantification
of the NO-to-NO 2 conversion and of O 3 yields, and can present difficulties in representation of chemical mechanisms, products, and product yields in O 3 air quality models. Major uncertainties in understanding the atmospheric chemistry of the VOCs with NO

x in both urban and rural atmospheres include chemistry of alkyl nitrate formation, mechanisms and products
of >C4 n -alkanes and branched alkanes, mechanisms and products of alkene-O 3 reactions, and mechanisms and products of aromatic hydrocarbons. It should be noted that the atmospheric chemical processes involved in the photooxidation of certain higher molecular weight VOCs and in the formation of O 3 also can lead to the formation of particulate-phase organic compounds. The OH radicals produced not only can oxidize VOCs to particulate-phase organic compounds but also can react with NO 2 and sulfur dioxide (SO 2 ) to form nitric acid (HNO 3 ) and sulfuric acid (H 2 SO 4 ), respectively, portions of which become incorporated into aerosols as particulate nitrate and sulfate.



### Meteorological Influences on Ozone Formation and Transport The surface energy (radiation) budget of the earth strongly influences the dynamics of the PBL. The redistribution of energy through the PBL creates thermodynamic conditions that influence vertical mixing. Growing evidence indicates that the strict use of mixing heights in modeling is an oversimplification of the complex processes by which pollutants are redistributed within urban areas, and that it is necessary to treat the turbulent structure of the atmosphere directly and acknowledge the vertical variations in mixing. Energy balances therefore require study so that more realistic simulations can be made of the structure of the
PBL. Day-to-day variability in O 3 concentrations depends heavily on day-to-day variations in meteorological conditions, including temperature, solar radiation, and the degree of mixing that occurs between release of a pollutant or its precursors and their arrival at a

1-3receptor; the occurrence of inversion layers (layers in which temperature increases with height above ground level); and the transport of O 3 left overnight in layers aloft and subsequent downward mixing of that O 3 to the surface. The transport of O 3 and its precursors beyond the urban scale ( ≤ 50 km) to neighboring rural and urban areas has been well documented. Episodes of high
O 3 concentrations in urban areas are often associated with high concentrations of O 3 in the surroundings. Areas of O 3 accumulation usually are characterized by synoptic-scale subsidence of air in the free troposphere, resulting in development of an elevated inversion layer; relatively low wind speeds associated with the weak horizontal pressure gradient around a surface high pressure system; a lack of cloudiness; and high temperatures. Ultraviolet (UV) radiation from the sun plays a key role in initiating the photochemical processes leading to O 3 formation and affects individual photolytic reaction steps. Still, there is little empirical evidence in the literature linking day-to-day variations in
observed UV radiation levels to variations in O 3 levels. An association, however, between tropospheric O 3 concentrations and temperature has been demonstrated. Empirical data from four urban areas, for example, show an apparent upper bound on O 3 concentrations that increases with temperature. A similar qualitative relationship exists at a number of rural
locations. The relationship between wind speed and O 3 buildup varies from one part of the country to another. Statistical techniques (e.g., regression techniques) can be used to help identify real
trends in O 3 concentrations, both intra- and interannual, by normalizing meteorological variability.



### Precursors Volatile Organic Compound Emissions Hundreds of VOCs, usually containing from 2 to 12 carbon atoms, are emitted by evaporative and combustion processes from a large number of source types. Total U.S. anthropogenic VOC emissions in 1991 were estimated at 21.0 Tg; the two largest source categories were (1) industrial processes (10.0 Tg) and (2) transportation (7.9 Tg). Emissions of VOCs from highway vehicles accounted for almost 75% of the transportation-related emissions; studies have shown that the majority of these VOC emissions come from about 20% of the automobiles in service, many, perhaps most, of which are older cars that are poorly maintained. The accuracy of VOC emission estimates is difficult to determine for both stationary and mobile sources. Vegetation emits significant quantities of VOCs into the atmosphere, chiefly monoterpenes and isoprene, but also oxygenated VOCs, according to recent studies. The most recent biogenic VOC emissions estimate for the United States showed annual emissions of 29.1 Tg/year. Although the biogenic VOC emission estimates exceed the anthropogenic estimates, the biogenic emissions are more diffusely distributed than the anthropogenic emissions, which tend to be concentrated in population centers. However, the large uncertainties in both biogenic and anthropogenic VOC emission inventories prevent establishing the relative contributions of these two categories.

1-4Nitrogen Oxides Emissions Anthropogenic NO

x is associated with combustion processes. The primary pollutant emitted is NO, formed at high combustion temperatures from nitrogen and oxygen in the air and from nitrogen in the combustion fuel. Emissions of NO

x in 1991 in the United States totaled 21.39 Tg. The two largest single NO

x emission sources are electric power generating plants and highway vehicles. Because a large proportion of anthropogenic NO

x emissions come from distinct point sources, published annual estimates are thought to be
much more reliable than VOC estimates.
Natural NO

x sources include stratospheric intrusion, oceans, lightning, soil, and wildfires. Lightning and soil emissions are the only two significant natural sources of NO

x in the United States. It is estimated that combined natural sources contribute about 2.2 Tg of
NO

x to the troposphere over the continental United States; however, uncertainties in natural
NO

x emission inventories are much greater than those for anthropogenic NO

x emissions.

Concentrations of Volatile Organic Compounds in Ambient Air The VOCs most frequently analyzed in ambient air are the nonmethane hydrocarbons (NMHCs). Morning (6:00 to 9:00 a.m.) concentrations most often have been measured because of the use of morning data in the Empirical Kinetic Modeling Approach (EKMA) and in air quality simulation models. Concurrent measurements of anthropogenic and biogenic NMHCs have shown that biogenic NMHCs usually constituted much less than 10% of the total NMHCs. For example, average isoprene concentrations ranged from 0.001 to 0.020 ppm carbon (C) and terpenes from 0.001 to 0.030 ppm C.

Concentrations of Nitrogen Oxides in Ambient Air
Measurements of NO

x made in 22 and 19 U.S. cities in 1984 and 1985, respectively, showed median 6:00-to-9:00 a.m. NO

x concentrations ranging from 0.02 to 0.08 ppm in most of these cities. Nonurban NO

x concentrations, reported as average seasonal or
annual NO

x , range from <0.005 to 0.015 ppm.

Ratios of Concentrations of Nonmethane Organic Compounds to Nitrogen Oxides Ratios of 6:00-to-9:00 a.m. nonmethane organic compounds (NMOC) to NO

x are higher in southeastern and southwestern U.S. cities than in northeastern and midwestern U.S. cities, according to data from EPA’s multicity studies conducted in 1984 and 1985. Rural
NMOC/NO

x ratios tend to be higher than urban ratios. The NMOC/NO

x ratios trended
downward to well below 10 in the South Coast Air Basin and in cities in the eastern United States during the 1980s. Based on these low ratios, hydrocarbon control should be more
effective than NO

x control within a number of cities. Morning (6:00-to-9:00 a.m.)
NMOC/NO

x ratios are used in the EKMA type of trajectory model. The correlation of
NMOC/NO

x ratios with maximum 1-h O 3 concentrations, however, was weak in a recent analysis.

Source Apportionment and Reconciliation Source apportionment (regarded as synonymous with receptor modeling) refers to determining the quantitative contributions of various sources of VOCs to ambient air pollutant concentrations. Source reconciliation refers to the comparison of measured ambient VOC

1-5concentrations with emissions inventory estimates of VOC source emission rates for the purpose of validating the inventories. Recent findings have shown that vehicle exhaust was the dominant contributor to ambient VOCs in seven of eight U.S. cities studied. Whole gasoline contributions were estimated to be equal to vehicle exhaust in one study and to 20% of vehicle exhaust in a second study. Estimates of biogenic VOCs at a downtown site in Atlanta, GA, in 1990 indicated a lower limit of 2% (24-h average) for the biogenic percentage of total ambient VOCs at that location (isoprene was used as the biogenic indicator species). The percentage varies during the 24-h period because of the diurnal (e.g., temperature, light intensity) dependence of isoprene concentrations. Source reconciliation data have shown disparities between emission inventory estimates and receptor-estimated contributions. For biogenics, emission estimates are greater than receptor-estimated contributions. The reverse has been true for natural gas contributions estimated for Los Angeles, CA; Columbus, OH; and Atlanta; and for refinery emissions in Chicago, IL.



### Ozone Air Quality Models Models and Their Components Photochemical air quality models are used to predict how O 3 concentrations change in response to prescribed changes in source emissions of NO

x and VOCs. These models operate on sets of input data that characterize the emissions, topography, and meteorology of a region and produce outputs that describe air quality in that region. Two kinds of photochemical models are recommended in guidelines issued by EPA: (1) the use of EKMA is accepted under certain circumstances, and (2) the grid-based Urban Airshed Model (UAM) is recommended for modeling O 3 over urban areas. The 1990 Clean Air Act Amendments mandate the use of three-dimensional (grid-based) air quality models such as UAM in developing state implementation plans for areas designated as "extreme", "severe", "serious", or "multistate moderate". General descriptions of EKMA and grid-based models were given in the 1986 EPA criteria document for O 3 . The EKMA-based method for determining O 3 control strategies has limitations, the most serious of which is that predicted emissions reductions are critically dependent on the
initial NMHC/NO

x ratio used in the calculations. This ratio cannot be determined with any certainty and is expected to be quite variable in time and space in an urban area. Spatial and temporal characteristics of VOC and NO

x emissions are major inputs to a grid-based photochemical air quality model. Greater accuracy in emissions inventories is needed for biogenics and for both mobile and stationary source components. Grid-based air quality models also require as input the three-dimensional wind field for the photochemical episode being simulated. A chemical kinetic mechanism, representing the important chemical reactions that occur in the atmosphere, is used in an air quality model to estimate the net rate of formation of each pollutant simulated as a function of time. Dry deposition is an important removal process for O 3 on both urban and regional scales and is included in all urban- and regional-scale models. Wet deposition is generally not included in urban-scale photochemical models, because O 3 episodes do not occur during periods of significant clouds or rain.

1-6Concentration fields of all species computed by the model must be specified at the beginning of the simulation ("initial conditions"). These initial conditions are determined mainly with ambient measurements, either from routinely collected data or from special studies; but interpolation can be used to distribute the surface ambient measurements.

Use of Ozone Air Quality Models Photochemical air quality models are used for control strategy evaluation by first demonstrating that a past episode or episodes can be simulated adequately. The hydrocarbon
or NO

x emissions or both are reduced in the model inputs, and the effects of these reductions
on O 3 in the region are assessed. The adequacy of control strategies based on grid-based models depends, in part, on the nature of input data for simulations and model validation, on input emissions inventory data, and on the mismatch between the spacial output of the model and the current form of the NAAQS for O 3 . Uncertainties in models obviously can affect their outputs. Uncertainties exist in all components of grid-based O 3 air quality models: emissions, meteorological modules, chemical mechanisms, deposition rates, and determination
of initial conditions. Grid-based models that have been widely used to evaluate control strategies for
O 3 or acid deposition, or both, are the UAM, the California Institute of Technology/Carnegie Institute of Technology model, the Regional Oxidant Model, the Acid Deposition and Oxidant Model, and the Regional Acid Deposition Model. The UAM (Version IV) is the grid model approved nationwide for control strategy development at this time. Despite the many uncertainties in photochemical air quality modeling, including emission inventories, these models are essential for regulatory analysis and solving the
O 3 problem. Grid-based O 3 air quality modeling is superior to the available alternatives for
O 3 control planning, but the chances of its incorrect use must be minimized.



### Analytical Methods for Oxidants and Their Precursors

Oxidants
Current methods used to measure O 3 are chemiluminescence (CL); UV absorption spectrometry; and newly developed spectroscopic and chemical approaches, including chemical approaches applied to passive sampling devices (PSDs) for O 3 . The CL method has been designated as the reference method by EPA. Detection limits of 0.005 ppm and a response time of <30 s are typical of currently available commercial instruments. A positive interference from atmospheric water vapor was reported in the 1970s and recently has been confirmed. Proper calibration can minimize this source of

error. Commercial UV photometers for measuring O 3 have detection limits of about 0.005 ppm and a response time of <1 min. Because the measurement is absolute, UV photometry is also used to calibrate O 3 methods. A potential disadvantage of UV photometry is that atmospheric constituents that absorb 254-nm radiation, the wavelength at which O 3 is measured, will cause a positive interference in O 3 measurements. Interferences have been reported in two recent studies, but assessment of the potential importance of such interferences (e.g., toluene, styrene, cresols, nitrocresols) is hindered by lack of absorption spectra data in the 250-nm range and by lack of aerometric data for the potentially interfering species. There also can be some interference from water, possibly from the condensation of moisture in sampling lines.

1-7Calibration of O 3 measurement methods (other than PSDs) is done by UV spectrometry or by gas-phase titration (GPT) of O 3 with NO. Ultraviolet photometry is the reference calibration method approved by EPA. Ozone is unstable and must be generated in situ at time of use to produce calibration mixtures. Peroxyacetyl nitrate and the higher peroxyacyl nitrates normally are measured by gas chromatography (GC) using an electron capture detector. Detection limits have been extended to 1 to 5 ppt. The preparation of reliable calibration standards is difficult because PAN is unstable, but several methods are available.

Volatile Organic Compounds The method recommended by EPA for total NMOC measurement involves the cryogenic preconcentration of NMOCs and the measurement of the revolatilized NMOCs using flame ionization detection (FID). The primary technique for speciated NMOC/NMHC measurements is cryogenic preconcentration followed by GC-FID. Systems for sampling and analysis of VOCs have been developed that require no liquid cryogen for operation.
Stainless steel canisters have become the containers of choice for collection of whole-air samples for NMHC/NMOC data. Calibration procedures for NMOC instrumentation require the generation, by static or dynamic systems, of dilute mixtures at concentrations expected to occur in ambient air. Preferred methods for measuring carbonyl species (aldehydes and ketones) in ambient air are spectroscopic methods; on-line colorimetric methods; and, the most common method currently in use for measuring gas-phase carbonyl compounds in ambient air, the high-performance liquid chromatography method, which employs 2,4-dinitrophenylhydrazine derivatization in a silica gel cartridge. Use of an O 3 scrubber has been recommended to prevent interference by O 3 in this method in ambient air.

Oxides of Nitrogen
Nitric oxide and NO 2 comprise the NO

x compounds involved as precursors to
O 3 and other photochemical oxidants. The most common method of NO measurement is the gas-phase CL reaction with
O 3 , which is essentially specific for NO. Commercial NO monitors have detection limits of a few parts per billion by volume (ppbv) in ambient air but may not have sensitivity sufficient
for surface measurements in rural or remote areas or for airborne measurements. Direct spectroscopic methods for NO exist that have very high sensitivity and selectivity for NO, but their complexity, size, and cost restrict these methods to research applications. No PSDs exist
for measurement of NO. Chemiluminescence analyzers are the tools of choice for NO 2 measurement, even though they do not measure NO 2 directly. Minimum detection levels for NO 2 have been reported to be 5 to 13 ppb, but more recent evaluations have indicated detection limits of 0.5 to 1 ppbv. Reduction of NO 2 to NO is required for measurement. These analyzers actually measure NO y (NO

x + PAN + HNO 3 + other reactive nitrogen species); however, for most urban atmospheres, NO

x is the predominant species measured diurnally. Several spectroscopic approaches to NO 2 detection have been developed but share the drawbacks of spectroscopic NO methods. Passive samplers for NO 2 exist but are still in the developmental stage for ambient air monitoring.

1-8Calibration of methods for NO measurement is done using standard cylinders of NO in nitrogen. Calibration of methods for NO 2 measurement include use of cylinders of
NO 2 in nitrogen or air, use of permeation tubes, and GPT.



## 1.4 Environmental Concentrations, Patterns, and Exposure Estimates

Ozone is measured at concentrations above the minimum detectable level at all monitoring locations in the world. In this section, hourly average concentration and exposure information is summarized for urban, rural forested, and rural agricultural areas in the United
States.
Because O 3 from urban area emissions is transported to rural downwind locations,
elevated O 3 concentrations can occur at considerable distances from urban centers. Urban
O 3 concentration values are often depressed because of titration by NO. Because of the absence of chemical scavenging, O 3 tends to persist longer in nonurban areas than in urban areas, and nonurban exposures may be higher than those in urban locations.



### Trends Ozone hourly average concentrations have been recorded for many years by the state and local air pollution agencies who report their data to EPA. The 10-year (1983 to 1992) composite average trend for the second highest daily maximum hourly average concentration during the O 3 season shows that the 1992 composite average for the trend sites was 21% lower than the 1983 average. The 1992 value was the lowest composite average of the 10-year period and was significantly less than each of the previous nine years, 1983 to 1991. The relatively high O 3 concentrations in 1983 and 1988 likely were attributable, in part, to hot, dry, stagnant conditions in some areas of the country, which were especially
conducive to O 3 formation. From 1991 to 1992, the composite mean of the second highest daily maximum
1-h O 3 concentrations decreased 7%, and the composite average of the number of estimated
exceedances of the O 3 standard decreased by 23%. Nationwide VOC emissions decreased 3% from 1991 to 1992. The composite average of the second daily maximum concentrations decreased in 8 of the 10 EPA regions from 1991 to 1992, and remained unchanged in Region VII. Except for Region VII, the 1992 regional composite means were lower than the corresponding 1990 levels. Although meteorological conditions in the east during 1993 were
more conducive to O 3 than those in 1992, the composite mean level for 1993 was the second lowest composite average of the decade, 1984 to 1993.



### Surface Concentrations Published data provide evidence showing the occurrence at some sites of multihour periods within a day of O 3 at levels of potential health effects. Although most of these analyses were made using monitoring data collected from sites in or near nonattainment areas, in one analysis of five sites (two in New York state, two in rural California, and one in rural Oklahoma), none of which was in or near a nonattainment area, O 3 concentrations showed only moderate peaks but showed multihour levels above 0.1 ppm.

1-9A small amount of the O 3 concentration measured at a monitoring site is produced by sources distant to the photochemical reactions occurring on an urban or regional scale. Typical sources include stratospheric intrusions into the troposphere, photochemical production by the CH 4 /CO/NO

x cycle in the troposphere, and transport of very distant anthropogenic or biogenic VOCs and NO

x . The specific concentrations of this "background"
O 3 vary with averaging times ranging from the daily 1-h maximum to daily, monthly, seasonal, or annual values. The background concentrations also vary with geographical region and with elevation of the monitoring site.
On the basis of O 3 data from isolated monitoring sites, EPA has indicated that a
reasonable estimate of O 3 background concentration near sea level in the United States is from 0.020 to 0.035 ppm for an annual average, 0.025 to 0.045 ppm for an 8-h daily summer seasonal average, and from 0.03 to 0.05 ppm for the average summertime 1-h daily maximum. This estimate includes a 0.005 to 0.015 ppm O 3 contribution from stratospheric intrusions into the troposphere.



### Diurnal Variations Diurnal patterns of O 3 may be expected to vary with location, depending on the balance among the many factors affecting O 3 formation, transport, and destruction. Although they vary with locality, diurnal patterns of O 3 typically show a rise in concentration from low levels, or levels near minimum detectable amounts, to an early afternoon peak. The diurnal pattern of concentrations can be ascribed to three simultaneous processes: (1) downward transport of O 3 from layers aloft, (2) destruction of O 3 through contact with surfaces and through reaction with NO at ground level, and (3) in situ photochemical production of O 3 .



### Seasonal Patterns

Seasonal variations in O 3 concentrations in urban areas usually show the pattern of high O 3 in the late spring or in the summer and low levels in the winter; however, weather conditions in a given year may be more favorable for the formation of O 3 and other oxidants than during the prior or following year. Average O 3 concentrations tend to be higher in the second versus the third quarter of the year for many isolated rural sites. This observation has been attributed to either stratospheric intrusions or an increasing frequency of slow-moving, high-pressure systems that promote the formation of O 3 . However, for several clean rural sites, the highest exposures have occurred in the third quarter rather than in the second. For rural O 3 sites in the southeastern United States, the daily maximum 1-h average concentration was found to peak during the summer months.



### Spatial Variations

Concentrations of O 3 vary with altitude and with latitude. There appears to be no consistent conclusion concerning the relationship between O 3 exposure and elevation.



### Indoor Ozone Until the early 1970s, very little was known about the O 3 concentrations experienced inside buildings; to date, the database on this subject is not large, and a wide range of indoor/outdoor O 3 concentration relationships can be found in the literature (reported
indoor/outdoor values for O 3 are highly variable). Indoor/outdoor O 3 concentration ratios

1-10generally fall in the range from 0.1 to 0.7 and indoor concentrations of O 3 almost invariably
will be less than outdoors.



### Estimating Exposure Both fixed-site monitoring information and human exposure models are used to
estimate risks associated with O 3 exposure. Because, for most cases, it is not possible to estimate population exposure solely from fixed-station data, several human exposure models have been developed. These models also contain submodels depicting the sources and concentrations likely to be found in each microenvironment, including indoor, outdoor, and in-transit settings. Two distinct types of O 3 exposure models exist: (1) those that focus narrowly on predicting indoor O 3 levels and (2) those that focus on predicting O 3 exposures on a community-wide basis. These latter models and their distinguishing features are: 1. pNEM/O 3 based on the National Air Quality Standards Exposure Model (NEM)
series of models · Uses mass-balance approach and seasonal considerations for I/O ratio
estimation. · Variables affecting indoor exposure obtained by Monte Carlo sampling from empirical distributions of measured data. 2. Systems Applications International (SAI)/NEM
· More districts and microenvironments and more detailed mass-balance model than pNEM/O 3 . · Human activity data outdated and inflexible. 3. Regional Human Exposure Model (REHEX) · More detailed geographic resolution than NEM. · Uses California-specific activity data and emphasizes in-transit and outdoor
microenvironments. 4. Event probability exposure model (EPEM) · Estimates probability that a randomly selected person will experience a particular exposure regime. · Lacks multiday continuity. Few data are available for individuals using personal exposure monitors. Results from a pilot study demonstrated that fixed-site ambient measurements may not adequately represent individual exposures. Models based on time-weighted indoor and outdoor concentrations explained only 40% of the variability in personal exposures.



### Peroxyacyl Nitrates Peroxyacetyl nitrate and peroxypropionyl nitrate (PPN) are the most abundant of
the non-O 3 oxidants in ambient air in the United States, other than the inorganic nitrogenous
oxidants such as NO 2 , and possibly HNO 3 . Most of the available data on concentrations of
PAN and PPN in ambient air are from urban areas. The levels to be found in nonurban areas will be highly dependent on the transport of PAN and PPN or their precursors from urban areas, because the concentrations of the NO

x precursors to these compounds are considerably
lower in nonurban areas than in urban areas.

1-11

### Co-occurrence Studies of the joint occurrence of gaseous NO 2 /O 3 and SO 2 /O 3 at rural sites have concluded that the periods of co-occurrence represent a small portion of the potential plant-growing period. For human ambient exposure considerations, in most cases, the
simultaneous co-occurrence of NO 2 /O 3 and SO 2 /O 3 was infrequent. Some researchers have reported the joint occurrence of O 3 , nitrogen, and sulfur in forested areas, combining cumulative exposures of O 3 with data on dry deposition of sulfur and nitrogen. One study reported that several forest landscapes with the highest dry deposition loadings of sulfur and nitrogen tended to experience the highest average O 3 concentrations and largest cumulative exposure. Although the authors concluded that the joint concentrations of multiple pollutants in forest landscapes were important, nothing was mentioned about the hourly co-occurrences
of O 3 and SO 2 or O 3 and NO 2 . Acid sulfates, which are usually composed of H 2 SO 4 , ammonium bisulfate, and ammonium sulfate, have been measured at a number of locations in North America. The potential for O 3 and acidic sulfate aerosols to co-occur at some locations in some form (i.e., simultaneously, sequentially, or complex-sequentially) is real and requires further characterization. For human ambient exposures, the simultaneous co-occurrence of
NO 2 and O 3 was infrequent. In one study, the relationship between O 3 and hydrogen ions in precipitation was explored using data from sites that monitored both O 3 and wet deposition simultaneously and within one minute latitude and longitude of each other. It was reported that individual sites experienced years in which both hydrogen ion deposition and total O 3 exposure were at least moderately high. With data compiled from all sites, it was found that relatively acidic precipitation occurred together with relatively high O 3 levels approximately 20% of the time, and highly acidic precipitation occurred together with a high O 3 level approximately 6% of the time. Sites most subject to relatively high levels of both hydrogen ions and O 3 were located in the eastern part of the United States, often in mountainous areas.
The co-occurrence of O 3 and acidic cloudwater in high-elevation forests has been characterized. The frequent O 3 -only and pH-only single-pollutant episodes, as well as the simultaneous and sequential co-occurrences of O 3 and acidic cloudwater, have been reported. Both simultaneous and sequential co-occurrences were observed a few times each month
above cloud base.



## 1.5 Environmental Effects of Ozone and Related Photochemical Oxidants
Ozone is the gaseous pollutant most injurious to agricultural crops, trees, and native vegetation. Exposure of vegetation to O 3 can inhibit photosynthesis, alter carbon (carbohydrate) allocation, and interfere with mycorrhizal formation in tree roots. Disruption of the important physiological processes of photosynthesis and carbon allocation can suppress the growth of crops, trees, shrubs, and herbaceous vegetation by decreasing their capacity to form the carbon (energy) compounds needed for growth and maintenance and their ability to absorb the water and mineral nutrients that they require from the soil. In addition, loss of vigor impairs the ability of trees and crops to reproduce and increases their susceptibility to insects and pathogens. The following section summarizes key environmental effects
associated with O 3 exposure.

1-12

### Effects on Agroecosystems Methodologies Used in Vegetation Research Most of the knowledge concerning the effects of O 3 on vegetation comes from the exposure-response studies of important agricultural crop plants and some selected forest and urban tree species, mostly as seedlings. A variety of methodologies have been used, ranging from field exposures without chambers to open-top chambers and to exposures conducted in chambers under highly controlled conditions. In general, the more controlled conditions are most appropriate for investigating specific responses and for providing the scientific basis for interpreting and extrapolating results. The greatest body of knowledge is from OTC studies.

Mode of Action Leaves are important regulators of plant stress and function. Stress resulting from exposure to O 3 produces a leaf-mediated response. Effects expressed within cells in the leaf (i.e., inhibition of photosynthesis) affect a plant’s carbon (energy) budget. Plant processes are impaired only by the O 3 that enters the plant through the stomata (opening in the leaves). An effect will occur only if sufficient O 3 reaches sensitive sites within the leaf cells. The uptake
and movement of O 3 to sensitive cellular sites within a leaf are subject to various biochemical and physiological controls. Leaf injury will not be detected if the rate of uptake is small enough for the plant to detoxify or metabolize O 3 and its derivatives, or the plant is able to repair or compensate for the impact at a rate equal to or greater than the rate of uptake. Impairment of leaf cellular processes is the basis for all other plant effects. The diurnal pattern of stomatal opening plays a critical role in O 3 uptake, particularly at the canopy level. Visible injury is usually the first observable indication of cellular response; injury can occur, however, with no visible effects. Early senescence of leaves or needles is also a result of cellular response. Impairment of cellular processes inhibits the rate of photosynthesis, reduces carbon (sugars, carbohydrate) production, and alters carbon allocation, causing a shift in growth pattern that favors shoots over roots. The reduced allocation of carbon to leaf repair and new leaf formation limits the availability of carbon for reproduction; stem and root growth; and, particularly, the formation of the mycorrhizae on roots necessary for nutrient and water uptake. Reduction of plant vigor by O 3 can result in mortality, particularly when plant susceptibility to insects and pathogens is increased.

Factors That Modify Plant Response Plant response to O 3 exposure is influenced by a variety of biological, chemical, and physical factors. When determining the impact of O 3 exposure on plants, both the influence of environmental factors on plant response and the effects of O 3 on that response must be considered. Biological factors within plants that affect their response to stresses include, genetic composition, stage of development, and the diurnal pattern of stomatal opening. Genotype significantly influences plant sensitivity to O 3 . Individuals, varieties, and cultivars of a species are known to differ greatly in their responses to a given O 3 exposure. Genotype also influences the ability of plants to compete with one another for space, nutrients, light, and water. The magnitude of response of a particular species, variety, or cultivar depends on a number of environmental factors. The plant’s present and past environmental milieu, which includes the temporal exposure pattern and stage of development, dictates the plant response. The corollary is also true: exposure to O 3 can modify plant response to other environmental

1-13variables. Available light, temperature, atmospheric turbulence and moisture, in both the atmosphere and soil; soil nutrition; and exposure to and interaction with other pollutants such as agricultural chemical sprays also influence the magnitude of plant response. Drought can reduce visible injury and the adverse effects of O 3 on growth and yield of crops. However, in the case of crops, drought, per se, much more adversely affects yield than the effects of O 3 . Ozone, on the other hand, tends to reduce the water-use efficiency of well-watered crops. In some plants, O 3 exposure reduces cold/winter hardiness. Although exposure to O 3 tends to reduce attacks by obligate pathogens, susceptibility of plants to facultative pests and pathogens increases.

Effects-Based Air Quality Exposure Indices Environmental scientists for many years have attempted to characterize and mathematically represent plant exposures to O 3 . A variety of averaging times have been used. Although most studies have characterized exposure by using mean concentrations, such as seasonal, monthly, weekly, daily, or peak hourly means, other studies have used cumulative measures (e.g., the number of hours above selected concentrations). None of these statistics completely characterizes the relationships among O 3 concentration, exposure duration, interval between exposures, and plant response. The use of a mean concentration with long averaging times implies that all
concentrations of O 3 are equally effective in causing plant responses and minimizes the contributions of the peak concentrations to the response. Ozone effects are cumulative; therefore, exposure duration should be included in any index if it is to be biologically relevant. Present evidence suggests that cumulative effects of episodic exposures to either peak or mid-range concentrations, or both, can play an important role in producing growth responses. The key to plant response is timing because peak and mid-range concentrations do not occur at the same time. Potentially, the greatest effect of O 3 on plants will occur when stomatal conductance is greatest. When peaks occur at the time of greatest stomatal conductance, the effect of mid-range concentrations will not be observable. Atmospheric conductivity also strongly influences plant response because O 3 must be in contact with the leaf surface if it is to be taken up by a plant. Effects on vegetation appear when the amount of pollutant entering exceeds the ability of the plant to repair or compensate for the impact. Increasing uptake of O 3 will inhibit photosynthesis and result in increased reductions in biomass production. An index of ambient exposures that relates well to plant response shouldincorporate, directly or indirectly, environmental influences (e.g., temperature, humidity, soilmoisture status) and exposure dynamics. Peak indices (e.g., second highest daily maximum) imply that a single high-concentration exposure (1- or 8-h concentration) during the course of a 70- to 120-day growing season is related to eventual yield or growth reductions. On the other hand, mean indices (e.g., 7-h seasonal mean) imply that duration of the exposure is not important, and that all concentrations have equal effect on plants. Neither of these indices
relates ambient O 3 concentrations to biological effects on plants because these indices do not consider the duration of exposure. An index that cumulates all hourly concentration during the season and gives greater weight to higher concentrations appears to be a more appropriate index for relating ambient exposures to growth or yield effects. No experimental studies have been designed specifically to evaluate the adequacy of the various peak-weighted indices that have been proposed. In retrospective analyses in
which O 3 is the primary source of variation in response, year-to-year variations in plant

1-14response are minimized by peak-weighted, cumulative exposure indices. However, a number of different forms of peak-weighted, cumulative indices have been examined for their ability to properly order yield responses from the large number of studies of the National Crop Loss Assessment Network (NCLAN) program. These exposure indices (i.e., SUM00, SUM06, SIGMOID, W126) all performed equally well, and it is not possible to distinguish among them on the basis of statistical fits of the data. The biological basis for these indices has not
been determined.

Exposure Response of Plant Species The emphasis of experimental studies usually has been on the more economically important crop plants and tree species, as seedlings. Crop species usually are monocultures that are fertilized and, in many cases, watered. Therefore, because crop plants are usually grown under optimal conditions, their sensitivity to O 3 exposures can vary from that of native trees, shrubs, and herbaceous vegetation. The concept of limiting values was used in both the 1978 and 1986 criteria documents to summarize visible foliar injury. Limiting values are defined as concentrations and durations of exposure below which visible injury does not occur. The limit for visible injury indicating reduced plant performance was an O 3 exposure of 0.05 ppm for several hours per day for more than 16 days. When the exposure period was decreased to 10 days,
the O 3 concentration required to cause injury was increased to 0.10 ppm. A short, 6-day exposure further increased the concentration to 0.30 ppm. These exposure and concentration periods apply for those crops where appearance or aesthetic value (e.g, spinach, cabbage, lettuce) is considered important. Limiting values for foliar injury to trees and shrubs range from 0.06 to 0.10 ppm for 4 h. The following assertions can be made based on information from the 1986 criteria document, its 1992 supplement, and literature published since 1986. Ambient
O 3 concentrations in several regions of the country are high enough to impair growth and yield of sensitive plant species. This clearly is indicated by comparison of data obtained from crop yield in charcoal-filtered and unfiltered (ambient) exposures. These elevated levels are further supported by data from studies using chemical protectants. These response data make possible the extrapolation to plants not studied experimentally. Both approaches mentioned above indicate that effects occur with only a few exposures above 0.08 ppm. Data from regression studies conducted to develop an exposure-response function for estimating yield loss indicated that at least 50% of the species and cultivars tested could be predicted to exhibit a 10% yield loss at 7-h seasonal mean O 3 concentrations of 0.05 ppm or less.



### Effects on Natural Ecosystems The responses of the San Bernardino mixed forest of Southern California to 50 or more years of chronic ozone exposures based on many studies, present a classic example of ecosystem response to severe stress. Data from an inventory conducted from 1968 through 1972 indicated that for 5 mo of each year, trees were exposed to O 3 concentrations greater than 0.08 ppm for more than 1,300 h. Concentrations rarely decreased below 0.05 ppm at night near the crest of the mountain slope, approximately 5,500 ft. In addition, during the years 1973 to 1978, average 24-h O 3 concentrations ranged from a background of 0.03 to 0.04 ppm in the eastern part of the San Bernardino Mountains to a maximum of 0.10 to 0.12 ppm in the western part during May through September.

1-15Plants accumulate, store, and use the energy in carbon compounds (sugars) produced during photosynthesis to build their structures and to maintain the physiological processes necessary for life. The patterns of carbon allocation to roots, stems, and leaves directly influence growth. The strategy for carbon allocation changes during the life of a plant, as well as with environmental conditions. Mature trees have a higher ratio of respiration to photosynthetic tissue. Impairment of photosynthesis shifts carbon allocation from growth and maintenance to repair; increased respiration can result in resource imbalances. The significant changes observed in the San Bernardino forest ecosystem were a possible outcome of the combined influences of O 3 on carbon, water, and nutrient allocation. The biochemical changes within the leaves of ponderosa and Jeffrey pine in the San Bernardino forest, expressed as visible foliar injury, premature needle senescence, reduced photosynthesis, and reduced carbohydrate production and allocation, resulted in reduced tree vigor, growth, and reproduction. Reduced vigor increases susceptibility of trees to insect pests and fungal pathogens. Premature needle senescence alters microorganismal succession on confer needles and changes the detritus-forming process and associated nutrient cycling. Altered carbon allocation is important in the formation of mycorrhizae (fungus roots), which are an extremely important but unheralded component of all ecosystems; the majority of all plants depend on them because they are integral in the uptake of mineral nutrients and water from the soil. Carbon-containing exudates from the roots are necessary for the formation of mycorrhizae. Reduced carbon allocation to plant roots affects mycorrhizal formation and impacts plant growth. Exposure to ozone, therefore, affects plant growth both above and below ground. Small changes in photosynthesis or carbon allocation can alter profoundly the structure of a forest. Ecosystem responses to stress begin with the response of the most sensitive individuals of a population. Stresses, whose primary effects occur at the molecular level (within the leaves), must be propagated progressively through more integrated levels of organ physiology (e.g., leaf, branch, root) to whole plant physiology, then to populations within the stand (community), and finally to the landscape level to produce ecosystem effects. Only a small fraction of stresses at the molecular level become disturbances at the tree, stand, or landscape level. The time required for a stress to be propagated from one level to the next (it can take years) determines how soon the effects of the stress can be observed or measured.

The primary effect of O 3 on ponderosa and Jeffrey pine, two of the more susceptible members of the San Bernardino forest community, was that the trees were no longer able to compete effectively for essential nutrients, water, light, and space. Decline in the sensitive trees, a consequence of altered competitive conditions, permitted the enhanced growth of more tolerant species. Removal of the ecosystem dominants at the population level changed its structure and altered the processes of energy flow and nutrient cycling, returning the ecosystem to a less complex stage. The San Bernardino Mountains continue to experience exposure to O 3 ; however, there has been a gradual decline in concentrations and length of exposure. Ozone concentrations of 0.06 ppm or higher of varying durations capable of causing injury to trees in forest ecosystems have been observed during the past 5 years in the Sierra Nevada Mountains and the Appalachian Mountains from Georgia to Maine. Visible injury to forest trees and other vegetation in these areas has been observed.

1-16Injury to sensitive trees from exposure to ozone concentrations 0.06 ppm or greater in the Sierra Nevada Mountains and the Appalachian Mountains has never had the impact on these ecosystems that it did on the San Bernardino forest. Forest stands differ greatly in age, species composition, stability, and capacity to recover from disturbance. In addition, the position in the stand or community of the most sensitive species is extremely important. Ponderosa and Jeffrey pine were the dominant species in the San Bernardino forest. Removal of populations of these trees altered both ecosystem structure and function. Both the Sierra Nevada Mountains and the Appalachian Mountains are biologically more diverse. Removal of sensitive individuals of eastern white pine and black cherry has not visibly altered the forest ecosystems along the Appalachian Mountains, possibly because of the absence of population changes in these species. Decline and dieback of trees on Mt. Mitchell, NC, and Camel’s Hump, VT, cannot be related solely to O 3 injury.



### Effects on Agriculture, Forestry, and Ecosystems: Economics

A number of economic assessments of the effects of O 3 on agriculture have been performed over the last decade. All use NCLAN response data to predict crop yield changes. Although these studies employ somewhat different economic assessment methodologies, each shows national-level economic losses to major crops in excess of $1 billion (1990 dollars) from exposure to ambient concentrations of O 3 . These studies also evaluate the sensitivity of the economic estimates to uncertainties in data, including the NCLAN response data. The economic assessment models used could be adapted to future O 3 -crop yield response findings,
if available. The plant science literature shows that O 3 adversely influences physiological performance of both urban and native tree species; the limited economic literature also demonstrates that changes in growth have economic consequences. However, the natural science and economic literature on the topic are not yet mature enough to conclude unambiguously that ambient O 3 is imposing economic costs. The economic effects of O 3 on ecosystems have not yet been addressed in the published literature. There is, however, an emerging interest in applying economic concepts and methods to the management of ecosystems.



### Effects on Materials

Over four decades of research show that O 3 damages certain materials such as elastomers, textile fibers, and dyes. The amount of damage to actual in-use materials and the economic consequences of that damage are poorly characterized. Natural rubber and synthetic polymers of butadiene, isoprene, and styrene, used in products like automobile tires and protective outdoor electrical coverings, account for most of the elastomer production in the United States. The action of O 3 on these compounds is well known, and concentration-response relationships have been established and corroborated by several studies. These relationships, however, must be correlated with adequate exposure information based on product use. For these and other economically important materials, protective measures have been formulated to reduce the rate of oxidative damage. When antioxidants and other protective measures are incorporated in elastomer production, the
O 3 -induced damage is reduced considerably, although the extent of reduction differs widely according to the material and the type and amount of protective measures used.

1-17Both the type of dye and the material in which it is incorporated are important
factors in the resistance of a fabric to O 3 . Some dyed fabrics, such as royal blue and red rayon-acetate and plum cotton are resistant to O 3 . On the other hand, anthraquinone dyes on nylon fibers are sensitive to fading by O 3 . Field studies and laboratory work show a positive
association between O 3 levels and dye fading of nylon materials. At present, the available research is insufficient to quantify the amount of damaged materials attributable to O 3 alone. The degradation of fibers from exposure to O 3 is poorly characterized. In general, most synthetic fibers, such as modacrylic and polyester, are relatively resistant, whereas cotton, nylon, and acrylic fibers have greater but varying sensitivities to O 3 . Ozone reduces the breaking strength of these fibers, and the degree of strength reduction depends on the amount of moisture present. The limited research in this area indicates that O 3 in ambient air may have a minimal effect on textile fibers, but additional research is needed to verify this
conclusion. A number of artists’ pigments and dyes are sensitive to O 3 and other oxidants; in particular, many organic pigments are subject to fading or other color changes when exposed
to O 3 . Although most, but not all, modern fine arts paints are more O 3 resistant, many older works of art are at risk of permanent damage due to O 3 -induced fading. A great deal of work remains to be done to develop quantitative estimates of the economic damage to materials from photochemical oxidants. Most of the available studies
are outdated in terms of O 3 concentrations, technologies, and supply-demand relationships. Additionally, little is known about the physical damage functions, so cost estimates have been simplified to the point of not properly recognizing many of the scientific complexities of the impact of O 3 .



## 1.6 Toxicological Effects of Ozone and Related Photochemical Oxidants Respiratory Tract Effects of Ozone Biochemical Effects Knowledge of molecular targets provides a basis for understanding mechanisms of effects and strengthening animal-to-human extrapolations. Ozone reacts with polyunsaturated fatty acids and sulfhydryl, amino, and some electron-rich compounds. These elements are shared across species. Several types of reactions are involved, and free radicals may be created. Based on this knowledge, it has been hypothesized that the O 3 molecule is unlikely to penetrate the liquid linings of the respiratory tract (RT) to reach the tissue, raising the possibility that reaction products exert effects. In acute and short-term exposure studies, a variety of lung lipid changes occur, including an increase in arachidonic acid, the further metabolism of which produces a variety of biologically active mediators that can affect host defenses, lung function, the immune system, and other functions. The level of lung antioxidant metabolism increases after O 3 exposure, probably as a result of the increase in the number of Type 2 cells, which are rich in antioxidant enzymes. Collagen (the structural protein involved in fibrosis) increases in O 3 -exposed lungs in a manner that has been correlated to structural changes (e.g., increased thickness of the tissue between the air and blood after prolonged exposure). Some studies found that the increased collagen persists after exposure ceases.

1-18Generally, O 3 enhances lung xenobiotic metabolism after both short- and long-term exposure, possibly as a result of morphological changes (increased numbers of nonciliated bronchiolar epithelial cells). The impact of this change is dependent on the xenobiotics involved; for example, the metabolism of benzo[ a ]pyrene to active metabolites was enhanced by O 3 .

Lung Inflammation and Permeability Changes
Elevated concentrations of O 3 disrupt the barrier function of the lung, resulting in the entry of compounds from the airspaces into the blood and the entry of serum components (e.g., protein) and white blood cells (especially polymorphonuclear leukocytes [PMNs]) into the airspaces and lung tissue. This latter impact reflects the initial stage of inflammation. These cells can release biologically active mediators that are capable of a number of actions, including damage to other cells in the lung. In lung tissue, this inflammation also can
increase the thickness of the air-blood barrier. Increases in permeability and inflammation have been observed at levels as low as 0.1 ppm O 3 (2 h/day, 6 days; rabbits). After acute exposures, the influence of the time of exposure (from two to several hours) increases as the concentration of O 3 increases. Long-term exposure effects are discussed under lung morphology. The impacts of these changes are not fully understood. At higher
O 3 concentrations (e.g., 0.7 ppm, 28 days), the diffusion of oxygen into the blood decreases, possibly because the air-blood barrier is thicker; cellular death may result from the enzymes released by the inflammatory cells; and host defense functions may be altered by mediators.

Effects on Host Defense Mechanisms Exposure to elevated concentrations of ozone results in alterations of all defense mechanisms of the RT, including mucociliary and alveolobronchiolar clearance, functional and biochemical activity of the alveolar macrophage (AM), and immunologic competence. These effects can cause susceptibility to bacterial respiratory infections. Mucociliary clearance, which removes particles and cellular debris from the conducting airways, is slowed by acute, but not repeated exposures to O 3 . Ciliated epithelial cells that move the mucous blanket are altered or destroyed by acute and chronic exposures. Neonatal sheep exposed to O 3 do not have normal development of the mucociliary system. Such effects could prolong the retention of unwanted substances (e.g., inhaled particles) in the lungs, allowing them to exert their toxicity for a longer period of time. Alveolar clearance mechanisms, which center on the functioning of AMs, are altered by O 3 . Short-term exposure to levels as low as 0.1 ppm O 3 (2 h/day, 1 to 4 days; rabbits) accelerates clearance, but longer exposures do not. Even so, after a 6-week exposure of rats to an urban pattern of O 3 , the retention of asbestos fibers in a region protected by alveolar clearance is prolonged. Alveolar macrophages engulf and kill microbes, as well as clear the deeper regions of the lungs of nonviable particles; AMs also participate in immunological responses, but
little is known about the effects of O 3 on this function. Acute exposures of rabbits to levels as low as 0.1 ppm O 3 decrease the ability of AMs to ingest particles. This effect is displayed in decreases in the ability of the lung to kill bacteria after acute exposure of mice to levels as low as 0.4 ppm O 3 .

1-19Both the pulmonary and systemic immune system are affected by O 3 , but in a poorly understood way. It appears that the part of the immune system dependent on T-cell function is more affected than is the part dependent on B-cell function. Dysfunction of host defense systems results in enhanced susceptibility to bacterial lung infections. For example, acute exposure to O 3 concentrations as low as 0.08 ppm for 3 h can overcome the ability of mice to resist infection with streptococcal bacteria, resulting in mortality. However, more prolonged exposures (weeks, months) do not cause greater effects on infectivity. Effects on antiviral defenses are more complex and less well understood. Only high concentrations (1.0 ppm O 3 , 3 h/day, 5 days; mice) increase viral-induced mortality. Apparently, O 3 does not impact antiviral clearance mechanisms. Although O 3 does not affect acute lung injury from influenza virus infection, it does enhance later phases of the course of an infection (i.e., postinfluenzal alveolitis).

Morphological Effects
Elevated concentrations of O 3 cause similar types of alterations in lung structure in all laboratory animal species studied, from rats to monkeys. In the lungs, the most affected cells are the ciliated epithelial cells of the airways and Type 1 epithelial cells of the gas-exchange region. In the nasal cavity, ciliated cells are also affected.The centriacinar region (CAR; the junction of the conducting airways and gasexchange regions) is the primary target, possibly because this area receives the greatest dose
of O 3 . The ciliated cells can be killed and replaced by nonciliated cells (i.e., cells not capable of clearance functions that also have increased ability to metabolize some foreign compounds). Mucous-secreting cells are affected, but to a lesser degree. Type 1 cells, across which gas exchange occurs, can be killed; they are replaced by Type 2 cells, which are thicker and produce more lipids. An inflammatory response also occurs in the tissue. The tissue is thickened further in later stages when collagen (a structural protein increased in fibrosis) and other elements accumulate. Although fibrotic changes have been observed in the CAR, they have not been distributed throughout the whole lung. The distal airway is remodeled; more specifically, bronchiolar epithelium replaces the cells present in alveolar ducts. Concurrent inflammation may play a role. This effect has been observed at 0.25 ppm O 3 (8 h/day, 18 mo) in monkeys; at a higher concentration, this remodeling persists after exposure stops. The progression of effects during and after a chronic exposure is complex. Over the first few days of exposure, inflammation peaks and then drops considerably, plateauing for the remainder of exposure, after which it largely disappears. Epithelial hyperplasia increases rapidly over the first few days and rises slowly or plateaus thereafter; when exposure ends, it begins to return toward normal. In contrast, fibrotic changes in the tissue between the air and blood increase very slowly over months of exposure, and, after exposure ceases, the changes sometimes persist or increase. The pattern of exposure can make a major difference in effects. Monkeys exposed to 0.25 ppm O 3 (8 h/day) every other month of an 18-mo period had equivalent changes in lung structure, more fibrotic changes, and more of certain types of pulmonary function changes than did monkeys exposed every day over the 18 mo. From this work and rat studies, it appears that natural seasonal patterns may be of more concern than more continuous exposures. Thus, long-term animal studies with uninterrupted exposures may
underestimate some of the effects of O 3 .

1-20The morphologic lesions described in early publications on laboratory animals exposed to O 3 do not meet the current criteria for emphysema of the type seen in human lungs.

Effects on Pulmonary Function Pulmonary function changes in animals resemble those observed in humans after acute exposure. During acute exposure, the most commonly observed alterations are increased frequency of breathing and decreased tidal volume (i.e., rapid, shallow breathing). This has been reported at exposures as low as 0.2 ppm O 3 for 3 h (rats). Typically, higher concentrations (around 1 ppm) are required to affect breathing mechanics (compliance and resistance). Extended characterizations of pulmonary function show types of changes generally seen in humans. For example, there are decreased lung volumes at levels ≥ 0.5 ppm
O 3 (a few hours; rats). When rats are exposed to O 3 for 2 h/day for 5 days, the pattern of attenuation of pulmonary function responses is similar to that observed in humans. Other biochemical indicators of lung injury did not return to control values by Day 5, and morphological changes increased in severity over the period of exposure. Thus, attenuation did not result in protection against all the effects of O 3 . Long-term exposures have provided mixed results on pulmonary function, including no or minimal effects, restrictive effects, and obstructive effects. When changes occurred and postexposure examinations were performed, pulmonary function recovered.

Genotoxicity and Carcinogenicity of Ozone
The chemical reactivities of O 3 give it the potential to be a genotoxic agent. In vitro studies are difficult to interpret because the culture systems used allowed the potential formation of artifacts, and high or very high concentrations of O 3 often were used. Generally, in these studies, O 3 causes DNA strand breaks, sometimes is weakly mutagenic, and causes cellular transformation and chromosomal breakage. The latter finding has been investigated in vivo, with mixed results in animals. The few earlier long-term carcinogenic studies in laboratory animals, with or without coexposure to known carcinogens, are either negative or ambiguous. The National Toxicology Program (NTP) completed chronic rat and mouse cancer bioassays using commonly accepted experimental approaches and designs. Both male and female rats and mice were studied. Animals were exposed for 2 years (6 h/day, 5 days/week) to 0.12, 0.5, and 1.0 ppm O 3 or for a lifetime to the same levels (except 0.12 ppm). Following their standard procedures for determination of weight-of-evidence for carcinogenicity, the NTP reported "no evidence" in rats, "equivocal evidence" in male mice,
and "some evidence" in female mice. The increases in adenomas and carcinomas were observed only in the lungs. There was no concentration response. One of the reasons for the designation of "some evidence" in female mice was that when the 2-year and lifetime exposure studies were combined, there was a statistically significant increase in total tumors at 1.0 ppm. Lung tumors from control and O 3 -exposed mice also were examined for the presence of mutated Ha-ras oncogenes. Although the types of mutations found were similar in both groups, a higher incidence of mutations was found in lung tumors from the
O 3 -exposed mice. At the present time, however, there is inadequate information to provide

1-21mechanistic support for the finding in mice. Thus, the potential for animal carcinogenicity
is uncertain. In a companion NTP study, male rats were treated with a tobacco carcinogen and exposed for 2 years to 0.5 ppm O 3 . Ozone did not affect the response and therefore had no tumor promoting activity.



### Systemic Effects of Ozone Ozone causes a variety of effects on tissues and organs distant from the lung.
Because O 3 itself is not thought to penetrate the lung, these systemic effects are either secondary to lung alterations or result from reaction products of O 3 . Effects have been observed on clinical chemistry, white blood cells, red blood cells, the circulatory system, the liver, endocrine organs, and the central nervous system. Most of these effects cannot be interpreted adequately at this time and have not been investigated in humans, but it is of
interest to note that O 3 exposures causing effects on the RT of animals cause a wide array of effects on other organs also. Several behavior changes occur in response to O 3 . For example, 0.12 ppm O 3 (6 h, rats) decreases wheel-running activity, and 0.5 ppm (1 min) causes mice to avoid exposure. These effects are not fully understood, but they may be related to lung irritation or decreased ability to exercise. Although cardiovascular effects, such as slowed heart rate and decreased blood pressure, occur in O 3 -exposed rats, some observed interactions with thermoregulation prevent qualitative extrapolation of these effects to humans at this time. Developmental toxicity studies in pregnant rats summarized in the 1986 O 3 criteria document showed that levels up to about 2.0 ppm O 3 did not cause birth defects. Rat pups from females exposed to 1.0 ppm O 3 during certain periods of gestation weighed less or had delays in development of behaviors (e.g., righting, eye opening). No "classical" reproductive assays with O 3 were found.
Other studies have indicated that O 3 can affect some endocrine organs (i.e., pituitary-thyroid-adrenal axis, parathyroid gland). It appears that the liver has less ability to detoxify drugs after O 3 exposure, but assays of liver enzymes involved in xenobiotic
metabolism are inconsistent.



### Interactions of Ozone with Other Co-occurring Pollutants

Animal studies of the effects of O 3 in combination with other air pollutants show that antagonism, additivity, and synergism can result, depending on the animal species, exposure regimen, and health endpoint. Thus, these studies clearly demonstrate the major complexities and potential importance of interactions but do not provide a scientific basis for predicting the results of interactions under untested ambient exposure scenarios.



## 1.7 Human Health Effects of Ozone and Related Photochemical Oxidants
This section summarizes key effects associated with exposure to O 3 , the major component of photochemical oxidant air pollution that is clearly of most concern to the health of the human population. Another, often co-occurring photochemical oxidant component of

1-22"smog" is PAN, but this compound has been demonstrated to be primarily responsible for induction of smog-related eye irritation (stinging of eyes). Limited pulmonary function studies have shown no effects of PAN at concentrations below 0.13 to 0.30 ppm, which are much higher than the generally encountered ambient air levels in most cities.



### Controlled Human Studies of Acute Ozone Effects Effects on Lung Function Controlled studies in healthy adult subjects have demonstrated O 3 -induced decrements in pulmonary function, characterized by alterations in lung volumes and flow and airway resistance and responsiveness. Respiratory symptoms, such as cough and pain on deep inspiration, are associated with these changes in lung function. Ozone-induced decreases in lung volume, specifically forced vital capacity (FVC) and forced expiratory volume i n 1 s (FEV 1 ), largely can be attributed to decreases in inspiratory capacity (the ability to take a deep breath), although at higher exposure concentrations, there is clearly an additional component that is not volume dependent. Lung volumes recover to a large extent within 2 to 6 h; normal baseline function typically is reestablished within 24 h, but not fully with more severe exposures. Ozone causes increased airway resistance and may cause reductions in expiratory
flow and the FEV 1 /FVC ratio. Ozone causes an increase in airway responsiveness to nonallergenic stimuli (e.g., histamine, methacholine) in healthy and asthmatic subjects. There is no clear evidence of a relationship between O 3 -induced lung volume changes and changes in airway responsiveness.

Inflammation and Host Defense Effects Controlled studies in healthy adult subjects also indicate that O 3 causes an inflammatory response in the lungs characterized by elevated levels of PMNs, increased epithelial permeability, and elevated levels of biologically active substances (e.g., prostaglandins, proinflammatory mediators, cytokines). Inflammatory responses to O 3 can be detected withi n 1 h after a single 1-h exposure with exercise to concentrations ≥ 0.3 ppm; the increased levels of some inflammatory cells and mediators persist for at least 18 h. The temporal response profile is not defined adequately, although it is clear that the time course of response varies for different mediators
and cells. Lung function and respiratory symptom responses to O 3 do not seem to be correlated with airway inflammation. Ozone also causes inflammatory responses in the nose, marked by increased numbers of PMNs and protein levels suggestive of increased permeability. Alveolar macrophages removed from the lungs of human subjects after 6.6 h of exposure to 0.08 and 0.10 ppm O 3 have a decreased ability to ingest microorganisms, indicating some impairment of host defense capability.

Ozone Exposure-Response Relationships Functional, symptomatic, and inflammatory responses to O 3 increase with increasing exposure dose of O 3 . The major determinants of the exposure dose are
O 3 concentration (C), exposure duration (T), and the amount of ventilation ( E ).  ̇ V

1-23Exercise increases response to O 3 by increasing E (greater mass delivered), tidal  ̇ V volume, inspiratory flow (greater percentage delivery), and the intrapulmonary
O 3 concentration. Repeated daily exposures to relatively high levels of O 3 doses ( C×T× E )  ̇ V causing substantial reductions in FEV 1 ( ≥ 20% decrement) typically cause exacerbation of the lung function and respiratory symptom responses on the second exposure day. However, attenuation of these responses occurs with continued exposures for a few days. Most inflammatory responses also attenuate; for example, the PMN influx is absent after five consecutive exposures. Multihour exposures (e.g., for up to 7 h) to O 3 concentrations as low as 0.08 ppm cause small but statistically significant decrements in lung function, increases in respiratory symptoms, and increases in PMNs and protein levels. Ozone C is a more important factor
than exercise E or T in predicting responses to multihour low-level O 3 exposure. There is  ̇ V clear evidence of a response plateau in terms of lung volume response to prolonged
O 3 exposure. This evidence suggests that for a given combination of exercise and
O 3 concentration (i.e., dose rate), there is a response plateau; continued exposure (i.e, increased T) at that dose rate will not increase response. Therefore, quantitative extrapolation of responses to longer exposure durations is not valid.

Mechanisms of Acute Pulmonary Responses The mechanisms leading to the observed pulmonary responses induced by O 3 are beginning to be better understood. The available descriptive data suggest a number of mechanisms leading to the alterations in lung function and respiratory symptoms, including
O 3 delivery to the tissue (i.e., the inhaled concentration, breathing pattern, airway geometry;
O 3 reactions with the airway lining fluid and epithelial cell membranes; local tissue responses, including injury and inflammation; and stimulation of neural afferents (bronchial C-fibers) and the resulting reflex responses and symptoms. The cyclooxygenase inhibitors block production of prostaglandin E 2 and interleukin-6 as well as reduce lung volume responses; however, these drugs do not reduce inflammation and levels of cell damage markers such as lactate dehydrogenase.

Effects on Exercise Performance Maximal oxygen uptake, a measure of peak exercise performance capacity, is reduced in healthy young adults if preceded by O 3 exposures sufficient to cause marked changes in lung function (i.e., decreases of at least 20%) and increased subjective symptoms of respiratory discomfort. Limitations in exercise performance may be related to increased symptoms, especially those related to breathing discomfort.

Factors Modifying Responsiveness to Ozone Many variables have the potential for influencing responsiveness to O 3 ; however, most are addressed inadequately in the available clinical data to make definitive conclusions. Active smokers are less responsive to O 3 exposure, which may reverse following smoking cessation, but these results should be interpreted with caution. The possibility of age-related differences in response to O 3 has been explored, although young adults historically have provided the subject population for controlled human studies. Children and adolescents have lung volume responses to O 3 similar to those of young adults, but lack respiratory symptoms. Pulmonary function responsiveness in adults

1-24appears to decrease with age, whereas symptom rates remain similar to young adults. Group mean lung function responses of adults over 50 years of age are less than those of children, adolescents, and young adults. The available data have not demonstrated conclusively that men and women respond differently to O 3 . Likewise, pulmonary function responses of women have been compared during different phases of the menstrual cycle, but the results are conflicting. If gender differences exist for lung function responsiveness to O 3 , they are not based on hormonal changes, differences in lung volume, or the ratio of FVC to E .  ̇ V There is no compelling evidence, to date, suggesting that any ethnic or racial groups have a different distribution of responsiveness to O 3 . Seasonal and ambient factors may vary responsiveness to O 3 , but further research is needed to determine how they affect individual subjects. Individual sensitivity to O 3 may vary throughout the year, related to seasonal variations in ambient O 3 concentrations. The specific inhalation route appears to be of minor importance in exercising adults. Exposure to O 3 by oral breathing (i.e., mouthpiece) yields results similar to exposure by oronasal breathing (i.e., chamber exposures).

Population Groups at Risk from Ozone Exposure Population groups that have demonstrated increased responsiveness to ambient
concentrations of O 3 consist of exercising healthy and asthmatic individuals, including children, adolescents, and adults. Available evidence from controlled human studies on subjects with preexisting disease suggests that mild asthmatics have similar lung volume responses, but greater airway resistance responses to O 3 than nonasthmatics; and that moderate asthmatics may have, in addition, greater lung volume responses than nonasthmatics. Of all the other population groups studied, those with preexisting limitations in pulmonary function and exercise capacity (e.g., chronic obstructive pulmonary disease, chronic bronchitis, ischemic heart disease) would be of primary concern in evaluating the
health effects of O 3 . Unfortunately, limitations of subject selection, standardized methods of subject characterization, and range of exposure hamper the ability to make definitive conclusions regarding the relative responsiveness of most chronic disease subjects.

Effects of Ozone Mixed with Other Pollutants No significant enhancement of respiratory effects has been demonstrated consistently for simultaneous exposures of O 3 mixed with SO 2 , NO 2 , H 2 SO 4 , HNO 3 , particulate aerosols, or combinations of these pollutants. It is fairly well established that simultaneous exposure of healthy adults and asthmatics to mixtures of O 3 and other pollutants for short periods of time (<2 h) induces pulmonary function responses not significantly different from those following O 3 alone when studies are conducted at the same
O 3 concentration. Exposure to PAN has been reported to induce greater pulmonary function responses than exposure to O 3 alone, but at PAN concentrations (>0.27 ppm) much higher than ambient levels. Unfortunately, only a limited number of pollutant combinations and exposure protocols have been investigated, and subject groups are small and are representative of only small portions of the general population. Thus, much is unknown about the relationships between O 3 and the complex mix of pollutants found in the ambient air.

1-25Prior exposure to O 3 in asthmatics may cause an increase in response to other pollutant gases, especially SO 2 . Likewise, prior exposure to other pollutants can enhance responses to O 3 exposure.



### Controlled Human Studies of Ambient Air Exposures Mobile laboratory studies of lung function and respiratory symptoms in a local subject population exposed to ambient photochemical oxidant pollution provide quantitative information on exposure-response relationships for O 3 . A series of these studies from Los Angeles has demonstrated pulmonary function decrements at mean ambient
O 3 concentrations of 0.14 ppm in exercising healthy adolescents and increased respiratory symptoms and pulmonary function decrements at 0.15 ppm in heavily exercising athletes and at 0.17 ppm in lightly exercising healthy and asthmatic subjects. Comparison of the observed effects in exercising athletes with controlled chamber studies at comparable O 3 concentrations showed no significant differences in lung function and symptoms, suggesting that coexisting ambient pollutants have a minimal contribution to the measured responses under typical
summer ambient conditions in Southern California.



### Field and Epidemiology Studies of Ambient Air Exposures Individual-level field studies and aggregate-level time-series studies have addressed
the acute effects of O 3 on lung function decrements and increased morbidity and mortality in human populations exposed to real-world conditions of O 3 exposure. Camp and exercise studies of lung function provide quantitative information on exposure-response relationships linking lung function declines with O 3 exposure occurring in ambient air. Combined statistical analysis of six recent camp studies in children yields an average relationship between decrements in FEV 1 and previous-hour O 3 concentration of −0.50 mL/ppb. Two key studies of lung function measurements before and after well-defined outdoor exercise events in adults have yielded exposure-response slopes of −0.40 and −1.35 mL/ppb. The magnitude of pulmonary function declines with O 3 exposure is consistent
with the results of controlled human studies. Daily life studies support a consistent relationship between O 3 exposure and acute respiratory morbidity in the population. Respiratory symptoms (or exacerbation of asthma) and decrements in peak expiratory flow rate are associated with increasing ambient O 3 , particularly in asthmatic children; however, concurrent temperature, particles, acidity (hydrogen ions), aeroallergens, and asthma severity or medication status also may contribute as independent or modifying factors. Aggregate results show greater responses in asthmatic individuals than in nonasthmatics, indicating that asthmatics constitute a sensitive group in epidemiologic studies of oxidant air pollution. Summertime daily hospital admissions for respiratory causes in various locations of eastern North America have consistently shown a relationship with ambient levels of O 3 , accounting for approximately one to three excess respiratory hospital admissions per hundred parts per billion O 3 per million persons. This association has been shown to remain even after statistically controlling for the possible confounding effects of temperature and copollutants (e.g., hydrogen ions, sulfate, and particles less than 10 μm), as well as when considering only concentrations below 0.12 ppm O 3 . Many of the time-series epidemiology studies looking for associations between
O 3 exposure and daily human mortality have been difficult to interpret because of

1-26methodological or statistical weaknesses, including the a failure to account for other pollutant
and environmental effects. One of the two most useful new studies on O 3 -mortality found a small but statistically significant association in Los Angeles when peak 1-h maximum
O 3 concentrations reached concentrations greater than 0.2 ppm during the study period. A second study in regions with lower ( ≤ 0.15 ppm) maximum 1-h O 3 concentrations (St. Louis, MO, and Kingston-Harriman, TN) did not detect a significant O 3 association with mortality. Only suggestive epidemiologic evidence exists for health effects of chronic
ambient O 3 exposure in the population. All of the available studies of chronic respiratory system effects in exposed children and adults are limited by a simplistic assignment of exposure or by their inability to isolate potential effects related to O 3 from those of other pollutants, especially particles.



## 1.8 Extrapolation of Animal Toxicological Data to Humans
There have been significant advances in O 3 dosimetry since 1986 that better enable quantitative extrapolation with marked reductions in uncertainty. Experiments and models describing the uptake efficiency and delivered dose of O 3 in the RT of animals and humans are beginning to present a clearer picture than has existed previously. The total RT uptake efficiency of rats at rest is approximately 50%. Within the RT of the rat, 50% of the O 3 taken up by the RT is removed in the head, 7% in the larynx/trachea, and 43% in the lungs. In humans at rest, the total RT uptake efficiency is between 80 and 95%. Total RT uptake efficiency falls as flow increases. As tidal volume increases, uptake efficiency increases and flow dependence lessens. Pulmonary function response data and O 3 uptake efficiency data in humans generally indicate that the mode of breathing (oral versus nasal versus oronasal) has little effect on upper RT or on total RT uptake efficiency, although one study suggests that the nose has a higher uptake efficiency than the mouth.
When all of the animal and human in vivo O 3 uptake efficiency data are compared, there is a good degree of consistency across data sets. This agreement raises the level of confidence with which these data sets can be used to support dosimetric model formulations. Several mathematical dosimetry models have been developed since 1986. Generally, the models predict that net O 3 dose to lung lining fluid plus tissue gradually decreases distally from the trachea toward the end of the tracheobronchial region and then rapidly decreases in the pulmonary region.
When the dose of O 3 to lung tissue is computed theoretically, it is found to be very low in the trachea; to increase to a maximum in the terminal bronchioles of the first generation pulmonary region; and then to decrease rapidly, moving further into the pulmonary region. The increased tidal volume and flow, associated with exercise in humans, shifts
O 3 dose further into the periphery of the lung and causes a disproportionate increase in distal lung dose. Predictions of delivered dose have been used to investigate both acute and chronic
O 3 responses in the context of intra- and interspecies comparisons. In the case of intraspecies comparisons, for example, the distribution of predicted O 3 tissue dose to a ventilatory unit in a rat as a function of distance from the bronchoalveolar duct junction is very consistent with

1-27the distribution of alveolar wall thickening. In the case of interspecies comparisons (using the
delivered O 3 dose to the proximal alveolar regions), although the functional responses (e.g., rapid, shallow breathing) differ markedly between rats and humans, there is similarity of acute dose-response patterns in inflammation (influx of cells and protein) among species, withhumans and guinea pigs more responsive than rats and rabbits, and similarity of chronic doseresponse patterns for increased alveolar interstitial thickness in the CAR of the lung, with monkeys being more responsive than humans and rats less responsive. In other words, the quantitative relationship between animal and human responses is dependent on the animal species and the endpoint. In summary, there is an emerging consistency among a variety of O 3 dosimetry data sets and between the experimental data and theoretical predictions of O 3 dose. The convergence of experimental data with theoretical predictions lends a degree of confidence to the use of theoretical models to predict total and regional O 3 dose. The use of O 3 dosimetry data and models is beginning to provide a useful extrapolation of effects between animals and humans. The data and models have thus far helped demonstrate that humans may be more responsive to O 3 than rats, but less responsive than monkeys with respect to acute and chronic inflammatory responses. However, the monkey, with its similarity to the human in distal airway structure, provides chronic effects data that may best reflect the degree to which a comparably exposed human would respond. These findings, therefore, suggest that long-term exposure to O 3 could impart a chronic effect in humans.



## 1.9 Integrative Summary of Ozone Health Effects

This section summarizes the primary conclusions derived from an integration of
the known effects of O 3 provided by animal toxicological, human clinical, and epidemiological studies.

1. What are the effects of short-term (<8-h) exposures to ozone? Recent epidemiology studies addressing the effects of short-term ambient exposure
to O 3 in the population have yielded significant associations with a wide range of health outcomes, including lung function decrements, aggravation of preexisting respiratory disease, increases in daily hospital admissions and emergency department visits for respiratory causes, and increased mortality. Results from lung function epidemiology studies generally are consistent with the experimental studies in laboratory animals and humans.
Short-term O 3 exposure of laboratory animals and humans causes changes in pulmonary function, including tachypnea (rapid, shallow breathing), decreased lung volumes and flows, and increased airway responsiveness to nonspecific stimuli. Increased airway resistance occurs in both humans and laboratory animals, but typically at higher exposure levels than other functional endpoints. In addition, adult human subjects experience
O 3 -induced symptoms of airway irritation such as cough or pain on deep inspiration. The changes in pulmonary function and respiratory symptoms occur as a function of exposure concentration, duration, and level of exercise. Adult human subjects with mild asthma have responses in lung volume and airway responsiveness to bronchoconstrictor drugs that are qualitatively similar to those of nonasthmatics. Respiratory symptoms are also similar, but wheezing is a prevalent symptom in O 3 -exposed asthmatics in addition to the other demonstrated symptoms of airway irritation. Airway resistance, however, increases relatively

1-28more in asthmatics from an already higher baseline. Recovery from the effects of O 3 on pulmonary function and symptoms is usually complete within 24 h of the end of exposure, although other responses may persist somewhat longer. · An association between daily mortality and O 3 concentration for areas with high O 3 levels (e.g., Los Angeles) has been suggested, although the magnitude
of such an effect is unclear.
· Increased O 3 levels are associated with increased hospital admissions and emergency department visits for respiratory causes. Analyses from data in the northeastern United States suggest that O 3 air pollution is associated with a substantial portion (on the order of 10 to 20%) of all summertime respiratory hospital visits and admissions. · Pulmonary function in children at summer camps in southern Ontario, Canada, in the northeastern United States, and in Southern California is associated with
O 3 concentration. Meta-analysis indicates that a 0.5-mL decrease in FEV 1 is associated with a 1-ppb increase in O 3 concentration. For preadolescent children exposed to 120 ppb (0.12 ppm) ambient O 3 , this amounts to an average decrement of 2.4 to 3.0% in FEV 1 . Similar responses are reported for children and adolescents exposed to O 3 in ambient air or O 3 in purified air for 1 to 2 h while exercising. · Pulmonary function decrements generally are observed in healthy subjects (8 to 45 years of age) after 1 t o 3 h of exposure as a function of the level of exercise performed and the O 3 concentration inhaled during the exposure. Group mean data from numerous controlled human exposure and field studies indicate that, in general, statistically significant pulmonary function decrements beyond the range of normal measurement variability (e.g., 3 to 5% for FEV 1 )

occur (1) at >0.50 ppm O 3 when at rest, (2) at >0.37 ppm O 3 with light exercise (slow walking), (3) at >0.30 ppm O 3 with moderate exercise (brisk walking), (4) at >0.18 ppm O 3 with heavy exercise (easy jogging), and (5) at >0.16 ppm O 3 with very heavy exercise (running). Smaller group mean changes (e.g., <5%) in FEV 1 have been observed at lower
O 3 concentrations than those listed above. For example, FEV 1 decrements have been shown to occur with very heavy exercise in healthy adults at 0.15 to 0.16 ppm O 3 , and such effects may occur in healthy young adults at levels as low as 0.12 ppm. Also, pulmonary function decrements have been observed in children and adolescents at concentrations of 0.12 and 0.14 ppm O 3 with heavy exercise. Some individuals within a study may experience FEV 1 decrements in excess of 15% under these exposure conditions, even when the group mean
decrement is less than 5%. · For exposures of healthy subjects performing moderate exercise during longer duration exposures (6 to 8 h), 5% group mean decrements in FEV 1 were observed at (1) 0.08 ppm O 3 after 5.6 h, (2) 0.10 ppm O 3 after 4.6 h, and (3) 0.12 ppm O 3 after 3 h.

1-29For these same subjects, 10% group mean FEV 1 decrements were observed at 0.12 ppm O 3 after 5.6 and 6.6 h. As in the shorter duration studies, some individuals experience changes larger than those represented by the group mean changes. · An increase in the incidence of cough has been reported at O 3 concentrations as low as 0.12 ppm in healthy adults during 1 t o 3 h of exposure with very heavy exercise. Other respiratory symptoms, such as pain on deep inspiration, shortness of breath, and lower respiratory scores (a combination of several symptoms), have been observed at 0.16 to 0.18 ppm O 3 with heavy and very heavy exercise. Respiratory symptoms also have been observed following exposure to 0.08, 0.10, and 0.12 ppm O 3 for 6.6 h with moderate levels of
exercise. · Increases in nonspecific airway responsiveness in healthy adults have been
observed after 1 t o 3 h of exposure to 0.40 but not 0.20 ppm O 3 at rest and have been observed at concentrations as low as 0.18 but not to 0.12 ppm
O 3 during exposure with very heavy exercise. Increases in nonspecific airway responsiveness during 6.6-h exposures with moderate levels of exercise have been observed at 0.08, 0.10, and 0.12 ppm O 3 . Short-term O 3 exposure of laboratory animals and humans disrupts the barrier function of the lung epithelium, permitting materials in the airspaces to enter lung tissue, allowing cells and serum proteins to enter the airspaces (inflammation), and setting off a cascade of responses. · Increased levels of PMNs and protein in lung lavage fluid have been observed following exposure of healthy adults to 0.20, 0.30, and 0.40 ppm with very heavy exercise and have not been studied at lower concentrations for 1- to 3-h exposures. Increases in lung lavage protein and PMNs also have been observed at 0.08 and 0.10 ppm O 3 during 6.6-h exposures with moderate exercise; lower concentrations have not been tested.
Short-term O 3 exposure of laboratory animals and humans impairs AM clearance of viable and nonviable particles from the lungs and decreases the effectiveness of host defenses against bacterial lung infections in animals and perhaps in humans. The ability of AMs to engulf microorganisms is decreased in humans exposed to 0.08 and 0.10 ppm O 3 for
6.6 h with moderate exercise.

2. What are the effects of repeated, short-term exposures to ozone? During repeated short-term exposures, some of the O 3 -induced responses are partially or completely attenuated. Over a 5-day exposure, pulmonary function changes are typically greatest on the second day, but return to control levels by the fifth day of exposure. Most of the inflammatory markers (e.g., PMN influx) also attenuate by the fifth day of exposure, but markers of cell damage (e.g., lactate dehydrogenase enzyme activity) do not attenuate but continue to increase. Attenuation of lung function decrements is reversed following 7 to 10 days without O 3 . Some inflammatory markers also are reversed during this time period, but others still show attenuation even after 20 days without O 3 . The mechanisms and impacts involved in attenuation are not known, although animal studies show that the underlying cell damage continues throughout the attenuation process. In addition, attenuation may alter the normal distribution of O 3 within the lung, allowing more O 3 to reach sensitive

1-30regions, possibly affecting normal lung defenses (e.g., PMN influx in response to inhaled microorganisms).

3. What are the effects of long-term exposures to ozone? Available data indicate that exposure to O 3 for months and years causes structural changes in several regions of the RT, but effects may be of the greatest importance in the CAR (where the alveoli and conducting airways meet); this region typically is affected in most chronic airway diseases of the human lung. This information on O 3 effects in the distal lung is extrapolated from animal toxicological studies because, to date, comparable data are not available from humans. The apparent lack of reversal of effects during periods of clean air exposure raises concern that seasonal exposures may have a cumulative impact over many years. The role of adaptive processes in this response is unknown but may be critically dependent on the temporal frequency or profile of exposure. Furthermore, the interspecies diversity in apparent sensitivity to the chronic effects of O 3 is notable, with the rat representing the lower limit of response, and the monkey the upper limit. Epidemiological studies attempting to associate chronic health effects in humans with long-term O 3 exposure provide only suggestive evidence that such a linkage exists. Long-term exposure of one strain of female mice to high O 3 levels (1 ppm) causeda small, but statistically significant increase in lung tumors. There was no concentrationresponse relationship, and rats were not affected. Genotoxicity data are either negative or weak. Given the nature of the database, the effects in one strain of mice cannot yet be extrapolated qualitatively to humans. Ozone (0.5 ppm) did not show tumor-promoting activity in a chronic rat study.

4. What are the effects of binary pollutant mixtures containing ozone? Combined data from laboratory animal and controlled human exposure studies of
O 3 support the hypothesis that coexposure to pollutants, each at low-effect levels, may result in effects of significance. The data from human studies of O 3 in combination with NO 2 , SO 2 , H 2 SO 4 , HNO 3 , or CO show no more than an additive response on lung spirometry or respiratory symptoms. The larger number of laboratory animal studies with O 3 in mixture
with NO 2 and H 2 SO 4 show that effects can be additive, synergistic, or even antagonistic, depending on the exposure regimen and the endpoint studied. This issue of exposure to copollutants remains poorly understood, especially with regard to potential chronic effects.

5. What population groups are at risk as a result of exposure to ozone? Identification of population groups that may show increased sensitivity to O 3 is based on their biological responses to O 3 , preexisting lung disease (e.g., asthma), activity patterns, personal exposure history, and personal factors (e.g., age, nutritional status). The predominant information on the health effects of O 3 noted above comes from clinical and field studies on healthy, nonsmoking, exercising subjects, 8 to 45 years of age. These studies demonstrate that, among this group, there is a large variation in sensitivity and responsiveness to O 3 , with at least a 10-fold difference between the most and least responsive individuals. Individual sensitivity to O 3 also may vary throughout the year, related to
seasonal variations in ambient O 3 exposure. The specific factors that contribute to this large intersubject variability, however, remain undefined. Although differences in response may be due to the dosimetry of O 3 in the RT, available data show little difference on O 3 deposition in the lungs for inhalation through the nose or mouth.

1-31Daily life studies reporting an exacerbation of asthma and decrease in peak expiratory flow rates, particularly in asthmatic children, appear to support the controlled studies; however, those studies may be confounded by temperature, particle or aeroallergen exposure, and asthma severity of the subjects or their medication use. In addition, field studies of summertime daily hospital admissions for respiratory causes show a consistent relationship between asthma and ambient levels of O 3 in various locations in the northeastern United States, even after controlling for independent contributing factors. Controlled studies on mild asthmatics suggest that they have similar lung volume responses but greater airway resistance changes to O 3 than nonasthmatics. Furthermore, limited data from studies of moderate asthmatics suggest that this group may have greater lung volume responses than
nonasthmatics. Other population groups with preexisting limitations in pulmonary function and exercise capacity (e.g., chronic obstructive pulmonary disease, chronic bronchitis, ischemic heart disease) would be of primary concern in evaluating the health effects of O 3 . Unfortunately, not enough is known about the responses of these individuals to make definitive conclusions regarding their relative responsiveness to O 3 . Indeed, functional effects in these individuals with reduced lung function may have greater clinical significance than comparable changes in healthy individuals. Currently available data follow on personal factors or personal exposure history known or suspected of influencing responses to O 3 . · Human studies have identified a decrease in pulmonary function responsiveness
to O 3 with increasing age, although symptom rates remain similar. Toxicological studies are not easily interpreted but suggest that young animals are not more responsive than adults. · Available toxicological and human data have not demonstrated conclusively that males and females respond differently to O 3 . If gender differences exist for lung function responsiveness to O 3 , they are not based on differences in baseline pulmonary function. · Data are not adequate to determine whether any ethnic or racial group has a different distribution of responsiveness to O 3 . In particular, the responses of nonwhite asthmatics have not been investigated.
· Information derived from O 3 exposure of smokers is limited. The general trend is that smokers are less responsive than nonsmokers. This reduced responsiveness may wane after smoking cessation. · Although nutritional status (e.g., vitamin E deficiency) makes laboratory rats more susceptible to O 3 -induced effects, it is not clear if vitamin E supplementation has an effect in human populations. Such supplementation has
no or minimal effect in animals. The role of such antioxidant vitamins in
O 3 responsiveness, especially their deficiency, has not been well studied. Based on information presented in this document, the population groups that have demonstrated increased responsiveness to ambient concentrations of O 3 consist of exercising, healthy and asthmatic individuals, including children, adolescents, and adults.

1-32The term "photochemical oxidants" historically has been defined as those atmospheric pollutants capable of 1 oxidizing neutral iodide ions (U.S. Environmental Protection Agency, 1978). A number of oxidants other than O are 3 measured, qualitatively if not quantitatively, by potassium iodide methods.

2-1 

# 2

Introduction

The photochemical oxidants found in ambient air in the highest concentrations are ozone (O ) and nitrogen dioxide (NO ). Other oxidants, such as hydrogen peroxide (H O ) and

3 2 2 2 the peroxyacyl nitrates, also have been observed, but in lower and less certain concentrations. In 1971, the U.S. Environmental Protection Agency (EPA) promulgated National Ambient Air Quality Standards (NAAQS) to protect the public health and welfare from adverse effects of photochemical oxidants. The 1971 photochemical oxidant standards were promulgated on the basis of (1) commercially available measurement methodology, (2) uncertainties over the 1

concentrations of O and non-O photochemical oxidants in the atmosphere resulting from the 3 3 nonspecificity of the measurement methodology, and (3) uncertainties regarding the health and
welfare effects of the non-O photochemical oxidants found in ambient air. After 1971, however, 3 O -specific commercial analytical methods became available, as did additional information on 3 concentrations and effects of the non-O photochemical oxidants. As a result, the chemical 3 designation of the standards was changed in 1979 from photochemical oxidants to O . This 3 document focuses primarily on the scientific air quality criteria for O and, to a lesser extent, on 3 those for H O and the peroxyacyl nitrates, particularly peroxyacetyl nitrate. The scientific air

2 2 quality criteria for NO are discussed in a separate document (U.S. Environmental Protection 2 Agency, 1993).

The previous O air quality criteria document (AQCD), Air Quality Criteria for Ozone 3 and Other Photochemical Oxidants (U.S. Environmental Protection Agency, 1986) was released by EPA in August 1986 and a supplement, Summary of Selected New Information on Effects of Ozone on Health and Vegetation (U.S. Environmental Protection Agency, 1992), was released in January 1992. These documents were the basis for a March 1993 decision by EPA that revision of the existing 1-h NAAQS for O was not appropriate at that time. That decision did 3 not take into account some of the newer scientific data that became available after completion of the 1986 criteria document. The purpose of this document is to summarize the air quality criteria
for O available in the published literature through early 1995. This review was performed in 3 accordance with provisions of the Clean Air Act (CAA) to provide the scientific basis for periodic
reevaluation of the O NAAQS. 3This text is excerpted and adapted from the Proposed Decision on the National Ambient Air Quality 2 Standards for Ozone (Federal Register, 1992a).

2-2

This chapter provides a general introduction to the legislative and regulatory background for decisions on the O NAAQS, as well as a general summary of the organization, 3 content, and major scientific topics presented in this document.



## 2.1 Legislative Background

Two sections of the CAA govern the establishment,review, and revision of the NAAQS. Section 108 (U.S. Code, 1991) directs the Administrator of EPA to identify certain ubiquitous pollutants that may reasonably be anticipated to endanger public health or welfare and to issue air quality criteria for them. These air quality criteria are to reflect the latest scientific information useful in indicating the kind and extent of all identifiable effects on public health or welfare that may be expected from the presence of the pollutant in ambient air.

Section 109(a) of the CAA (U.S. Code, 1991) directs the Administrator of EPA to propose and promulgate primary and secondary NAAQS for pollutants identified under Section 108. Section 109(b)(1) defines a primary standard as one the attainment and maintenance of which, in the judgment of the Administrator and based on the criteria and allowing for an adequate margin of safety, are requisite to protect the public health. The secondary standard, as defined in Section 109(b)(2), must specify a level of air quality the attainment and maintenance of which, in the judgment of the Administrator and based on the criteria, are requisite to protect the public welfare from any known or anticipated adverse effects associated with the presence of the pollutant in ambient air.

Section 109(d) of the CAA (U.S. Code, 1991) requires periodic review and, if appropriate, revision of existing criteria and standards. Thus, the Administrator may find that EPA's review and revision of criteria make appropriate the proposal of new or revised standards. Alternatively, the Administrator may find that revision of the standards is inappropriate and conclude the review by leaving the existing standards unchanged.



## 2.2 Regulatory Background 2

On April 30, 1971, EPA promulgated primary and secondary NAAQS for photochemical oxidants under Section 109 of the CAA (Federal Register, 1971). These standards were set at an hourly average of 0.08 ppm total photochemical oxidants not to be exceeded more than 1 h/year. On April 20, 1977, EPA announced (Federal Register, 1977) the first review and updating of the 1970 Air Quality Criteria for Photochemical Oxidants in accordance with Section 109(d) of the CAA. In preparing a revised AQCD, EPA made two external review drafts of the document available for public comment, and these drafts were peer reviewed by the Subcommittee on Scientific Criteria for Photochemical Oxidants of EPA's Science Advisory Board (SAB). A final revised AQCD for O and other photochemical oxidants was published on 3 June 22, 1978.

Based on the 1978 revised AQCD and taking into account the advice and recommendations of the Subcommittee and the comments received from the public, EPA announced (Federal Register, 1979) a final decision to revise the NAAQS for photochemical2-3

oxidants on February 8, 1979. The final ruling revised the level of the primary standard from 0.08 to 0.12 ppm, set the secondary standard identical to the primary standard, changed the chemical designation of the standards from photochemical oxidants to O , and revised the 3 definition of the point at which the standard is attained to "when the expected number of days per calendar year with maximum hourly average concentrations above 0.12 ppm is equal to or less than one" (see Table 2-1).

Table 2-1. National Ambient Air Quality Standards for Ozone a

Date of Promulgation Primary and Secondary NAAQS Averaging Time

February 8, 1979 0.12 ppm (235 g/m ) 1 h b 3 c

See Appendix A for abbreviations and acronyms. a 1 ppm = 1,962 g/m , 1 g/m = 5.097 × 10 ppm at 25 C, 760 mm Hg. b 3 3 -4 The standard is attained when the expected number of days per calendar year with a maximum hourly average c concentration above 0.12 ppm (235 g/m ) is equal to or less than one. 3

On March 17, 1982, in response to requirements of Section 109(d) of the CAA, EPA announced (Federal Register, 1982) that it was undertaking plans to revise the existing 1978 AQCD for O and other photochemical oxidants, and, on August 22, 1983, it announced (Federal

3 Register, 1983) that review of the primary and secondary NAAQS for O had been initiated. 3 Public peer-review workshops on draft chapters of a revised AQCD were held December 15 through 17, 1982, and November 16 through 18, 1983. The EPA considered comments made at both workshops in preparing the first external review draft that was made available (Federal Register, 1984) on July 24, 1984, for public review.

On February 13, 1985 (Federal Register, 1985), and on April 2, 1986 (Federal Register, 1986), EPA announced two public meetings of the Clean Air Scientific Advisory Committee (CASAC) of EPA's SAB to be held March 4 through 6, 1985, and April 21 and 22, 1986, respectively. At these meetings, CASAC reviewed external review drafts of the revised AQCD for O and other photochemical oxidants. After completion of this review, CASAC sent

3 the EPA Administrator a closure letter, dated October 22, 1986, indicating that the document "represents a scientifically balanced and defensible summary of the extensive scientific literature." The EPA released the final draft document in August 1986.

The first draft of the Staff Paper "Review of the National Ambient Air Quality Standards for Ozone: Assessment of Scientific and Technical Information" was reviewed by CASAC at a public meeting on April 21 and 22, 1986. At that meeting, CASAC recommended that new information on prolonged exposure effects of O be considered in a second draft of the 3 Staff Paper prior to closure. The CASAC reviewed this second draft and also a presentation of new and emerging information on the health and welfare effects of O at a public review meeting 3 held on December 14 and 15, 1987. The CASAC concluded that sufficient new information existed to recommend incorporation of relevant new data into a supplement to the 1986 AQCD (O supplement) and in a third draft of the Staff Paper. 32-4 A draft O supplement, Summary of Selected New Information on Effects of Ozone on

3 Health and Vegetation: Draft Supplement to Air Quality Criteria for Ozone and Other
Photochemical Oxidants , and the revised Staff Paper were made available to CASAC and to the public for review in November 1988. The O supplement reviewed and evaluated selected 3 literature concerning exposure- and concentration-response relationships observed for health effects in humans and experimental animals and for vegetation effects. This literature appeared as peer-reviewed journal publications or as proceedings papers from 1986 through late 1988.

On December 14 and 15, 1988, CASAC held a public meeting to review these documents. The CASAC sent the EPA Administrator a closure letter dated May 1, 1989, indicating that the draft O supplement, along with the 1986 AQCD, and the draft Staff Paper 3 "provide an adequate scientific basis for the EPA to retain or revise the primary and secondary standards of ozone." The CASAC concluded that it would be some time before enough new information on the health effects of multihour and chronic exposure to O would be published in 3 scientific journals to receive full peer review and, thus, be suitable for inclusion in a criteria
document. The CASAC further concluded that such information could better be considered in the
next review of the O NAAQS. A final version of the O supplement has been published (U.S. 3 3 Environmental Protection Agency, 1992).

On October 22, 1991, the American Lung Association and other plaintiffs filed suit to compel EPA to complete its review of the criteria and standards for O . On May 4, 1992, the 3 U.S. District Court for the Eastern District of New York issued an order requiring the Administrator of EPA to sign a proposed decision on whether to revise the standards for O by 3 August 1, 1992, and to sign EPA's final decision by March 1, 1993.

On August 1, 1992, the Administrator signed a proposed decision not to revise the existing NAAQS for O (Federal Register, 1992a), then, on March 1, 1993, signed EPA's final 3 decision, concluding that revision of the NAAQS was inappropriate at that time (Federal Register, 1993a). For reasons indicated in the proposed and final decisions, the March 1993 decision did
not take into consideration a number of recent studies on the health and welfare effects of O that 3 had been published since the last literature review in early 1989. The EPA estimated that approximately 3 years would be necessary to (1) incorporate the new studies into a revised criteria document, (2) complete mandated CASAC review, (3) evaluate the significance of the key information for regulatory decision-making purposes, and (4) publish a proposed decision on the
O NAAQS in the Federal Register

. 3 The EPA intends to complete the current review of the criteria and standards for O as 3 rapidly as possible. Accordingly, the National Center for Environmental Assessment (formerly the Environmental Criteria and Assessment Office [ECAO]) of EPA's Office of Research and Development, located in Research Triangle Park, NC, has given very high priority to review and revision of the air quality criteria for O . The ECAO began by announcing the commencement of 3 the review and identification of new information (Federal Register, 1992b). After assessing and evaluating pertinent new studies, ECAO prepared a preliminary draft of a revised AQCD that was reviewed in a series of expert peer reviewed workshops (Federal Register, 1993b,c). Comments received at the workshops were used to revise the preliminary draft for external review (Federal Register, 1994a). Public peer review meetings were held by CASAC to provide advice on the scientific and technical adequacy of the external review draft (Federal Register, 1994b) and the subsequent revised draft (Federal Register, 1995). The final document was prepared on the basis of comments received from the public and CASAC reviews and provides a scientific basis for review of the existing O standards. The EPA's Office of Air Quality Planning and Standards 32-5

(OAQPS) is completing its preparation of a draft staff paper assessing the most significant information contained in this AQCD and presenting staff recommendations on whether revisions to the NAAQS for O are appropriate. After reviews of the draft staff paper by the public and 3 CASAC, the Administrator will decide whether to propose revisions to the O NAAQS. 3



## 2.3 Summary of Major Scientific Topics Presented

A number of separate topics and issues are addressed in this O criteria document. 3 Some of the key questions addressed are highlighted below by document section.



### 2.3.1 Air Chemistry

• What concerns still exist regarding precision and accuracy of measurements of O 3 and its precursors?

• What is the order of magnitude of current estimates of natural emissions of
O precursors and emissions from anthropogenic sources and their relevance to 3 tropospheric O photochemistry?

3

• What new scientific information exists on the roles of meteorologic and climatologic factors in O formation and transport? 3

• Are the reaction pathways of all major precursors to O understood? Have all 3 major reaction products been identified? How are the reactions and products represented in air quality models?

• What is the status of development, application, evaluation, and verification of air quality models?



### 2.3.2 Air Quality

• What are the trends and geographic differences in O concentrations across the 3 United States?

• What are diurnal and seasonal patterns of 1-h average O concentrations for urban 3 and nonurban sites and for attainment versus nonattainment areas?

• What is known about patterns of co-occurrence of O with other pollutants in the 3 atmosphere?

• What O exposure assessment data are available for agricultural crops and forests?

3

• To what level and to what extent are humans typically exposed to O in the course 3 of normal, everyday activities?



### 2.3.3 Environmental Effects

• What are the effects of ambient O concentrations on vegetation (i.e., agricultural 3 and horticultural crops; urban landscape trees, shrubs, and flowers; forest tree species)?

• What characteristics of air quality (e.g., summary statistics) are relevant to these effects on vegetation?

• What are the long-term effects of O exposures on natural ecosystems? 3

• Is there important new information on the effects of O on nonbiological materials? 32-6



### 2.3.4 Health Effects

• What O concentration and exposure duration relationships exist for effects on lung

3 structure, function, and host defense mechanisms, and what are the important
modifiers of these effects?

• What are the mechanisms of O -induced lung injury? 3

• Can dosimetry models predict human population responses to O on the basis of 3 laboratory animal data?

• Does long-term exposure to O lead to the development of chronic lung disease or 3 to an increased frequency or exacerbation of other chronic respiratory outcomes?

• What segments of the population are most susceptible to effects from exposure to
O ? 3



## 2.4 Organization and Content of the Document

This document critically evaluates and assesses scientific information on the health and welfare effects associated with exposure to the concentrations of O and related photochemical 3 oxidants present in ambient air. Although the document is not intended to be an exhaustive literature review, it is intended to selectively cover the pertinent literature through 1995. The references cited in the document should be reflective of the state of knowledge on those issues most relevant to review of the NAAQS for O , now set at 0.12 ppm for 1 h. Although emphasis 3 is placed on the presentation of health and welfare effects data, other scientific data will be presented and evaluated in order to provide a better understanding of the nature, sources, distribution, measurement, and concentrations of O and related photochemical oxidants in 3 ambient air, as well as the characterization of population exposure to these pollutants.

To aid in the development of this document, summary tables of the relevant published literature have been provided to supplement a selective discussion of the literature. Most of the
scientific information selected for review and comment in the text comes from the more recent literature published since completion of the previous O criteria document (U.S. Environmental 3 Protection Agency, 1986). Some of these newer studies were reviewed briefly in the supplement to that document (U.S. Environmental Protection Agency, 1992), but more intense evaluation of these studies has been included. Other studies, however, are included if they contain unique data, such as the documentation of a previously unreported effect or of a mechanism of an effect, or if they were multiple-concentration studies designed to provide exposure-response relationships. Emphasis is placed on studies conducted at or near O concentrations found in ambient air. For 3 animal toxicology studies, typically only those studies conducted at less than 1 ppm O are 3 considered. Studies that are presented in the previous criteria document and whose data were judged to be significant because of their usefulness in deriving the current NAAQS are discussed briefly in the text. Other, older studies also are discussed in the text if they were judged to be (1) open to reinterpretation because of newer data or (2) potentially useful in deriving revised
standards for O . The reader should, however, consult the more extensive discussion of these 3 "key" studies in the previous document. Generally, only published information that has undergone scientific peer review is included in the criteria document.

Certain issues of direct relevance to standard setting are not explicitly addressed in this document, but instead are analyzed in documentation prepared by OAQPS as part of its regulatory review process. Such issues include (1) determining what constitutes an "adverse2-7

effect" and delineation of particular adverse effects that the primary and secondary NAAQS are intended to protect against, (2) exposure assessment, (3) assessment of consequent risks based on health and exposure analyses, and (4) factors to be considered in determining an adequate margin of safety. Key points and conclusions from such analyses are summarized in the Staff Paper prepared by OAQPS and reviewed by CASAC and the public. Although scientific data contribute significantly to decisions regarding the above issues, their resolution cannot be achieved solely on the basis of experimentally acquired information. Final decisions on items 1 and 4 are made by the EPA Administrator, as mandated by the CAA.

A fourth issue directly pertinent to standard setting is identification of populations at risk, which is basically a determination by EPA of the subpopulations to be protected by the promulgation of a given standard. This issue is addressed only partially in the criteria document. For example, information is presented on factors, such as preexisting disease, that biologically may predispose individuals and subpopulations to more severe effects from exposures to O . The 3 identification of a population at risk, however, requires information above and beyond data on biological predisposition, such as information on levels of exposure, activity patterns, and personal habits. Such information is included in the Staff Paper developed by OAQPS.

Finally, the O air quality document considers only the scientific and technical issues

3 that are important for standard setting, not those issues relative to implementation of the
O NAAQS. For example, certain issues related to the control strategies for attainment of the 3 standard and to possible atmospheric consequences of control strategy design are not discussed in this document. This limitation also includes discussion of impacts consequent to possible changes
in the O NAAQS. These issues would be better addressed in regulatory impact analyses or cost-

3 benefit analyses that may be prepared as part of the O NAAQS decision package. 3 This document is structured as follows: Chapter 1 (executive summary and conclusions) provides a concise presentation of key information and conclusions from all subsequent chapters. This is followed by this brief introduction (Chapter 2) containing information on the legislative and regulatory background for review of the O NAAQS, as well as 3 an overview of the organization of this document. Chapter 3 provides information on the chemistry, sources, emissions, measurement, and transport of O and related photochemical 3 oxidants and their precursors, and Chapter 4 covers environmental concentrations, patterns, and exposure estimates of O and oxidant air quality. This is followed by Chapter 5, which deals with 3 environmental effects of O and related photochemical oxidants. Chapters 6, 7, and 8 discuss 3 animal toxicological studies, human health effects, and extrapolation of animal toxicological data to humans, respectively. Finally, Chapter 9, provides an integrative and interpretive evaluation of health effects associated with exposure to O

. 32-8



### References

Federal Register. (1971) National primary and secondary ambient air quality standards. F. R. (April 30) 36: 8186-8201.

Federal Register. (1977) Review of the photochemical oxidant and hydrocarbon air quality standards. F. R. (April 20)

42: 20493-20494.

Federal Register. (1979) National primary and secondary ambient air quality standards: revisions to the national ambient

air quality standards for photochemical oxidants. F. R. (February 8) 44: 8202-8221.

Federal Register. (1982) Air quality criteria document for ozone and other photochemical oxidants. F. R. (March 17) 47:

11561.

Federal Register. (1983) Review of the national ambient air quality standards for ozone. F. R. (August 22) 48: 38009.

Federal Register. (1984) Draft air quality criteria document for ozone and other photochemical oxidants. F. R. (July 24)

49: 29845.

Federal Register. (1985) Science Advisory Board; Clean Air Scientific Advisory Committee; open meeting.

F. R. (February 13) 50: 6049.

Federal Register. (1986) Science Advisory Board; Clean Air Scientific Advisory Committee; open meeting. F. R. (April

2) 51: 11339.

Federal Register. (1992a) National ambient air quality standards for ozone; proposed decision. F. R. (August 10) 57:

35542-35557.

Federal Register. (1992b) Air quality criteria for ozone and related photochemical oxidants: call for information.

F. R. (August 27) 57: 38832.

Federal Register. (1993a) National ambient air quality standards for ozone—final decision. F. R. (March 9)

58: 13008-13019.

Federal Register. (1993b) Peer-review workshops on the health effects of ozone and related photochemical oxidants:

notice of public meeting. F. R. (July 1) 58: 35454.

Federal Register. (1993c) Three review workshops on draft chapters of a revised air quality criteria for ozone and

related photochemical oxidants: notice of public meetings. F. R. (September 14) 58: 48063.

Federal Register. (1994a) External review draft of revised air quality criteria for ozone and related photochemical

oxidants: notice of availability of external review draft. F. R. (January 31) 59: 4278.

Federal Register. (1994b) Science Advisory Board Clean Air Scientific Advisory Committee review of a draft revised

air quality criteria for ozone and related photochemical oxidants. F. R. (June 3) 59: 28857-28858.

Federal Register. (1995) Science Advisory Board; notification of public advisory committee meetings. F. R. (March 3)

60: 11971-11974.

U.S. Code. (1991) Clean Air Act, §108, air quality criteria and control techniques, §109, national ambient air quality

standards. U. S. C. 42: §§7408-7409.

U.S. Environmental Protection Agency. (1978) Air quality criteria for ozone and other photochemical oxidants.

Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA-600/8-78-004. Available from: NTIS, Springfield, VA; PB80-
124753.2-9

U.S. Environmental Protection Agency. (1986) Air quality criteria for ozone and other photochemical oxidants.

Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report nos. EPA-600/8-84-020aF-eF. 5v. Available from: NTIS, Springfield, VA;
PB87-142949.

U.S. Environmental Protection Agency. (1992) Summary of selected new information on effects of ozone on health and

vegetation: supplement to 1986 air quality criteria for ozone and other photochemical oxidants. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA/600/8-88/105F. Available from: NTIS, Springfield, VA;
PB92-235670.

U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen. Research Triangle Park, NC:

Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v. Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525,
and PB95-124517.

# 3

Tropospheric Ozone and Its Precursors

3.1 Introduction

Ozone (O 3 ) and other oxidants found in ambient air, such as peroxyacyl nitrates (PANs) and hydrogen peroxide (H 2 O 2 ), are formed as the result of atmospheric physical and chemical processes involving two classes of precursor pollutants, volatile organic compounds (VOCs) and nitrogen oxides (NO

x ). The formation of O 3 and other oxidants from these precursors is a complex, nonlinear function of many factors, including temperature, the intensity and spectral distribution of sunlight, atmospheric mixing and related meteorological conditions, the concentrations of the precursors in ambient air and the ratio between VOC and
NO

x , and the reactivity of the organic precursors. An understanding of the atmospheric chemistry and meteorological parameters and processes responsible for the formation and occurrence of elevated concentrations of O 3 in ambient air is basic to the formulation of strategies and techniques for its abatement. Such an understanding is required for representing those parameters and processes adequately in predictive models used to determine the emission reductions needed for complying with the National Ambient Air Quality Standards (NAAQS) for O 3 . In addition, the identification and quantification of O 3 precursors in ambient air are essential, along with emission inventories or emission models, for the development, verification, and refinement of photochemical air quality models and for comparisons of ambient concentrations with emission inventories (source reconciliation), as a check on the accuracy of measurements and of inventories. Product identification and quantification of yields, in both chambers and ambient air, are helpful in the verification of photochemical air quality models and in testing theoretical chemical mechanisms. Likewise, product identification and quantification are useful in determining the need for research on the potential effects of the simultaneous or sequential co-occurrence with O 3 and related oxidants of multiple air pollutants. The ability to measure O 3 and its precursors, its reaction products, and the products of the atmospheric reactions of its respective precursors is essential for understanding the atmospheric chemistry of O 3 formation, verifying chemical mechanisms and models, quantifying emission rates, and adequately characterizing exposure-response factors for both biological and nonbiological receptors. For these reasons, this chapter presents information on a broad range of topics. The chapter describes the chemical processes by which O 3 and other photochemical oxidants are formed in ambient air (Section 3.2). The chapter also characterizes the nature of the precursors in terms of their sources and emissions into the atmosphere and their

3-1concentrations in ambient air (Section 3.4), as well as the methods by which their concentrations in ambient air are measured (Section 3.5). In addition to information on the chemistry of oxidants and their precursors, this chapter includes a discussion of meteorological processes (Section 3.3) that contribute to the
formation of O 3 and other oxidants and govern their transport and dispersion. Finally, an overview is given (Section 3.6) of models of the relationships between precursor emissions
and O 3 formation in the atmosphere. Readers are referred to other sources (e.g., Finlayson-Pitts and Pitts, 1986; Seinfeld, 1986; the U.S. Environmental Protection Agency, 1986a; National Research Council, 1991) for additional information on the chemical and physical aspects of photochemical air pollution.



## 3.2 Tropospheric Ozone Chemistry 3.2.1 Background Information Ozone is formed photochemically in the stratosphere and transported downward, resulting in the presence of O 3 in the natural or "clean" troposphere. The presence of O 3 in the clean troposphere, in the absence of perturbations caused by human activities, is highly important because O 3 is a precursor to the hydroxyl (OH) radical, the key intermediate species in the tropospheric degradation of VOCs emitted into the atmosphere. Although O 3 at relatively low concentrations is an integral part of the clean troposphere, its presence at higher
concentrations is detrimental. The chemical processes occurring in the atmosphere that lead to the formation of
O 3 and other photochemical air pollutants are complex. Tropospheric O 3 is formed as a result of (1) the emissions of NO

x and VOCs into the atmosphere from anthropogenic and natural sources, (2) the transport of these emissions and their reaction products, and (3) chemical reactions occurring in the atmosphere concurrent with transport and dispersion of the emissions. These processes lead to the formation of O 3 and other photochemical oxidants, such as peroxyacetyl nitrate (PAN), nitric acid (HNO 3 ), and sulfuric acid (H 2 SO 4 ), and to other compounds, such as particulate matter and formaldehyde (HCHO) and other carbonyl compounds. Additionally, deposition of gases and particles along the trajectory of an air parcel occurs, reducing the concentrations of precursors and products in the atmosphere, but possibly leading to adverse impacts on the earth’s environment. The basic process leading to the photochemical formation of O 3 in the troposphere involves the photolysis of nitrogen dioxide (NO 2 ) to yield nitric oxide (NO) and a groundstate oxygen atom, O( 3 P),

(3-1) NO 2 h ν → NO O( 3 P),

which then reacts with molecular oxygen to form O 3 :

(3-2) O( 3 P) O 2 M → O 3 M, where M air.

The NO and O 3 react to reform NO 2 :

3-2(3-3) NO O 3 → NO 2 O 2 .

The presence of reactive VOCs leads to the conversion of NO to NO 2 without the intermediary of O 3 (Reaction 3-3), and the photolysis of NO 2 then leads to the formation of
elevated levels of O 3 :

The photochemical cycles leading to O 3 production are best understood through a knowledge of the chemistry of the atmospheric oxidation of methane (CH 4 ), which can be viewed as being the chemistry of the clean or unpolluted troposphere (although this is a simplification because vegetation releases large quantities of complex VOCs into the atmosphere). Although the chemistry of the VOCs emitted from anthropogenic and biogenic sources in polluted urban and rural areas is more complex, a knowledge of the CH 4 oxidation reactions aids in understanding the chemical processes occurring in the polluted atmosphere because the underlying chemical principles are the same. This section first describes the structure of the atmosphere, followed by discussions of the formation of the OH radical and of tropospheric NO

x chemistry. The photochemical formation of tropospheric O 3 from the oxidation of CH 4 then is discussed in some detail
because the CH 4 oxidation cycle serves as a model for the photochemical formation of O 3 . In Section 3.2.4, the chemistry of the major classes of nonmethane VOCs and the formation of
O 3 from these VOCs are discussed. Finally, in Section 3.2.5, a brief account of the photochemical formation of aerosols is given because the same processes that lead to the
formation of elevated levels of O 3 result in the formation of both particulate matter (leading to visibility degradation) and atmospheric acidity.



### 3.2.2 Structure of the Atmosphere Earth’s atmosphere is composed of a number of layers (McIlveen, 1992). For the purposes of this chapter, those of concern are the troposphere and the stratosphere, and the boundary between them, the tropopause. The troposphere extends from the earth’s surface to the tropopause ( ≈ 10 to 18 km altitude, depending on latitude and season). The altitude of the tropopause is greatest in the tropics and lowest in the wintertime polar regions, with an average altitude of ≈ 14 km. The temperature in the troposphere decreases with increasing altitude from an average of 290 K at
the earth’s surface to ≈ 210 to 220 K at the tropopause, and the pressure decreases from ≈ 760
torr at the earth’s surface to ≈ 100 torr at the tropopause. The stratosphere extends from the tropopause to an altitude of ≈ 50 km. In the stratosphere, the temperature increases with increasing altitude from ≈ 210 to 220 K at the tropopause to ≈ 270 K at the top of the stratosphere. The pressure in the stratosphere decreases with increasing altitude from ≈ 100 torr at the tropopause to ≈ 1 torr at the top of the stratosphere.

3-33.2.2.1 Vertical and Horizontal Mixing in the Atmosphere In the troposphere, temperature generally decreases with increasing altitude. As will be discussed in Section 3.3, the lowest 1 to 2 km of the troposphere is influenced by the planetary boundary layer (PBL) and, in certain locales, by inversion layers. These boundary and inversion layers inhibit the vertical movement of pollutants into the free troposphere. Above inversion and boundary layers, vertical mixing in the "free" troposphere
has a time scale of ≈ 10 to 30 days (Langner et al., 1990; World Meteorological Organization, 1990a). Because temperature increases with increasing altitude in the stratosphere, vertical mixing in the stratosphere is slow, with a time scale of months to a few years. Horizontal mixing in the troposphere occurs both within and between the hemispheres. The time scale for mixing between the Northern and Southern Hemispheres is

≈ 1 year (Cicerone, 1989; Singh and Kanakidou, 1993). Transport within a hemisphere is more rapid (Graedel et al., 1986a), and local, regional, and global transport distances of <100 km, 100 to 1,000 km, and >1,000 km, respectively, are observed. For a wind speed of
15 km h -1 ( ≈ 4ms -1 ), transport times over these local, regional, and global distances are a few hours, a few hours to a few days, and ≥ 10 days, respectively.

3.2.2.2 Formation of Stratospheric Ozone At altitudes between approximately 20 and 35 km, the stratosphere has a layer of air containing O 3 at mixing ratios up to approximately 10 ppm. The sun emits radiation ~ >170 nm, and this radiation impacts the upper levels of the atmosphere. The bulk composition of the atmosphere (78.1% nitrogen [N 2 ], 21.0% molecular oxygen [O 2 ], 0.9% argon [Ar], 0.03% carbon dioxide [CO 2 ], with variable trace gas concentrations) is invariant up to at least 50 km (McIlveen, 1992). The shorter wavelength radiation (175 to 240 nm) is absorbed by O 2 in the stratosphere, leading to dissociation into two ground-state oxygen atoms, O( 3 P),

(3-5) O 2 h ν → 2 O( 3 P),

followed by the reaction of O( 3 P) atoms with O 2 in the presence of a third body, M, to form
O 3 :

(3-2) O( 3 P) O 2 M → O 3 M, where M air.

Ozone also photolyzes, at wavelengths <360 nm (DeMore et al., 1992),

(3-6) O 3 h ν → O 2 O,

where the oxygen atom produced can be in the ground state, O( 3 P), or electronically excited state, O( 1 D). The O( 1 D) atoms produced are deactivated to the ground-state O( 3 P) atom by
N 2 , O 2 , CO 2 , and Ar:

3-4(3-7) O( 1 D) M → O( 3 P) M, where M N 2 , O 2 , Ar, and CO 2 .

The reaction of O( 3 P) atoms with O 3 is the termination step of this reaction sequence,

(3-8) O( 3 P) O 3 → 2 O 2 .

These reactions, called the Chapman reactions (Chapman, 1930), are responsible for the layer of O 3 found in the stratosphere. Because the stratospheric O 3 layer absorbs the
sun’s radiation below ≈ 290 nm, only radiation of wavelengths ~ >290 nm can penetrate into the troposphere and impact the earth’s surface. Any depletion of the stratospheric O 3 layer allows shorter wavelength ultraviolet radiation (<320 nm) to be transmitted through the stratosphere and into the troposphere. In addition to the biological effects expected from increased ultraviolet B (UV-B) radiation (290 to 320 nm), increased penetration of UV-B into the troposphere can lead to changes in tropospheric O 3 . Model calculations indicate that O 3 in the troposphere could increase with increasing UV-B in urban and rural areas impacted by anthropogenic NO

x emissions (Gery et al., 1988; Liu and Trainer, 1988; Thompson et al., 1989; Thompson, 1992) but could decrease with increasing UV-B in remote tropospheric areas characterized by low
NO

x levels (Liu and Trainer, 1988; Thompson et al., 1989). Besides the implications of longterm trends in stratospheric O 3 concentrations leading to corresponding changes in the intensity of UV-B radiation impacting the troposphere, short-term changes (including daily changes) in stratospheric O 3 levels lead to short-term changes in the rates of photolysis of several important species. These include the photolysis of formaldehyde to produce radicals
and of O 3 to form the OH radical. These changes in photolysis rates affect the formation rates and ambient concentrations of key radical intermediates, specifically of the OH radical, in the troposphere. Information concerning such short-term changes in stratospheric
O 3 concentrations is needed as input to urban and regional airshed computer models of photochemical air pollution formation. In the clean atmosphere, stratospheric O 3 also is influenced by the emission of nitrous oxide (N 2 O) from soils and oceans (World Meteorological Organization, 1992).
Because N 2 O is chemically inert in the troposphere and does not photolyze (Prinn et al., 1990), it therefore is transported into the stratosphere, where it undergoes photolysis and also reacts with O( 1 D) atoms (DeMore et al., 1992; Atkinson et al., 1992a). The reaction of N 2 O with the O( 1 D) atom is the major source of stratospheric NO, which then participates in a
series of reactions known as the NO

x catalytic cycle (Crutzen, 1970; Johnston, 1971).

(3-3) NO O 3 → NO 2 O 2

The Chapman reactions and the NO

x catalytic cycle reactions control the O 3 concentrations in the lower clean stratosphere. Additional reaction sequences leading to the removal of stratospheric O 3 arise from
the ClO

x and BrO

x catalytic cycles, which result when chlorine (Cl)- and bromine

3-5(Br)-containing organic compounds are emitted into the atmosphere. These O 3 -depleting (3-9)

Net: NO 2 O( 3 P) → NO O 2

O( 3 P) O 3 → 2 O 2

compounds include the chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), carbon tetrachloride (CCl 4 ), methyl chloroform, halons, and methyl bromide (Anderson et al., 1991; Rowland, 1990, 1991; World Meteorological Organization, 1992). Analogous to N 2 O, the CFCs, CCl 4 , and certain halons (CF 3 Br and CF 2 ClBr) are inert in the troposphere and are transported into the stratosphere, where they photolyze to generate Cl or Br atoms (World Meteorological Organization, 1992). Methyl bromide and the HCFCs react to a large extent in the troposphere, so that only a fraction of these Cl- and Br-containing species that are emitted into the troposphere are transported into the stratosphere (World Meteorological Organization, 1990b, 1992).



### 3.2.3 Background Ozone in the Troposphere As noted in Section 3.2.1, O 3 is present in the troposphere even in the absence of human activities. Although this "natural" O 3 has received widely varying estimates in the literature, there has not been an attempt to standardize the definition because natural background O 3 is a multidimensional and complex concept. Concentrations of background O 3 can vary with temperature; wind speed and direction; vertical motion; geographical location, including latitude and altitude; and season of the year. Because of the decrease of total pressure with increasing altitude, the O 3 concentration in the clean troposphere may be taken to be reasonably independent of altitude at ≈ 7 × 10 11 molecules cm −3

. For purposes of this document, the primary focus is on the background
O 3 concentration near the surface over the United States during the O 3 season. However, the length of the O 3 season varies from state-to-state, depending predominantly on latitude (see Chapter 4). These variations in O 3 season affect the determination of which seasonal averaging period to apply when estimating O 3 concentrations. Based on available assessments
of O 3 monitoring measurements, a daytime, 7-h (0900 to 1559 hours), seasonal (April to October) average O 3 concentration of 25 to 45 ppb can be assumed (Altshuller and Lefohn, 1996) as an estimated background concentration (see Chapter 4). Although one component of this background is of natural origin, the other can be attributed to anthropogenic contributions associated with long-range transport of O 3 . This assumption is consistent with the relatively long lifetime of O 3 in the troposphere, which can be as long as 30 to 60 days. The background of O 3 can be attributed to the following sources: downward transport of stratospheric O 3 through the free troposphere to near ground level, in situ
O 3 production from methane emitted from swamps and wetlands reacting with natural NO

x emitted from soils and lighting strikes and from the downward transport of NO from the stratosphere into the troposphere, and in situ production of O 3 from the reactions of biogenic
VOCs with natural NO

x (National Research Council, 1991). Another source to be considered is the long-range transport of O 3 from distant pollutant sources. It is important to appreciate that NO

x has a limited lifetime, often estimated to be
as short a s 6 h in plumes (Altshuller, 1986) and possibly up to 1 to 2 days under less polluted conditions. Because of this relatively short lifetime, the NO

x emitted from cultivated areas in

3-6the United States as a result of agricultural practices may not survive long enough to interact with substantial emissions of biogenic VOCs in distant forested areas. There also is no direct way to distinguish natural NO

x from anthropogenic NO

x in a rural or remote location.

3.2.3.1 Tropospheric Hydroxyl Radicals It is now recognized that the key reactive species in the troposphere is the OH radical, which is responsible for initiating the degradation reactions of almost all VOCs. In the presence of NO, these OH radical reactions with VOCs lead to the formation of
O 3 and, hence, to O 3 concentrations above those encountered in the clean troposphere. The OH radical is produced from the ultraviolet (UV) photolysis of O 3 . Ozone photolyzes in the UV radiation at wavelengths <320 nm to generate the electronically excited O( 1 D) atom (DeMore et al., 1992; Atkinson et al., 1992a),

(3-6a) O 3 h ν → O 2 O( 1 D).

The O( 1 D) atoms either are deactivated to the ground state O( 3 P) atom by Reaction 3-7 or they react with water vapor to form the OH radical:

(3-10) O( 1 D) H 2 O → 2 OH.

The O( 3 P) atoms formed directly in the photolysis of O 3 or formed from deactivation of O( 1 D) atoms (Reaction 3-7) reform O 3 through Reaction 3-2. At room temperature and 50% relative humidity, 0.2 OH radicals are formed per O( 1 D) atom generated from the photolysis of O 3 . Hydroxyl radical production from Reactions 3-6a and 3-10 is balanced by reaction of the OH radical with carbon monoxide (CO) and CH 4 . Because the water vapor mixing ratio decreases with increasing altitude in the troposphere (Logan et al., 1981; World Meteorological Organization, 1992) and the O 3 mixing ratio generally increases with increasing altitude, the OH radical concentration is expected to be reasonably independent of altitude (Dentener and Crutzen, 1993). A knowledge of ambient tropospheric OH radical concentrations is needed for an understanding of tropospheric chemistry and to reliably calcuate the lifetimes of chemical compounds. Because OH and hydroperoxyl (HO 2 ) radicals are interrelated through a series of reactions (Section 3.2.3.3), concurrent measurements of OH and HO 2 radical concentrations improve the knowledge of tropospheric chemistry. Only in the past few years have measurements been made of lower tropospheric OH radical concentrations (see, for example, Felton et al., 1990; Hofzumahaus et al., 1991; Eisele and Tanner, 1991; Mount and Eisele, 1992; Comes et al., 1992; Hard et al., 1992). The limited data available show that, as expected, the OH radical concentrations exhibit a diurnal profile, with daytime maximum
concentrations of several times 10 6 molecules cm -3 . A global, annually, seasonally, and diurnally averaged tropospheric OH radical concentration also can be derived from the estimated emissions and measured atmospheric concentrations of methylchloroform (CH 3 CCl 3 )
and the rate constant for the reaction of the OH radical with CH 3 CCl 3 (its major tropospheric loss process). Using this method, Prinn et al. (1992) have derived a 24-h average OH radical
concentration o f 8 × 10 5 molecules cm -3 (equivalent to a 12-h daytime average of 1.6 × 10 6

molecules cm -3 [ ≈ 0.1 ppt]). Ambient air measurements of the decay of nonmethane

3-7hydrocarbons in urban plumes (Blake et al., 1993) give OH radical concentrations of similar magnitude to those derived from direct tropospheric measurements and globally averaged
estimates.

3.2.3.2 Tropospheric Nitrogen Oxides Chemistry The presence of NO

x is necessary for the formation of O 3 from the oxidation of
CH 4 and other VOCs. Sources of tropospheric NO

x include downward transport from the stratosphere, in situ formation from lightning (National Research Council, 1991; World Meteorological Organization, 1992) (see Section 3.4.1.2), and emission from soils (National Research Council, 1991; World Meteorological Organization, 1992). Recent measurements
show that the NO

x concentrations over maritime areas increase slightly with increasing altitude, from ≈ 15 ppt in the marine boundary layer (Carroll et al., 1990) to ≈ 30 to 40 ppt at 3 to 7 km altitude (Ridley et al., 1989; Carroll et al., 1990). Significantly higher NO

x concentrations ( ≈ 100 ppt) have been observed in the boundary layer over relatively unpolluted continental areas (Carroll et al., 1990), with the NO

x concentrations decreasing with increasing altitude to ≈ 50 ppt at 3 to 7 km (Ridley et al., 1989; Carroll et al., 1990). In the troposphere, NO, NO 2 , and O 3 are interrelated by the following reactions:

(3-3) NO O 3 → NO 2 O 2

(3-1) NO 2 h ν → NO O( 3 P)

(3-2) O( 3 P) O 2 M → O 3 M.

Because Reaction 3-2 is fast (the lifetime of an O( 3 P) atom at 298 K and 760 torr of air is

≈ 10 -5 s), the O 3 concentration at photoequilibrium is given by

(3-11) [O 3 ] J 1 [NO 2 ]/k 3 [NO],

where J 1 and k 3 are the photolysis rate of NO 2 ( ≈ 0.5 min -1 for an overhead sun) and the rate
constant for the reaction of NO with O 3 , respectively. There are other important reactions involving NO

x . The reaction of NO 2 with
O 3 leads to the formation of the nitrate (NO 3 ) radical,

(3-12) NO 2 O 3 → NO 3 O 2 ,

which in the lower troposphere is nearly in equilibrium with dinitrogen pentoxide (N 2 O 5 ):

(3-13, -3-13) NO 3 NO 2 M N 2 O 5 .

3-8However, because the NO 3 radical photolyzes rapidly (with a lifetime of ≈ 5 s for an overhead sun [Atkinson et al., 1992a]),

its concentration remains low during daylight hours, but can increase after sunset to nighttime
concentrations of <5 × 10 7 to 1 × 10 10 molecules cm -3 (<2 to 430 ppt) over continental areas influenced by anthropogenic emissions of NO

x (Atkinson et al., 1986). Nitrate radical
concentrations over marine areas are low because NO

x concentrations are low over lower tropospheric marine areas (Noxon, 1983), and an NO 3 radical mixing ratio of 0.25 ppt has been measured at 3 km altitude in Hawaii (Noxon, 1983). Atkinson (1991) has suggested the use of a 12-h nighttime average NO 3 radical concentration o f 5 × 10 8 molecules cm -3 in the lower troposphere over continental areas, with an uncertainty of a factor of ≈ 10. The tropospheric chemical removal processes for NO

x involve the daytime reaction
of NO 2 with the OH radical and the nighttime wet and dry deposition of N 2 O 5 to produce
HNO 3 .

(3-15) OH NO 2 M → HNO 3

The gas-phase reaction of the OH radical with NO 2 is the major and ultimate removal process
for NO

x in the troposphere. This reaction removes radicals (OH and NO 2 ) and competes with
the reaction of the OH radical with VOCs. Gaseous HNO 3 formed from Reaction 3-15 undergoes wet and dry deposition, including combination with gaseous ammonia (NH 3 ) to form particulate phase ammonium nitrate (N 2 H 4 O 3 ). The tropospheric lifetime of NO

x due to chemical reaction (mainly Reaction 3-15) is ≈ 1 to 2 days. The tropospheric NO

x reactions are shown schematically in Figure 3-1. It should be noted that OH radicals also can react with NO to produce nitrous acid (HNO 2 ):

In urban areas, HNO 2 also can be formed during nighttime hours (Harris et al., 1982; Pitts (3-17) OH NO M → HNO 2 .

et al., 1984a; Rodgers and Davis, 1989), apparently from the heterogeneous hydrolysis of NO 2 or NO

x , or both (Sakamaki et al., 1983; Pitts et al., 1984b; Svensson et al., 1987;

3-9Figure 3-1. The cyclic reactions of tropospheric nitrogen oxides.

Jenkin et al., 1988; Lammel and Perner, 1988; Notholt et al., 1992a,b). The photolysis of
HNO 2 during the early morning hours,

(3-18) HNO 2 h ν → OH NO,

thus can become an important source of OH radicals, leading to the rapid initiation of photochemical activity (Harris et al., 1982). In the troposphere, the initially emitted NO is converted to NO

x (NO + NO 2 ), then to reservoir and termination species (PAN and its homologues, organic nitrates, HNO 3 , and particulate nitrate). These reservoir and termination species are referred to as NO

z . The term
"NO y " refers to the total amount of nitrogen, with NO y = (NO

x + NO

z ). Parrish et al. (1993) have investigated the partitioning between the individual nitrogen-containing species at several rural sites in the eastern United States, and Trainer et al. (1993) and Olszyna et al. (1994) have shown that, in rural areas in the eastern United States, there is a good correlation
between the O 3 levels and NO y . Trainer et al. (1993) further showed that O 3 levels correlate
even better with NO

z than with NO y , as may be expected because NO

z quantifies the amount of initially emitted NO that has been processed photochemically, forming O 3 in the process.

3.2.3.3 The Methane Oxidation Cycle Methane is emitted into the atmosphere from swamps and wetlands, as well as from ruminants (Fung et al., 1991a; World Meteorological Organization, 1992). The major tropospheric removal process for CH 4 is by reaction with the OH radical, with the CH 4 lifetime equal to

(k 21 [OH]) -1 , (3-19)

3-10where k 21 is the rate constant for Reaction 3-21, and [OH] is the (variable) atmospheric
OH radical concentration. The calculated lifetime of CH 4 in the troposphere is ≈ 10 to 12 years. As for other saturated organic compounds, the OH radical reaction with CH 4 proceeds by hydrogen (H)-atom abstraction from the carbon (C)-H bonds to form the methyl (CH 3 ) radical:

(3-20) OH CH 4 → H 2 O  ̇ CH 3 .

In the troposphere, the methyl radical reacts solely with O 2 to yield the methyl peroxy (CH 3 O 2 . ) radical (Atkinson et al., 1992a):

In the troposphere, the methyl peroxy radical can react with NO, NO 2 , HO 2 (3-21)  ̇ CH 3 O 2 M → CH 3 O 2 .

radicals, and other organic peroxy (RO 2 . ) radicals, with the reactions with NO and HO 2 radicals being the most important (see, for example, World Meteorological Organization, 1990b). The reaction with NO leads to the formation of the methoxy (CH 3 ) radical,  ̇ O

(3-22) CH 3 O 2 NO → CH 3  ̇ O NO 2 .

The reaction with the HO 2 radical leads to the formation of methyl hydroperoxide (CH 3 OOH),

(3-23) CH 3 O 2 HO 2 → CH 3 OOH O 2 ,

which can photolyze or react with the OH radical (Atkinson et al., 1992a):

(3-24) CH 3 OOH h ν → CH 3  ̇ O OH,

Methyl hydroperoxide also undergoes wet and dry deposition or incorporation into cloud
water. The lifetime of CH 3 OOH in the troposphere due to photolysis and reaction with the

3-11OH radical is calculated to be ≈ 2 days. Methyl hydroperoxide is then a temporary sink of radicals, with its wet or dry deposition being a tropospheric loss process for radicals. The only important reaction for the methoxy radical in the troposphere is with
O 2 to form HCHO and the HO 2 radical,

(3-26) CH 3  ̇ O O 2 → HCHO HO 2 .

Formaldehyde is a "first-generation" product that reacts further, by photolysis:

where the percentages are for overhead sun conditions (Rogers, 1990) and also by reaction with the OH radical,

(3-28) OH HCHO → H 2 O H  ̇ CO.

In the troposphere, the H atom and H O (formyl) radical produced in these processes react  ̇ C solely with O 2 to form the HO 2 radical:

(3-29) H O 2 M → HO 2 M

(3-30) H  ̇

CO O 2 → HO 2 CO.

The lifetimes of HCHO due to photolysis and OH radical reaction are ≈ 4 h and 1.5 days, respectively, leading to an overall lifetime of ≈ 3 h for overhead sun conditions. The final step in the oxidation of CH 4 in the earth’s atmosphere involves the oxidation of CO by reaction with the OH radical (the only tropospheric reaction of CO) to
form CO 2 :

(3-31) OH CO → H CO 2

(3-29) H O 2 M → HO 2 M.

The lifetime of CO in the lower troposphere is ≈ 2 mo. The overall reaction sequence leading to CO 2 formation, through the HCHO and CO intermediate products, is shown in Figure 3-2.

3-12Figure 3-2. Atmospheric reactions in the complete oxidation of methane.

There is competition between NO and the HO 2 radical for reaction with the CH 3 O 2 . radical, and the reaction route depends on the rate constants for these two reactions and the tropospheric concentrations of HO 2 radicals and NO. The rate constants for the reaction of
the CH 3 O 2 . radicals with NO (Reaction 3-22) and HO 2 radicals (Reaction 3-23) are of comparable magnitude (Atkinson et al., 1992a). Based on the expected HO 2 radical concentration in the troposphere, Logan et al. (1981) calculated that the reaction of the CH 3 O 2 . radical with NO dominates for NO mixing ratios of >30 ppt (equivalent to an NO
concentration of >7 × 10 8 molecules cm -3 in the lower troposphere). For NO mixing ratios <30 ppt, the reaction of the CH 3 O 2 . radical with HO 2 dominates. Hydroperoxy radicals formed from, for example, Reactions 3-26, 3-29, and 3-30 can react with NO, O 3 , or themselves, depending mainly on the concentration of NO. The reaction with NO leads to regeneration of the OH radical,

(3-32) HO 2 NO → OH NO 2 ,

whereas the reactions with O 3 and HO 2 radicals lead to a net destruction of tropospheric O 3 :

(3-33) HO 2 HO 2 → H 2 O 2 O 2

(3-34) HO 2 O 3 → OH 2 O 2 .

3-13This net loss of tropospheric O 3 occurs because the photolytic production of the OH radical
from O 3 , via the intermediary of the O( 1 D) atom, represents a loss process for tropospheric
O 3 . Hence, the absence of any O 3 formation from the CH 4 oxidation cycle is equivalent to a
net O 3 loss. Using the rate constants reported for Reactions 3-32 and 3-34 (Atkinson et al., 1992a) and the tropospheric O 3 mixing ratios given above, it is calculated that the HO 2 radical
reaction with NO dominates over reaction with O 3 for NO mixing ratios >10 ppt. The rate constant for Reaction 3-33 is such that an NO mixing ratio of this magnitude also means that
the HO 2 radical reaction with NO dominates over the self-reaction of HO 2 radicals. There are therefore two regimes, depending on the fate of HO 2 and CH 3 O 2 radicals: (1) a high-NO regime in which HO 2 and CH 3 O 2 radicals react with NO to convert NO to
NO 2 , regenerate the OH radical, and, through the photolysis of NO 2 , produce O 3 ; and (2) a low-NO regime in which HO 2 and CH 3 O 2 radicals combine (Reaction 3-23), and HO 2 radicals undergo self-reaction and react with O 3 (Reactions 3-33 and 3-34), leading to a net
destruction of O 3 and inefficient OH radical regeneration (see also Ehhalt et al., 1991; Ayers et al., 1992). Under high-NO conditions, the oxidation of CH 4 leading to the formation of HCHO can be written as the net reaction,

(3-35) OH CH 4 2 NO 2 O 2 H 2 O HCHO 2 NO 2 OH,

indicating the conversion of two molecules of NO to NO 2 and regeneration of the OH radical.
Because NO 2 photolyzes to form O 3 in the presence of O 2 ,

the oxidation of CH 4 to HCHO under high-NO conditions can be written as

(3-36) OH CH 4 4 O 2 H 2 O HCHO 2 O 3 OH,

showing the formation of O 3 from CH 4 oxidation in the troposphere. The reaction cycles oxidizing CH 4 to HCHO, converting NO to NO 2 , and forming O 3 are shown schematically in Figure 3-3. In a similar manner, under high-NO conditions, the photolysis of HCHO and its reaction with the OH radical is given approximately by

(3-37) 0.2 OH HCHO 0.92 NO O 2 → CO 0.44 H 2 0.2 H 2 O 0.92 NO 2 0.92 OH.

3-14Figure 3-3. Cyclic reactions of methane oxidation to formaldehyde, conversion of nitric oxide to nitrogen dioxide, and concomitant formation of ozone in the atmosphere.

Formaldehyde photooxidation is thus a source of HO 2 radicals (and of OH radicals in highNO conditions) (Ehhalt et al., 1991), especially in urban areas where the concentration of HCHO is elevated because it is produced during the oxidation of anthropogenic nonmethane VOCs (Finlayson-Pitts and Pitts, 1986). Nitric oxide mixing ratios are sufficiently low in the lower troposphere over
marine areas that oxidation of CH 4 will lead to a net destruction of O 3 (low-NO conditions), as discussed by Carroll et al. (1990) and Ayers et al. (1992). However, in the upper troposphere and over continental areas impacted by NO

x emissions from combustion sources, NO mixing ratios are high enough (high NO-conditions) for CH 4 oxidation to lead to net
O 3 formation (Carroll et al., 1990; World Meteorological Organization, 1992).

3.2.3.4 Cloud Processes in the Methane-Dominated Troposphere In addition to the dry and wet deposition of certain products of the NO

x -CH 4 -air photooxidation (e.g., wet and dry deposition of HNO 3 and CH 3 OOH [Atkinson, 1988, and references therein; Hellpointner and Gäb, 1989]), cloud processes can have significant effects on the gas-phase chemistry of the clean troposphere (Lelieveld and Crutzen, 1990, 1991; Warneck, 1991, 1992). Lelieveld and Crutzen (1990, 1991) have postulated from modeling studies that the uptake of HCHO, HO 2 radicals, and N 2 O 5 into clouds can decrease markedly the production of O 3 . The incorporation of HCHO into cloudwater removes HCHO from the gas phase and, hence, reduces the gas-phase formation of HO 2 . The uptake of HO 2 radicals into cloudwater has the same effect. Moreover, the aqueous-phase reactions of CH 2 (OH) 2 (the hydrated form of HCHO) lead to the formation of O 2 - , which reacts with dissolved O 3 to act as a sink for O 3 during cloudy periods. During nighttime, N 2 O 5 formed in the gas phase from Reactions 3-3, 3-12, and 3-13 can be readily incorporated into cloudwater with hydrolysis to
HNO 3 , precluding the reformation of NO

x during the following day from Reactions -3-13 and 3-14. Dentener and Crutzen (1993) have also concluded from a computer modeling study that

3-15the heterogeneous reactions of NO 3 radicals and N 2 O 5 on aerosols can have significant effects on global O 3 mixing ratios and on OH concentrations by reducing NO

x levels. The net effect of these cloud processes is to reduce the gas-phase concentrations of HCHO, NO

x , HO

x , and O 3 . Additional, but related, processes can occur in the polluted troposphere (see, for example, Jacob et al., 1989; Dentener and Crutzen, 1993; Section 3.2.5).



### 3.2.4 Photochemistry of the Polluted Atmosphere

Human activities lead to the emissions of NO

x (NO + NO 2 ) and both CH 4 - and nonmethane organic compounds (NMOC) into the atmosphere (Table 3-1). Methane emissions are important on a global scale (World Meteorological Organization, 1992), whereas nonmethane VOC emissions are most important in urban and regional areas. In addition to the emissions of nonmethane VOCs from anthropogenic sources, large quantities of biogenic nonmethane VOCs (mainly of isoprene [2-methyl-1, 3-butadiene] and monoterpenes, [C 10 H 16 ]) are emitted, both in polluted and nonpolluted areas, into the atmosphere from vegetation (see, for example, Isidorov et al., 1985; Lamb et al., 1987; Arey et al., 1991a,b).

Table 3-1. Estimated Emissions of Methane, Nonmethane Organic Compounds, Nitrous Oxide, and Nitrogen Oxides into the Earth’s Atmosphere from Biogenic and Anthropogenic Sources a
Emissions (Tg/year b )

Chemical Biogenic Sources Anthropogenic Sources

CH 4 c ≈ 150 ≈ 350

NMOC d
≈ 1,000 ≈ 100

N 2 O (as N) e ≈ 7 ≈ 6

NO

x (as N) f
≈ 10 ≈ 40

a See Appendix A for abbreviations and acronyms.

b Teragram = 10 12 g, or ≈ 10 6 metric tons.

c Fung et al. (1991a); World Meteorological Organization (1992). Emissions from ruminants, rice paddies, and biomass burning are considered as anthropogenic emissions.

d Logan et al. (1981); World Meteorological Organization (1992), with biogenic emissions being assumed to be 50% isoprene and 50% monoterpenes.

e Prinn et al. (1990).

f National Research Council (1991); World Meteorological Organization (1992); biogenic sources ≈ 50% from
soils and ≈ 50% from lightning.

Analogous to the photooxidation of CH 4 , the interaction of NO

x with nonmethane VOCs from anthropogenic and biogenic sources under the influence of sunlight leads to the formation of photochemical air pollution (National Research Council, 1991). In urban areas,
emissions of NO

x and VOCs from human activities (combustion sources, including transportation; industrial sources; solvent usage; landfills; etc.) dominate over biogenic sources (National Research Council, 1991; Chameides et al., 1992). However, the emissions

3-16of VOCs from vegetation have been implicated in the formation of photochemical air pollution in urban (Chameides et al., 1988; 1992) as well as rural areas (Trainer et al., 1987; Roselle et al., 1991; Chameides et al., 1992). In essence, the chemistry of the polluted urban and regional atmosphere is an extension of that of the clean, CH 4 -dominated troposphere, with a number of additional complexities due to the number and types of VOCs emitted from anthropogenic and biogenic sources. At least in certain urban areas, the NMOC content of ambient air is similar to the composition of typical gasolines (Mayrsohn and Crabtree, 1976; Mayrsohn et al., 1977; Harley et al., 1992; see Section 3.4.3). For example, gasolines typically consist of ≈ 55 to 65% alkanes, ≈ 5 to 10% alkenes, and ≈ 25 to 35% aromatic hydrocarbons (Lonneman et al., 1986; Sigsby et al., 1987), whereas in Los Angeles, CA, the ambient urban air composition is

≈ 50 to 55% alkanes, ≈ 5 to 15% alkenes, ≈ 25 to 30% aromatic hydrocarbons, and ≈ 5 to 15% carbonyls (Grosjean and Fung, 1984; California Air Resources Board, 1992). Emissions of
NO

x and VOCs are dealt with in detail in Section 3.4.1.

3.2.4.1 Tropospheric Loss Processes of Volatile Organic Compounds The chemical loss processes of gas-phase VOCs include photolysis and chemical reaction with the OH radical during daylight hours, reaction with the NO 3 radical during nighttime hours, and reaction with O 3 , which often is present throughout the 24-h period (Atkinson, 1988). As discussed earlier, photolysis of chemical compounds in the troposphere is restricted to the wavelength region above ≈ 290 nm. Because of the strength of chemical bonds, the tropospheric wavelength region in which photolysis can occur extends from

≈ 290 to 800 nm, and this wavelength region often is referred to as the "actinic" region. For photolysis to occur, a chemical compound must be able to absorb radiation in the actinic region (and hence have a nonzero absorption cross-section, σ λ , in this wavelength region). Having absorbed radiation, a chemical compound must then undergo chemical change (i.e., have a nonzero quantum yield, φ λ , for photodissociation or photoisomerization). The quantum yield, φ λ , is defined as (number of molecules of the chemical undergoing change)/(number of photons of light absorbed). The photolysis rate, k photolysis , for the process,

(3-38) C h ν → products

is given by

(3-39) k photolysis ⌡ ⌠ J λ σ λ φ λ d λ

,

where J λ is the radiation flux at wavelength λ , and σ λ and φ λ are the absorption cross-section and photolysis quantum yield, respectively, at wavelength λ . Photolysis is therefore a pseudofirst-order process (depending on the radiation flux and spectral distribution) and the lifetime of a chemical with respect to photolysis is given by

(3-40) τ photolysis k photolysis 1

.

3-17For the reaction of a VOC with a reactive species, X (for tropospheric purposes, X = OH, NO 3 , and O 3 ), the lifetime for the reaction process ,C+X → products, is given by

(3-41) τ X (k X [X]) 1

and depends on the concentration of the reactive species X and the rate constant (k X ) for reaction of the VOC with X. In general, the ambient atmospheric concentrations of OH radicals, NO 3 radicals, and O 3 are variable, depending on time of day, season, latitude, altitude, etc. For the purpose of comparing lifetime calculations for various classes of VOCs, average ambient tropospheric concentrations of these three species often are used. The concentrations used here have been presented in the sections above and are OH radicals, a 12- h average daytime concentration of 1.6 × 10 6 molecule cm -3 (equivalent to a 24-h average
concentration o f 8 × 10 5 molecule cm -3 ) (Prinn et al., 1992); NO 3 radicals, a 12-h nighttime average concentration o f 5 × 10 8 molecule cm -3 (Atkinson, 1991); and O 3 , a 24-h average of
7 × 10 11 molecule cm -3 (30 ppb) (Logan, 1985). The major classes of VOCs are the alkanes, alkenes (including alkenes from biogenic sources), aromatic hydrocarbons, carbonyl compounds, alcohols, and ethers (see California Air Resources Board, 1992). The calculated lifetimes with respect to the individual atmospheric loss processes of compounds representing a range of reactivities in each class are given in Table 3-2. Note that the lifetimes given are dependent on the reaction rate constants and the assumed ambient concentrations of OH radicals, NO 3 radicals, and O 3 . Uncertainties in the ambient concentrations of the reactive species translate directly into corresponding
uncertainties in the lifetimes. The following brief discussions of the tropospheric chemistry of the important classes of VOCs are based on the recent review and evaluation article of Atkinson (1994), which should be consulted for further details of the tropospheric reactions of VOCs.

Alkanes Because gasoline and diesel fuels contain alkanes of carbon number C 4 to ≥ C 15 , a large number of alkanes are present in ambient air (see, for example, Grosjean and Fung, 1984; California Air Resources Board, 1992; Section 3.4). Table 3-2 shows that the only important tropospheric loss process for the alkanes is by reaction with the OH radical, with
calculated lifetimes of the C 3 to C 10 alkanes ranging from ≈ 1 to 15 days. As for methane, the OH radical reaction proceeds by H-atom abstraction from the various C-H bonds. The nighttime reactions of the NO 3 radical with alkanes (calculated to be generally of minor importance, but see Penkett et al. [1993]) also proceed by initial H-atom abstraction. For an alkane (RH), the initially formed radical is an alkyl radical (R),

(3-42) OH RH → H 2 O  ̇ R,

3-18Table 3-2. Calculated Tropospheric Lifetimes of Selected Volatile Nonmethane Organic Compounds Due to Photolysis and Reaction with Hydroxyl and Nitrate Radicals and with Ozone a

Lifetime Due to Reaction with
Organic OH NO 3 O 3 h v

n -Butane 5.7 days 2.8 years >4,500 years
2-Methylbutane 3.7 days 290 days >4,500 years

n -Octane 1.7 days 250 days >4,500 years
Ethane 1.7 days 230 days 10 days
Propene 6.6 h 4.9 days 1.6 days
Isoprene 1.7 h 0.8 h 1.3 days

Limonene 1.0 h 3 min 2.0 h
Benzene 12 days >4 years >4.5 years
Toluene 2.4 days 1.9 years >4.5 years

m -Xylene 7.4 h 200 days >4.5 years
Formaldehyde 1.5 days 80 days >4.5 years 4 h
Acetaldehyde 11 h 17 days >4.5 years 6 days
Acetone 66 days — >4.5 years 60 days
2-Butanone 13 days — >4.5 years
Methanol 15 days >77 days —
Ethanol 4.4 days >50 days —
Methyl t -butyl ether 4.9 days — —
Ethyl t -butyl ether 1.6 days — —
Methylglyoxal 10 h

— >4.5 years 2 h

a See Appendix A for abbreviations and acronyms.

Sources: Lifetimes resulting from reaction with OH, NO 3 , and O 3 were calculated using rate constants given in Atkinson and Carter (1984) and Atkinson (1989, 1991, 1994); data for photolysis lifetimes are from Horowitz and Calvert (1982), Meyrahn et al. (1982, 1986), Plum et al. (1983), and Rogers (1990). The OH radical, NO 3 radical, and O 3 concentrations used (molecule cm -3 were: OH, 12-h average of 1.6 × 10 6 ; NO 3 , 12-h average of 5
× 10 8 ; O 3 , 24-h average o f 7 × 10 11

.

which rapidly adds O 2 to form an alkyl peroxy radical (RO 2 . ),

(3-43)  ̇ R O 2 M → RO 2 ,

3-19with the simplest of the RO 2 . radicals being the methylperoxy radical, described in Section 3.2.3.3 dealing with methane oxidation. Alkyl peroxy radicals can react with NO,
NO 2 , and HO 2 radicals, and other organic peroxy radicals (R ′ O 2 . ):

(3-44a) RO 2 NO → R  ̇ O NO 2

(3-45) RO 2 NO 2 ROONO 2

(3-46) RO 2 HO 2 → ROOH O 2

(3-47a) RO 2 R ′ O 2 → R  ̇ O R ′  ̇ O O 2

(3-47b) RO 2 R ′ O 2 → α carbonyl (1 α ) ROH O 2 products of R ′ O 2 .

The reactions with organic peroxy radicals are expected to be of less importance in the troposphere than the other reactions listed. Because low NO conditions occur even in air masses in urban areas, the HO 2 radical reactions with RO 2 radicals and the subsequent chemistry must be considered. However, because of space constraints and a general lack of knowledge concerning the tropospheric chemistry of RO 2 radicals under low-NO conditions, only the reactions occurring under high-NO conditions are presented and discussed here. For
the ≥ C 3 alkyl peroxy radicals, in addition to the reaction pathway leading to NO-to-NO 2 conversion (Reaction 3-44a), a second reaction pathway leading to formation of an alkyl nitrate becomes important:

For a given alkyl peroxy radical, the alkyl nitrate yield increases with increasing pressure and (3-44b) RO 2 NO M → RONO 2 .

with decreasing temperature (Carter and Atkinson, 1989a). Analogous to the case for the methoxy radical, those alkoxy radicals (RO 2 . ) formed from the higher alkanes that have an abstractable H atom can react with O 2 to form the HO 2 radical and a carbonyl; for example,

(3-48) (CH 3 ) 2 CH  ̇ O O 2 → CH 3 C(O)CH 3 HO 2 .

In addition, unimolecular decomposition by C-C bond scission and unimolecular isomerization via a six-member transition state (Atkinson and Carter, 1991; Atkinson, 1994) can be important for the larger alkoxy radicals. For example, the following chemistry can occur for the 1-pentoxy radical:

3-20with the alkyl radicals C 4 H 9 . and HOCH 2 CH 2 CH 2 HCH 3 undergoing further reaction.  ̇ C The majority of the reaction rate constants and reaction pathways in the alkane degradation schemes are arrived at by analogy from the chemistry of C 1 -C 3 alkyl, alkyl peroxy, and alkoxy radicals (Atkinson, 1990, 1994; Carter, 1990; Atkinson et al., 1992a). A number of areas of uncertainty still exist for the tropospheric chemistry of the alkanes (Atkinson, 1993). These include the relative importance of alkoxy radical reaction with O 2 , decomposition and isomerization, and the reactions occurring subsequent to the isomerization reaction; the formation of alkyl nitrates from the reactions of the peroxy radicals with NO; and reactions of the alkyl peroxy radicals with HO 2 and other peroxy radicals, reactions that can be important in the nonurban troposphere.

Alkenes (Anthropogenic and Biogenic) The alkenes emitted from anthropogenic sources are mainly ethene, propene, and the butenes, with lesser amounts of the ≥ C 5 alkenes. The major biogenic alkenes emitted from vegetation are isoprene (2-methyl-1,3-butadiene) and C 10 H 16 monoterpenes (Isidorov et al., 1985; Winer et al., 1992), and their tropospheric chemistry is currently the focus of much attention (see, for example, Hatakeyama et al., 1989, 1991; Arey et al., 1990; Tuazon and Atkinson, 1990a; Pandis et al., 1991; Paulson et al., 1992a,b; Paulson and Seinfeld, 1992a; Zhang et al., 1992; Hakola et al., 1993, 1994). As evident from Table 3-2, the alkenes react with OH and NO 3 radicals and O 3 . All three processes are important atmospheric transformation processes, and all three reactions proceed by initial addition to the >C=C< bonds. These reactions are briefly discussed below. Hydroxyl Radical Reactions. As noted above, the OH radical reactions with the alkenes proceed mainly by OH radical addition to the >C=C< bonds. For example, the OH radical reaction with propene leads to the formation of the two OH-containing radicals,

The subsequent reactions of these radicals are similar to those of the alkyl radicals formed by (3-49) OH CH 3 CH CH 2 M → CH 3 CH(OH)  ̇ CH 2 CH 3  ̇ CHCH 2 OH.

H-atom abstraction from the alkanes. Taking the CH 3 HCH 2 OH radical as an example,  ̇ C under high-NO conditions, the following chemistry occurs:

3-21The underlined species represent products that, although stable, can undergo further reaction; and, hence, they can lead to "second-generation" products. For the simple ≤ C 4 alkenes, the intermediate OH-containing radicals appear to undergo mainly decomposition at room temperature and atmospheric pressure of air. Hence, for propene, the "first-generation" products of the OH radical reaction in the presence of NO are HCHO and CH 3 CHO, irrespective of which OH-containing radical is formed. However, this is not the case for the more complex alkenes of biogenic origin. The product studies of Tuazon and Atkinson (1990a) and Paulson et al. (1992a) for the OH radical reaction with isoprene in the presence of NO

x show that the products expected from reaction schemes analogous to that shown above for propene (i.e., HCHO + methyl vinyl ketone [CH 3 C(O)CH=CH 2 ] and HCHO + methacrolein [CH 2 =C(CH 3 )CHO], arising from
initial OH radical addition to the CH 2 =CH- and CH 2 =C< bonds, respectively) do not account for the entire reaction pathways. The product yields obtained from the studies of Tuazon and Atkinson (1990a) and Paulson et al. (1992a) are methyl vinyl ketone, 34%; methacrolein, 24%; 3-methylfuran, 5%; organic nitrates, ≈ 12%; and unidentified carbonyl compounds, ≈ 25% (Tuazon and Atkinson, 1990a; Paulson et al., 1992a; Atkinson, 1994). The HCHO yield was consistent with being a co-product formed with methyl vinyl ketone and methacrolein (Tuazon and Atkinson, 1990a). Aerosol formation for isoprene photooxidation has been shown to be negligible under atmospheric conditions (Pandis et al., 1991; Zhang et al., 1992). To date, few quantitative product studies have been carried out for the monoterpenes (Arey et al., 1990; Hatakeyama et al., 1991; Hakola et al., 1993, 1994). Arey et al. (1990) and Hakola et al. (1993, 1994) have observed the C 7 -C 10 carbonyl compounds, as had been based on the analogous reaction scheme shown above for propene, but with total carbonyl formation yields of ≤ 50%. These data (Arey et al., 1990; Hakola et al., 1993, 1994) indicate the formation of other products in significant, and often dominant, yields. Hatakeyama et al. (1991) used Fourier transform infrared (FTIR) absorption spectroscopy and reported carbonyl compounds to be formed in high yield from α -pinene and β -pinene, in apparent disagreement with the data of Arey et al. (1990) and Hakola et al. (1994). Although Hatakeyama et al. (1991) ascribed these carbonyl products to those expected from oxidative

3-22cleavage of the >C=C< bonds, it is possible that the yields reported for these carbonyls included contributions by other, as yet unidentified, carbonyl-containing products. Nitrate Radical Reactions. The NO 3 radical reactions proceed by reaction schemes generally similar to the OH radical reactions, except that, when NO 3 radicals are present, NO concentrations are low (see above) and RO 2 . + RO 2 . and RO 2 . + HO 2 radical reactions are expected to dominate over RO 2 . + NO reactions. For propene, the initial reaction is

(3-50) NO 3 CH 3 CH CH 2 → CH 3  ̇ CHCH 2 ONO 2 and CH 3 CH(ONO 2 )  ̇ CH 2 ,

followed by a series of reactions that are expected (Atkinson, 1991) to lead to the formation of, among others, carbonyls and nitrato-carbonyls (for example, HCHO, CH 3 CHO,
CH 3 CH(ONO 2 )CHO, and CH 3 C(O)CH 2 ONO 2 from propene). Few data are presently available concerning the products and detailed mechanisms of NO 3 -alkene reactions (Atkinson, 1991, 1994, and references therein). In particular, the reaction products and mechanisms for the
NO 3 radical reactions with isoprene and the monoterpenes are still not quantitatively understood (Kotzias et al., 1989; Barnes et al., 1990; Hjorth et al., 1990; Skov et al., 1992). Ozone Reactions. The O 3 reactions also proceed by addition of O 3 to the alkene, to form an energy-rich ozonide that rapidly decomposes to form carbonyls and energy-rich biradicals ([ ] * ):

The energy-rich biradicals, [ H 2 O ] * and [CH 3 HO ] * , undergo collisional stabilization or  ̇ C  ̇ O  ̇ C  ̇ O decomposition:

3-23There are still significant uncertainties concerning the reactions of the energy-rich biradicals (Horie and Moortgat, 1991; Atkinson, 1990, 1994), with recent studies showing the production of OH radicals in high yields for several alkenes (Niki et al., 1987; Paulson et al., 1992a; Atkinson et al., 1992b; Paulson and Seinfeld, 1992b; Atkinson and Aschmann, 1993). For isoprene, the major products are methacrolein and methyl vinyl ketone (Kamens et al., 1982; Niki et al., 1983; Paulson et al., 1992b). Paulson et al. (1992b) derived an OH radical and an O( 3 P) atom formation yield of 0.68 ± 0.15 and 0.45 ± 0.2, respectively,
from the O 3 reaction with isoprene, indicating the dominance of secondary reactions. However, Atkinson et al. (1992b) derived a significantly lower OH radical formation yield of 0.27 (uncertain to a factor of ≈ 1.5). Clearly, further studies of this important reaction are
needed. The only quantitative studies of the gas-phase O 3 reactions with the monoterpenes are those of Hatakeyama et al. (1989) for α - and β -pinene and Hakola et al. (1993, 1994) for a series of monoterpenes. Additionally, Atkinson et al. (1992b) derived OH radical formation yields from these reactions under atmospheric conditions. Several groups (Gäb et al., 1985; Becker et al., 1990, 1993; Simonaitis et al., 1991; Hewitt and Kok, 1991) have reported the formation of H 2 O 2 and organic peroxides from
O 3 reactions with alkenes. However, there are significant disagreements in the quantitative results reported by Becker et al. (1990, 1993) and Simonaitis et al. (1991).

Aromatic Hydrocarbons The chemistry of aromatic hydrocarbons is one of the major sources of uncertainty in the atmospheric chemistry of VOCs (National Research Council, 1991; Atkinson, 1994). The most abundant aromatic hydrocarbons in urban atmospheres are benzene, toluene, the xylenes, and the trimethylbenzenes (Grosjean and Fung, 1984; California Air Resources Board, 1992). As shown in Table 3-2, the only tropospherically important loss process forbenzene and the alkyl-substituted benzenes is by reaction with the OH radical. For the alkylsubstituted benzenes, the OH radical reactions proceed by two pathways: (1) H-atom abstraction from the C-H bonds of the alkyl substituent groups and (2) OH radical addition to the aromatic ring, as shown below, for p -xylene:

with the OH radical addition pathway being reversible above ≈ 325 K (Atkinson, 1989).

3-24The radical formed in Reaction 3-52a reacts analogously to an alkyl radical (Atkinson, 1994), leading, in the presence of NO, to aromatic aldehydes and organic nitrates.

The OH-containing radical formed in Reaction 3-52b can undergo reaction with both NO 2 and
O 2 . Knispel et al. (1990) reported rate constants for the reactions of NO 2 and O 2 with the OH-containing radicals formed from benzene and toluene. The magnitude of the rate constants they obtained implies that, in the troposphere, the major reactions of these radicals
will be with O 2 . Laboratory studies of the formation of selected products from the gas-phase reactions of the OH radical with toluene, o -xylene, and 1,2,3-trimethylbenzene (Atkinson and Aschmann, 1994), and, in particular, of the formation of the ring-cleavage product 2,3-butanedione from o -xylene, are consistent with the kinetic data of Knispel et al. (1990). Thus, at least for the monocyclic aromatic hydrocarbons such as benzene, toluene, and the xylenes, the OH-aromatic adducts formed in Reaction 3-52b react with O 2 under atmospheric conditions. However, care must be taken in using product yields obtained in the laboratory at higher than ambient NO 2 concentrations because data may be influenced by the NO 2 reaction and, hence, may not be applicable to the O 2 reaction with the OH-aromatic adduct. Clearly, the products formed and their yields from the O 2 and NO 2 reactions with the OH-aromatic adducts need to be determined, and the detailed reaction mechanisms elucidated. Despite these uncertainties, however, products from the OH radical addition pathway have been identified, and their formation yields determined (Atkinson, 1994, and references therein). The major products identified from the OH radical addition pathway are phenolic compounds (e.g., phenol from benzene and o- , m- , and p- cresol from toluene) and

α -dicarbonyls (glyoxal, methylglyoxal, and 2,3-butanedione) resulting from the cleavage of the aromatic ring (see, for example, Atkinson, 1990, 1994, and references therein). Significant fractions ( ≥ 50% for benzene, toluene, and the xylenes) of the reaction products are, however, still not accounted for.

3-25Carbonyl Compounds As noted above, the OH radical reactions with the alkanes, alkenes, and aromatic hydrocarbons lead, often in large yield, to the formation of carbonyl compounds. Likewise, carbonyls are formed during the reactions of NO 3 radicals and O 3 with alkenes. As a first approximation, the carbonyl compounds of tropospheric interest are HCHO (see Section 3.2.3.3), acetaldehyde, and the higher aliphatic aldehydes; benzaldehyde; acetone, 2-butanone, and the higher ketones; and simple dicarbonyls such as glyoxal, methylglyoxal, and 2,3-butanedione. The tropospheric photooxidation of isoprene leads to the formation of methyl vinyl ketone (CH 3 C(O)CH=CH 2 ) and methacrolein (CH 3 C(CHO)=CH 2 ). The OH radical-initiated reactions of these two carbonyl compounds in the presence of NO

x have been studied by Tuazon and Atkinson (1989, 1990b). The tropospherically important loss processes of the carbonyls not containing >C=C< bonds are photolysis and reaction with the OH radical. As shown in Tables 3-2, photolysis is a major tropospheric loss process for the simplest aldehyde (HCHO) and the simplest ketone (CH 3 C(O)CH 3 ), as well as for the dicarbonyls. For the higher aldehydes and ketones, the OH radical reactions are calculated to be the dominant gas-phase loss process (Table 3-2). For CH 3 CHO, the reaction proceeds by H-atom abstraction from the -CHO group to form the acetyl (CH 3 C . O) radical,

(3-53) OH CH 3 CHO → H 2 O CH 3  ̇ CO,

which rapidly adds O 2 to form the acetyl peroxy radical:

This O 2 addition pathway is in contrast to the reaction of O 2 with the formyl (H O) radical (3-54) CH 3  ̇ CO O 2 M → CH 3 C(O)O  ̇ O.

 ̇ C formed from HCHO, which reacts by an H-atom abstraction pathway (Reaction 3-30). The acetyl peroxy radical reacts with NO and NO 2 ,

with the NO 2 reaction forming the thermally unstable PAN. The higher aldehydes also lead (3-56, -3-56) CH 3 C(O)O  ̇ O NO 2 M CH 3 C(O)OONO 2 ,

to PANs (Roberts, 1990); for example, propionaldehyde reactions lead to the formation of

3-26peroxypropionyl nitrate (PPN). Although the rate constant at atmospheric pressure for the thermal decomposition of PAN (Atkinson et al., 1992a) is such that the lifetime of PAN, with respect to thermal decomposition, is ≈ 30 min at 298 K in the lower troposphere, the thermal lifetime of PAN is calculated to be several hundred years in the upper troposphere. Reaction with OH radicals or photolysis, or both, therefore will dominate as the loss processes of PANs in the upper troposphere (Atkinson et al., 1992a). The transport of PAN out of urban areas into colder air masses (e.g., to higher altitude) leads to PAN becoming a temporary reservoir of NO

x , allowing the long-range transport of NO

x to less polluted areas. Release of NO 2 in these less polluted areas via Reaction -3-56, with subsequent photolysis of NO 2 , then leads to O 3 formation and the pollution of remote areas.
Because the H 3 radical formed from the NO reaction with CH 3 C(O)O leads to  ̇ C  ̇ O HCHO formation, the OH radical reaction with CH 3 CHO subsequently leads to the formation of HCHO. The same process occurs for propionaldehyde, which reacts to form CH 3 CHO and then HCHO. Benzaldehyde appears to behave as a phenyl-substituted aldehyde, with respect to its OH radical reaction, and the analog to PAN is then peroxybenzoyl nitrate, PBzN (C 6 H 5 C(O)OONO 2 ).
The formation of HCHO from CH 3 CHO and of CH 3 CHO and then HCHO from propionaldehyde are examples of "cascading", in which the photochemical degradation of emitted VOCs leads to the formation of further VOCs, typically containing fewer carbon atoms than the precursor VOC. This process continues until the degradation products are removed by wet and dry deposition or until CO or CO 2 are the degradation products. The reactions of each of these VOCs (i.e., the initially emitted VOC and its first-, second-, and successive-generation products), in the presence of high concentrations of NO, can lead to the
formation of O 3 . As discussed in Section 3.2.3.3 for HCHO, the photolysis of carbonyl compounds can lead to the formation of new radicals that result in enhanced photochemical activity. The OH radical reactions of the ketones are generally analogous to the reaction schemes for the alkanes and aldehydes.

Alcohols and Ethers A number of alcohols and ethers are used in gasolines and in alternative fuels. The alcohols include methanol, ethanol, and tert -butyl alcohol, and the ethers include methyl

tert -butyl ether (MTBE) and ethyl tert -butyl ether (ETBE). Table 3-2 shows that in the troposphere these VOCs react only with the OH radical. The relatively long calculated lifetime of methanol in the troposphere (15 days), due to reaction with the OH radical (Table 3-2), suggests that methanol also will be removed from the troposphere by wet and dry deposition and that these physical loss processes may dominate the OH radical reactions proceded by H-atom abstraction from the C-H bonds (and to a minor extent from the O-H bonds in the alcohols), for example, for methanol,

(15%) (3-57a) OH CH 3 OH → H 2 O CH 3 O

(85%) (3-57b) OH CH 3 OH → H 2 O CH 2 OH.

3-27In the troposphere, both of the CH 3 O and CH 2 OH radicals react only with O 2 to form HCHO.

(3-58) CH 3 O O 2 → HCHO HO 2

(3-59) CH 2 OH O 2 → HCHO HO 2

The overall reaction is then

(3-60) OH CH 3 OH O 2 → H 2 O HCHO HO 2 .

The reaction sequence for ethanol is similar (Atkinson, 1994). Product studies of the OH radical-initiated reactions of MTBE and ETBE in the presence of NO

x have been carried out by Japer et al. (1990), Smith et al. (1991a, 1992), Tuazon et al. (1991), and Wallington and Japar (1991). The major products from MTBE are tert -butyl formate, HCHO, and methyl acetate [CH 3 C(O)OCH 3 ] and, from ETBE, tert -butyl formate, tert -butyl acetate, HCHO,
CH 3 CHO, and ethyl acetate. The available product data and the reaction mechanisms have been reviewed by Atkinson (1994), and that reference should be consulted for further details. In addition to the use of alcohols and ethers in gasolines and alternative fuels, unsaturated alcohols have been reported as emissions from vegetation (Arey et al., 1991a; Goldan et al., 1993), and kinetic and product studies have begun to be reported for these biogenic VOCs (Grosjean et al., 1993a).

Primary Products and Areas of Uncertainty for the Tropospheric Degradation Reactions of Volatile Organic Compounds The tropospheric degradation reactions of the alkanes, alkenes (including those of biogenic origin), aromatic hydrocarbons, carbonyls (often formed as products of the degradation reactions of alkanes, alkenes, and aromatic hydrocarbons), and other oxygenates have been briefly discussed above. A more lengthy and detailed discussion of theatmospheric chemistry of alkanes, alkenes, aromatic hydrocarbons, and oxygen- and nitrogencontaining organic compounds emitted into the atmosphere from anthropogenic and biogenic sources and of their atmospheric transformation products is given in the review and evaluation of Atkinson (1994), which also provides an assessment of the uncertainties in the product yields and the reaction rate constants. The first-generation products of the alkanes, alkenes, and aromatic hydrocarbons follow (unfortunately, complete product distributions have not been obtained for most of the VOCs studied).

Alkanes. · Carbonyl compounds (i.e., aldehydes and ketones) are formed as major products for the smaller ( ≤ C 4 ) alkanes. · Alkyl nitrates are formed from the ≥ C 3 alkanes studied to date. The yields increase with
the size of the alkane from ≈ 4% for propane to ≈ 30% for n -octane.

· δ -Hydroxycarbonyls are expected to be formed after the alkoxy radical isomerization reaction. To date, no direct evidence for the formation of these compounds exists. For the larger alkanes, the formation yields of these compounds could be high.

3-28· Alkyl hydroperoxides are formed under low-NO conditions. · Alkyl peroxynitrates (ROONO 2 ) are formed but have short lifetimes (a few seconds at 298 K) due to thermal decomposition. · Alcohols are formed from the combination reactions of the peroxy radicals under low-NO experimental conditions. These compounds are expected to be formed in low concentrations in the troposphere. The major uncertainties in the atmospheric chemistry of the alkanes concern the
formation of RONO 2 from the reactions of the peroxy radicals with NO (Reaction 3-44b) and the reactions of the alkoxy radicals in the troposphere. These uncertainties affect the amount
of NO to NO 2 conversion occurring and, hence, the amounts of O 3 that are formed during the
NO

x -air photooxidations of the alkanes.

Alkenes. · Carbonyl compounds (aldehydes and ketones) are formed as major products of the OH radical, NO 3 radical, and O 3 reactions. · Organic acids are formed from the O 3 reactions, but probably in low yields. · Hydroxynitrates and nitratocarbonyls are formed from the OH radical reactions and NO 3 radical reactions, respectively. The hydroxynitrates are formed in low yields from the OH radical reactions, whereas the nitratocarbonyls may be major products of the NO 3 radical
reactions. · Hydroxycarbonyls and carbonyl-acids are also expected to be formed, although few, if any,
data exist to date. · Decomposition products are produced from the initially energy-rich biradicals formed in
the O 3 reactions; these include CO, CO 2 , esters, hydroperoxides, and, in the presence of
NO

x , peroxyacyl nitrates (RC(O)OONO 2 and PANs). The major areas of uncertainty concern the products and mechanisms of the
O 3 reactions (in particular, the radical yields from these reactions that affect the O 3 formation yields from the NO

x -air photooxidations of the alkenes) and the reaction products and mechanisms of the OH radical reactions with the alkenes containing more than four carbon

atoms.

Aromatic Hydrocarbons. · Phenolic compounds, such as phenol and cresols, have been shown to be major products of the atmospheric reactions of the aromatic hydrocarbons under laboratory conditions. · Aromatic aldehydes, such as benzaldehyde, are formed in ≤ 10% yield.

· α -Dicarbonyls, such as glyoxal, methylglyoxal, and biacetyl, are formed in fairly high (10 to 40%) yields. These dicarbonyls photolyze rapidly to form radicals and, therefore, are important products with respect to the photochemical activity of the aromatic hydrocarbons. · Unsaturated carbonyl or hydroxycarbonyl compounds are formed, although there is little direct information concerning the formation of these products. There is a lack of knowledge of the detailed reaction mechanisms and products for the aromatic hydrocarbons under tropospheric conditions (i.e., for the NO

x concentrations encountered in urban and rural areas). It is possible that the products observed in laboratory studies and their formation yields are not representative of the situation in the troposphere. This then leads to an inability to formulate detailed reaction mechanisms for the atmospheric

3-29degradation reactions of the aromatic hydrocarbons, and the chemical mechanisms used in urban airshed models then must rely heavily on environmental (or "smog") chamber data.

Oxygenated Compounds. · The products observed from the atmospheric photooxidations of oxygenated organics are carbonyls, organic acids (e.g., RC(O)OH), esters, alcohols, and, in the presence of NO

x , PANs. The major area of uncertainty concerns the importance of photolysis of carbonyl compounds in the troposphere, and the products formed. In particular, there is a lack of information concerning the absorption cross-sections and photoodissociation quantum yields for most of the aldehydes and ketones other than HCHO, CH 3 CHO, and CH 3 COCH 3 .

3.2.4.2 Chemical Formation of Ozone in Polluted Air Major Steps in Ozone Formation As discussed earlier, NO

x and VOCs interact under the influence of sunlight to
form O 3 and other photochemical air pollutants. The major steps in this process are the
conversion of NO to NO 2 by peroxy radicals, with the photolysis of NO 2 leading to
O 3 production. In the absence of a VOC, Reactions 3-1 through 3-3,

do not lead to any net formation of O 3 . The reaction of a VOC with the OH radical, or its (3-3) NO O 3 → NO 2 O 2

(3-1, 3-2) NO 2 h ν O 2 → NO O 3 ,

photolysis, leads to the formation of HO 2 and organic peroxy (RO 2 ) radicals, which react with NO under high-NO conditions:

(3-61) VOC( OH, h ν ) O 2 → R  ̇ O 2

(3-44a) R  ̇

O 2 NO → R  ̇ O NO 2

(3-1, 3-2) NO 2 h ν O 2 → NO O 3

Net: (3-62) VOC( OH, h ν ) O 2 → R  ̇ O 2 O 3 .

with the alkoxy (RO) radical producing further HO 2 or RO 2 radicals or both, and, hence, resulting in further production of O 3 . This process is shown schematically in Figure 3-4.

3-30Figure 3-4. Major steps in production of ozone in ambient air (R = H, alkyl or substituted alkyl, or acyl).

The general time-concentration profiles for selected species during irradiation of an
NO

x -VOC-air mixture are shown in Figure 3-5 for a constant light intensity and in Figure 3-6 for diurnally varying light intensity. These general features of an NMOC-NO

x -air irradiation are described by the reactions described below.
The conversion of NO to NO 2 occurs through the oxidation reactions:

(3-63) organic( OH, h ν , O 3 ) → R  ̇ O 2
(3-44a) R  ̇

O 2 NO → R  ̇ O NO 2

(3-64) R  ̇

O O 2 → carbonyl HO 2

(3-32) HO 2 NO → OH NO 2 .

The maximum concentration of NO 2 is less than the initial NO + NO 2 concentration because NO 2 is removed through the reaction

This reaction removes radicals (OH and NO 2 ) and NO

x from the system. In addition to the (3-15) OH NO 2 M → HNO 3 .

removal of NO

x through Reaction 3-15, NO

x also can be removed through the formation of temporary reservoir species such as organic nitrates (Reaction 3-44b) and PAN (Reaction 3-56).

3-31Figure 3-5. Time-concentration profiles for selected species during irradiations of a nitrogen oxide-propene-air mixture in an indoor chamber with constant light intensity.

Figure 3-6. Time-concentration profiles for selected species during irradiations of a nitrogen oxide-propene-air mixture in an outdoor chamber with diurnally varying light intensity.

3-32The O 3 concentration increases with the NO 2 /NO concentration ratio, and (3-44b) RO 2 NO → RONO 2

(3-56) CH 3 C(O)OO NO 2 → CH 3 C(O)OONO 2

O 3 formation ceases when NO 2 (and hence NO

x ) has been removed by reaction. Formation of PAN occurs by Reaction 3-56. Because of Reactions 3-55 and 3-56,
the PAN concentration also increases with the NO 2 /NO concentration ratio, and PAN
formation also ceases when NO

x has been depleted. The removal processes for NO

x are by reaction of NO 2 with the OH radical to
form HNO 3 (Reaction 3-16), the formation of organic nitrates from the RO + NO Reaction  ̇ O 3-44b pathway, and the formation of PAN through Reaction 3-56. The initially present NO

x is converted to organic nitrates, HNO 3 , and thermally unstable PANs. At ambient temperature, the PANs will gradually thermally decompose to yield NO 2 and the acylperoxy radicals; hence, the ultimate fate of NO

x will be to form HNO 3 and organic nitrates.

Effects of Varying Initial Nitrogen Oxide and Nonmethane Volatile Organic Compound
Concentrations As discussed in Section 3.2.4.2, NO

x and VOC interact in sunlight to form O 3 and other photochemical air pollutants. The formation of O 3 from the NO

x and VOC precursors is nonlinear with respect to the precursor emissions (or ambient concentrations). As discussed in detail in Section 3.6, computer models incorporating emissions, meteorology, and chemistry are necessary for a full understanding of the complexities of the NO

x -VOC-O 3 system. The major reactions in irradiated VOC-NO

x -air mixtures (see Section 3.2.4.2) include

which is in competition with Reaction 3-15, (3-61) OH VOC O 2
→ RO 2 ,

(3-15) OH NO 2 → HNO 3 ,

which removes both radicals and NO

x .

(3-44a) RO 2 NO → RO NO 2

(3-1, 3-2)

NO 2 h ν O 2
→ NO O 3

(3-3) NO O 3 → NO 2 O 2

Based on these reactions, as the VOC/NO

x ratio decreases, Reaction 3-15 competes more successfully with Reaction 3-61 in removing radicals from the system for a constant source of OH radicals, and, thus, slows down the formation of O 3 (and vice-versa). The ultimate
amount of O 3 formed depends on the NO 2 available for photochemical processing through

3-33Reactions 3-44a, 3-1, 3-2, and 3-3. Because of the diurnal light intensity variation and the length of daylight available for reactions and photolysis to occur, O 3 formation from VOCNO

x -air mixtures, especially as a function of the VOC/NO

x ratio, is complex and depends on the time or distance scale being considered (for example, transport of urban plumes to rural areas) (Wolff, 1993; Finlayson-Pitts and Pitts, 1993). In general, reducing the VOC/NO

x ratio by reducing VOC emissions slows down the formation rate of O 3 , leading to lower O 3 levels in urban areas and in areas downwind of urban complexes. Reduction of NO

x emissions leads to a more rapid formation of O 3 , although with less O 3 formed, and, hence, inner-urban areas may experience higher O 3 levels with NO

x control than with VOC control, whereas suburban and rural areas may have lower O 3 levels with NO

x control (Trainer et al., 1993; Olszyna et al., 1994).

Effects of Biogenic Nonmethane Volatile Organic Compound Emissions Biogenic VOC emissions can be important in urban and rural areas (Trainer et al., 1987; Chameides et al., 1988, 1992; Roselle et al., 1991) and can contribute to O 3 formation in much the same way as anthropogenic VOCs. Modeling simulations in which urban biogenic VOC emissions are first included and then excluded from the calculations generally indicate little effect of the biogenic emissions on the predicted O 3 levels. This is not unexpected from the shape of the O 3 isopleths at high VOC/NO

x ratios (Chameides et al., 1988; Section 3.6). However, results of modeling studies in which anthropogenic VOC emissions are removed from the simulations (but anthropogenic NO

x emissions are left unaltered) suggest that anthropogenic NO

x together with biogenic VOCs may form sufficient
O 3 to exceed the NAAQS, at least in certain areas (Chameides et al., 1988). Thus, for the anthropogenic and biogenic VOC emissions considered by Chameides et al. (1988) for Atlanta, GA, changes in either the anthropogenic VOC emissions or biogenic VOC emissions
have little effect on O 3 levels. Therefore, as discussed for the Atlanta region (Chameides et al., 1988), NO

x control may be more favorable than VOC control in urban areas with substantial biogenic NMOC emissions. Although it is known that isoprene is reactive with respect to the formation of
O 3 (Section 3.2.4.3) and that the monoterpenes react rapidly with OH radicals, NO 3 radicals,
and O 3 , the O 3 -forming potentials of the various monoterpenes emitted into the atmosphere
are not known.

3.2.4.3 Hydrocarbon Reactivity with Respect to Ozone Formation As discussed in Section 3.2.4, VOCs are removed and transformed in the troposphere by photolysis and by chemical reaction with OH radicals, NO 3 radicals, and O 3 . In the presence of sunlight, the degradation reactions of the VOCs lead to the conversion of
NO to NO 2 and the formation of O 3 and various organic products. However, different VOCs react at differing rates in the troposphere because of their differing tropospheric lifetimes (Table 3-2). The lifetimes of most VOCs with respect to reaction with OH radicals and
O 3 are in the range ≈ 1 h to ≈ 10 years. In large part, because of these differing tropospheric lifetimes and rates of reaction, VOCs exhibit a range of reactivities with respect to the
formation of O 3 (Altshuller and Bufalini, 1971, and references therein). A number of "reactivity scales" have been developed over the years (see, for example, Altshuller and Bufalini, 1971, and references therein; Darnall et al., 1976), including the rate of VOC disappearance in NO

x -VOC-air irradiations, the rate of NO to NO 2 conversion in NO

x -VOC-air irradiations, O 3 formation in NO

x -single VOC-air irradiations, eye

3-34irritation, and the rate constant for reaction of the VOC with the OH radical. It appears, however, that a useful definition of "reactivity" is that of "incremental reactivity" ( IR ),
defined as the amount of O 3 formed per unit of VOC added or subtracted from the VOC mixture in a given air mass under high-NO conditions (Carter and Atkinson, 1987, 1989b):

(3-65) IR ∆ [O 3 ]/ ∆ [VOC],

at the limit of ∆ [VOC] → 0. The concept of incremental reactivity and some further details of this approach are illustrated by the general reaction mechanism for the photooxidation of an alkane, RH:

(3-42) OH RH → H 2 O  ̇ R

(3-43)  ̇ R O 2 → R  ̇ O 2
(3-44a) R  ̇

O 2 NO → R  ̇ O NO 2
(3-65) R  ̇

O → carbonyl HO 2 (3-32) HO 2 NO → OH NO 2 .

The net reaction,

can be viewed as involving the two separate reaction sequences: (3-66) OH RH 2 NO O 2 → carbonyl 2 NO 2 OH,

(1) the formation of organic peroxy (R 2 ) radicals from the reactions,  ̇ O

3-35and (2) the conversion of NO to NO 2 and the formation of O 3 and other products,

The photolysis of NO 2 then leads to O 3 formation (Reactions 3-1 and 3-2). The first reaction sequence determines how fast R 2 radicals are generated from the VOC, which is called  ̇ O "kinetic reactivity" (Carter and Atkinson, 1989b). For the case given above, where the only reaction of the VOC is with the OH radical, the kinetic reactivity depends solely on the OH radical reaction rate constant. The second reaction sequence, leading to NO to NO 2 conversion, regeneration of OH radicals, and the formation of product species, determines the efficiency of formation of O 3 from the R 2 radicals formed from the first reaction sequence  ̇ O and is termed "mechanistic reactivity" (Carter and Atkinson, 1989b). The second reaction sequence can be represented as

(3-67) R  ̇

O 2 α NO → β NO 2 γ OH δ products.

In general, the faster a VOC reacts in the atmosphere, the higher the incremental reactivity. However, the chemistry subsequent to the initial reaction does affect the
O 3 -forming potential of the VOC. Thus, the existence of NO

x sinks in the reaction mechanism (low values of β or values of α

- β > 0) leads to a decrease in the amount of
O 3 formed. Examples of NO

x sinks are the formation of organic nitrates and PANs (which are also sinks for radicals). The generation or loss of radical species can lead to a net formation or net loss of OH radicals ( γ > 1 or γ < 1, respectively). This, in turn, leads to an enhancement or suppression of radical concentrations in the air parcel and to an enhancement or suppression of the overall reactivity of all VOCs in that air parcel by affecting the rate of
formation of R 2 radicals.  ̇ O These effects vary in importance depending on the VOC/NO

x ratio. Nitrogen oxides sinks are most important at high VOC/NO

x ratios (NO

x -limited), affecting the
maximum O 3 formed; although the formation or loss of OH radicals is most important at low
VOC/NO

x ratios, affecting the initial rate at which O 3 is formed (Carter and Atkinson, 1989b). In addition to depending on the VOC/NO

x ratio (Table 3-3), incremental reactivity depends on the composition of the VOC mixture and on the physical conditions encountered by the air mass (including the dilution rate, light intensity, and spectral distribution (Carter and Atkinson, 1989b; Carter, 1991).

3-36Table 3-3. Calculated Incremental Reactivities of Selected Volatile Organic Compounds as a Function of the Volatile Organic Compound/Nitrogen Oxide Ratio for an Eight-Component Volatile Organic Compound Mixture a and Low-Dilution Conditions b

VOC/NO

x Ratio (ppmC/ppm)

NMOC 4 8 16 40

CO 0.011 0.022 0.016 0.005

Ethane 0.024 0.041 0.018 0.007

n -Butane 0.10 0.16 0.069 0.019

n -Octane 0.068 0.12 0.027 −0.031

Ethene 0.85 0.90 0.33 0.14 Propene 1.28 1.03 0.39 0.14

trans -2-Butene 1.42 0.97 0.31 0.054

Benzene 0.038 0.033 −0.002 −0.002

Toluene 0.26 0.16 −0.036 −0.051

m -Xylene 0.98 0.63 0.091 −0.025 Formaldehyde 2.42 1.20 0.32 0.051 Acetaldehyde 1.34 0.83 0.29 0.098

Methanol 0.12 0.17 0.066 0.029

Ethanol 0.18 0.22 0.065 0.006

Urban Mix 0.41 0.32 0.088 0.011

a Eight-component VOC mixture used to simulate NMOC emissions in an urban area.

b See Appendix A for abbreviations and acronyms.

Source: Carter and Atkinson (1989b).

The O 3 -forming potentials of large numbers of VOCs, including emissions from automobiles using gasoline and various alternative fuels such as methanol and ethanol blends and compressed natural gas, have been investigated by airshed computer models (Chang and Rudy, 1990; Chang et al., 1991a; Derwent and Jenkin, 1991; Andersson-Sköld et al., 1992; McNair et al., 1992, 1994; Carter, 1994). Consistent with the modeling studies of Carter and Atkinson (1987, 1989b), these computer-modeling studies show that VOCs differ significantly
in terms of their O 3 -forming potential, for single-day as well as multiday conditions (Derwent and Jenkin, 1991; Andersson-Sköld et al., 1992; Carter, 1994; McNair et al., 1994). However, there are some differences between the O 3 -forming potentials derived by Derwent and Jenkin (1991) for multiday transport conditions over Europe and by Carter (1994) for 1- day urban airshed "maximum incremental reactivity" conditions, especially for HCHO, an important direct emission and atmospheric transformation product of most VOCs. Recent modeling studies have been carried out by Carter (1994) and McNair et al. (1994) to determine the O 3 -forming potential of alternative fuels. Emissions from vehicles using 85% methanol and 15% gasoline (M-85) were shown to have ≈ 40% of the O 3 -forming

3-37potential of emissions from gasoline-fueled vehicles. Emissions from vehicles using liquefied petroleum gas (LPG) and compressed natural gas (CNG) were shown to have O 3 -forming potentials that are ≈ 50% and ≈ 18%, respectively, of the O 3 -forming potential of emissions from gasoline-fueled vehicles (McNair et al., 1994).



### 3.2.5 Photochemical Production of Aerosols The chemical processes involved in the formation of O 3 and other photochemical pollutants from the interaction of NO

x and VOCs lead to the formation of OH radicals and oxidized VOC reaction products that often are of lower volatility than the precursor VOC. The OH radicals that oxidize the VOCs and lead to the generation of RO 2 radicals and
conversion of NO to NO 2 (with subsequent photolysis of NO 2 form O 3 ) also react with NO 2 and sulfur dioxide (SO 2 ) to form HNO 3 and H 2 SO 4 , respectively, which can become incorporated into aerosols as particulate nitrate (NO 3 - ) and sulfate (SO 4 2- ). The low-volatility VOC reaction products can condense onto existing particles in the atmosphere to form secondary organic aerosol matter. Hence, O 3 formation, acid formation, and secondary aerosol formation in the atmosphere are so related that controls aimed at reducing O 3 levels can impact (positively or negatively) acid and secondary aerosol formation in the atmosphere.

3.2.5.1 Phase Distributions of Organic CompoundsChemical compounds are emitted into the atmosphere in both gaseous and particleassociated forms. The emissions from combustion sources (e.g., vehicle exhaust) are initially at elevated temperature, and compounds that may be in the particle phase at ambient atmospheric temperature may be in the gas phase when emitted. In addition, atmospheric reactions of gas-phase chemicals can lead to the formation of products that then condense onto particles, or self-nucleate (Pandis et al., 1991; Wang et al., 1992; Zhang et al., 1992). Measurements of ambient atmospheric gas- and particle-phase concentrations of several classes of organic compounds indicate that the phase distribution depends on the liquid-phase vapor pressure, PL (Bidleman, 1988; Pankow and Bidleman, 1992). The available experimental data and theoretical treatments show that, as a rough approximation, organic compounds with PL > 10 -6 torr at ambient temperature are mainly in the gas phase (Bidleman, 1988). As expected, the gas-particle phase distribution in the atmosphere depends on the ambient temperature, with the chemical being more particle-associated at lower temperatures. The gas-to-particle adsorption-desorption process can be represented as,

(3-68) A TSP F,

where A is the gas-phase compound, F is the particle-phase compound, and TSP is the total suspended particulate matter. The relationship among these three species is expressed using a particle-gas partition coefficient, K:

(3-69) K F/(TSP)A.

3-38Because K is a constant at a given temperature, if TSP increases (for example, in going from a clean remote atmosphere to an urban area), F/A also must increase and the chemical becomes more particle-associated (Pankow and Bidleman, 1991, 1992). Gaseous and particulate species in the atmosphere are subject to wet and dry deposition. Dry deposition refers to the uptake of gases and particles at the earth’s surface by vegetation, soil, and water, including lakes, rivers, oceans, and snow-covered ground. Wet deposition refers to the removal of gases and particles from the atmosphere through incorporation into rain, fog, and cloud water, followed by precipitation to the earth’s surface. These processes are discussed further in Section 3.6. For gases, dry deposition is important primarily for HNO 3 , SO 2 , and H 2 O 2 as well
as for O 3 and PAN, whereas wet deposition is important for water-soluble gases such as
HNO 3 , H 2 O 2 , phenols, and, under atmospheric conditions, SO 2 . Dry deposition of particles depends on the particle size; those of a mean diameter of ≈ 0.1 to 2.5 μm have lifetimes with respect to dry deposition of ≈ 10 days (Graedel and Weschler, 1981; Atkinson, 1988), sufficient for long-range transport. However, particles are efficiently removed from the atmosphere by wet deposition (Bidleman, 1988). Particles can form in the atmosphere by condensation or by coagulation, generally occurring by coagulation in urban and rural areas. The photooxidation reactions of VOCs typically lead to the formation of more oxidized and less volatile product species. When the vapor pressures exceed the saturated vapor pressure (i.e., vapor pressure < 10 -6 Torr), the products will become particle-associated (Pandis et al., 1991, 1992). Accumulation-size particles are in the size range 0.08 to 2.5 μm diameter (Whitby et al., 1972). In urban areas, the major sources of particulate matter (Larson et al., 1989; Solomon et al., 1989; Wolff et al., 1991; Hildemann et al., 1991a,b; Rogge et al., 1991, 1993; Chow et al., 1993) are · direct emissions of elemental carbon from, for example, diesel-powered vehicles (Larson et al., 1989); · direct emissions of primary organic carbon from, for example, meat cooking operations, paved road dust, and wood-burning fireplaces and other combustion sources (Hildemann et al., 1991a,b; Rogge et al., 1991, 1993); · secondary organic material formed in the atmosphere from the atmospheric photooxidations of gas-phase NMOC (Turpin and Huntzicker, 1991; Pandis et al., 1992);
· the conversion of NO and NO 2 to HNO 3 , followed by neutralization by NH 4 or through combination with other cations to form aerosol nitrates:

(3-69a) NH 3 (gas) HNO 3 (gas) NH 4 NO 3 (aerosol);

· the conversion of SO 2 (and other sulfur-containing species) to H 2 SO 4 , which has sufficiently low volatility to move to the aerosol phase; and · emission into the atmosphere of "fine dust", for example, crustal material. Because the fine-particle size range is the same magnitude as the wavelength of visible light, particulate matter present in the atmosphere leads to light scattering and absorption, and hence to visibility reduction (Larson et al., 1989; Eldering et al., 1993).

3-393.2.5.2 Acid Deposition As noted above, the chemical processes involved in the formation of O 3 and other photochemical pollutants from the interaction of NMOC and NO

x also lead to the formation of acids in the atmosphere. The two major acidic species in ambient air are HNO 3 and
H 2 SO 4 , arising from the atmospheric oxidation of NO

x and SO 2 , respectively. Reduced sulfur compounds emitted from biogenic sources and certain anthropogenic sources also may lead to
SO 2 or sulfonic acids (Tyndall and Ravishankara, 1991). The major sulfur-containing compound emitted into the atmosphere from anthropogenic sources is SO 2 . In the troposphere, the important loss processes of SO 2 are drydeposition (Atkinson, 1988, and references therein), reactions within cloud water, and gasphase reaction with the OH radical. The rate constant for the reaction of SO 2 with the OH
radical is such that the lifetime of SO 2 with respect to gas-phase reaction with the OH radical
is ≈ 15 days. The reaction proceeds by (Stockwell and Calvert, 1983; Atkinson et al., 1992a)

(3-70) OH SO 2 M → HOSO 2

(3-71) HOSO 2 O 2 → HO 2 SO 3

(3-72) SO 3 H 2 O → H 2 SO 4 .

The reaction of SO 3 with water vapor is slow in the gas-phase (Atkinson et al., 1992a) and, hence, this may be a heterogeneous reaction. Because of its low vapor pressure, H 2 SO 4 exists in the aerosol or particle phase in the atmosphere. Dry deposition is an important atmospheric loss process for SO 2 , because SO 2 has a fairly long lifetime, due to gas-phase chemical processes, and also has a high deposition velocity. A lifetime, in relation to dry deposition of 2 to 3 days, appears reasonable (Schwartz, 1989). Sulfur dioxide is not very soluble in pure water (Schwartz, 1989). However, the presence of pollutants such as H 2 O 2 or O 3 , in the aqueous phase, displaces the equilibrium and allows gas-phase SO 2 to be incorporated into cloud, rain, and fog water, where it is oxidized rapidly (Schwartz, 1989; Pandis and Seinfeld, 1989, and references therein):

(3-73) SO 2 (gas) SO 2 (aqu)

(3-74) SO 2 (aqu) H 2 O HSO 3 H SO 2
3 2 H

(3-75) HSO 3 H 2 O 2 → SO 2
4 H H 2 O

(3-76) SO 2
3 O 3 → SO 2
4 O 2 .

In addition, aqueous sulfur can be oxidized in a process catalyzed by transition metals such as iron(III) (Fe 3+ ) and manganese(II) (Mn 2+ ) (Graedel et al., 1986b; Weschler et al., 1986; Pandis and Seinfeld, 1989).

3-40(3-77) SO 2
3 1/2 O 2 → SO 2
4

The oxidation rate of aqueous sulfur by O 3 decreases as the pH decreases (i.e., as the acidity increases) and this oxidation route is therefore self-limiting and generally of minor importance in the atmosphere. The oxidation of SO 2 by H 2 O 2 appears to be the dominant aqueous-phase oxidation process of SO 2 (Chandler et al., 1988; Gervat et al., 1988; Schwartz, 1989; Pandis and Seinfeld, 1989; Fung et al., 1991b), although the transition metal-catalyzed oxidation of
SO 2 may also be important (Jacob et al., 1989). It should be noted that aqueous-phase H 2 O 2 arises, in part, from the absorption of HO 2 radicals and H 2 O 2 into the aqueous phase, with
HO 2 radicals being converted into H 2 O 2 (Zuo and Hoigne, 1993).
The oxidation of SO 2 to sulfate in clouds and fogs is often much faster than the homogeneous gas-phase oxidation of SO 2 initiated by reaction with the OH radical. The gasphase oxidation rate is ≈ 0.5 to 1% h -1 , whereas the aqueous-phase (cloud) oxidation rate may be as high as 10 to 50% h -1 (Schwartz, 1989).
The oxidation of NO

x to HNO 3 and nitrates was discussed in Section 3.2.3. During daylight hours, oxidation occurs by the gas-phase reaction of NO 2 with the OH
radical:

with the lifetime of NO 2 due to Reaction 3-15 calculated to be ≈ 1.4 days. Nitric acid is (3-15) OH NO 2 M → HNO 3 ,

removed from the troposphere by wet and dry deposition, with wet deposition being efficient. During nighttime hours, NO 2 can be converted into NO 3 radicals and N 2 O 5 :

with N 2 O 5 undergoing wet or dry deposition, or both. The reader is referred to Schwartz (3-12) NO 2 O 3 → NO 3 O 2

(3-13, -3-13)

NO 3 NO 2 M N 2 O 5 ,

(1989) for further discussion of the conversion of NO

x to NO 3 and HNO 3 and of acid deposition.



## 3.3 Meteorological Processes Influencing Ozone Formation and Transport
Day-to-day variability in O 3 concentrations is, to a first approximation, the result of day-to-day variations in meteorological conditions. This section presents a succinct overview of those atmospheric processes that affect the concentrations of O 3 and other oxidants in urban and rural areas. Included in this list of processes are the vertical structure and

3-41dynamics of the PBL; transport processes, including thermally-driven mesoscale circulations such as lake and sea breeze circulations; complex terrain effects on transport and dispersion; vertical exchange processes; deposition and scavenging; and meteorological controls on biogenic emissions and dry deposition.



### 3.3.1 Meteorological Processes 3.3.1.1 Surface Energy Budgets Knowledge of the surface energy budget is fundamental to an understanding of the dynamics of the PBL. The PBL is defined as that layer of the atmosphere in contact with the surface of the earth and directly influenced by the surface characteristics. In combination with synoptic winds, it provides the forces for the vertical fluxes of heat, mass, and momentum. The accounting of energy inputs and outputs provides a valuable check on modeled PBL dynamics. Figure 3-7 illustrates the surface radiation budget for short-wave (wavelength roughly <0.4 μm) and long-wave radiation. The radiation budget for the surface can be described in terms of its components as

(3-78) Q sfc K ↓ K ↑ L ↓ L ↑ Q H Q E .

where K ↓ is the incoming short-wave radiation, K ↑ is the outgoing short-wave radiation, L ↓ is the incoming long-wave radiation from the atmosphere, L ↑ is the outgoing long-wave radiation, and Q H and Q E are the heat flux and latent heat flux to the soil, respectively. On a global annual average, Q sfc is assumed to be near zero (i.e., the planet is not heating or cooling systematically, an assumption clearly being questioned with the growing debate on climatic change). On a day-to-day basis, however, Q sfc will certainly vary from zero and will cause changes in surface temperature. Cloud cover, for example, will reduce the amount of short-wave radiation reaching the surface and will modify all the subsequent components of the radiation budget. Moreover, the redistribution of energy through the PBL creates thermodynamic conditions that influence vertical mixing. The treatment of energy budgets has been attempted on the scale of individual urban areas. These studies are summarized by Oke (1978). For many of the modeling studies of the photochemical production of O 3 , the vertical mixing has been parameterized by a single, well-mixed layer. However, because this is a great simplification of a complex structure, and because the selection of rate and extent of vertical mixing may influence local control options, future modeling and observational studies need to address the energy balances so that more realistic simulations can be made of
the structure of the PBL.

3-42Figure 3-7. Surface radiation budget for short- and long-wave radiation. The surface radiation budget is driven by the input of short-wave radiation (1). This direct input is reduced by scatter (2) and absorption passing through the atmosphere. The amount that remains can be absorbed or reflected at the surface. Thereflected light (3) also can be scattered back to the surface (4). The shortwave energy absorbed at the surface will ultimately be emitted back to the atmosphere as long-wave radiation (5). The atmosphere absorbs much of this radiation and radiates it back to the surface (7) and out to space (6). This energy cycle is completed as some of the absorbed energy is transmitted to the atmosphere as sensible and latent heat (8).

3.3.1.2 Planetary Boundary Layer The concentration of an air pollutant depends significantly on the degree of mixing that occurs between the time a pollutant or its precursors are emitted and the arrival of the pollutant at the receptor. Atmospheric mixing is the result of either mechanical turbulence, often associated with wind shear, or thermal turbulence associated with vertical redistribution of heat energy. The potential for thermal turbulence can be characterized by atmospheric stability. The more stable the air layer, the more work is required to move air vertically. As air is moved vertically through the atmosphere, as might happen in a convective thermal, its temperature will decrease with height as the result of adiabatic expansion. It is the comparison of how the temperature should change with height in the absence of external heating or cooling against the actual temperature change with height that is a measure of atmospheric stability. Those layers of the atmosphere where temperature increases with height (inversion layers) are the most stable as air, cooling as it rises, becomes denser than its new warmer environment. In an atmospheric layer with relatively low turbulence, pollutants do not redistribute vertically as rapidly as they do in an unstable layer.

3-43Also, because a stable layer has a relatively low rate of mixing, pollutants in a lower layer will not mix through it to higher altitudes. The stability of the atmosphere is often measured through computation of potential temperature as

(3-79) θ         P

P

o R/c
p

,

where θ is the virtual potential temperature, P is the pressure of the air parcel, P

o is the reference pressure to which the air parcel will be moved (usually 1,000 mb), R is the gas law constant, and c p is the specific heat of air at constant pressure. The faster θ increases with height, the less the potential for mixing. A stable layer can also act as a trap for air pollutants lying beneath it. Hence, an elevated inversion is often referred to as a "trapping" inversion. On the other hand, if pollutants are emitted into a stable layer aloft, such as might occur from an elevated stack, the lack of turbulence will keep the effluents from reaching the ground while the inversion persists. Traditionally, atmospheric mixing has been treated through use of a mixing height

, which is defined as the base of an elevated inversion layer. In this model, the O 3 precursors are mixed uniformly through the layer below the mixing height. As this layer grows, it both
entrains remnant O 3 from previous days and redistributes fresh emissions aloft. The vertical mixing profile through the lower layers of the atmosphere is assumed to follow a typical and predictable cycle on a generally clear day. In such a situation, a nocturnal surface inversion would be expected to form during the night as L ↑ exceeds L ↓ . This surface layer inversion persists until surface heating becomes significant, probably 2 o r 3 h after sunrise. Pollutants initially trapped in the surface inversion may cause relatively high, local concentrations, but these concentrations will decrease rapidly when the surface inversion is broken by surface heating. The boundary formed between the rising, cooling air of the growing mixing layer and that of the existing PBL is often sharp and can be observed as an elevated temperature
inversion. Elevated temperature inversions, when the base is above the ground, are also common occurrences (Hosler, 1961; Holzworth, 1964, 1972). This condition can form simply as the result of rapid vertical mixing from below, but is exacerbated in regions of subsiding air when the sinking air warms to a point at which it is warmer than the rising and cooling underlying air. Because these circumstances are associated with specific synoptic conditions, they are less frequent than the ubiquitous nighttime radiation inversion. An elevated inversion is, nevertheless, a very significant air pollution feature, because it may persist throughout the day and, thus, restrict vertical mixing. When compared to a source near the surface and the effects of a radiation (surface) inversion, the pollutant dispersion pattern is quite different for an elevated source plume trapped in a layer near the base of an elevated inversion. This plume will not be in contact with the ground surface in the early morning hours because there is no mixing through the surface radiation inversion. Thus, the elevated plume will not affect surface pollutant concentrations until the mixing processes become strong enough to reach the altitude of the plume. At that time, the plume may be mixed downward quite rapidly in a process called

3-44fumigation . During fumigation, surface O 3 concentrations will increase if the morning
O 3 concentration is higher aloft than at the ground and if insufficient scavenging by NO occurs at ground-level. In fact, the rapid rise in O 3 concentrations in the morning hours is often the result of vertical (downward) transport from an elevated reservoir of O 3 . After this initial increase, surface concentrations can continue to increase as a result of photochemistry or transport of O 3 -rich air to the receptor. When surface heating decreases in the late afternoon and early evening, the surface inversion will form again under most conditions. The fate of the elevated inversion is less clear, however. Although O 3 and its precursors have been mixed vertically, the reduction of turbulence and mixing at the end of the daylight hours leaves O 3 in a remnant layer that is often without a well-defined thermodynamic demarcation. This layer is then transported through the night, often to regions far removed from pollution sources, where its pollutants can influence concentrations at remote locations the next morning, as mixing entrains the elevated remnant layer. This overnight transport can be aided by the development of a nocturnal jet that forms many nights at the top of the surface inversion layer. Geography can have a significant impact on the dispersion of pollutants (such as along the coast of an ocean or one of the Great Lakes). Near the coast or shore, the temperatures of land and water masses can be different, as can the temperature of the air above these land and water masses. When the water is warmer than the land, there is a tendency toward reduction in the frequency of surface inversion conditions inland over a relatively narrow coastal strip (Hosler, 1961). This in turn tends to increase pollutant dispersion in such areas. The opposite condition also occurs if the water is cooler than the land, as in summer or fall. Cool air near the water surface will tend to increase the stability of the boundary layer in the coastal zone, and thus decrease the mixing processes that act on pollutant emissions. These conditions occur frequently along the New England coast (Hosler, 1961). Similarly, pollutants from the Chicago area have been observed to be influenced by a stable boundary layer over Lake Michigan (Lyons and Olsson, 1972). This has been observed especially in summer and fall when the lake surface is most likely to be cooler than the air that is carried over it from the adjacent land. Sillman et al. (1993) investigated abnormally high concentrations of O 3 observed in rural locations on the shore of Lake Michigan and on the Atlantic coast in Maine, at a distance of 300 km or more from major anthropogenic sources. A dynamical-photochemical model was developed that represented formation of O 3 in shoreline environments and was used to simulate case studies for Lake Michigan and the northeastern United States. Results suggest that a broad region with elevated O 3 , NO

x , and VOC forms as the Chicago plume travels over Lake Michigan, a pattern consistent with observed O 3 at surface monitoring sites. Near-total suppression of dry deposition of O 3 and NO

x over the lake is needed to produce high O 3 . Results for the East Coast suggest that the observed peak O 3 can be reproduced only by a model that includes suppressed vertical mixing and deposition over water, 2-day transport of a plume from New York, and superposition of the New York and Boston, MA, plumes. Hence, the thermodynamics associated with the water bodies seem to play a significant role in some regional-scale episodes of high O 3 concentrations. There is concern that the strict use of mixing height unduly simplifies the complex atmospheric processes that redistribute pollutants within urban areas. There is growing
evidence that some O 3 precursors may not be evenly redistributed over some urban areas. These are cases where the sources are relatively close to the urban area and atmospheric mixing is not strong enough to redistribute the material over a short travel time. In these

3-45cases, it is necessary to treat the turbulent structure of the atmosphere directly and acknowledge the vertical variations in mixing. Methods that are being used to investigate these processes include the use of a diffusivity parameter to express the potential for mixing as a function of height. A simple expression of how the mean concentration, — χ , changes with time, t, in an air parcel, assuming all concentrations are homogeneous in the horizontal, is

where w χ is the vertical turbulent eddy flux of pollutant. The term on the right hand side of the equation changes mean concentration through flux divergence (i.e., turbulence either disperses the pollutant to or from the point being considered). The problem with this representation is that the flux divergence term is virtually impossible to measure directly. The turbulent eddy flux needed to understand the vertical distribution of O 3 and its precursors often is parameterized in photochemical models, if included at all, through use of eddy diffusivity . The eddy diffusivity is set using an analogy to mixing length theory as

which allows estimation of flux divergence from measured or estimated vertical gradients in concentration and estimation of the eddy diffusivity. The selection of diffusivity is often somewhat arbitrary, but can be related to the eddy diffusivity for heat or momentum, depending on circumstances. Large values result in rapid mixing. Thus, the appropriate selection of eddy diffusivity is necessary to simulate whether elevated plumes will enter an
urban airshed.The use of an eddy diffusivity approach to turbulent diffusion assumes local downgradient diffusion, a situation not always realistic in the atmosphere. It is important to note that eddy diffusivity is not valid under convective conditions because counter-gradient flows occur (Sun, 1986). Eddy diffusivity also does not work in the presence of multiple stable layers. Moreover, the form of the eddy diffusivity used in the existing air quality models is rather arbitrary. More research will be needed to remove this arbitrariness. Another method used for convective situations is a technique called "large-eddy simulation", employed to recreate the probability of redistribution within the mixing height. This method explicitly simulates the larger eddies occurring under convective situations. These techniques require meteorological information that is not normally available from the National Weather Service but that is now becoming available as part of several O 3 field experiments.

3.3.1.3 Cloud Venting
Vertical redistribution of O 3 out of the PBL is achieved by the venting of pollutants in clouds. Clouds represent the top-most reaches of thermals of air rising through the PBL and can act as chemical reactors for soluble pollutants, returning the "processed" air to the PBL. They also can result in physical redistribution of O 3 and its precursors from the

3-46PBL if convection is sufficiently vigorous (Greenhut, 1986; Dickerson et al., 1987). Clouds also act to influence photolysis rates and chemical transformation rates. Greenhut (1986) showed that the net O 3 flux in the cloud layer was a linear
function of the difference in O 3 concentration between the boundary and cloud layers. Ozone fluxes between clouds were usually smaller than those found within clouds, but the slower rate is at least partially offset by the larger region of cloud-free air relative to cloudy air. Large clouds, such as cumulonimbus, offer considerably more potential for
redistribution of O 3 and its precursors. Additionally, the cumulonimbus clouds also are associated with precipitation, a scavenger of pollutants, and with lightning, a potential source
for NO

x . Using CO as a tracer, Dickerson et al. (1987) and Pickering et al. (1990) have illustrated the redistribution potential of cumulonimbus cloud systems. Lyons et al. (1986) provided an illustration of the potential for groups of cumulonimbus clouds to vent the polluted boundary layer. The role of cloud venting is thought to be largely a cleansing process for the boundary layer, although a portion of the material lifted into the free troposphere could be entrained back to the surface in subsequent convection. Aircraft observations frequently have documented the occurrence of relatively high O 3 concentrations above lower concentration surface layers (e.g., Westberg et al., 1976). This is a clear indication that O 3 is preserved essentially in layers above the surface and can be transported over relatively long distances, even when continual replenishment through precursor reactions is not a factor, such as at night.

3.3.1.4 Stratospheric-Tropospheric Ozone Exchange
The fact that O 3 is formed in the stratosphere, mixed downward, and incorporated into the troposphere, where it forms a more or less uniformly mixed background concentration, has been known in various degrees of detail for many years (Junge, 1963). The mechanisms by which stratospheric air is mixed into the troposphere have been examined by a number of authors, as documented previously by the U.S. Environmental Protection Agency (EPA) (U.S. Environmental Protection Agency, 1986a, and references therein). Although the portion of background O 3 near the surface attributed to stratospherictropospheric O 3 can be in the 5 to 15 ppb range for a seasonal average, this amount of O 3 by itself cannot account for peak urban O 3 values or regional episodes of elevated O 3 levels (Johnson and Viezee, 1981; Ludwig et al., 1977; Singh et al., 1980; and Viezee et al., 1979). Johnson and Viezee (1981) concluded that the O 3 -rich intrusions studied sloped downward toward the south. In terms of dimensions, the average crosswind width (north to south), at an altitude of 5.5 km (ca. 18,000 ft), for six spring intrusions, averaged 226 km, and, for four fall tropopause fold systems, averaged 129 km. Ozone concentrations at 5.5 km averaged 108 ppb in the spring systems and 83 ppb in the fall systems. From this and other research described in the previous criteria document for O 3 and other photochemical oxidants (U.S. Environmental Protection Agency, 1986a), Viezee and coworkers (Viezee and Singh, 1982; Viezee et al., 1983) concluded (1) that direct ground-level impacts by stratospheric O 3 may be infrequent, occurring <1% of the time; (2) that such ground-level events are short-lived and episodic; and (3) that they are most likely to be associated on a 1- to 4-h average with
O 3 concentrations in the range 60 to 100 ppb. The monthly stratospheric-tropospheric total O 3 flux from tropospheric folding events in the Northern Hemisphere has been estimated. These fluxes, in units of
10 35 O 3 molecules per month per tropospheric folding event, increase from 1.0 in January to

3-472.1 in April and May, decline to 1.0 in August, and reach a minimum of 0.5 in October and November (Viezee et al., 1983). The spring-to-fall variation resembles the seasonal variations
of O 3 near ground-level often observed at more remote sites (e.g., Logan, 1985). Four of the 10 episodes in which ground-level O 3 has been attributed to stratospheric O 3 transport occurred during March, but several such episodes during summer months have been reported (see below). Using the 7 Be-to-O 3 ratio as an indicator of O 3 of stratospheric origin and SO 4 2- concentrations as a tracer for anthropogenic sources, Altshuller (1987) estimated stratospheric
contributions of O 3 in the range 0 to 40 ppb (0 to 95% of observed O 3 ) at ground level at Whiteface Mountain, NY, for July 1975 and mid-June to mid-July 1977. Monthly average stratospheric contributions were estimated at 5 to 10 ppb. Extant 7 Be and O 3 data for a number of lower-elevation rural locations in the western, midwestern, and southeastern United States also were examined, and stratospheric or upper tropospheric contributions of 6 to 8 ppb
were calculated. The author concluded that the calculated values for such contributions should be viewed with caution and regarded as probable upper limits because of scatter in the

7 Be and corresponding O 3 data that hindered definition of the 7 Be-to-O 3 ratio. Altshuller also concluded that removal and dilution processes result in the loss of most stratospheric O 3 before it reaches ground level. In other work performed in England, O 3 of stratospheric origin was estimated to contribute 10 to 15 ppb to the daily maximum hourly mean
O 3 concentrations in an April through October period (Derwent and Key, 1988; United Kingdom Photochemical Oxidant Review Group, 1993).



### 3.3.2 Meteorological Parameters This section focuses on analyses of data from previous and ongoing measurement programs to address two key questions: (1) are there meteorological parameters which are systematically associated with O 3 levels? and (2) are relationships between O 3 and meteorological parameters sufficiently strong such that meteorological fluctuations can be filtered from the data to allow examination of longer term trends? The meteorological factors that theoretically could influence surface O 3 levels include ultraviolet radiation, temperature, wind speed, atmospheric mixing and transport, and surface scavenging. The following examines the theoretical basis for each of these factors and identifies to what degree empirical evidence supports the hypotheses.

3.3.2.1 Sunlight Ultraviolet radiation plays a key role in initiating the photochemical processes leading to O 3 formation. Sunlight intensity (specifically the UV portion of sunlight) varies with season and latitude, but the latter effect is strong only during winter months. The importance of photolysis to the formation of O 3 provides a direct link between O 3 and time of year. However, during the summer, the maximum UV intensity is fairly constant throughout the contiguous United States, and only the duration of the solar day varies to a small degree
with latitude. The effects of light intensity on individual photolytic reaction steps and on the overall process of oxidant formation have been studied in the laboratory (Peterson, 1976; Demerjian et al., 1980). Early studies, however, employed constant light intensities, in contrast to the diurnally varying intensities that occur in the ambient atmosphere. The diurnal variation of light intensity was subsequently studied as a factor in photochemical oxidant

3-48formation (e.g., Jeffries et al., 1975, 1976). Such studies showed that the effect of this factor varies with initial reactant concentrations. Most important was the observation that similar
NMOC/NO

x systems showed different oxidant-forming potential depending on whether studies of these systems were conducted using constant or diurnal light. This led to incorporation of the effects of diurnal or variable light into photochemical models (Tilden and Seinfeld, 1982). There is little empirical evidence in the literature, however, linking day-to-day
variations in observed UV radiation levels with variations in O 3 levels. Samson and Shi (1988) illustrated that the number of O 3 concentrations exceeding 120 ppb did not track well with potential solar radiation, as shown in Figure 3-8. Although variations in day-to-day concentrations could well be influenced by cloud cover or attenuated by haze, the seasonal peak in O 3 concentrations usually lags the peak in potential solar radiation that occurs at the
Summer Solstice on or about June 23.

3.3.2.2 Temperature There is an association between tropospheric O 3 concentration and tropospheric temperature that has been demonstrated from measurements in outdoor smog chambers and from measurements in ambient air. A linear relationship between maximum O 3 and temperature was obtained in the smog chambers with little scatter around the regression line (Kelly and Gunst, 1990). Numerous ambient studies done over more than a decade have reported that successive occurrences or episodes of high temperatures characterize seasonally high O 3 years (Clark and Karl, 1982; Kelly et al., 1986). The relationship has been observed for the South Coast Air Basin of California (Kuntasal and Chang, 1987), in New England (Wolff and Lioy, 1978; Atwater, 1984; Wackter and Bayly, 1988), and elsewhere. Figures 3-9 and 3-10 show the daily maximum O 3 concentrations versus maximum daily temperature for summer months (May to October), 1988 to 1990, for Atlanta and New York City, NY, and for Detroit, MI, and Phoenix, AZ, respectively. There appears to be an upper-bound on O 3 concentrations that increases with temperature. Likewise, Figure 3-11 shows that a similar qualitative relationship exists between O 3 and temperature even at a
number of rural locations. The notable trend in these plots is the apparent upper-bound to O 3 concentrations as a function of temperature. It is clear that, at a given temperature, there is a wide range of possible O 3 concentrations because other factors (e.g., cloudiness, precipitation, wind speed)
can reduce the O 3 production. The upper bound presumably represents the maximum
O 3 concentration achieved under the most favorable conditions. These plots, based on ambient air measurements, show wide scatter in O 3 concentration with temperature because of
the contributions to variations from several of these factors that do not influence the results from smog chamber studies (Kelly and Gunst, 1990). Table 3-4 lists the results of a statistical regression performed on the paired O 3 -temperature data used in Figures 3-9 and 3-10 with separate slopes listed for temperatures above and below 30 °C. Results show that, for T > 30 °C, the O 3 -temperature relationship is statistically significant at all sites. The rate
of increase fo r T > 30 °C is 3 to 5 ppb/°C at eastern United States rural sites and ranges from 4 to 9 ppb/°C at the three eastern U.S. urban sites (New York, Detroit, and Atlanta). At two western sites, Williston, ND, and Billings, MT, there is a much weaker

Figure 3-8. The number of reports of ozone concentrations ≥ 120 ppb at the 17 cities studied in Samson and Shi (1988). (1 April = Week 14, 1 May = Week 18, 1 June = Week 22, 1 July = Week 27, 1 August = Week 31, 1 September =

3-49Week 35, 1 October = Week 40, 1 November = Week 44). A representation of the annual variation in solar radiation reaching the earth's surface at 40°N latitude (units = cal cm -2 ) is shown.

Source: Samson and Shi (1988).

dependence on temperature, possibly reflecting the lower level of anthropogenic activity. At a third western site, Medford, OR, the O 3 -temperature relationship is comparable to that at
rural eastern sites. Relationships between peak O 3 and temperature also have been recorded by Wunderli and Gehrig (1991) for three locations in Switzerland. At two sites near Zurich, peak O 3 increased 3 to 5 ppb/°C for diurnal average temperatures between 10 and 25 °C, and little change in peak O 3 occurred for temperatures below 10 °C. At the third site, a high-altitude location removed from anthropogenic influence, much less variation of O 3 with temperature was observed. The hypotheses for this correlation of O 3 with temperature include, but are not necessarily limited to: 1. Reduction in photolysis rates under meterological conditions associated with low temperatures;
2. Reduction in H 2 O concentrations at low temperatures; 3. Thermal decomposition of PAN and its homologues; 4. Increased anthropogenic emissions of reactive hydrocarbons or NO

x , or both; 5. Increased natural emissions of reactive hydrocarbons; and 6. Relationships between high temperatures and stagnant circulation patterns.

3-50Figure 3-9. A scatter plot of maximum daily ozone concentration in Atlanta, GA, and New York, NY, versus maximum daily temperature.

Figure 3-10. A scatter plot of maximum daily ozone concentration in Detroit, MI, and Phoenix, AZ, versus maximum daily temperature.

3-51Figure 3-11. A scatter plot of maximum ozone concentration versus maximum daily temperature for four nonurban sites. The relationship with temperature is still apparent, although the slope is reduced from that of the urban areas.

Table 3-4. Rates of Increase of Peak Ozone with Diurnal Maximum Temperature (ppb/ ° C) for Temperature <300 K (27 ° C) and Temperature >300 K, Based on Measurements for April 1 to September 30, 1988 a

T < 300 K T > 300 K

Location ∆ O 3 / ∆ T T-Statistic ∆ O 3 / ∆ T T-Statistic
Urbanized Regions

NY-NJ-CT 1.5 −5.2 8.8 −7.4

Detroit 1.4 −6.4 4.4 −6.3

Atlanta 3.2 −4.2 7.1 −5.9

Phoenix — — 1.4 −4.1

Southern California 11.3 −8.9 — —

Nonurban Sites Williamsport, PA 1.2 −5.0 4.0 −7.4
Saline, MI 0.8 −3.5 3.1 −4.9
Mammoth Cave, KY 0.1 −0.3 4.4 −7.3 Kentucky, cleanest site 3 0.3 −0.7 3.4 −6.6
Williston, ND 0.2 −1.0 0.8 −3.7 Billings, MT 0.1 −0.5 0.7 −2.2
Medford, OR 0.5 −2.6 3.3 −13.7

a See Appendix A for abbreviations and acronyms.

3-52The relationship with temperature is well known, but not yet reproduced by air quality models. Although it has been argued that this striking relationship with temperature is an indirect result of the stagnant synoptic meteorological conditions that lead to higher
O 3 levels, the correlation is not strong with other parameters of stagnation, notably wind speed, which is discussed later.

Reduction in Photolysis Rates It is possible that, on a seasonal scale, the correlation between temperature and
O 3 may be an indirect correlation with UV radiation variability. This is insufficient, however, to explain the day-to-day correlation between the two variables. Changes in photolysis rates and in H 2 O concentrations are related in that both are linked to the supply of OH radicals, which determines the rate of O 3 production in clean atmospheres and contributes to O 3 production in polluted atmospheres. A reduction in either photolysis rates or H 2 O would reduce the source of OH radicals. Calculations by Sillman and Samson (1995) showed that the difference between summer and fall photolysis rates (at 40° N latitude) has a significant impact on the rate of O 3 production in urban photochemical simulations, roughly equal to the impact of PAN thermal decomposition (discussed below). However the impact of photolysis rates and of water vapor was much lower in simulations for polluted rural environments. In the simulations by Sillman et al. (1993), O 3 production in urban environments was limited largely by the supply of OH radicals to react with hydrocarbons; whereas in rural environments the limiting factor was the source of NO

x . Consequently, photolysis rates and H 2 O had less impact on O 3 production in rural
environments.

Thermal Decomposition of Peroxyacetyl Nitrate Temperature-dependent photochemical rate constants provide a link between
O 3 and temperature (Sillman et al., 1990a; Cardelino and Chameides, 1990). The reason for
the decline in O 3 in rural areas when the PAN decomposition rate decreases is that PAN represents a sink for NO

x in rural environments. When the rate of PAN decomposition is decreased, NO

x drops sharply, whereas OH and HO 2 remain largely unaffected. Consequently, the rate of the important HO 2 + NO reaction (see Section 3.2) shows a
substantial decrease. The photochemical response in an urban environment is fundamentally different, although the final result, a decrease in O 3 with temperature, is similar. The impact of PAN in urban environments is attributable to its role as a sink for odd hydrogen rather than to its
effect on NO

x (Cardelino and Chameides, 1990). Sillman et al. (1990a) have shown that the
well-known division of O 3 photochemistry into NO

x - and VOC-sensitive regimes is associated with the relative magnitude of odd-hydrogen sinks. Sillman and Samson (1995) found that the thermal decomposition of PAN was enough to explain an increase of 1 to 2 ppb peak O 3 /°C increase in temperature in rural locations in the eastern United States, based on photochemical simulations. This increase represents a significant fraction of the observed increase in peak O 3 with a rise in temperature (3 to 5 ppb/°C, Table 3-4).

Increased Anthropogenic Emissions Emission rates for anthropogenic hydrocarbons (VOCs) also can increase with temperature (U.S Environmental Protection Agency, 1989; Stump et al., 1992). Increased

3-53anthropogenic VOC emissions might be expected to cause increased rates of O 3 production in
urban areas where O 3 is sensitive to VOC, but would be less likely to have impact on rural areas where biogenic VOC emissions can predominate. However, O 3 in rural areas is NO

x dependent. The NO

x -sensitive rural areas also would show increased O 3 production with a rise in temperature as biogenic NO

x emissions increase with temperature.

Increased Natural Emissions Emissions of biogenic hydrocarbons increase sharply with a rise in temperature (Lamb et al., 1987). In ambient temperatures from 25 to 35 °C, the rate of natural hydrocarbon emissions from isoprene-emitting deciduous trees increased by about a factor of 4. From coniferous trees, the increase was on the order of 1.5. Recently, Jacob et al. (1993) found that the photochemistry of O 3 production in a polluted rural environment (Blue Ridge Mountains, VA) is significantly different in September and October, when natural emissions from deciduous forests have ceased. The difference in chemistry between summer and fall leaf production also may have an impact on
the O 3 -temperature correlation.

Correlation with Stagnation Recently, Jacob et al. (1993b) found that model-simulated O 3 formation in the rural United States shows a tendency to increase with a rise in temperature, based solely on the difference in atmospheric circulation between relatively warm and relatively cool days. The
model-simulated O 3 -temperature correlation was less than observed but large enough torepresent a significant component of the observed correlation. However, the temperaturemeteorology correlation identified by Jacob et al. (1993b) was based on simulated meteorology from a General Circulation Model rather than on direct observations.

3.3.2.3 Wind Speed Ozone is expected to be influenced by wind speed because lower wind speeds should lead to reduced ventilation and the potential for greater buildup of O 3 and its precursors. Abnormally high temperatures are frequently associated with high barometric pressure, stagnant circulation, and suppressed vertical mixing resulting from subsidence (Mukammal et al., 1982), all of which may contribute to elevated O 3 levels. However, in reality this relationship varies from one part of the country to another. Figure 3-12 shows the frequency of 24-h trajectory transport distances to Southern cities on days with resulting
concentrations of O 3 ≥ 120 ppb (Samson and Shi, 1988). The frequency for Southern cities is biased toward lower wind speeds. The same bias was shown for all 17 cities in the study. A similar plot for cities in the northeastern United States (Figure 3-13) shows an opposite pattern, in which the bias is toward higher wind speeds than normal. It is unclear how much meteorological information is needed in order to perform accurate urban-area O 3 simulations using advanced photochemical models. To understand the significance of variations between upper-air wind measurements during the Southern Oxidant Study (SOS), 1992, Atlanta, an intensive, intercomparison test of the precision of upper-air measurements was conducted. Collocated measurements were made at an SOS measurement site, using a boundary-layer lidar, a wind profiler, and a rawinsonde balloon. There was generally good agreement between the profiler and rawinsonde, although some large outliers existed. Figure 3-14 illustrates that the root-mean-square difference (RMSD) varied with altitude. The RMSD reached a minimum near 1,200 m above ground level (AGL) of about 2 m/s, rising to over

3-54Figure 3-12. The frequency of 24-h trajectory transport distance en route to city when

ozone was ≥ 120 ppb in four Southern U.S. cities, compared with the percent frequency distribution for all 17 cities (scale on right) of a nationwide study,
1983 to 1985.

Source: Samson and Shi (1988).

Figure 3-13. The frequency of 24-h trajectory transport distance en route to city when

ozone was ≥ 120 ppb in four New England cities, compared with the percent frequency distribution for all 17 cities (scale on right) of a nationwide study,
1983 to 1985.

Source: Samson and Shi (1988).

3-55Figure 3-14. The root-mean-square difference (RMSD) between CLASS observations and profiler observations as a function of height above ground level.

3 m/s near the surface and above 1,200 m AGL. Figure 3-15 illustrates the RMSD for the lidar comparison with CLASS observations. There is slightly greater RMSD at all heights than for the profiler-rawinsonde comparison, with a relative minimum observed at about 1,200

m. Although the measurements were significantly correlated, the results illustrate that there was still considerable disagreement between methods. The profiler had better precision than the lidar had, although the differences were negligible if the first four runs were excluded from the data set. The profiler obtained values biased slightly higher than the CLASS system (+0.2 m/s), whereas the lidar system was biased low (−0.3 m/s or −0.5 m/s). The statistical comparisons of both the profiler and the boundary-layer lidar with the rawinsonde system suggest that variations in wind speed at a particular level must be larger than about 3 m/s to be considered significant.

3.3.2.4 Air Mass Characteristics In meteorology, an "air mass" is a region of air, usually of multistate dimensions, that exhibits similar temperature, humidity, or stability characteristics. Air masses are created when air becomes stagnant over a "source region" and subsequently takes on the characteristics of the source region. Similarly, when dealing with air pollution meteorology, it is possible to identify a "chemical air mass" as a region of air that has become stagnant over an emissions source area. Air that is stagnant over, say, the center of Canada will exhibit relatively cold, dry conditions and will be relatively devoid of pollutants. Air that

3-56Figure 3-15. The root-mean-square difference (RMSD) between CLASS observations and lidar observations as a function of height above ground level.

resides over the industrial regions of the midwestern United States will exhibit low visibility and, often, high O 3 levels on a regional scale. Meteorological processes play an important role in determining the amount of "accumulation" of O 3 and its precursors that occurs under such stagnant conditions. Episodes of high O 3 concentrations in urban areas often are associated with high
concentrations of O 3 in the surroundings. This accumulated O 3 forms under the same atmospheric conditions that lead to high O 3 levels in urban areas and exacerbates the urban problem by supplying relatively high O 3 and precursor concentrations to the urban area from upwind. The transport of O 3 and its precursors beyond the urban scale ( ≤ 50 km) to neighboring rural and urban areas has been well documented (e.g., Wolff et al., 1977a,c; Wolff and Lioy, 1978; Clark and Clarke, 1984; Sexton, 1982; Wolff et al., 1982; Altshuller, 1988). A summary of most of these reports was given in the 1986 O 3 criteria document (U.S. Environmental Protection Agency, 1986a) and will not be reiterated here. The phenomena of high nonurban O 3 levels was illustrated by Stasiuk and Coffey (1974) for transport within New York State; by Ripperton et al. (1977) for sites in the Middle Atlantic States; and by Samson and Ragland (1977) for the midwestern United States.
These areas of O 3 accumulation are characterized by synoptic-scale subsidence of air in the free troposphere, resulting in development of an elevated inversion layer; relatively low wind speeds associated with the weak horizontal pressure gradient around a surface high pressure system; lack of cloudiness; and high temperatures. On occasion, O 3 at levels greater than 120 ppb can occur in rural areas far
removed from urban or industrial sources. Ozone levels at the summit of Whiteface Mountain exceeded this value during the summer of 1988 when O 3 accumulated across a

3-57wide expanse of the eastern United States at levels ≥ 120 ppb. Nonetheless, even when the regional accumulation is at a level below the current O 3 NAAQS, the increment needed to bring the level above the NAAQS in an urban area is not large. The identification and understanding of the transport of photochemical O 3 and other oxidants and their precursors by weather systems represent a significant advance in comprehending photochemical air pollution and the potential extent of its effects. Considerable progress has been made in the development of long-range photochemical modeling techniques so that the likely impact of synoptic systems can be anticipated. Such tools are very much in the research stage, however, because the local impact of O 3 and other oxidants results from a complex interaction of distant and local precursor sources, urban plumes, mixing processes, atmospheric chemical reactions, and general meteorology.



### 3.3.3 Normalization of Trends The degree to which meteorological factors can be "normalized" out of the
O 3 concentration and "trends" data depends in large part on the strength of the relationships
between O 3 and meteorological components. As part of the SOS Atlanta intensive field campaign, an attempt was made to model statistically the O 3 levels in Atlanta to build a predictive tool for forecasting days of specialized measurement. Figure 3-16 shows the fit of the data used to create the model to the model simulations. Figure 3-17 shows the fit obtained from independent data collected in 1992.

Figure 3-16. Model of ozone (O 3 ) levels using regression techniques. The use of wind speed, temperature, and previous-day ozone provided a means to forecast O 3 levels.

3-58Figure 3-17. Simulated versus observed ozone levels using regression techniques on an independent data set obtained in the summer of 1992 in Atlanta, GA.

This model was used successfully to predict next-day O 3 levels in Atlanta. Ozone levels in a number of American cities should be analyzed using regression tools such as this in order to normalize meteorological variability. Through such analyses, it is possible that trends, if any, represented as systematic deviations from the model, may become observable. A summary of other techniques for removing meteorological variability is contained in the recent monograph from the National Research Council (1991). Table 3-5 lists a sample of
studies aimed at evaluation of O 3 trends.



## 3.4 Precursors of Ozone and Other Oxidants

3.4.1 Sources and Emissions of Precursors
3.4.1.1 Introduction As described elsewhere in this chapter, O 3 is formed in the atmosphere through a
series of chemical reactions that involve VOCs and NO

x . Control of O 3 depends on reducing
emissions of VOCs or NO

x or both. Thus, it is important to understand the sources and source strengths of these precursor species in order to devise the most appropriate oxidant control strategies. In the following sections, anthropogenic and biogenic NO

x and VOC sources will be described, and the best estimates of their current emission levels and trends will be provided. Confidence levels for the assigned source strengths will be discussed. Both English and metric units have been utilized in emission inventories. Thousands or millions of short tons are the common scales in the English system. The metric unit most

3-59Table 3-5. Recent Studies Examining Trends in Ozone Data After Removal of Variability Associated with Meteorological Factors

Study Variables Approach

Jones et al. (1989) Surface temperature Compared number of days with
ozone concentrations above 120 ppb to days with temperature
above 30 °C.

Pollock et al. (1988) Surface temperature Compared number of days with
ozone concentrations above 105 ppb to days with temperature
above 30 °C.

Kuntasal and Chang (1987) 850-mb temperature Regression of ozone versus temperature for Southern
California.

Wakim (1989) Surface temperature Regression of ozone versus temperature for Houston, New York, and Washington, DC.

Chock et al. (1982) Surface temperature, wind speed, relative humidity, sky cover, wind direction, dew point temperature, sea level pressure, precipitation. Regression versus a variety of meteorological parameters.

Kumar and Chock (1984) Surface temperature, wind speed, relative humidity, sky cover, wind direction, dew point temperature, sea level pressure, precipitation. Regression versus a variety of meteorological parameters.

Korsog and Wolff (1991) Surface temperature, wind speed, relative humidity, sky cover, wind direction, dew point temperature, sea level pressure, precipitation. Regression versus a variety of meteorological parameters.

Source: National Research Council (1991).

often employed is millions of metric tons, which is equivalent to teragrams (Tg). To convert English tons to teragrams, multiply English tons by 0.907 × 10 -6 . For consistency, teragrams have been employed throughout the ensuing discussion.

3.4.1.2 Nitrogen Oxides Manmade Emission Sources Anthropogenic NO

x sources are associated with combustion processes. The primary pollutant is NO, which is formed from nitrogen and oxygen atoms that are produced at high combustion temperatures when air is present. In addition, NO

x is formed from nitrogen contained in the combustion fuel. Major NO

x source categories include transportation, stationary source fuel combustion, industrial processes, solid waste disposal,

3-60and some miscellaneous combustion-related activities. Table 3-6 provides a more detailedsummary of each of these source categories. The transportation category includes gasolineand diesel-powered motor vehicles, aircraft, railcars, vessels, and off-highway vehicles. Electric utilities, industrial and commercial/institutional boilers, industrial furnaces, and space heaters comprise the stationary source fuel combustion category. Industrial processes include petroleum refining and paper, glass, steel, cement, and chemical production. The incineration and open burning of waste leads to emissions of NO

x in the solid waste disposal category. The miscellaneous sources category includes prescribed forest slash burning, agricultural burning, coal refuse burning, and structure fires. It should be noted at this point that, even though NO is the pollutant emitted, NO

x emission inventories are quantified relative to NO 2 (mol wt = 46). Nitrogen dioxide is a secondary pollutant produced via oxidation of NO in the atmosphere. Quantifying NO

x emissions in all of these categories generally requires multiplying an emission factor and an activity level. Nitrogen oxides emission factors are obtained from Compilation of Air Pollution Factors , AP-42 (U.S. Environmental Protection Agency, 1985), and from the current mobile source emission factor model (e.g., MOBILE5) recommended by EPA. Activity levels are derived from information sources that provide consumption levels. This takes the form of fuel type and amount consumed for stationary sources and, for transportation sources, the number of vehicle miles traveled (VMT). Point-source emissions are tallied at the individual plant level. These plant-by-plant NO

x emissions are first summed at the state level, and then state totals are added to arrive at the national emissions total. Data on VMT are published for three road categories: (1) highways, (2) rural roads, and (3) urban

streets. Table 3-7 provides a summary of NO

x emissions from the various categories mentioned previously (U.S. Environmental Protection Agency, 1993b). The 1991 total is 21.39 Tg of NO

x emissions in the United States. About half of the emissions (10.69 Tg) is associated with the stationary source fuel combustion category. Transportation-related activities are the second largest source, accounting for about 45% of the national total. The remaining 7% of emissions are divided among the industrial processes, solid waste disposal, and miscellaneous sources categories. The two largest single NO

x emission sources are electric power generation and highway vehicles. Local NO

x source apportionment may differ substantially from these national figures. Because of the dominance of the electric utility and transportation sources, the geographical distribution of NO

x emissions is related to areas with a high density of power-generating stations and urban regions with high traffic densities. Figure 3-18 shows the location of the 50 largest electric power generating sources of NO

x in the United States. The majority of these power plants are concentrated in the upper Mississippi-Ohio River corridor. Because of this congregation of large point sources, 69% of U.S. NO

x emissions occur within U.S. Environmental Protection Agency Regions III, IV, V, and VI (Figure 3-19). It is interesting to compare the annual NO

x emissions from a large electrical generating plant with the yearly transportation-related emissions in a major metropolitan region. The largest utility plants currently release between 0.06 and 0.09 Tg of NO

x annually, which compares to approximately 0.12 Tg of NO

x emitted by transportation sources in the Atlanta urban area (U.S. Environmental Protection Agency, 1993b).

3-61Table 3-6. Source Categories Used to Inventory Nitrogen Oxides EmissionsTransportationStationary SourceFuel CombustionIndustrial ProcessesSolid Waste DisposalMiscellaneousHighway vehiclesGasoline-poweredPassenger carsLight trucks - 1Light trucks - 2Heavy-duty vehiclesMotorcyclesCoalElectric utilitiesIndustrialCommercial/InstitutionalResidentialPulp millsOrganic chemicalsAmmoniaNitric acidPetroleum refiningGlassCementLimeIron and steelIncinerationOpen burningForestriesOther burningDiesel-poweredPassenger carsLight trucksHeavy-duty vehiclesFuel oilElectric utilitiesIndustrialCommercial/InstitutionalResidentialAircraftRailcarsVesselsFarm machineryConstruction machineryIndustrial machineryOther off-highway vehiclesNatural gasElectric utilitiesIndustrialCommercial/InstitutionalResidentialWoodIndustrialResidentialOther FuelsIndustrialResidentialSource: U.S. Environmental Protection Agency (1992a).

3-62Table 3-7. 1991 Emission Estimates for Manmade Sources of Nitrogen Oxides in the United States
Source Category Emissions (Tg)
Transportation Highway vehicles Off-highway vehicles 7.20
2.51 9.71

Stationary fuel combustion
Electric utilities
Industrial
Other 6.74
3.27
0.68 10.69

Industrial processes 0.80
Solid waste disposal 0.07

Miscellaneous Forest burning Other burning Miscellaneous organic solvents 0.12

Total of all sources 21.39

Source: U.S. Environmental Protection Agency (1993b).

Figure 3-18. The 50 largest sources of nitrogen oxides (power plants) in the United States.

Source: U.S. Environmental Protection Agency (1992a).

3-63Figure 3-19. Nitrogen oxides emissions (Tg/year) from manmade sources in the 10 U.S. Environmental Protection Agency regions of the United States, 1991.

Source: U.S. Environmental Protection Agency (1993b).

Seasonal variations are available for the 1993 NO

x emissions from 14 source categories in the United States (U.S. Environmental Protection Agency, 1994). Very little seasonal variation occurred for categories contributing approximately three-quarters of the
total annual NO

x emissions, including the categories of highway vehicles, electric utilities, and industrial combustion sources. For off-highway sources (comprising 20% of the total annual
NO

x emissions), 29% of the off-highway NO

x emissions occur in the summer and 21% in the winter. In contrast, the category of other combustion sources (comprising 5% of the total
annual NO

x emissions) emit 47% of the NO

x emissions in the winter and only 8% in the summer. An earlier inventory for 1985 (U.S. Environmental Protection Agency, 1989), which considered only total point and area anthropogenic NO

x emissions, indicated that very little
variation occurred in NO

x emissions among the winter, spring, summer, and fall seasons. The
contributions of these NO

x categories also were shown to vary seasonally by region of the
United States.

Trends in Nitrogen Oxides Emissions
Estimates of NO

x emissions have been made back to 1900, when approximately 2.3 Tg were emitted into the atmosphere in the United States (U.S. Environmental Protection Agency, 1992a). Figure 3-20 summarizes the growth in NO

x emissions at 10-year intervals since the 1940s. Emissions grew rapidly until the 1970s and then leveled off at about Figure 3-20. Changes in nitrogen oxides emissions from manmade sources in the United States, 10-year intervals, 1940 through 1990.

3-64Source: U.S. Environmental Protection Agency (1992a).

20 Tg/year. Currently, more than 90% of the national NO

x emissions result from transportation activities and stationary fuel combustion. Figure 3-21 illustrates the growth in each of these categories over the last 50 years. Transportation-related NO

x emissions grew steadily until the 1980s and then exhibited a moderate decrease. However, the recent trends in transportation-related NO

x emissions shown in Table 3-8 indicate no trend between 1987
and 1991. Emissions of NO

x from fuel combustion sources have increased continually from 1940 to the present time. Recent trends in the major NO

x emission categories are shown in Table 3-8. Between 1987 and 1991, transportation-related NO

x emissions have remained essentially constant, whereas the stationary source NO

x emissions have increased about 10%. Transportation and stationary source fuel combustion will likely show downward
trends in their NO

x emissions during the next 20 years. This will result from the provisions of the Clean Air Act, which was passed in 1990. Emission limits for electric utility boilers have been prescribed to reduce acidic deposition, automobile tailpipe emission standards will be tightened, and current technology-based applications will be required for industrial boilers (non-utility) in O 3 nonattainment areas. In addition, the average grams of NO

x per mile from passenger cars is expected to decrease because of new on-board diagnostic systems and expanded inspection and maintenance requirements. As a result of new emission limits and revised performance standards, NO

x emissions from electric utilities are expected to decrease by 16% by the year 2000. Control requirements in the industrial non-utility sector are expected to reduce NO

x emissions by 10% between 1990 and 2000. Projections based on VMT and emission factors from the MOBILE model suggest nearly a 50% decrease in NO

x emissions from highway vehicles manufactured from 1990 to 2000 (U.S. Environmental Protection Agency, 1992a).

3-65Figure 3-21. Changes in nitrogen oxides emissions from stationary source fuel combustion (SF) and transportation (TR) from 1940 through 1990. a

a The values for 1990 do not agree with those in Table 3-8 because different models were employed for deriving the short- and long-term trends.

Source: U.S. Environmental Protection Agency (1992a).

Table 3-8. Recent Trends in Nitrogen Oxides Emissions for Major Manmade Source Categories (Tg)

Year Transportation Stationary Source
Fuel Combustion

1991 9.7 11.0

1990 a 9.9 10.7

1989 9.7 10.7

1988 9.9 10.6

1987 9.7 10.1

a The values for 1990 do not agree with those in Figure 3-21 because different models were employed for deriving the short- and long-term trends.

Source: U.S. Environmental Protection Agency (1993b).

3-66Uncertainty of Anthropogenic Nitrogen Oxides Emission Estimates Because a large proportion of the U.S NO

x emissions are derived from distinct point sources, it generally is believed that published estimates are very reliable. For example, the National Acid Precipitation Assessment Program (NAPAP) NO

x inventory for U.S. emissions in 1985 (18.6 Tg) was assigned a 90% relative confidence interval in the range of 6 to 11% (Placet et al., 1991). This confidence level was based on judgments used to assign uncertainty to component inputs of emission models and on statistical assumptions used to aggregate uncertainty values. Sources of error are associated with both the emission factors and the activity levels utilized in the inventorying process. Emission factors provide quantitative estimates of the average rate of emissions from many sources. Consequently, these factors are best applied to a large number of sources over relatively long time periods. In other words, an
NO

x emission estimate for a single point source on a particular day in 1990 may be highly inaccurate; but the emission value for this same source for the entire year of 1990 may be very good. It appears that the emission factors assigned to the transportation sectors may be the most uncertain. This results from the emission factors having been derived from mobile source models that require multiple inputs. This type of model requires information on temperatures, vehicle speeds, gasoline volatility, and several other parameters. Recent attempts to validate NO

x emission factors or inventories have involved comparing ambient NO

x concentrations with values predicted using emissions-based models. These generally have taken one of two forms: (1) comparisons between NO

x concentrations measured in a tunnel and those predicted from emission factors, activity levels, and dilution factors in the tunnel; or (2) whole-city integration procedures in which ambient NO

x concentrations are compared to ambient NO

x levels that have been predicted using a model such as the Urban Airshed Model (UAM). The latter approach has been applied in the South Coast Air Basin (Fujita et al., 1992). It was reported that measured and predicted NO

x concentrations agreed within 20% for a 2-day period in August 1987. Likewise, the results from tunnel studies (Pierson et al., 1990; Robinson et al., 1996) have shown reasonably good agreement between predicted and measured NO

x concentrations. It is important to keep in
mind that ambient NO

x levels predicted using a modeling method cannot be assigned true value status. There could be as much or more uncertainty in the model outputs as there is in the emission inputs that are being tested. The fact, however, that an emissions-based model predicts ambient concentrations that are close to those measured tends to lend credence to the
NO

x emission estimates. No systematic study of the effect of these uncertainties on model predictions has been published, but a limited summary of sensitivity analyses appears in Seinfeld (1988). In addition, NO

x inventory validation has involved comparing annual emission estimates reported by different groups. Table 3-9 shows several annual U.S. NO

x emission estimates. In 1982, the estimates vary by less than 12% and this decreases to about 9% in the 1985 comparison.

Natural Emission Sources
Natural sources of NO

x include lightning, soils, wildfires, stratospheric intrusion, and the oceans. Of these, lightning and soils are the major contributors. The convention is to include emissions from all soils in the biogenic or natural category even though cultivated soil emissions are in a sense anthropogenic; cultivated soils also appear to produce higher emissions than those from undisturbed forest and prairie soils, as discussed later. Although

3-67Table 3-9. Comparison of Estimates of Nitrogen Oxides Emissions from Manmade Sources in the United States a

Emissions/year (Tg)

Inventory 1982 1985

NAPAP

— 18.6

EPA 19.6 19.8

MSCET 18.8 18.2

EPRI 20.7

—

a See Appendix A for abbreviations and acronyms.

Source: U.S. Environmental Protection Agency (1993a).

NO

x emitted from large wildfires can be significant on a regional scale, this source is overall considered to be of minor importance for the United States. Injection of NO

x into the upper troposphere via subsidence from the stratosphere is estimated at less than 0.1 Tg/year for all of North America. Because of the relatively short lifetime of NO

x (1 to 3 days) and small
NO

x emissions from sea water, transport of NO

x from oceans is thought to be a negligible
source in the United States. Lightning. Lightning produces high enough temperatures to allow N 2 and O 2 to be converted to NO. Two methods have been employed to estimate the NO

x source strength from lightning: (1) Multiply the frequency of lightning flashes by the energy dissipated per flash and the NO production per unit of energy dissipated; or (2) Relate NO

x production to NO 3 - deposition in remote areas where lightningproduced NO

x is thought to be the dominant NO 3 - precursor. Method 1 yields an annual NO

x production of approximately 1.2 Tg for North America (Placet et al., 1991). The deposition-based estimate (Method 2) gives a somewhat larger value of 1.7 Tg/year (Placet et al., 1991). The NAPAP inventory included lightning-produced
NO

x on a gridded 10° × 10° latitude-longitude scale. Most of the continental United States fits within 30° to 50° N latitude and 80° to 120° W longitude. The estimated annual lightning-produced NO

x for this region (continental United States) is about 1.0 Tg. Roughly 60% (0.6 Tg) of this NO

x is generated over the southern tier of states (30° to 40° N latitude; 80° to 120° W longitude). Soils. Both nitrifying and denitrifying organisms in the soil can produce NO

x . The relative importance of these two pathways is probably highly variable from biome to biome. Nitric oxide is the principal NO

x species emitted from soils, with emission rates depending mainly on fertilization levels and soil temperature. Several reports have noted a large increase in NO

x emissions from agricultural soils treated with NO 3 - -containing fertilizers (Johansson and Granat, 1984; Kaplan et al., 1988; Johansson, 1984). Measurements of soil
NO

x emissions have established that the relationship with temperature is exponential,

3-68consisting of approximately a twofold increase for each 10 °C rise in temperature (Williams et al., 1992; Valente and Thornton, 1993). Inventorying soil NO

x emissions is difficult because of the large temporal and spatial variability in emissions. The existing inventories have been developed using emission algorithms that are functions of soil temperature and land-use type. Two broad, land-use categories—natural and agricultural—have been assigned. The natural soils are broken down into biome types, and the agricultural soils subdivided according to fertilizer applications. The highest biogenic NO emissions are in corn-growing regions of the midwest (Nebraska, Iowa, and Illinois) during summer months. Of the total U.S. biogenic emissions of NO from soils, 85% occur during the spring and summer months. Table 3-10 provides a summary of the annual soil NO

x emissions from the 10 U.S. Environmental Protection Agency regions. Approximately 60% of this NO

x is emitted in Regions V, VII, and VIII (see Figure 3-19), which contain the central U.S. corn belt. The total estimate for U.S. soil emissions is 1.2 Tg.

Table 3-10. Annual Nitrogen Oxides Emissions from Soils by U.S. Environmental Protection Agency Region a

U.S. Environmental Protection Agency Region NO

x Emissions (Tg)

I, II, and III
IV
V
VI
VII
VIII

IX
X 0.05
0.11
0.26
0.18
0.27
0.21
0.04
0.01

Total 1.20 b

a See Appendix A for abbreviations and acronyms.

b Values do not sum to total due to independent rounding.

Source: Placet et al. (1991).

Uncertainty in Estimates of Natural Nitrogen Oxides Emissions As previously indicated, inventorying NO

x produced from lightning requires multiplying the number of flashes by average energy factors. No attempt has been made to assign confidence limits to these variables. A measure of the uncertainty associated with lightning-produced NO

x is provided, however, by comparing emission estimates generated independently. Two estimates of the amount of lightning-generated, summertime NO

x in the southeastern United States (2.4 and 8.5 × 10 -2 Tg) varied by approximately a factor of 4 (Placet et al., 1991).

3-69Sources of uncertainty when inventorying NO

x emissions from soils include: land-use assignments, soil temperature, and emission algorithm development. Confidence levels assigned to categories 1 and 2 are about ±50%. The emission algorithm is developed
from field measurements of NO

x emission rates versus temperature for various land-use categories. Measurement accuracy is approximately ±30%. However, because of the natural variability of NO

x emissions within a specific soil category, uncertainty in the exponential relationship that relates emission rate to temperature is estimated to be in the range of a factor
of 2 to 4.

Comparison of Emissions from Manmade and Natural Sources On an annual basis, natural sources (lightning and soils) contribute approximately 2.2 Tg of NO

x to the troposphere over the United States. This compares to the 1990 anthropogenic emission estimate of 19.4 Tg. Annual NO

x emissions from soils (1.2 Tg) are about 6% of the manmade emissions in the United States. This percentage increases to about 14% when the comparison includes only the summer months of July, August, and September. Even larger biogenic contributions can occur in certain regions of the United States. For example, it is estimated that biogenic NO

x emissions from soils account for about 19% of
summertime NO

x emissions in Tennessee (Valente and Thornton, 1993) and actually exceed emissions from manmade sources during the summer months in the states of Nebraska and South Dakota (Williams et al., 1992).

3.4.1.3 Volatile Organic Compounds
Manmade Emission Sources Volatile organic compounds are emitted into the atmosphere by evaporative and combustion processes. Many hundreds of different organic species are released from a large number of source types. The species commonly associated with atmospheric O 3 production contain from 2 to about 12 carbon atoms. They can be true hydrocarbons, which possess only carbon and hydrogen atoms (e.g., alkanes, alkenes, aromatics), or substituted hydrocarbons that contain a functional group such as alcohol, ether, carbonyl, ester, or halogens. The emissions of methane have been ignored because of their largely natural origin and the fact that the importance of methane is limited primarily to global scale processes. In addition, the atmospheric oxidation rate of methane is very slow compared to the higher molecular weight organics. In 1991, the total U.S. emissions of VOCs was estimated to be 21.0 Tg (U.S. Environmental Protection Agency, 1993b). The two largest source categories were industrial processes (10.0 Tg) and transportation (7.9 Tg). Lesser contributions were attributed to waste disposal and recycling (2.0 Tg), stationary source fuel combustion (0.7 Tg), and miscellaneous area sources (0.5 Tg). Table 3-11 provides a more detailed breakdown of VOC source contributions. Within the industrial category, solvent utilization, petroleum product storage and transfer, and chemical manufacturing are the major contributors. Volatile organiccompounds released from highway vehicles account for almost 75% of the transportationrelated emissions. Speciated hydrocarbon emissions from manmade sources were reported in the 1985 NAPAP Emissions Inventory. Emissions of each main hydrocarbon family exceeded 1 Tg. Alkanes comprised about 33%, aromatics 19%, and alkenes 11% of anthropogenic VOC emissions in the 1985 inventory (Placet et al., 1991). None of the major oxygenated

3-70Table 3-11. Estimated 1991 Emissions of Volatile Organic Compounds from Manmade Sources in the United States
Source Category Emissions (Tg)
Transportation 7.87
Highway vehicles Off-highway vehicles 6.00
1.87
Stationary fuel combustion 0.68

Electric utilities
Industrial
Other 0.03
0.26
0.39
Industrial processes 9.97

Chemical manufacture
Petroleum and related industries
Solvent utilization Petroleum product storage and transport
Other 1.61
0.68
5.50
1.69
0.49
Waste disposal and recycling 2.01

Miscellaneous 0.51

Total all sources 21.04

Source: U.S. Environmental Protection Agency (1993b).

hydrocarbon groups (e.g., carbonyls, organic acids, phenols) listed in the speciated inventory exceeded 1 Tg. The carbonyl group, which included formaldehyde, higher aldehydes, acetone, and higher ketones, was the largest contributor of oxygenated hydrocarbons at 0.73 Tg. Seasonal variations are available for the 1993 anthropogenic VOC emissions from 14 source categories in the United States (U.S. Environmental Protection Agency, 1994). Very few seasonal variations occur for categories contributing approximately 85% of the total annual VOC emissions. The only category of VOC emissions that showed significant seasonal variation was off-highway sources, which comprises 13% of the total annual VOC emissions. These off-highway sources contribute 31% of their VOC emissions in summer and 19% in winter. An earlier inventory for 1985 (U.S. Environmental Protection Agency, 1989), which considered total point and area anthropogenic VOC emissions, indicated that very little variation in VOC emissions occurred between the winter, spring, summer, and fall seasons. The contribution of these VOC emissions also were shown to vary seasonally by region of the
United States.

Trends in Emissions Emissions of nonmethane VOCs peaked in the early 1970s and have decreased continually since then. Emissions of VOCs increased from 15.5 Tg in 1940 to 27.4 Tg in

3-711970 and now are estimated to be back down to approximately the same level as in 1940 (U.S. Environmental Protection Agency, 1992a). Figure 3-22 illustrates these changes at 10-year intervals from 1940 to 1990. Until 1970, highway vehicles were the major source of VOC emissions. As automobiles have been equipped with more and better emission controls, however, emissions from the transportation sector have dropped below those from industrial processes, the category that is now the leading contributor of VOC emissions to the atmosphere. Transportation, industrial processes, and the miscellaneous burning and solvent-use categories have accounted for 83 to 93% of VOC emissions over the past 50 years. Figure 3-23 shows the emission trends for these three categories. The transportation-related emissions of VOCs are currently estimated to be at about the same level as in 1940. Industrial process VOC emissions nearly tripled between 1940 and 1980, followed by a small decline in more recent years. The miscellaneous category exhibited a decrease in emissions from 4.5 Tg in 1940 to a 1990 level estimated at 2.8 Tg/year.

Figure 3-22. Changes in emissions of volatile organic compounds from major manmade sources in the United States, 10-year intervals, 1940 through 1990.

Source: U.S. Environmental Protection Agency (1992a).

Trends for the dominant VOC emissions categories from 1987 through 1991 are shown in Table 3-12. Projections for the year 2000 forecast a 62% reduction in VOC emissions from highway vehicles compared to 1990 levels. The major reduction in the transportation area will contribute to a predicted overall 25% decrease in total national VOC emissions between 1990 and 2000 (U.S. Environmental Protection Agency, 1992a).

Uncertainty in Estimates of Emissions from Manmade Sources It has proven difficult to determine the accuracy of VOC emission estimates. Within an area source such as an oil refinery, emission factors and activity levels are

3-72Figure 3-23. Changes in emissions of volatile organic compounds from major manmade sources, 1940 through 1990. a

a The values for 1990 do not agree with those in Table 3-12 because different models were employed for deriving the short- and long-term trends.

Source: U.S. Environmental Protection Agency (1992a).

Table 3-12. Recent Trends in Emissions of Volatile Organic Compounds from Major Categories of Manmade Sources (Tg)

Year Transportation Industrial Processes Waste Disposal and Recycling

1991 7.87 7.86 2.01

1990 a 8.07 9.96 2.05

1989 8.26 9.92 2.08

1988 9.15 10.00 2.10

1987 9.29 9.65 2.05

a The values for 1990 do not agree with those in Figure 3-23 because different models were employed for deriving the short- and long-term trends.

Source: U.S. Environmental Protection Agency (1993b).

3-73assigned for thousands of individual sources (e.g., valves, flanges, meters, processes), and emission estimates for each of these sources are summed to produce the emissions total. Because it would be impractical to determine an emission factor for each of these sources within a refinery individually, average emission factors for the various source categories are
utilized. This can lead to substantial error if emissions from the individual sources deviate from the assigned average factor. Even more troublesome are area sources that include a large evaporative emissions component. These sources are dependent on environmental factors such as temperature, which add to the difficulty in establishing reliable emission estimates. Such sources fall into a miscellaneous solvent evaporation category that includes emissions from processes such as dry cleaning, degreasing, printing, automobile body repair, furniture manufacture, and motor vehicle manufacture. Assigning accurate VOC emission estimates to the mobile source category has proven troublesome, as well. Models are used that incorporate numerous input parameters, each of which has some degree of uncertainty. For example, activity models are employed to
characterize the mobile source fleet. This includes the number of vehicles in various categories (e.g., gasoline-fueled, diesel-fueled, catalyst-equipped, non-catalyst-equipped, etc.), miles accumulated per year for each type of vehicle, and ages of the vehicles. Vehicle registration statistics are employed for category assignment. Errors can arise because registration data are not always up to date, and unregistered vehicles are completely omitted. Military vehicles, foreign-owned automobiles, and old "junkers" that are on the highways but not registered are included in the inventorying process. The activity models assume that vehicles of the same age accumulate mileage at the same rate. This is most likely not correct; there is a need to assess the uncertainty in this assumption through a systematic collection of vehicle type, age, and mileage accumulation statistics. Experiments carried out in tunnels have looked at the relationship between
measured VOC emission factors and those derived from automotive emission models. In a study designed to verify automotive emission inventories for the South Coast Air Basin, measurements in the Van Nuys Tunnel indicated that automotive VOC emissions were a factor of 4 larger than predicted using emission models (Pierson et al., 1990). Results from two tunnel studies conducted in 1992 (Robinson et al., 1996) show much better agreement between VOC measurements and model predictions than those obtained in the Van Nuys Tunnel. Comparisons were made for the Tuscarora Tunnel on the Pennsylvania Turnpike in south-central Pennsylvania and at the Fort McHenry Tunnel under Baltimore Harbor. For Tuscarora, MOBILE4.1 gives 131% of the average measured VOC values and MOBILE5 gives 216% of the average measured VOCs. For Fort McHenry, MOBILE4.1 gives 53% of the average measured VOC values and MOBILE5 gives 81% of the average measured VOCs. Somewhat better agreement also was obtained from the Van Nuys Tunnel data when the updated California emission model EMFACTEP was used (Robinson et al., 1996). At the Cassier Tunnel in Vancouver, Canada, agreement within ±30% was obtained between the VOC measurements and the Canadian version of the MOBILE models (Gertler et al., 1994). The 1993 results from the Caldecott Tunnel in the San Francisco, CA, area show deviations between VOC measurements and model predictions similar to those obtained in the Van Nuys Tunnel, possibly because of similar urban/local fleets. Differences in test results between the newer tunnel studies and those obtained in the early Van Nuys Tunnel study are likely due to the better condition of the vehicles in the newer studies and the lack of power enrichment or other transients because of the steady-speed driving. However, it is important to appreciate that results from tunnel measurements do not necessarily predict equivalency of VOC

3-74measurements with model predictions under highly transient urban/local driving and fleet
conditions. Recent developments in remote sensing have permitted more accurate measurement of hydrocarbon exhaust emissions from on-road vehicles (Stedman et al., 1991). These studies have demonstrated a highly skewed distribution, with the majority of VOC emissions coming from about 20% of the automobiles. Emission factors developed from laboratory dynamometer testing most likely do not properly account for the high-emitting vehicle contribution (Pitchford and Johnson, 1993). In many cases, these high emitters are older cars that are poorly maintained. In order to reduce this source of uncertainty, it may be necessary to reassess the life spans assigned to vehicles. Vehicles manufactured more than 25 years prior to 1993 are not included in the inventory. However, these older vehicles are likely to be high emitters, and, if they are underrepresented in the model, emissions will be underestimated. Activity models provide data in terms of national averages. This can contribute to inaccuracies in emissions estimates if a particular region varies from the national average in terms of vehicle types, age, or VMT.
Ambient measurements of VOCs and NO

x have been employed in order to better define uncertainty levels in VOC inventories. Some of the earliest work was carried out in the Atlanta area in the 1980s. Using a simple model and measured ambient VOC and NO

x concentrations, it was shown that ambient NO

x levels were consistent with the urban NO

x emission estimates. However, measured ambient VOC concentrations were as much as a factor of 6 greater than predicted (Westberg and Lamb, 1985). Improvements in mobile source emission models have resulted in somewhat higher emission estimates, so that the discrepancy between model estimates and ambient data has been reduced to about a factor of 2.5 (Fujita et al., 1992; Cadle et al., 1993). It is clear that the relationship between emission
inventories and ambient concentrations of NO

x and VOCs warrants further study. In addition to improving the mobile source emission inventories, it will be necessary to place uncertainty bounds on stationary source inventories. Whether stationary source emissions of VOCs are underpredicted using current emission inventory methodology is not known (Finlayson-Pitts and Pitts, Jr., 1993).

Biogenic Emissions Vegetation emits significant quantities of reactive VOCs into the atmosphere. Many of these biogenic VOCs may contribute to O 3 production in urban (Chameides et al., 1988) and rural (Trainer et al., 1987) environments. The VOC emissions of primary interest are isoprene and the monoterpenes (e.g., α -pinene, β -pinene, myrcene, limonene, etc.), which are hydrocarbons. Recent field measurements have shown that a variety of oxygenated organics also are emitted from plants (Winer et al., 1992). A thorough discussion of biogenic emissions and their implication for atmospheric chemistry has been published recently by Fehsenfeld et al. (1992), who reviewed the techniques used to measure VOC emissions from vegetation, laboratory emissions studies that have been used to relate emission rates to temperature and light intensity, development of emission models, and the use of emission models in the preparation of emission inventories. Since the late 1970s, a number of regional and national biogenic emission inventories have been reported (Zimmerman, 1979; Winer et al., 1983; Lamb et al., 1985, 1987, 1993). These inventories are based on algorithms that relate VOC emissions from a particular vegetation class to ambient temperature, land-use, and, in the case of isoprene, photosynthetically active radiation. Most biogenic VOC emissions from vegetation increase

3-75exponentially with temperature. Isoprene emissions are light-dependent, being minimal at night and increasing with solar intensity during the day. Deciduous vegetation is the dominant source of isoprene; whereas coniferous trees emit primarily monoterpenes. Other things being equal, isoprene is emitted at a much higher rate than the monoterpenes. For example, in a southern forest of mixed pine and hardwoods, the isoprene emission rate from an oak tree is about 10 times larger than the flux of α -pinene from an adjacent loblolly pine during the midday period. The most recent biogenic VOC emissions estimate for the United States totals 29 Tg/year (Lamb et al., 1993). This estimate includes 5.9 Tg isoprene, 4.4 Tg α -pinene, 6.5 Tg other monoterpenes, and 12.3 Tg other VOCs. Table 3-13 provides a summary of the contributions from the various vegetation categories based on an inventory of monthly statewide data for eight land-cover types. In preparing this inventory, algorithms were developed that related VOC emissions to temperature and light for each of the biomass categories shown in the table. On a national scale, coniferous forests are the largest vegetative contributor because of their extensive land coverage. The category "Other VOCs" is the dominant biogenic hydrocarbon contributor to the national total. From the standpoint of inventory accuracy, this is somewhat unfortunate because the identities of most of the
"Other VOCs" are uncertain. This classification has carried over from the extensive field-measurement program conducted by Zimmerman (1979) and coworkers in the mid-1970s. The category "Other VOCs", includes peaks that showed up in sample chromatograms at retention times that could not be matched to known hydrocarbons. It is likely that if the Zimmerman study were repeated today, most of the species making up this "Other VOCs" category could be identified. Recent field studies have made use of GC/MS techniques that were not available to Zimmerman in the 1970s. Biogenic emissions, because of their dependence on temperature and vegetational growth, vary by season. In addition, the southern tier of states is expected to produce more biogenic emissions than those in the north because of higher average temperatures. Table 3-14 shows a spatial and temporal breakdown of U.S. biogenic emissions. Summertime emissions comprise 16.7 of the 29.1 Tg (or 57%) of the annual totals in all regions. The EPA Regions IV and VI in the southeastern and southcentral United States, respectively, have the highest summertime and annual biogenic VOC emission rates. Region IV contributes 16% of the summertime and 18% of the annual biogenic VOC emissions in the United States, whereas Region VI contributes 21% of the summertime and 23% of the annual biogenic emissions in the United States. Compared to Regions IV and VI, regions to the north have more rapid increases in biogenic VOC emissions in the spring and more rapid decreases in biogenic VOC emissions in the fall.

Uncertainty in Estimates of Biogenic Emissions Sources of error in the biogenic inventorying process arise from uncertainties in emission measurements, determination of biomass densities, land-use characterization, and measurement of light intensity and temperature. Within each of these categories, the error is relatively small. However, when emission measurements are combined with temperature or light intensity, or both, into a single algorithm, the uncertainty increases greatly. This results from the fact that temperature and light are only surrogates for the real physiological processes that control biogenic emissions. Emission rate and ambient temperature can be highly correlated for data collected from one tree branch over a 24-h period; but, when these
data are combined with measurements from other branches and other trees the correlation is

3-76Table 3-13. Annual Biogenic Hydrocarbon Emission Inventory for the United States (Tg)aLand UseCompoundOakForestsOtherDeciduousForestsConiferousForestsScrublandsGrass-landsCrop-landsInlandWatersUrbanAreasU.S. TotalIsoprene 2.31 1.01 0.61 1.17 0.49 0.2 0.02 0.08 5.9α-pinene 0.19 0.23 2.07 0.78 0.13 0.85 0.06 0.04 4.4Other terpenes 0.41 0.44 3.08 1.41 0.24 0.81 0.06 0.06 6.5Other VOCs 1.12 0.88 2.72 2.49 0.45 4.51 0.07 0.08 12.3Total 4.032.568.485.851.316.370.210.2629.1Percent of Total13.98.829.220.14.521.90.70.9aSee Appendix A for abbreviations and acronyms.Source: Lamb et al. (1993).

3-77Table 3-14. Annual Biogenic Hydrocarbon Emission Inventory by Month andby U.S. Environmental Protection Agency Region for United States Emissions (Tg)U.S. Environmental Protection Agency RegionMonthIIIIVVVIVIIVIIIIXXTotalPercentof Total1 0.018 0.092 0.004 0.084 0.007 0.022 0.060 0.043 0.3 1.12 0.017 0.139 0.004 0.123 0.007 0.020 0.054 0.039 0.4 1.43 0.071 0.428 0.067 0.519 0.078 0.108 0.113 0.102 1.5 5.14 0.169 0.460 0.189 0.567 0.211 0.303 0.320 0.202 2.4 8.35 0.206 0.475 0.240 0.586 0.226 0.362 0.331 0.208 2.6 9.16 0.427 0.874 0.550 1.146 0.508 0.809 0.710 0.424 5.5 18.77 0.441 0.903 0.568 1.184 0.524 0.836 0.734 0.438 5.6 19.38 0.439 0.903 0.568 1.184 0.524 0.820 0.734 0.438 5.6 19.39 0.123 0.461 0.137 0.561 0.136 0.280 0.357 0.212 2.3 7.810 0.069 0.286 0.066 0.394 0.026 0.290 0.369 0.219 1.7 5.911 0.066 0.162 0.063 0.174 0.025 0.110 0.130 0.109 0.8 2.912 0.0180.0800.0040.0730.0070.0220.0600.0430.31.1Total2.15.32.56.62.34.04.02.529.1Source: Lamb et al. (1993).

3-78not nearly as good. The uncertainty associated with the algorithms used to generate the U.S. inventory described previously is estimated to be a factor of 3 (Lamb et al., 1987). Because other sources of error in the inventorying process are much smaller, a factor of 3 is the current best estimate of the overall uncertainty associated with biogenic VOC inventories. However, this may be a lower limit if it is shown that oxygenated species are emitted in significant quantities by vegetation. Emission measurement methods employed in the past have not been adequate for quantifying polar, oxygenated organics.

Comparison of Manmade and Biogenic Emissions The most recent anthropogenic and biogenic VOC emissions estimates for the United States indicate that natural emissions (29 Tg) exceed manmade emissions (23 Tg). During the summer months in the United States, anthropogenic emissions constitute 25% of the annual anthropogenic VOC emissions, whereas biogenic emissions constitute 57% of the annual biogenic emissions. On a teragram basis, anthropogenic VOC emissions during the summer contribute 0.25 × 23 Tg = 5.75 Tg, whereas summer biogenic VOC emissions contribute 0.57 × 29 Tg = 16.5 Tg. Therefore, on a national basis, the ratio of biogenic to anthropogenic VOC emissions is approximately 2.9 for the United States. However, this ratio varies with region in the summer months. These calculations depend on the assumption that regional summertime anthropogenic VOC emissions are one-quarter of the annual VOC emissions. With this assumption, the ratios of biogenic to anthropogenic VOC emissions for three selected regions are as follows: 2.2 for Region IV, 1.6 for Region V, and 3.2 for Region VI (Lamb et al., 1993; U.S. Environmental Protection Agency, 1994). However, in a recent National Research Council review (1991), it was concluded that emissions from manmade sources are currently underestimated by a significant amount (60 to 80%). Because uncertainty in both biogenic and anthropogenic VOC emission inventories is large, it is not possible to establish whether the contribution of emissions from natural or manmade sources of VOCs is larger.

3.4.1.4 Relationship of Summertime Precursor Emissions and Ozone Production
Peak O 3 levels are recorded in most regions of the country during the months of June, July, and August. From the foregoing discussion, it is obvious that natural emissions of
NO

x and VOCs peak during this same time frame. Biogenic emissions are very dependent on temperature; and, as ambient temperatures rise during the summer months, NO

x and VOC emissions reach a maximum. Figure 3-24 clearly demonstrates this for biogenic VOC emissions, and a plot of monthly biogenic NO

x emissions would show a similar pattern. Well over 50% of biogenic NO

x and VOC emissions occur during the period of maximum photochemical activity. Seasonal changes in anthropogenic emissions of NO

x are believed to be relatively small. The transportation sector produces slightly less NO

x during the warmer months, but there is probably a small increase from the stationary source category because of higher summertime power demands. Because these are the major U.S. sources of NO

x and changes in seasonal emissions tend to offset each other, there is no reason to expect that NO

x emissions will vary significantly by season on the national level. Evaporative emissions of VOCs are enhanced during the warm summer months. Because evaporation is an important component of anthropogenic VOC emissions, there is a summertime increase. In 1993, U.S. VOC emissions during June, July, and August were estimated to exceed annual monthly average VOC emissions by about 17% (U.S. Environmental Protection Agency, 1994). The

3-79Total NMHC Emissions (Tg) Jan. Feb. March

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## #PCN[VKECN/GVJQFUHQT1ZKFCPVUCPF6JGKT2TGEWTUQTU5CORNKPICPF#PCN[UKUQH1\QPGCPF1VJGT1ZKFCPVU

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4KUGVKOG OKP 
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2TGEKUKQPQHWRRGTTCPIGNKOKV RRO QHWRRGTTCPIGNKOKV RRO 

5QWTEG (GFGTCN 4GIKUVGT 
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accuracy. Formaldehyde standards generally are prepared by one of several methods. The first method utilizes dilute commercial formalin (37% HCHO, w/w). Calibration is accomplished by the direct spiking into sampling impingers of the diluted mixture or by evaporation into known test volumes, followed by impinger collection. Formaldehyde also can be prepared by heating known amounts of paraformaldehyde, passing the effluent gases through a methanol-liquid nitrogen slush trap to remove impurities, and collecting the remaining HCHO. Paraformaldehyde permeation tubes also have been used (Tanner and Meng, 1984). For the higher molecular weight carbonyl species, liquid solutions can be evaporated, or pure vapor can be generated in dynamic gas-flow systems (permeation tubes, diffusion tubes, syringe delivery systems, etc.). These test atmospheres are then passed through the appropriate collection system and analyzed. A comparison of these data, with the direct spiking of liquid carbonyl species into the particular collection system, provides a measure of the overall collection efficiency.

3.5.2.4 Polar Volatile Organic Compounds The VOCs discussed earlier in this chapter (Section 3.5.2.2) have included aliphatic, aromatic, alkenic, and acetylenic hydrocarbons. These compounds are relatively nonpolar, nonreactive species, and measurement methods have been easily applied in determining ambient concentrations. Recently, attention also has been directed toward the more reactive oxygen- and nitrogen-containing organic compounds, in part by the inclusion of many of these compounds on a list of 189 hazardous air pollutants specified in the 1990 CAAA (U.S. Congress, 1990). Many of these compounds are emitted directly from a variety of industrial processes, mobile sources, and consumer products, and some also are formed in the atmosphere by photochemical oxidation of hydrocarbons. However, as indicated earlier in this document, very few ambient data exist for these species. These compounds have been referred to collectively as PVOCs, although it is their reactivity and water solubility, more than simple polarity, that make their measurement difficult with existing methodology. Two approaches have been utilized in developing analytical methods for PVOCs. One approach has incorporated the use of cryogenic trapping techniques similar to those discussed earlier for the nonpolar hydrocarbon species; the second approach has utilized adsorbent material for sample preconcentration. To be effective for sensitive parts-per-billion measurement of PVOCs, both approaches require some type of water management system to mitigate the adverse effects that water has on the chromatography and detector sensitivity and reliability. Several researchers have reported the use of cryogenic trapping with two-dimensional chromatography to selectively remove water vapor from the analytical process (Pierotti, 1990; Cardin and Lin, 1991). Although this column "heart cutting" technique has been successful for selected compounds, additional studies are needed to determine its potential use for the wide range of PVOCs. Ogle et al. (1992) developed a novel water management system based on the condensation of moisture from the saturated carrier gas stream during thermal desorption of a cryogenic trap. The moisture management system was found to be effective for reducing the amount of water delivered to the column during laboratory analyses of spiked mixtures. However, the system has not yet been extended to field monitoring. Gordon and Miller (1989) have used cryogenic trapping and

3-116GC/MS spectrometry techniques to demonstrate the potential of chemical ionization (CI) within an ion trap to detect PVOCs. The water vapor present in the sample served as the CI reagent gas and appeared to be an effective reagent gas; however, deleterious chromatography results were also encountered. The authors concluded that further laboratory work is needed before this methodology can be applied to ambient air monitoring. Martin et al. (1991) also reported the use of cryogenic trapping with a GC-FID system to measure ambient levels of isoprene and two of its oxidation products, methacrolein and methylvinyl ketone (detection level of 0.5 ppb). The water vapor was selectively removed by using a potassium carbonate (K 2 CO 3 ) trap ahead of the cryogenic trap. Frequent replacement of the K 2 CO 3 trap was required. The use of solid adsorbents for sample preconcentration of PVOCs has been reported by Kelly et al. (1993). The analytical method was used extensively at two field sites that formerly were used in EPA’s Toxic Air Monitoring Study (TAMS). The analytical method consisted of GC separation of PVOCs with quantification by a ion-trap mass spectrometer. A two-stage adsorbent trap containing Carbopack B and Carbosieve S-III (Supelco catalog number 2-0321) was used to separate water vapor from the PVOCs. The optimum room temperature trapping and drying procedure consisted of a 320-cc sample (100 cc/min) followed by a dry nitrogen purge of 1,300 cc (100 cc/min). The trap was then backflushed and thermally desorbed with helium at 220 °C. A 5-min, 260 °C trap bakeout followed each collection-analysis cycle. The target list contained 14 PVOCs, including alcohols, ethers, esters, and nitrile species. Individual detection limits ranged from 0.2 to 1 ppb.



### 3.5.3 Sampling and Analysis of Nitrogen Oxides
3.5.3.1 Introduction
The measurement of NO

x in ambient air is of interest because of the role that certain of those compounds play as precursors to O 3 and because NO 2 has been shown to impact health effects. Most of the NO

x emitted from combustion sources are NO and NO 2 . Collectively these two compounds are called NO

x . They contribute to O 3 formation by means of reactions discussed in Section 3.2. As a result, measurement of NO

x is important in efforts
to understand and control O 3 and NO 2 in ambient air. The atmospheric photochemistry that produces O 3 also results in conversion of NO
and NO 2 to products such as HNO 3 , nitrous acid (HNO 2 ), organic nitrates such as PAN (CH 3 C(O)O 2 NO 2 ), and other species. The total of all of these labile nitrogen species in air,
NO

x included, is termed NO y . Such compounds may be labile via photolysis (e.g., HNO 2 ) or thermal decomposition (e.g., PAN), and may be toxic, irritating, or acidic. The organic nitrates can occur in the atmosphere as reservoirs for NO 2 . However, in general, they do not play the same critical role that NO 2 and NO play as O 3 precursors. For that reason, this
section focuses on measurement methods for NO and NO 2 , as the primary O 3 precursors of
NO

x . Nitrogen oxides other than NO

x may be important, however, as interferents in efforts to
measure NO and NO 2 . These non-NO

x species are considered in this section in that regard.
Measurements of NO

x may involve measurements of NO, of NO 2 , or of the sum of
NO

x . Nitrogen dioxide, but not NO, is a criteria air pollutant, and, thus, reference and equivalent methods are specified for NO 2 measurements. In this section, the current state of
measurement methods for NO and NO 2 will be summarized separately. Such methods in some cases rely on measurements of total NO

x , or at least an approximation of NO

x . This

3-117discussion focuses on current methods and on promising new technologies, but no attempt is made here to cover the extensive history of the development of these methods. More detailed discussions of such methods may be found elsewhere (U.S. Environmental Protection Agency, 1993c; National Aeronautics and Space Administration, 1983). Wet chemical methods are no longer commonly used and are not discussed here; a review of such methods is given by Purdue and Hauser (1980).

3.5.3.2 Measurement of Nitric Oxide Gas-Phase Chemiluminescence Methods By far the most common method of NO measurement is gas-phase CL with O 3 . In this method, excess O 3 is added to air containing NO in a darkened, internally reflective chamber monitored by a photomultiplier tube. A small portion of the NO reactions with
O 3 produce electronically excited NO 2 molecules that decay by emission of light of wavelengths longer than 600 nm. The emitted light is detected by a red-sensitive photomultiplier tube, through an optical filter that prevents passage of wavelengths shorter than 600 nm. This optical filtering minimizes interference from CL produced by O 3 reactions with other species (e.g., hydrocarbons). The excited NO 2 is readily quenched in air, so that, in typical instruments, air and O 3 are mixed at reduced pressure (i.e., at least 20 in. of Hg vacuum). The intensity of the emitted light is linearly proportional to the NO content of the sample air over several orders of magnitude in concentration.
Commercial CL instruments for continuous measurement of NO are available from several manufacturers. The chemiluminescence approach is also an EPA-designated measurement principle for measuring ambient NO 2 ; it requires a means of converting NO 2 to NO for detection. The complexities of this conversion are discussed in Section 3.5.3.3, on
NO 2 methods. The commercial NO monitors typically are claimed to have detection limits of a few parts per billion by volume in air, with response time of a few minutes. Field
evaluations of several commercial instruments have indicated that minimum levels of
detection for NO 2 are 5 to 13 ppbv (Michie et al., 1983; Holland and McElroy, 1986). However, more recent evaluations have indicated better performance. Rickman et al. (1989) reported detection limits of 0.5 to 1 ppbv, and precision of ±0.3 ppbv, from laboratory and field evaluations of two commercial instruments operated on their 50 ppbv full-scale ranges. Commercial NO analyzers are portable and quite reliable and now are commonly used in ambient air monitoring networks. Commercial NO analyzers may not have sensitivity sufficient for surface measurements in urban, rural, or remote areas, or for airborne measurements. As a result, several investigators have devised modifications to commercial instruments to improve their sensitivity and response time (Delany et al., 1982; Tanner et al., 1983; Dickerson et al., 1984; Kelly et al., 1986). Those modifications include operating at low pressure and high sample flow rate; using a larger, more reflective reaction chamber that promotes mixing of the reactants close to the photomultiplier tube; increasing the O 3 supply; for example, by use of oxygen in the O 3 source; cooling of the photomultiplier to reduce noise; adopting photon-counting techniques for light detection; and adding a prereactor to obtain a more stable and appropriate background signal. Commercial instruments modified in these ways are generally reported to have detection limits of 0.1 ppbv or less, with response times of 30 s
or less. Research-grade NO instruments specially designed for ultra-high sensitivity also have been built for use in remote ground-level or airborne applications (e.g., Ridley and

3-118Howlett, 1974; Kley and McFarland, 1980; Kelly et al., 1980; Helas et al., 1981; Drummond et al., 1985; Torres, 1985; Kondo et al., 1987; Parrish et al., 1990). These instruments typically have detection limits of 10 ppt (i.e., 0.01 ppbv) or less, with response times from a
few seconds to 1 min. A number of studies indicate that the CL method is essentially specific for NO. Operation at reduced pressure prevents interference resulting from quenching by water vapor (Michie et al., 1983; Drummond et al., 1985). In air sampling, no significant interferences have been found in NO detection from sulfur-, chlorine-, and nitrogen-containing species (Joshi and Bufalini, 1978; Sickles and Wright, 1979; Grosjean and Harrison, 1985b; Fahey et al., 1985). However, H 2 S and possibly other sulfur-containing compounds from seawater have been reported to give false NO signals (Zafiriou and True, 1986). This effect should not be important for ambient air measurements. Fahey et al. (1985) and Drummond et al. (1985) also reported no significant NO interference from a variety of other nitrogen-containing species, including NO 2 , HNO 3 , PAN, N 2 O 5 , NH 3 , HCN, N 2 O, and HO 2 NO 2 ; as well as no
interference from CH 4 , propylene, and H 2 O 2 . Several ambient air intercomparisons have been done of CL NO instruments (Walega et al., 1984; Hoell et al., 1987; Fehsenfeld et al., 1987; Gregory et al., 1990a). These studies have focused on high-sensitivity research instruments, rather than the commercial instruments used for widespread ambient air measurements. These studies have shown excellent agreement among the CL NO instruments, even at NO levels in the low ppt range (Hoell et al., 1987; Gregory et al., 1990a). These results support the validity of the CL approach for NO. Good agreement also has been found between CL measurements and spectroscopic NO measurements in these studies (see Section 3.5.3.2).

Spectroscopic Methods for Nitric Oxide Direct spectroscopic methods for NO include two-photon laser-induced fluorescence (TPLIF), TDLAS, and two-tone frequency-modulated spectroscopy (TTFMS). The primary characteristics of these methods are their very high sensitivity and selectivity for NO. For example, a detection limit of 10 ppt has been quoted for TPLIF with a 30-s integration time, with no significant interferences from atmospheric species (Davis et al., 1987). An accuracy of ±16% as a 90% confidence limit has been calculated for NO measurement by TPLIF from an aircraft (Davis et al., 1987). The TDLAS method is similarly highly selective for NO and achieves a detection limit of 0.5 ppbv (Schiff et al., 1983). The response time of the TDLAS instrument is about 1 min for NO, and is limited by stabilization of concentrations with the large surface area of the multi-pass White cell. The newest method is TTFMS, which appears in laboratory studies to be very sensitive, fast, and selective. With a 100-m path length in a 20-torr multiple-pass cell, and a 1-min averaging time, the detection limit of NO is estimated to be 4 ppt (Hansen, 1989). Spectroscopic methods have compared well with the CL method for NO in ambient measurements. Walega et al. (1984) reported good agreement between CL and TDLAS results for NO in laboratory air, in ambient air, and in downtown Los Angeles air. Gregory et al. (1990a) reported comparisons of TPLIF and CL NO methods in airborne measurements. Agreement at levels below 20 ppt was within the expected accuracy and precision of the instruments (i.e., within 15 to 20 ppt). The major drawbacks of these spectroscopic methods are their complexity, size, and cost. Although possessing remarkable characteristics, these methods are restricted to

3-119research applications. The TTFMS approach, in fact, is still in the laboratory development stage.

Passive Samplers At present, no passive sampler exists that directly measures NO. Instead, passive samplers developed for NO 2 have been adapted for NO measurement, using an oxidizing
material that converts NO to NO 2 . Palmes tubes (Palmes and Tomczyk, 1979) have been adapted for NO measurement by using two tubes in parallel. One tube collects NO 2 on a triethanolamine (TEA)-coated grid, whereas the other collects NO 2 on a TEA grid, plus NO oxidized by a chromic acid-coated surface. The grids are then extracted and analyzed for
NO 2 - ion. Nitric oxide is determined by difference between the two results, after accounting
for the different diffusivities of NO and NO 2 . The sampling rates depend on temperature and air velocity. The tubes cannot be used for periods longer than 24 h and are intended for use at ppm NO levels important in the workplace (e.g., 2 to 200 ppm h). Applicability to
ambient NO levels has not been demonstrated. A more sensitive passive sampler for NO has been reported (Yanagisawa and Nishimura, 1982) that uses the same TEA chemistry, with CrO 3 as the NO oxidizer. A detection limit of 70 ppbv-h has been reported. As with any currently available passive sampler, the disadvantages of the method are the potential for interferences, relatively poor precision, and low sensitivity for ambient air measurements.

3.5.3.3 Measurements for Nitrogen Dioxide Gas-Phase Chemiluminescence Methods In 1976, the gas-phase CL approach described above for NO detection was designated as the measurement principle on which EPA reference methods for ambient NO 2must be based. The CL method thus filled the vacancy left by withdrawal of the JacobsHochheiser method, because of technical problems, in 1973. To be designated as a reference method, an NO 2 detection method must use the CL approach and be calibrated by the specified methods (gas-phase titration of NO with O 3 , or use of an NO 2 permeation device). In addition the instrument must meet the performance specifications shown in Table 3-18. An equivalent method, either manual or automated, must meet certain requirements for comparability with a reference method when measuring simultaneously in a real atmosphere. Those comparability requirements are shown in Table 3-19. An automated equivalent method must also meet the performance requirements shown in Table 3-18.
The selection of the O 3 -CL method as the reference measurement principle for
ambient NO 2 was the result of comparison tests of CL and wet chemical methods. Chemiluminescence analyzers were found superior to the wet chemical methods in response time, zero and span drift, and overall operation, although agreement among all the methods tested was good, at the NO 2 spike levels provided (Purdue and Hauser, 1980). Table 3-20 lists the methods currently designated (as of August 1, 1994) by EPA as reference and equivalent methods for ambient NO 2 . Three wet chemical methods are shown as equivalent methods, but these rarely are used for ambient air measurements.
The O 3 CL method does not measure NO 2 directly, because the CL is produced by
reaction of NO with O 3 . As a result, NO 2 must first be reduced to NO for detection. In principle, such a reduction should readily result in measurement of NO + NO 2 (i.e.,

3-120Table 3-18. Performance Specifications for Nitrogen Dioxide Automated Methods a

Performance Parameter Units NO 2 Range ppm 0-0.5

Noise

0% Upper range limit 80% Upper range limit ppm
ppm 0.005
0.005 Lower detectable limit ppm 0.01 Interference equivalent

Each interferant (SO 2 ,NO,NH 3 ,H 2 O)
Total interferant ppm
ppm ±0.02

≤ 0.04 Zero drift, 12 and 24 hours ppm ±0.02 Span drift, 24 hours

20% Upper range limit 80% Upper range limit %
% ±20.0
±5.0 Lag time min 20

Rise time min 15

Fall time min 15

Precision

20% Upper range limit 80% Upper range limit ppm
ppm 0.02
0.03

a See Appendix A for abbreviations and acronyms.

Source: Code of Federal Regulations (1987), Ambient Air Monitoring Reference and Equivalent Methods, C.F.R. Title 40, Part 53.

Table 3-19. Comparability Test Specifications for Nitrogen Dioxide Nitrogen Dioxide Concentration Range (ppm) Maximum Discrepancy Specification (ppm)

Low 0.02 to 0.08 0.02
Medium 0.10 to 0.20 0.02 High 0.25 to 0.35 0.03

NO

x ), and allow indirect measurement of NO 2 by difference between NO and NO

x responses, measured either sequentially, or simultaneously by separate detectors. In practice, however,
selective measurement of NO

x by this approach has proven very difficult. Several methods have been employed to convert NO 2 to NO, including catalytic reduction with heated molybdenum or stainless steel, reaction with CO over a gold catalyst surface, reaction with ferrous sulfate (FeSO 4 ) at room temperature, reaction with carbon at 200 °C, and photolysis of NO 2 at wavelengths of about 320 to 400 nm (Kelly et al., 1986). It has been found in many separate investigations that the heated converters reduce NO 2 to

3-121Table 3-20. Reference and Equivalent Methods for Nitrogen Dioxide Designated by U.S. Environmental Protection Agency a

Method Designation
Number Method
Code

Reference Methods (Continuous CL Analyzers)
Advanced Pollution Instrumentation 200 RFNA-0691-082 082
Beckman 952A RFNA-0179-034 034
Bendix 8101-B RFNA-0479-038 038

Bendix 8101-C RFNA-0777-022 022

CSI 1600 RFNA-0977-025 025
Dasibi 2108 RFNA-1192-089 089 Lear Siegler ML9841 RFNA-1292-090 090 Meloy NA53OR RFNA-1078-031 031

Monitor Labs 8440E RFNA-0677-021 021

Monitor Labs 8840 RFNA-0280-042 042
Monitor Labs 8841 RFNA-0991-083 083 Philips PW9762/02 RFNA-0879-040 040
Thermo Electron 14B/E RFNA-0179-035 035
Thermo Electron 14D/E RFNA-0279-037 037
Thermo Environmental 42 RFNA-1289-074 074

Equivalent Methods (Wet Chemical) Sodium arsenite EQN-1277-026 084 Sodium arsenite/Technicon II EQN-1277-027 084

TGS-ANSA b EQN-1277-028 098

a As of August 1, 1994.

b Triethanolamine-guaiacol-sulfite with 8-amino-1-naphthalene-sulfonic acid ammonium salt.

NO effectively, but also reduce other NO y species as well (e.g., Winer et al., 1974; Cox, 1974; Joseph and Spicer, 1978; Grosjean and Harrison, 1985b; Fahey et al., 1985). Efficiencies of conversion near 100% are reported in these studies for NO 2 and for NO y species such as HNO 3 , HNO 2 , PAN, and organic nitrates. This finding is particularly important for widespread monitoring networks that use commercial instruments, because such instruments without exception use heated catalytic converters (typically molybdenum). Thus,
such instruments measure not NO and NO

x , but more nearly NO and total NO y . Although
NO

x is the predominant NO y species during early morning hours, other NO y species constitute a substantial percent of the NO y later in the day, especially in rural areas. The NO 2 value inferred from such measurements may be significantly in error (see below), and may in turn
affect the results of models of ambient O 3 . The completeness of the measured NO y value is also questionable because, for example, HNO 3 is readily lost to surfaces, and, in ambient sampling, may be removed within the sampling system before reaching the heated converter.

3-122Other conversion methods for NO 2 have been tried in an effort to achieve higher selectivity. Ferrous sulfate has been used for ambient NO 2 measurements using highsensitivity research grade CL instruments (e.g., Kelly et al., 1980; Helas et al., 1981; Dickerson et al., 1984). This material is an efficient reducer of NO 2 , but also has been found to convert a portion of PAN, and possibly a portion of HNO 2 and organic nitrates (Fehsenfeld et al., 1987). Memory effects and reduction in efficiency can occur because of humidity effects (Fehsenfeld et al., 1987). As a result of these characteristics, use of FeSO 4 has given high readings in comparison with spectroscopic instruments and the photolytic NO 2 converter, and its use likely results in overestimating ambient NO

x by a significant amount (Fehsenfeld et al., 1987; Ridley et al., 1988a; Gregory et al., 1990b). Ferrous sulfate has never been used
in commercial NO

x instruments and is no longer used in research measurements. The most specific method for converting NO 2 to NO is photolysis (Kley and McFarland, 1980). In the most common approach, ambient NO 2 is photolyzed to NO by light of 350 to 410 nm from a xenon arc lamp. The method does not produce NO from the major potential interferents present in air (i.e., HNO 3 , PAN, and organic nitrates), but less abundant
NO y species such as HNO 2 or HO 2 NO 2 may interfere. A detailed description of steps to minimize such interferences is given by Ridley et al. (1988b). As currently used, the photolytic converter appears to be essentially specific for NO 2 . However, it does not provide complete conversion of NO 2 . Conversion efficiencies are 50 to 60% with a new lamp but may decline to 20% over the course of several weeks (Parrish et al., 1990). Thus, the conversion efficiency must be calibrated repeatedly. This approach has not been implemented with commercial NO detectors but has been implemented with research-grade CL NO
instruments for studies of NO

x and NO y chemistry at a variety of locations (e.g., Buhr et al., 1990; Parrish et al., 1990; Trainer et al., 1991; Parrish et al., 1992, 1993). The photolytic method compares well with other techniques, including spectroscopic methods, even at NO 2 levels as low as 0.05 ppbv (Gregory et al., 1990b). Further improvement of the photolytic converter approach is continuing. Bradshaw et al. (1994) reported on plans to minimize wall effects in the photolytic converter and to use a metal halogen lamp in place of the xenon arc lamp. The metal halogen lamp emits strongly in the proper wavelength region and is much less expensive than the xenon arc lamp, allowing more frequent replacement of the lamp and consequently higher long-term photolytic efficiency. As noted above, the commercial CL analyzers used for most ambient air NO and
NO

x measurements actually measure NO and NO y . The magnitude of the resulting
overestimation of NO 2 , determined by difference, obviously depends on the portion of NO y that is NO

x . The smaller the portion of NO y that is NO

x , the greater will be the error in the
NO 2 determined by difference. In rural/remote areas, where NO

x has undergone extensive conversion to other products during transport from a source region, NO

x may contribute a
small fraction of NO y . In urban areas, close to sources, NO

x may comprise nearly all of NO y . For example, in measurements at Point Arena, Parrish et al. (1992) report NO

x /NO y ratios averaging 0.3 in air of marine origin and 0.75 in air subject to continental influence. Buhr et al. (1990) and Parrish et al. (1993) reported measurements at rural sites in eastern North
America that indicate NO

x /NO y ratios ranging from about 0.25 to 0.75, varying with the time of day, with the lowest ratios occurring during daytime, photochemically active periods. Clearly, although the commercial CL instruments are designated as reference methods for
NO 2 , the great majority of existing ambient air data for NO 2 or NO

x are biased high, due to the inclusion of some portion of other NO y species. The magnitude of this bias may not be large in urban areas, but, in any case, it is essentially unknown at this time.

3-123Luminol Chemiluminescence Method This approach is based on the CL reaction of gaseous NO 2 with the surface of an aqueous solution of luminol (5-amino-2,3-dihydro-1,4-phthalazinedione). Emission occurs primarily between 380 and 520 nm. In commercial instruments, luminol solution flows down a fabric wick that lies vertically on a clear window viewed by a photomultiplier tube. Nitrogen dioxide in sample air passing over the wick produces light, the intensity of which is proportional to the NO 2 concentration. Commercial instruments using this approach are compact, light, and relatively inexpensive and can provide detection limits as low as 0.01 ppbv, with response times below 30 s. The instrument has the advantage of detecting
NO 2 directly. However, several difficulties have had to be dealt with in developing the
method. Original reports of the approach (Maeda et al., 1980) indicated positive
interferences from O 3 and SO 2 and a negative one from CO 2 . Reformulation of the luminol reagent solution has minimized, although not fully eliminated, those interferences (Wendel et al., 1983; Schiff et al., 1986). Reported effects include a slight negative response from NO, and sensitivity to PAN, HNO 2 , and O 3 (Wendel et al., 1983; Schiff et al., 1986; Rickman et al., 1989; Kelly et al., 1990; Spicer et al., 1991). Response to NO 2 may be nonlinear at low concentrations (Kelly et al., 1990), although recent reformulation of the reagent apparently has reduced this behavior (Busness, 1992). Evaluation of the luminol NO 2 monitor indicates that great care must be taken in using and calibrating the instrument in order to achieve good precision and accuracy in ambient measurements (Kelly et al., 1990). The monitor has been used widely as a research tool, but has not been used widely in ambient air monitoring nor has it been designated an equivalent method for NO 2 . An O 3 scrubber is available to eliminate the O 3 interference noted above, but it also was found to remove a portion of the NO 2 (Kelly et al., 1990). The luminol approach also has been modified to measure NO, by using a CrO 3 converter that oxidizes NO to NO 2 for detection. Thus NO is detected by difference. This method has the potential for
measurement of total NO

x ; however, evaluations of the CrO 3 converter are still underway at several laboratories. Given the known interferences in the luminol approach, careful evaluation of this method must be completed before it gains acceptance as an NO
measurement method. An adaptation of the commercial luminol NO 2 detector has been reported to provide measurements of total NO y , NO 2 , and NO

x (Drummond et al., 1992). This adaptation, called the LNC-3M, uses a commercial luminol instrument for NO 2 detection, with a
CrO 3 converter for NO

x detection. The NO

x measurement must be corrected for the few percent of the ambient NO 2 that is lost in the CrO 3 converter (Drummond et al., 1992). The
NO y measurement is achieved using a stainless steel converter maintained at 400 °C. Tests indicate that this converter provides a more complete conversion of alkyl nitrates, and consequently a more complete measurement of NO y , than is provided by either the heated molybdenum converters used in commercial O 3 CL NO

x detectors or the gold converters with CO addition used in research instruments (Drummond et al., 1992). The LNC-3M adds a
small amount of NO 2 to the sample to eliminate the nonlinearity at low concentrations, and uses a zeroing scrubber that greatly reduces the interference from PAN. However, this scrubber must be replaced weekly when it is in continuous use (Drummond et al., 1992).

Spectroscopic Methods

3-124Several spectroscopic approaches to NO 2 detection have been developed; TDLAS, TTFMS, DOAS, and differential absorption lidar (DIAL) are absorption methods that have been used. The TDLAS method is probably the most commonly used spectroscopic NO 2 method. It can provide high selectivity for NO 2 , with a detection limit of 0.1 ppbv, accuracy of ±15%, and a response time on the order of 1 min because of the White cell (Mackay and Schiff, 1987b). The DOAS method is an open-path, long-pathlength system. The detection
limit for NO 2 with a 0.8-km pathlength and 12-min averaging time has been reported as 4 ppbv, with measurement accuracy reported as ±10% (Biermann et al., 1988). However, recent improvements have resulted in a commercial DOAS instrument capable of an NO 2 detection limit of 0.6 ppbv, based on a 557-m path and a 1-min averaging time (Stevens et al., 1993). The detection limit for NO 2 by the DIAL technique has been reported as 10 ppbv with a 6-km pathlength (Staehr et al., 1985). The novel TTFMS method noted above for NO is reported to have an NO 2 detection limit of 0.3 ppt, but is not fully proven for ambient

measurements. Fluorescence methods also have been used for NO 2 , including photofragmentation TPLIF (PF/TPLIF) (Davis, 1988). This method uses two cells in which NO is measured by TPLIF. In one of the cells, an excimer laser emitting at 353 nm photolyzes NO 2 to NO for
detection. Thus NO 2 is ultimately measured, by difference, as NO, but the NO is formed directly by photolysis of NO 2 . With a 2-min integration time, an NO 2 detection limit of 12 ppt is reported. The method is highly selective for NO 2 , because an interferant would have to photolyze to produce NO. Several potential atmospheric species have been ruled out in this regard (Davis, 1988). The drawbacks of most of these methods are, as noted earlier, complexity, size, and cost. At present, these factors outweigh the obvious advantages of the sensitivity and selectivity of these spectroscopic methods and largely have restricted the use of these
NO 2 methods to specific research applications or as reference methods in intercomparisons. In such intercomparisons, absorption measurements have been used most commonly. The TDLAS method has been used in ground-level comparisons with O 3 CL and luminol instruments to provide specific NO 2 measurements (Walega et al., 1984; Sickles et al., 1990; Fehsenfeld et al., 1990) and in an airborne comparison with PF/TPLIF and O 3 CL instruments (Gregory et al., 1990b). A finding of these studies was that the TDLAS consistently read higher than other established methods at very low NO 2 levels (i.e., <0.4 ppbv) (Fehsenfeld et al., 1990; Gregory et al., 1990b). The spectroscopic NO 2 method most fully developed beyond the research stage is the DOAS technique. Stevens et al. (1993) report testing of a commercial DOAS instrument in North Carolina over 17 days in the fall of 1989. The DOAS measured NO 2 using wavelengths between 400 and 460 nm and achieved a detection limit of 0.6 ppbv, as noted
above. Simultaneous measurements of O 2 , SO 2 , HCHO, and HNO 2 also were provided by the DOAS instrument. Comparison of the DOAS NO 2 results to those from a commercial CL detector showed (DOAS NO 2 ) = 1.14 × (CL NO 2 ) + 2.7 ppbv, with an r 2 of 0.93, at NO 2 levels up to 50 ppbv (Stevens et al., 1993). The sensitivity, stability, response time, and multicomponent capability are the primary advantages of the DOAS approach. Further intercomparisons and interference tests are recommended (Stevens et al., 1993).

Passive Samplers

3-125Passive samplers are attractive, inexpensive, and simple means to obtain long-term or personal exposure data for NO 2 or NO

x . The simplest passive sampler for NO 2 is the nitration plate, which is essentially an open dish containing filter paper impregnated with TEA. Nitrogen dioxide diffuses to the paper and is extracted later as NO 2 - for analysis. No diffusion barrier exists in this approach or in a similar approach using a candle-shaped absorber (Kosmus, 1985). Consequently, results are very much subject to ambient conditions and give, at best, a qualitative indication of NO 2 or NO

x . Addition of a diffusion barrier to the nitration plate concept has led to badge-type passive samplers for NO 2 (e.g., Mulik and Williams, 1986, 1987; Mulik et al., 1989, 1991). In general, such devices use perforated screens, plates, or filters as diffusion barriers on the chemically reactive material, which may be exposed on one or both sides, depending on the application. Extraction of the sorbent then allows measurement of the NO 2 collected, typically as NO 2 - ion. Such a device using TEA as the active material gave very good agreement relative to a CL analyzer in laboratory tests with NO 2 at 10 to 250 ppbv (Mulik and Williams, 1987). However, interferences from PAN and HNO 2 (the latter in both outdoor and indoor air) are expected (Sickles and Michie, 1987). Comparison of ambient NO 2 results in the 5 to 25 μg/m 3 range (i.e., about 2.5 to 12.5 ppbv) from the passive device to those from TDLAS showed good agreement on average values, but a correlation coefficient (r) of only 0.47 on daily values (Mulik et al., 1989). Badge-type personal samplers for NO 2 also have been developed by Yanagisawa and Nishimura (1982) (YN) and by Cadoff and Hodgeson (1983) (CH). Triethanolamine is used as the active collecting medium in both samplers, and both use colorimetry as the analytical method for detection of NO 2 - . The samplers differ in that the YN device uses TEA-coated on a cellulose filter, with a Teflon® filter as a diffusion barrier; whereas the CH sampler uses TEA-coated on a glass fiber filter, with a polycarbonate filter as a diffusion barrier. Detection limits are reported to be 0.07 ppm h (Yanagisawa and Nishimura, 1982) and 0.06 ppm h (Cadoff and Hodgeson, 1983) for the YN and CH samplers, respectively.
Interferences from PAN and HNO 2 are expected (Sickles and Michie, 1987); likewise, the devices are sensitive to the speed of ambient air movement. Palmes tubes have been developed for NO 2 measurement and adapted to NO measurement as described above. The device has been used for workplace and personal exposure monitoring (Wallace and Ott, 1982) and for ambient air measurements (U.S. Environmental Protection Agency, 1993c). A detection limit of 0.03 ppm h can be achieved
if IC is used to determine the extracted NO 2 - (Mulik and Williams, 1986). Adsorption of NO 2 to the tube walls may raise this limit considerably (Miller, 1988), but this effect can be counteracted by use of stainless steel tubes. The device is sensitive to temperature and wind speed, and PAN and HNO 2 are likely interferences (Sickles and Michie, 1987). In a comparison with two commercially produced NO 2 passive samplers, the Palmes tube showed reasonable accuracy and precision at loadings of 1 to 80 ppm h. However, the commercial devices were designed for use at relatively high loadings; therefore, this comparison does not support the use of Palmes tubes for ambient air monitoring.

3.5.3.4 Calibration Methods Calibration of NO measurement methods is done using standard cylinders of NO in nitrogen. Typical NO concentrations in such cylinders are 1 to 50 ppmv. Dilution of such standards with clean air using mass flow controllers can accurately provide NO concentrations in the ambient (i.e., 1 to 100 ppbv) range for calibration. Nitric oxide standards are available

3-126as SRMs from NIST and as commercially available Certified Reference Standards. Commercially available certified NO standards have been shown to be stable and accurate for the specified concentrations. Standard cylinders of NO 2 in nitrogen or air are sometimes used for NO 2calibration. These standards are commercially available and are readily diluted to parts-perbillion-volume levels in the same manner as for NO standards. However, instability of the
NO 2 levels in such standards has been reported, and caution must be used in relying on NO 2 standards as the primary means of calibration.
Two calibration methods for NO 2 are specified in the Code of Federal Regulations (1987) for calibration of ambient NO 2 measurements: (1) permeation tubes and (2) gas-phase
titration.
An NO 2 permeation tube is an inert enclosure, generally of Teflon®, glass and Teflon®, or stainless steel and Teflon®, that contains liquid NO 2 . As long as liquid NO 2 is present, NO 2 will permeate through the Teflon® at a rate that depends on the temperature of the tube. Maintaining the permeation tube at a constant temperature (i.e., ±0.1 °C) results in permeation of NO 2 at a constant rate. Dilution of the emitted NO 2 with a flow of dry air or
N 2 results in known low NO 2 concentrations for calibration. Nitrogen dioxide permeation tubes are supplied as SRMs by NIST, and tubes are commercially available with a wide range of permeation rates. Permeation tubes are small, simple, reliable, and relatively inexpensive, although constant temperature ovens and dilution systems are required to obtain good results. Nitrogen dioxide permeation tubes are susceptible to moisture, and changes in permeation rate or emission of other species (HNO 3 , HNO 2 , NO) may occur if they are not kept dry. As with
NO 2 cylinder standards, the NO 2 permeation tube requires care as a calibration method for
NO 2 . Gas-phase titration uses the rapid reaction of NO with O 3 to produce NO 2 with 1:1 stoichiometry. In practice, excess NO generated from a standard cylinder containing 50 to 100 ppmv NO is reacted with O 3 from a stable source. The resultant decrease in NO concentration, usually measured on the NO channel of a CL NO

x analyzer, equals the
concentration of NO 2 generated. Varying amounts of NO 2 can be produced by varying the
amount of O 3 .



## 3.6 Ozone Air Quality Models
To plan control strategies to achieve compliance with the NAAQS for O 3 at some future date, it is necessary to predict how O 3 concentrations change in response to prescribed changes in source emissions of precursor species (NO

x and VOCs). This assessment requires an air quality model, which in the case of O 3 prediction is often called a photochemical air quality model. The model, in effect, is used to determine the emission reductions needed to
achieve the O 3 air quality standard. For at least a decade, EPA has offered guidelines on the selection of air quality modeling techniques for use in SIP revisions, new source reviews, and studies aimed at the prevention of significant deterioration of air quality. It is worth noting the interrelated nature of O 3 and other air quality issues. Ozone,
PM 10 , visibility, and acid deposition are all connected as a result of similar sources and complex chemical mechanisms. Consequently, strategies for O 3 abatement that involve
reductions of VOC and NO

x emissions also will impact particulate matter, visibility, and acid deposition.

3-127Ozone air quality models provide the ability to address "what if" questions, such as, what if emissions of VOCs or NO

x are reduced? The model can be used as an experiment that cannot be run in the atmosphere. Sensitivity questions can be asked, such as, how important is emissions change A relative to emissions change B? or what is the effect of an X% uncertainty in a particular chemical reaction rate constant on the predicted O 3 levels? Models are the ultimate integrators of our knowledge of the comprehensive chemistry and physics of the atmosphere. As such, they are an indispensable tool for understanding the complex interactions of transport, transformation, and removal in the atmosphere. Models are useful in the design of field measurement programs and are essential in the interpretation of data from such programs. Models can be verified by the demonstration of agreement between observations and predictions, but confirmation is inherently partial. Verification of mathematical models of natural systems is always incomplete because complete information on the natural system is never available. Furthermore, model results always include some degree of nonuniqueness because model inputs and parameters are never precisely known. Ozone air quality model applications are most reliable in the domain and conditions where model predictions have been evaluated by extensive, valid data and the comparisons of observations and predictions fall within accepted guidelines. Historically, the primary measure of model performance has been degree of agreement between observed and predicted O 3 concentrations during simulated episodes, although it now is recognized that comparisons of observations and predictions for other compounds, such as organics and NO y components, are also important in assessing model performance. The purpose of Section 3.6 is to review briefly the main elements of O 3 air quality models, to describe several of the current models, to discuss the performance evaluation of these models, and to present examples of the use of the models for determining VOC and
NO

x control strategies.



### 3.6.1 Definitions, Description, and Uses Air quality models are mathematical descriptions of the atmospheric transport, diffusion, removal, and chemical reactions of pollutants. These models operate on sets of input data that characterize the emissions, topography, and meteorology of a region and produce outputs that describe air quality in that region. Mathematical models for photochemical air pollution first were developed in the early 1970s and have been improved, applied, and evaluated since that time. Much of the history of the field is described in reviews by Tesche (1983), Seinfeld (1988), and Roth et al. (1990). Photochemical air quality models include treatments of the important physical and chemical processes that contribute to O 3 formation in and downwind of urban areas. In particular, such models contain a representation of the following phenomena (Roth et al., 1990): · Precursor emissions. The spatial and temporal characteristics of reactive hydrocarbon, CO, and NO

x emissions sources must be supplied as inputs to the model. Hydrocarbon emissions generally are apportioned into groups (e.g., alkanes, alkenes, aromatics, etc.) according to the speciation requirements of the
chemical kinetic mechanism embedded in the model.

3-128· Pollutant transport. Once the O 3 precursors are emitted into the atmosphere, they are transported by the wind. When O 3 is formed, it also is subject totransport by the wind. Grid-based models require the preparation of threedimensional, time-varying fields of wind speed and direction. These values must be specified for each grid cell. Cloud venting and mixing processes that are important on the regional scale also can be included in the pollutant transport description.· Turbulent diffusion. Ozone and its precursors also are subject to turbulencerelated dispersion processes that take place on a subgrid scale. These turbulent diffusion effects usually are represented in grid-based models by the so-called gradient transport hypothesis, where the pollutant flux is assumed to be proportional to the spatial gradient in the concentration field. The turbulent diffusivities employed in the model are dependent on atmospheric stability and other meteorological variables. · Chemical reactions. Ozone results from chemical transformations involving reactive organics and NO

x (See Section 3.2). A chemical kinetics mechanism representing the important reactions that occur in the atmosphere is employed to estimate the net rate of change of each pollutant simulated by the model. Description of chemical reactions requires actinic flux, cloud cover, temperature, and relative humidity. · Removal processes. Pollutants are removed from the atmosphere via interactions with surfaces at the ground, so-called "dry deposition", and by precipitation, called "wet deposition". Guidelines issued by EPA (U.S. Environmental Protection Agency, 1986b) identify two kinds of photochemical model: (1) the grid-based UAM is the recommended model for modeling O 3 over urban areas, and (2) the trajectory model EKMA is identified as anacceptable approach. The 1990 CAAA (U.S. Congress, 1990) mandate that threedimensional, or grid-based, air quality models, such as UAM, be used in SIPs for
O 3 nonattainment areas designated as extreme, severe, serious, or multistate moderate (U.S. Environmental Protection Agency, 1991b).

3.6.1.1 Grid-Based Models The basis for grid-based air quality models is the atmospheric diffusion equation that expresses the conservation of mass of each pollutant in a turbulent fluid in which chemical reactions occur (Seinfeld, 1986). The region to be modeled is bounded on the bottom by the ground, on the top by some height that characterizes the maximum extent of vertical mixing, and on the sides by east-west and north-south boundaries. The choice of the size of the modeling domain will depend on the spatial extent of the O 3 problem, including the distribution of emissions in the region, the meteorological conditions, and, to some extent, the computational resources available. This space then is subdivided into a three-dimensional array of grid cells. The horizontal dimensions of each cell are usually a few kilometers for urban applications up to tens of kilometers for regional applications. Some older grid-based models assumed only a single, well-mixed vertical cell extending from the ground to the inversion base; current models subdivide the region into layers. Vertical dimensions can vary, depending on the number of vertical layers and the vertical extent of the region being modeled. Increasing the vertical resolution in the computation should be accompanied by increased vertical resolution of the physical parameters used. A compromise generally must

3-129be reached between the better vertical resolution afforded by the use of more vertical layers and the associated increase in computing time. Although aerometric data, such as the vertical temperature profile, which are needed to define the vertical structure of the atmosphere, are generally lacking, it is still important to use enough vertical layers so that vertical transport processes are represented accurately. There are practical and theoretical limits to the minimum horizontal grid cell size. Increasing the number of cells increases computing and data acquisition effort and costs. In addition, the choice of the dimension of a grid cell implies that the input data information about winds, turbulence, and emissions, for example, are resolved to that scale. The spatial resolution of the concentrations predicted by a grid-based model corresponds to the size of the grid cell. Thus, effects that have spatial scales smaller than those of the grid cell cannot be resolved. Such effects include the depletion of O 3 by reaction with NO near strong sources of
NO

x like roadways and power plants. Ozone predictions are sensitive to the choice of grid cell size. The use of a larger grid tends to smooth out VOC and NO

x precursor concentrations, affecting the computed chemical production of O 3 . Multigrid models, in which a region with a finer grid resolution is embedded within a larger grid, are an approach
to obtain a better resolution of O 3 formation processes in regions of intense source emissions (Odman and Russell, 1991). Jang (1992) has examined the sensitivity of O 3 predictions to model grid resolution in regional air quality models. A high-resolution version of the Regional Acid Deposition Model (RADM) was used to simulate O 3 formation over the northeastern United States at different grid resolutions. The high-resolution version of RADM, with horizontal grid cell sizes of 20, 40, and 80 km, was operated within the 80-km RADM domain. Coarser grid
sizes were found to result in lower resolved emission intensities of NO

x and VOCs. Because of the smearing effect of the large grid sizes, the coarser grid model tended to underpredict
the O 3 highs in the areas downwind of cities and overpredict the O 3 lows in the intense NO

x emissions areas. It was found that the impact of model grid resolution on the chemistry of
NO

x is more important than that on the chemistry of VOCs, and that model grid resolution has no significant impact on the total amount of odd oxygen (O

x = O 3 + NO 2 ) produced in the models but has great impact on the interactions of chemistry and transport processes that
control the balance of O

x . As a result, the coarser grid model tends to predict higher O 3 and
lower NO 2 than does the finer grid model, and the coarser grid tends to transport O

x more in
the form of O 3 , whereas the finer grid model tends to transport the O

x more in the form of
NO 2 . Uncertainties arise in photochemical modeling from the basic model components (chemical mechanism and numerical techniques in solving the governing equations) and from inputs to the simulations that reflect the particular episode (boundary and initial conditions, emission inventory, wind field, and mixing depth). Sensitivity studies aim to determine the range of uncertainty in model predictions corresponding to ranges of uncertainty in the basic model components and input quantities. Such studies are valuable in pinpointing those quantities to which model predictions are most sensitive and, therefore, in directing future efforts in reducing the uncertainty in key parameters. These studies are also valuable in assessing the sensitivity of future air quality changes to uncertainties in the base case episode. It is not possible to state general, widely applicable levels of uncertainty for photochemical model inputs and parameters. These will depend on the particular region being modeled, and, in the case of meteorological and emissions inputs, may even depend on the time of day

3-130during the simulation. All model application exercises should include, to the extent possible, an analysis of the uncertainties in model inputs and parameters. Several grid-based photochemical air quality models have been developed to
simulate O 3 production in urban areas or in larger regions. They differ primarily in their treatment of specific atmospheric processes, such as chemistry, and in the numerical procedures used to solve the governing system of equations. These models will be reviewed
in Section 3.6.3.

3.6.1.2 Trajectory Models In the trajectory model approach, a hypothetical air parcel moves through the area of interest along a path calculated from wind trajectories. Emissions are injected into the air parcel and undergo vertical mixing and chemical transformations. The data requirements for trajectory models include: (1) initial concentrations of all relevant pollutants and species; (2) rates of emissions of VOC and NO

x precursors into the parcel along its trajectory; (3) meteorological characteristics, such as wind speed and direction, needed to define the path of the air parcel through the region; (4) mixing depth; and (5) solar ultraviolet radiation. The key assumption inherent in the trajectory model is that a hypothetical air parcel maintains its integrity along the trajectory. Almost certainly, the parcel assumption fails at night, when flows drift and the atmosphere stratifies; for hilly or mountainous terrain; and under convergence conditions. Thus, the trajectory model concept does not apply in many areas and under a variety of conditions (Liu and Seinfeld, 1975). Trajectory models provide a dynamic description of atmospheric source-receptor relationships that is simpler and less expensive to derive than that obtained from grid models. Trajectory models are designed to study the photochemical production of O 3 in the presence of sources and vertical diffusion of pollutants; otherwise the meteorological processes are highly simplified. A simple trajectory model is used in EKMA (Dodge, 1977a). This modeling approach relates the maximum level of O 3 observed downwind of an urban area to the levels
of VOCs and NO

x observed in the urban area. It is based on the use of a simple, one-cell moving box model. As the box moves downwind, it encounters emissions of organics and
NO

x that are assumed to be uniformly mixed within the box. The height of the box is allowed to expand to account for the breakup of the nocturnal inversion layer. As the height of the box increases, pollutants above the inversion layer are transported into the box. The model is first used to generate a series of constant O 3 lines (or isopleths) as depicted in Figure 3-25. The isopleths show the downwind, peak 1-h O 3 levels as a function of the
concentrations of VOCs and NO

x for a hypothetical urban area. These isopleths were generated by carrying out a large number of model simulations in which the initial concentrations and anthropogenic emissions of VOCs and NO

x were varied systematically, whereas all other model inputs were held constant. When it was first conceived, EKMA employed a very simple, highly empirical chemical mechanism and the isopleths generated were for a hypothetical situation in Los Angeles. As understanding of the chemical processes responsible for O 3 formation increased, the EKMA model was updated to include more complete representations of atmospheric chemistry. Although EKMA has employed the CBM-IV mechanism, the same mechanism that is currently being used in several grid-based models, the most recent version allows the input of any mechanism. The EKMA method is

3-131Figure 3-25. Example of Empirical Kinetic Modeling Approach diagram for high-oxidant
urban area.

Source: Derived from U.S. Environmental Protection Agency (1986a).

now used to generate city-specific isopleth diagrams using information on emissions, transport, and dilution that are appropriate to the particular city being modeled. City-specific O 3 isopleths can be used to estimate the reduction in NMHC or NO

x levels needed to achieve the NAAQS for O 3 in a specific urban area. The first step is to determine the early-morning NMHC/NO

x ratio for the urban area in question and the
maximum 1-h downwind O 3 concentration. Both the NMHC/NO

x ratio and the peak
O 3 concentration are obtained from air monitoring data. These two values define a point on the isopleth surface and, from this point, the percentage reductions in NMHC or NO

x , or both,
needed to achieve the O 3 NAAQS can be determined. As examination of Figure 3-25 reveals, for an NMHC concentration of 0.6 ppmC, for example, increasing NO

x leads to increased O 3 until NMHC/NO

x ratios of about 5:1 to 6:1 are reached; further NO

x increases, leading to lower NMHC/NO

x ratios, inhibit
O 3 formation. Thus, in this example, there is a "critical" ratio (in the range of 5:1 to 6:1) at
which the NO

x effect on O 3 changes direction. Besides this "critical" ratio, an "equal control"
NMHC/NO

x ratio also exists, above which the reduction of NO

x is more beneficial in terms of
O 3 reduction than an equal percentage reduction in NMHC. This ratio, for the isopleths

3-132shown in Figure 3-25, is roughly 8:1 to 9:1 for low levels of control and as high as 20:1 for
the levels of control needed to reduce O 3 to 0.12 ppm. Thus, for this particular case (Figure 3-25), the chemical mechanism modeling evidence suggests that NO

x control will increase the peak downwind O 3 concentration at NMHC/NO

x ratios of between 5:1 and 6:1 or lower; both
NO

x control and NMHC control will be beneficial at somewhat higher ratios, with control of NMHC being more effective; and, for ratios above 20:1, NO

x control is relatively more effective in reducing O 3 to attain the O 3 NAAQS. The EKMA-based method for determining control strategies has some limitations, the most serious of which is that predicted emissions reductions are critically dependent on
the initial NMHC/NO

x ratio used in the calculations. This ratio cannot be determined with any certainty because it is expected to be quite variable in time and space in an urban area. Another limitation is that trajectory models have limited spatial and temporal scopes of application. They are generally 1-day models, simulating only one cell at a time. Another problem with the use of morning NMHC/NO

x ratios is the failure to account for photochemical evolution as urban emissions are carried downwind. As demonstrated in simulations by Milford et al. (1989) and in smog chamber studies by Johnson and Quigley (1989), an urban plume that is in the VOC-controlling regime (low NMHC/NO

x ratio) near city center can move increasingly into the NO

x -controlling regime (high NMHC/NO

x ratio) as the air parcels age and move downwind. This progression occurs because NO

x is photochemically removed from an aging plume more rapidly than VOCs, causing the
VOC/NO

x ratio to increase. As demonstrated by Milford et al. (1989), the implication of this evolution is that different locations in a large urban area can show very different
O 3 sensitivities to VOC and NO

x changes. Because of this and other drawbacks, the 1990 CAAA (U.S. Congress, 1990) require that grid-based models be used in most
O 3 nonattainment areas.



### 3.6.2 Model Components 3.6.2.1 Emissions Inventory The spatial and temporal characteristics of VOC and NO

x emissions must be supplied as inputs to a photochemical air quality model. Emissions from area and point sources are injected into ground-level grid cells, and emissions from large point sources are injected into upper level cells. Total VOC emissions generally are apportioned into groups of chemically similar species (e.g., alkanes, alkenes, aromatics, etc.) according to the requirements of the chemical mechanism. This apportionment may be accomplished using actual emission sampling and analysis or be based on studies of similar emission sources. Recognition of potential undercounting in existing inventories has spurred efforts to improve the accuracy of emissions inventories. In fact, at present, the emissions inventory is the most rapidly changing component of photochemical models. It has been recognized that both mobile and stationary source components have been highly uncertain and that there is significant ongoing effort to improve the accuracy of emissions inventories. Some emissions terminology is as follows (Tesche, 1992): · Emissions data—the primary information used as input to emissions models. · Emissions model—the integrated collection of calculational procedures, or algorithms, properly encoded for computer-based computation. · Emissions estimates—the output of emissions models; used as input to photochemical models.

3-133· Emissions inventory—the aggregated set of emissions estimate files. · Emissions model evaluation—the testing of a model’s ability to produce accurate emissions estimates over a range of source activity and physicochemical and meteorological conditions. Emissions input requirements for the UAM, for example, include: · Spatial allocation of precursor emissions (VOCs, NO

x , CO): — Actual location of individual point sources; — Spatial allocation by gridding surrogates; — Assignment of surrogates to other categories. · Stack parameters for point sources: — Temperature, height, diameter, and exit velocity. · Speciation of VOC emissions for CBM-IV mechanism: — Region-specific speciation profiles; — EPA default speciation profiles. · Temporal allocation of precursor emissions: — Operating schedules for individual point sources; — Assignment of diurnal profiles for area and mobile sources. The emissions inventory component of modeling is moving in the direction of the use of emissions models rather than inventories. Emissions models are being developed for the Lake Michigan Oxidant Study (LMOS), the San Joaquin Valley Air Quality Study (SJVAQS), and the Atmospheric Utility Signatures, Predictions, and Experiments (AUSPEX), designated as the SJVAQS/AUSPEX Regional Model Adaptation Project (SARMAP). The consistency of existing inventories was improved in 1990 when EPA released the Emissions Preprocessor System (EPS) as a component of the UAM (U.S. Environmental Protection Agency, 1992b). The EPS was updated in 1992 to EPS Version 2 (EPS2). It is an emissions model that considers spatial and temporal disaggregation factors, speciation data, and meteorological data to convert daily emissions estimates for each point source and for area source categories and mobile source emissions factors computed by the EPA MOBILE5 model into hourly, gridded speciated estimates that are needed by a photochemical grid
model. A step beyond the EPS is the Emissions Modeling System (EMS) 1 (Tesche, 1992). The EMS utilizes emissions estimation and information processing methods to provide gridded, temporally resolved, and chemically speciated base-year emissions estimates for all relevant source categories; to provide flexibility in forecasts of future-year emissions rates; and to provide modular code design for use in module updating and replacement. The EMS provides for easy substitution of alternative assumptions, theories, or input parameters (e.g., emissions factors, activity levels, spatial distributions) and facilitates sensitivity and uncertainty testing. As a result of a variety of independent studies, it recently has been determined that urban VOC emissions inventories, particularly motor vehicle emissions, have been significantly understated. These studies include tunnel studies (Pierson et al., 1990) and comparisons of ambient and emission inventory VOC/NO

x ratios (Fujita et al., 1992).

1 The EMS has been renamed the GMEP (Geocoded Model of Emissions and Projections).

3-1343.6.2.2 Meteorological Input to Air Quality Models Grid-based air quality models require, as input, the three-dimensional wind field for the episode being simulated. This input is supplied by a so-called meteorological module. Meteorological modules for constructing wind fields for air quality models fall into one of four categories (Tesche, 1987; Kessler, 1988): (1) Objective analysis procedures that interpolate observed surface and aloft wind speed and direction data throughout the modeling domain; (2) Diagnostic methods in which the mass continuity equation is solved to determine the wind field; (3) Dynamic, or prognostic, methods based on numerical solution of the governing equations for mass, momentum, energy, and moisture conservation, along with the thermodynamic state equations on a three-dimensional, finite-difference mesh; or (4) Hybrid methods that embody elements from both diagnostic and prognostic approaches.

Objective Analysis Objective wind-field analysis involves the interpolation and extrapolation of wind speed and direction measurements (collected at a number of unequally spaced monitoring stations) to grid points throughout the region (Goodin et al., 1980). For flat terrain settings away from complex mesoscale forcings, this class of techniques may provide an adequate method for estimating the wind field, provided that appropriate weighting and smoothing functions are used (Haltiner, 1971). For complex terrain or coastal/lake environments, however, it is tenuous to interpolate between and extrapolate from surface observational sites except with an unusually dense monitoring network. In most cases, the routinely available rawinsonde network sounding data are even more severely limited because of the large distances (300 to 500 km) between sites and because soundings are made only every 12 h. The limitations of even the best available data sets are most severe above the surface layer, where upper level observations are less frequent and more expensive to obtain. It will remain economically unfeasible to obtain sufficiently dense atmospheric observations to allow any direct objective analysis scheme to provide the required detail and accuracy necessary for use in advanced, high-resolution photochemical models.

Diagnostic Modeling In diagnostic wind modeling, the kinematic details of the flow are estimated by solving the mass conservation equation. Dynamic interactions such as turbulence production and dissipation and the effects of pressure gradients are parameterized. Various diagnostic wind models have been developed, many employing the concepts introduced by Sherman (1978) and Yocke (1981). In recent years, attempts have been made to combine the best features of objective analysis and pure diagnostic wind modeling. The current release of EPA’s UAM-IV includes the Diagnostic Wind Model (DWM) as the suggested wind-field generator for this urban-scale photochemical model. The DWM (U.S. Environmental Protection Agency, 1990c) is representative of this class of hybrid objective-diagnostic models. The DWM combines the features of the Complex Terrain Wind Model (CTWM) (Yocke, 1981) and the objective wind interpolation code developed at the California Institute of Technology (Goodin et al., 1980). In the DWM, a two-step procedure normally is followed. First, a "domain-scale" wind is

3-135estimated from available surface and upper-air synoptic data. This initial field consists of a single wind vector (e.g., horizontal homogeneity) for each elevation. The domain-scale wind is adjusted using procedures derived from the CTWM for the kinematic effects of terrain such as lifting, blocking, and flow acceleration. Thermodynamically generated influences such as mountain-valley winds are parameterized. This first step produces a horizontally varying field of wind speed and direction for each vertical layer within the DWM modeling domain. Typically, 10 to 12 vertical layers are used. In the second step, available hourly surface and upper air measurements are combined objectively with the step 1 hourly diagnostic flow fields to produce a resultant wind field that matches the observations at the monitoring points and obeys the general constraints of topography in regions where data are absent. The DWM contains a number of user-specified options whereby different final flow fields may be produced, depending on selection of various smoothing and weighting parameters. The final output of the DWM is a set of hourly averaged horizontal wind fields for each model layer. Diagnostic models may invoke scaling algorithms that propagate the influence of the surface-flow field into upper levels according to the local height of the inversion and the Pasquill-Gifford-Turner stability category for the hour. Once the winds are created by DWM, they must be "mapped" onto the photochemical model’s vertical grid structure. This function is normally accomplished in a two-step process. First, the DWM winds are interpolated ontothe photochemical model grid using simple linear interpolation. Second, the threedimensional divergence is computed in each grid cell and an iterative scheme is used to minimize this divergence to a user-specified level. Typically, the output consists of "nondivergent" x- and y-direction wind components for direct input to the photochemical
model. Among the advantages of the diagnostic modeling approach are its intuitive appeal and modest computing requirements. The method generally reproduces the observed wind values at the monitoring locations and provides some information on terrain-induced airflows in regions where local observations are absent. In addition, diagnostic model parameters for a particular locale based on site-specific field measurements may be calibrated. However, there are several disadvantages. Diagnostic models cannot represent complex mesoscale circulations, unless these features are well represented by surface and aloft observations. Often the vertical velocities produced by a diagnostic model are unrealistic and, in regions of complex terrain, local horizontal flow velocities often may be an order of magnitude too high (Tesche et al., 1987). Because the diagnostic model is not time-dependent, there is no inherent dynamic consistency in the winds from one hour to the next. That is, calculation of the flow field at 1200 hours, for example, is not influenced by the results of the 1100-hour winds. This is a particular problem in applications involving important flow regimes, such as land-sea breezes, mountain-valley winds, eddy circulations, and nocturnal valley jets, that take several hours to develop and whose three-dimensional character is poorly characterized by even the most intensive sampling networks. Finally, the inadequacy of the upper-air synoptic data causes significant difficulty in the validation of the model wind fields.

Prognostic Modeling In prognostic meteorological modeling, atmospheric fields are computed based on numerical solutions of the coupled, nonlinear conservation equations of mass, momentum, energy, and moisture. Derivations of these equations are presented extensively in the literature (Haltiner, 1971; Pielke, 1984; Seinfeld, 1986; Cotton and Anthes, 1989). Many prognostic models have been developed for computing mesoscale wind fields, as shown in the

3-136recent survey by Pielke (1989), and they have been applied to a variety of problems, including the study of land-sea and land-lake circulations. Available prognostic models range from relatively simple one-dimensional representations to complex three-dimensional codes. Prognostic wind models are attractive because they explicitly address the various physical processes governing atmospheric flows. Consequently, they have the potential for describing a number of wind regimes that are particularly relevant to air pollution modeling, such as flow reversal, daytime upslope flows, wind shear, and other mesoscale thermally induced circulations. Drawbacks of prognostic models include the need to gather detailed data for model performance testing and the large computational costs. Indeed, prognostic models may require as much or more computer time than regional-scale photochemical models. More intensive data sets are needed to evaluate prognostic models than for diagnostic models, but this is not necessarily a disadvantage. Rather, it provides the modeler and decision-maker with a far better basis for judging the adequacy of the model than can be achieved with objective or diagnostic models. Summaries of prognostic models available for use in air quality modeling are presented extensively in the literature (e.g., Pielke, 1989; Benjamin and Seaman, 1985; McNally, 1990; Stauffer et al., 1985; Stauffer and Seaman, 1990; Ulrickson, 1988; Wang and Warner, 1988; and Yamada et al., 1989). From these reviews, two models stand out as representing the present state-of-science in applications-oriented prognostic modeling. These are the Mesoscale Model Versions 4 and 5 (MM4/MM5) developed by Pennsylvania State University and the National Center for Atmospheric Research (NCAR) (Anthes and Warner, 1978; Anthes et al., 1987; Zhang et al., 1986; Seaman, 1990; Stauffer and Seaman, 1990), and the Coast and Lake Regional Atmospheric Modeling System (CAL-RAMS) (Tripoli and Cotton, 1982; Pielke, 1974, 1984, 1989; Lyons et al., 1991). Three ongoing regional O 3 modeling programs in the United States are using prognostic models to drive regional O 3 models. These include LMOS; SARMAP; and a regional O 3 modeling program in Southeast Michigan, Northern Ohio, and Southwest Ontario. Part of EPA’s long-range plan (in the Office of Research and Development [ORD]) for model development is to construct a "third" generation modeling framework referred to as MODELS 3 (Dennis and Novak, 1992). This modeling system will consolidate all of the agency’s three-dimensional models. The current plan calls for meteorological inputs to the MODELS 3 system to be supplied by prognostic models. The MM4 model (the hydrostatic version of MM5) is presently being examined by EPA for this purpose. Activities are currently underway in LMOS to supply prognostic model fields to EPA’s ROM for use in simulating regional O 3 distributions in four multiple-day O 3 episodes extensively monitored during the 1991 field program in the midwest. The EPA will be utilizing ROM2.2 (version 2.2) with fields obtained from CAL-RAMS (Lyons et al., 1991) to examine whether prognostic model output gives improved regional O 3 estimates (Guinnup and Possiel, 1991). The SARMAP program is the modeling and data analysis component of a multiyear collaboration between two projects, SJVAQS and AUSPEX. The major near-term objective of SARMAP is to understand the processes that lead to high O 3 concentrations in the San Joaquin Valley of California. An overview of the regional meteorological and air quality modeling approach of SARMAP is described by Tesche (1993). For SARMAP, the MM5 model was chosen as the "platform" prognostic meteorological model because of its broad application history; its demonstrated reliability on large domains, requiring spatially and temporally varying boundary conditions; and its capability for four-dimensional data

3-137assimilation (FDDA)—needed for longer-range simulations (see Section 3.6.2.2). All of these attributes are crucial to the success of mesoscale meteorological modeling. Prognostic models are believed to provide a dynamically consistent, physically realistic, three-dimensional representation of the wind and other meteorological variables at scales of motion not resolvable by available observations. However, the meteorological fields generated by a prognostic model do not always agree with observational data. Numerical approximations, physical parameterizations, and initialization problems are among the potential sources of error growth in model forecasts that can cause model solutions to deviate from actual atmospheric behavior. Methods that have been devised over the past 20 years to mitigate these problems are described below. "Post-processing" refers to methods whereby output fields from prognostic models are selectively adjusted through a series of objective techniques with the aim of improving the realism of the resultant fields. Examples of this procedure (sometimes referred to as objective combination) are given by Cassmassi et al. (1991) in the Los Angeles Basin, Kessler and Douglas (1989) in the South Central Coast Air Basin, and Moore et al. (1987) in the San Joaquin Valley.Ideally, a prognostic model should be initialized with spatially varying, threedimensional fields (i.e., wind, temperature, moisture) that represent the state of the atmosphere at the initial simulation time. A prognostic model that is initialized with such fields, however, can generate large nonmeteorological "waves" when the initial conditions do not contain a dynamic balance consistent with the model formulation (Hoke and Anthes, 1976; Errico and Bates, 1988). The objective of an initialization procedure is to bring the initial conditions into dynamic balance so that the model can integrate forward with a minimum of noise and a maximum of accuracy (Haltiner and Williams, 1980). Dynamic initialization makes use of a model’s inherent adjustment mechanism to bring the wind and temperature into balance prior to the initial simulation time. In this technique, a "presimulation" integration of the model equations produces a set of dynamically balanced initial conditions. By allowing the simulation to begin with a balanced initial state, this technique reduces the generation of meteorological noise and thus improves the quality of the
simulation.

Four-Dimensional Data-Assimilation Techniques Four-dimensional data assimilation refers to a class of procedures in which observational data are used to enhance the quality of meteorological model predictions (Harms et al., 1992). The most common use of FDDA today in applications-oriented models is known as Newtonian relaxation, or simply as "nudging". With this method, model estimates at a particular time interval are adjusted toward the observations by adding artificial tendency terms to the governing prognostic equations. The objective of this method is to improve prognostic model estimates through the use of valid, representative observational data. As an example of this procedure, a linear term is added to the momentum equations to "nudge" the dynamic calculation towards the observed state at each time step in regions where data are available. The FDDA procedures may be thought of as the joint use of a dynamic meteorological model in conjunction with observed data (or analysis fields based on these data) in such a manner that the prognostic equations provide temporal continuity and dynamic coupling of the hourly fields of monitored data (Seaman, 1990). A recent example of the use of FDDA in regional-scale applications with the MM4/RADM model is given by Stauffer and Seaman (1990). Attempts to apply FDDA in

3-138support of urban-scale photochemical grid modeling are described by Tesche et al. (1990b) and McNally (1990) for the San Diego Air Basin and by Stauffer et al. (1993) for the Grand Canyon region of Arizona. Currently, FDDA is being used in the CAL-RAMS simulations in the LMOS program (Lyons et al., 1991) and in the MM5 simulations for SARMAP (Seaman, 1992).

3.6.2.3 Chemical Mechanisms A chemical kinetic mechanism (a set of chemical reactions), representing the important reactions that occur in the atmosphere, is used in an air quality model to estimate the net rate of formation of each pollutant simulated as a function of time. Various grid models employ different chemical mechanisms. Because so many VOCs participate in atmospheric chemical reactions, chemical mechanisms that explicitly treat each individual VOC component are too lengthy to be incorporated into three-dimensional atmospheric models. "Lumped" mechanisms are therefore used (e.g., Lurmann et al., 1986; Gery et al., 1989; Carter, 1990; Stockwell et al., 1990). These lumped mechanisms are highly condensed and do not have the ability to follow explicit chemistry because of this lumping. Lumped-molecule mechanisms group VOCs by chemical classes (alkanes, alkenes, aromatics, etc.). Lumped-structure mechanisms group VOCs according to carbon structures within molecules. In both cases, either a generalized (hypothetical) or surrogate (actual) species represents all species within a class. Organic product and radical chemistry is limited to a few generic compounds to represent all products; thus, chemistry after the first oxidation step is overly uniform. Some mechanisms do not conserve carbon and nitrogen mass. Some molecules do not easily "fit" the classes used in the reduced mechanisms. Because different chemical mechanisms follow different approaches to lumping, and because the developers of the mechanisms made different assumptions about how to represent chemical processes that are not well understood, models can produce somewhat different results under similar conditions (Dodge, 1989). No single chemical mechanism is currently considered "best". Both UAM-IV and ROM utilize the carbon-bond mechanism (CBM-IV), which, along with the SAPRC (Statewide Air Pollution Research Center, University of California, Riverside) and RADM mechanisms, is considered to represent the state of the science (Tesche et al., 1993; National Research Council, 1991). Agreement among mechanisms is better for O 3 than for other secondary pollutants (Dodge, 1989, 1990; National Research Council, 1991), raising concern that the mechanisms may suffer from compensating errors. These mechanisms are at least 5 years old and often are tested on much older smog chamber data. The chemical mechanisms used in existing photochemical O 3 models contain uncertainties that may limit the accuracy of the model predictions. The reactions that are included in these mechanisms generally fall into one of three categories. (1) Reactions for which the magnitude of their rate constants and their product distribution is well known. These include mostly the inorganic reactions and those for the simple carbonyls. (2) Reactions with known rate constants and known products but with uncertain product yields. These are mostly organic reactions, and the actual product yields assumed may vary among mechanisms. (3) Reactions with known rate constants but unknown products. Each mechanism assumes its own set of products for reactions in this class. This class includes
aromatic oxidation reactions.

3-139Most inorganic gas-phase processes are understood. Regarding classes of VOCs the following general comments can be made:
· The n -alkanes comprise approximately one-half of the major carbon emissions in urban areas. Reaction rates are relatively slow. The only important reaction is with the hydroxyl radical. For alkanes C 4 or below, the chemistry is well
understood and the reaction rates are slow. For C 5 and higher alkanes the situation is more complex because few reaction products have been found. · Branched-chain alkanes have rates of reaction that are highly dependent on structure. Rate constants have been measured for only a few of the branched alkanes, and reaction products for this class of organics are not well
characterized. · Alkenes are reactive with OH, O 3 , and the NO 3 radical. Most rate constants of these reactions are known. Alkenes make up ≤ 15% of the emitted carbon and constitute about 25% of the hydrocarbon reactions in urban areas. Ozone reaction products are not well characterized, and the mechanisms are poorly
understood. Mechanisms for the NO 3 radical are also uncertain. · Aromatics constitute about 15 to 20% of the carbon compounds emitted and 25% of the hydrocarbons reacting in urban areas. Aromatics have been studied frequently, but only a few reaction products have been well characterized. Aromatics act as strong NO

x sinks under low NO

x conditions. Mechanisms used in photochemical air quality models thus have uncertainties, largely attributable to a lack of fundamental data on products and product yields. The missing information necessitates that assumptions be made. Current mechanisms provide acceptable overall simulation of O 3 generation in smog chamber experiments. Specific VOCs may, however, be simulated poorly, and products other than O 3 may not be simulated accurately. Existing mechanisms are mostly applicable to single-day, high NO

x conditions because those are the conditions of almost all smog chamber experiments. Low NO

x condition simulations are verified less thoroughly. Fundamental kinetic data are needed on the photooxidation of aromatics, higher alkanes, and higher alkenes to fill in areas of uncertainty in current mechanisms. Whereas these uncertainties are important and require continued research to remove, the uncertainties are likely not such that general conclusions about the relative roles of hydrocarbons and NO

x in O 3 formation will be changed by new
data.

3.6.2.4 Deposition Processes Species are removed from the atmosphere by interaction with ground-level surfaces, so-called dry deposition, and by absorption into airborne water droplets followed by transport of the water droplets, wet deposition. Dry deposition is an important removal process for ozone and other species on both the urban and regional scales and is included in all urban and regional scale models as a contribution to the ground-level flux of pollutants. Wet deposition is a key removal process for gaseous species on the regional scale and is included in regional scale acid deposition models. Urban-scale photochemical models generally have not included a treatment of wet deposition as O 3 episodes do not occur during periods of significant clouds or rain.

Dry Deposition

3-140It is generally impractical to simulate, in explicit detail, the complex of multiple physical and chemical pathways that result in dry deposition to individual surface elements. Because of this, the usual practice has been to adopt simple parameterizations that consolidate the multitude of complex processes. For example, it generally is assumed that the dry deposition flux is proportional to the local pollutant concentration [at a known reference height (z

r ), typically 10 m], resulting in the expressio n F = −v d C, where F represents the dry deposition flux (the amount of pollutant depositing to a unit surface area per unit time), and C is the local pollutant concentration at the reference height. The proportionality constant, v d , has units of length per unit time and is known as the deposition velocity. It is customary to interpret the dry deposition process in terms of the electrical resistance analogy, where transport of material to the surface is assumed to be governed by three resistances in series: (1) the aerodynamic resistance (r

a ), (2) the quasi-laminar layer resistance (r b ), and (3) the surface or canopy resistance (r

s ) (Wu et al., 1992). The aerodynamic resistance characterizes the turbulent transport through the atmosphere from reference height z

r down to a thin layer of stagnant air very near the surface. The molecularscale diffusive transport across the thin quasi-laminar sublayer near the surface is characterized by r b . The chemical interaction between the surface and the pollutant of interest once the gas molecules have reached the surface is characterized by r

c . The total resistance (r t ) is the sum of the three individual resistances, and is, by definition, the inverse of the deposition velocity, 1/v d = r t = r a + r b + r s . Note that the deposition velocity is small when any one of the resistances is large. Hence, either meteorological factors or the chemical interactions on the surface can govern the rate of dry deposition. Dry deposition velocities of HNO 3 and SO 2 are typically ≈ 2 cm s -1 , and those of
O 3 and PAN are generally ≈ 0.5 and ≈ 1 cm s -1 , respectively (Dolske and Gatz, 1985; Colbeck and Harrison, 1985; Huebert and Robert, 1985; Shepson et al., 1992). Wit h a 1 km-deep inversion or boundary layer, the time scale for dry deposition is on the order of 1 day for a deposition velocity of 1 cm s -1 . Dry deposition is important for those chemicals with high or fairly high deposition velocities and long or fairly long lifetimes ( ≥ 10 days) due to photolysis and chemical reaction (for example, HNO 3 , SO 2 , and H 2 O 2 , as well as O 3 and PAN). A number of researchers have reviewed the deposition literature and provided summaries of deposition velocity data. The rank ordering of deposition velocity values among pollutant species based on several such studies is summarized as follows: McRae and Russell (1984):
HNO 3 > SO 2 > NO 2 ≈ O 3 > PAN > NO; Derwent and Hov (1988):
HNO 3 > SO 2 = O 3 > NO 2 > PAN; McRae et al. (1982b):
O 3 > NO 2 > PAN > NO > CO; and Chang et al. (1987):
HNO 3 > H 2 O 2 > NH 3 > HCHO > O 3 = SO 2 = NO 2 = NO > RCHO. There is general agreement that HNO 3 is removed at the highest observed rates, which is consistent with the relative deposition rates observed by Huebert and Robert (1985). Most of the surveys are roughly consistent with the relative deposition velocity ordering seen in the experiments of Hill and Chamberlain (1976): diffusion-limited acids > SO 2 > NO 2 ≈ O 3 > PAN > NO > CO. This suggests surface resistance values should be ordered approximately as CO > NO > PAN > O 3 ≈ NO 2 > SO 2 > HNO 3 = 0. However, there is still a substantial range of variability in reported deposition velocities. For example, McKeen et al.

3-141(1991) calculated dry deposition velocities for HNO 3 , O 3 , and PAN of 10, 0.5, and 0.3 cm s -1

, respectively. Note that a large deposition velocity for HNO 3 will limit the lifetime of HNO 3 relative to O 3 in photochemically aged air. There are a significant number of other gases for which there are no surface resistance data and for which values must be estimated using expert judgment. The values should be consistent with the existing experimental values for vegetative surfaces and should preserve the apparent rank ordering among the pollutant species (discussed above). For O 3 , surface resistance values by land-use type and season recommended by Sheih et al. (1986) and Wesely (1988) are appropriate. For NO, NO 2 , NH 3 , H 2 O 2 , HCHO, and CH 3 CHO, the
surface resistance values for each land use can be estimated from that for SO 2 (Wesely, 1988), except different proportionality factors should be used for NO and NO 2 . The treatment of dry deposition is perhaps the most primitive of the scientific modules in photochemical air quality models. Knowledge of deposition rates is limited, and uncertainties in deposition velocities are high. For travel times of one to several days, the quantities of pollutants that are predicted to be removed by dry deposition can be substantial for those species with appreciable deposition velocities. Setting all species deposition velocities to zero in a model provides an indication of the importance of dry deposition relative to other processes influencing pollutant dynamics. Further effort to describe the dynamics of deposition are needed, together with evaluation against available data that can be used to test deposition modules.

Wet Deposition Wet deposition refers to the removal of gases and particles from the atmosphere by precipitation events, through incorporation of gases and particles into rain, cloud, and fog water followed by precipitation at the earth’s surface. Removal of gases and particles during snow falls is also wet deposition. Wet removal of gases arises from equilibrium partitioning of the chemical between the gas and aqueous phases (Bidleman, 1988; Mackay, 1991). This partitioning can be defined by means of a washout ratio, Wg, with Wg = [C]rain/[C]air, where [C]rain and [C]air are the concentrations of the chemical in the aqueous and gas phases, respectively. Because Wg is the inverse of the air/water partition coefficient, K

aw , then Wg = RT/H, where R is the gas constant, T is the temperature, and H is the Henry’s Law constant (Mackay, 1991). Particles and particle-associated chemicals are efficiently removed from the atmosphere by precipitation events, and the washout ratios for particles, Wp, are typically in the range 10 4 to 10 6 (Eisenreich et al., 1981; Bidleman, 1988). Wet deposition is important for particles (and particle-associated chemicals) and for those gas-phase compounds with washout ratios of Wg ≥ 10 4 . Examples of such gaseous chemicals are HNO 3 , H 2 O 2 , phenol, and cresols, all of which are highly soluble in water. Formaldehyde is present in the aqueous phase as the glycol, H 2 C(OH) 2 , and has an effective washout ratio o f 7 × 10 3 at 298 K (Betterton and Hoffmann, 1988; Zhou and Mopper, 1990). Note that the importance of wetdeposition may depend on whether the chemical is present in the gas phase or is particleassociated. For example, the gas-phase alkanes have low values of Wg and are inefficiently removed by wet deposition, whereas the particle-associated alkanes are efficiently removed by wet deposition (Bidleman, 1988), through removal of the host particles.

3.6.2.5 Boundary and Initial Conditions

3-142When a grid-based photochemical model is applied to simulate a past pollution episode, it is necessary to specify the concentration fields of all the species computed by the model at the beginning of the simulation. These concentration fields are called the initial conditions. Throughout the simulation, it is necessary to specify the species concentrations, called the boundary conditions, in the air entering the three-dimensional geographic domain. Three general approaches for specifying boundary conditions for urban-scale applications can be identified: (1) use the output from a regional-scale photochemical model, (2) use objective or interpolative techniques with ambient observational data, or (3) use default regional background values and expand the area that is modeled for urban areas sufficiently isolated from significant upwind sources. In the ideal case, observed data would provide information about the concentrations for all the predicted species at the model’s boundaries. An alternative approach is to use regional models to set boundary and initial conditions. This is, in fact, preferred when changes in these conditions are to be forecast. In any event, simulation studies should use boundaries that are far enough from the major source areas of the region that concentrations approaching regional values can be used for the upwind boundary conditions. Boundary conditions at the top of the area that is being modeled should use measurements taken from aloft whenever they are available. Regional background values often are used in lieu of measurements. An emerging technique for specifying boundary conditions is the use of a nested grid, in which concentrations from a larger, coarse grid are used as boundary conditions for a smaller, nested grid with finer resolution. This technique reduces computational requirements compared to those of a single-size, fine-resolution grid. Initial conditions are determined mainly with ambient measurements, either from routinely collected data or from special studies. Where spatial coverage with data is sparse, interpolation can be used to distribute the surface ambient measurements. Because few measurements of air quality data are made aloft, it generally is assumed that species concentrations are initially uniform in the mixed layer and above it. To ensure that the initial conditions do not dominate the performance statistics, model performance should not be assessed until the effects of the initial conditions have been swept out of the grid.

3.6.2.6 Numerical Methods The core of a grid-based O 3 air quality model is the numerical solution of the three-dimensional atmospheric diffusion equation (McRae et al., 1982c). The central numerical schemes involve horizontal advection and simultaneous vertical mixing, advection, and chemistry. A possible source of model inaccuracy is the numerical method used to solve the governing equations. The solution of chemical kinetics is generally the most computationally intensive step in O 3 air quality models. To compute the rate of chemical reaction one essentially must solve a system of stiff nonlinear ordinary differential equations. The desirable characteristics of the integration routine are speed and stability, at a certain prescribed level of accuracy. The chemistry integration routines used in several ozone air quality models are based on the implicit, hybrid, exponential scheme developed by Young and Boris (1977). The integration is stable, efficient, and sufficiently accurate. It has been concluded from several studies that the numerical solution of the vertical/chemical portion of the model is less likely to be a source of O 3 prediction inaccuracy (Odman et al., 1992) than the horizontal advection, numerical method (McRae et al., 1982; Chock, 1985, 1991; Dabdub and Seinfeld, 1994). Although the horizontal transport computations typically consume only a small fraction of the total computer time, it is well known that numerical diffusion and

3-143dispersion degrade the computed solution and that available methods differ greatly in their numerical performance in this regard (Chock, 1985, 1991; Dabdub and Seinfeld, 1994). Continued work on optimizing the numerical methods used in O 3 air quality models is

necessary.



### 3.6.3 Urban and Regional Ozone Air Quality Models Several grid-based models have been widely used to evaluate O 3 and acid deposition control strategies. · The Urban Airshed Model, developed by Systems Applications, Inc., has been, and continues to be, applied to urban areas throughout the country. It is described in Section 3.6.3.1. The current EPA-approved version is UAM-IV. The UAM-V, which has been developed for LMOS, is a nested regional-scale
model. · The California Institute of Technology (CIT) model has been applied to California’s South Coast Air Basin (McRae et al., 1982a,b; McRae and Seinfeld, 1983; Milford et al., 1989; Harley et al., 1993). · The ROM, developed by EPA, has been applied to the northeastern and southeastern United States (Schere and Wayland, 1989a,b). It is described in
Section 3.6.3.2. · The Acid Deposition and Oxidant Model (ADOM) was developed by ENSR Consulting and Engineering for the Ontario Ministry of the Environment and Environment Canada (Venkatram et al., 1988) and the German Umweltbundesamt. Its primary application has been to acidic deposition. · The RADM was developed by NCAR and the State University of New York for NAPAP. The primary objective of RADM applications is the calculation of changes in sulfur and nitrogen deposition over the eastern United States and southeastern Canada, resulting from changes in emissions (National Acid Precipitation Assessment Program, 1989). See Section 3.6.3.3 for a description
of RADM. A summary of the major applications of the above air quality models, including the Sulfur Transport Eulerian Model (STEM-II), is presented in Table 3-21. All of the models are based nominally on a 1-h time resolution. The horizontal spatial resolutions vary from 5 to 120 km. Typical spatial resolutions used in past model applications are summarized in Table 3-22. It is important to note that the spatial scale at which a model is applied is governed by the manner in which physical processes are treated and the spatial scale of the inputs. The regional models can have a vertical resolution on the order of 10 to 15 layers extending up to 6 to 10 km in order to treat vertical redistribution of species above the planetary boundary layer. This increased vertical resolution often comes at the expense of decreased horizontal resolution. Urban models typically have two to five layers extending up to 1,000 to 2,000 m. The treatment of meteorological fields by the six models is summarized in Table 3-23. Generally, the treatment of meteorology is separate from the air quality model itself, and models can employ wind fields prepared by different approaches as long as consistent assumptions, such as nondivergent wind field, are employed in each model. The regional models, ROM, RADM, ADOM, and STEM-II, address the vertical redistribution of pollutants resulting from the presence of cumulus clouds.

3-144Table 3-21. Grid-Based Urban and Regional Air Pollution Models:Overview of Three-Dimensional Air Quality ModelsaModelMajor ApplicationsMajor References forModel FormulationSelected References for Model PerformanceEvaluation and ApplicationUAM Urban and nonurban areas in theUnited States and EuropeReynolds et al. (1973, 1974, 1979)Tesche et al. (1992)U.S. Environmental Protection Agency(1990a,b,c; 1992b)Scheffe and Morris (1993)Tesche et al. (1993)CIT Los Angeles Basin McRae et al. (1982a) McRae and Seinfeld (1983)Russell et al. (1988a,b)Harley et al. (1993)ROM Eastern United States(E of 99° W longitude)Lamb (1983) Schere and Wayland (1989a,b)Meyer et al. (1991b)RADM Eastern North America Chang et al. (1987) Middleton et al. (1988, 1993)Middleton and Chang (1990)Dennis et al. (1993a)Cohn and Dennis (1994)ADOM Eastern North America andNorthern EuropeVenkatram et al. (1988) Venkatram et al. (1988)Macdonald et al. (1993)Karamchandani and Venkatram (1992)STEM-IIPhiladelphia area, Kentucky, andnortheastern United States, centralJapanCarmichael et al. (1986)Carmichael et al. (1991)Saylor et al. (1991)aSee Appendix A for abbreviations and acronyms.

3-145Table 3-22. Grid-based Urban and Regional Air Pollution Models: Treatment of Emissions and SpatialResolutionaModelEmitted SpeciesPoint-Source EmissionsArea-Source EmissionsVertical ResolutionUAM SO2, sulfate, NO, NO2, CO,NH3, and 8 classes of ROG andPM (4 size classes)Released into grid cell in layercorresponding to plume rise inUAM; treated with a reactiveplume model in PARISGrid-average with resolutionranging from 4 km × 4 km to10 km × 10 km in pastapplicationsTypically, 5-6 layers up to about 1.5 kmCIT SO2, sulfate, NO, NO2, CO,NH3, and 6 classes of ROG andPM (4 size classes)Treated with a plume modelwith simple NOxand O3chemistryGrid-average with 5 km × 5 kmresolution in past applicationsFive layers up to about 1.5 kmROM CO, NO, NO2, and 8 classesof ROGReleased into grid cell in layercorresponding to plume riseGrid-average with 18.5 km ×18.5 km resolution in presentapplicationsThree layers up to about 4 kmRADM SO2, sulfate, NO, NO2, CO,NH3, and 12 classes of ROGReleased into grid cell in layercorresponding to plume riseGrid-average with 80 km ×80 km resolution in pastapplicationsFifteen layers up to about 16 kmADOM SO2, sulfate, NO, NO2, NH3,and 8 classes of ROG and PMReleased into grid cell in layercorresponding to plume riseGrid-average with resolutionranging from 60 km × 60 km toabout 120 km × 120 km in pastapplicationsTwelve layers up to about 10 kmSTEM-IISO2, sulfate, NO, NO2, NH3,and 8 classes of ROGReleased into grid cell in layercorresponding to plume riseGrid-average with resolutionranging from 10 km × 10 km to56 km × 56 km in pastapplicationsTen to 14 layers up to about 6 kmaSee Appendix A for abbreviations and acronyms.

3-146Table 3-23. Grid-based Urban and Regional Air Pollution Models:Treatment of Meteorological Fields, Transport, and DispersionaModelMeteorologyTransportTurbulent DiffusionUAM Constructed through data interpolationor calculated with land-sea breeze orcomplex terrain wind model.3-D wind field. Finite differencenumerical technique.Vertical turbulent diffusion function of atmosphericstability and friction velocity. Constant horizontalturbulent diffusion coefficient.CIT Constructed through data interpolationwith diagnostic wind model.3-D wind field. Finite elementnumerical technique.Vertical turbulent diffusion function of atmosphericstability and friction velocity. Horizontal turbulentdiffusion function of mixing height and convectivevelocity scale.ROM Constructed through data interpolation. 3-D wind field with vertical transportthrough cumulus clouds. Finitedifference numerical technique.Vertical turbulent diffusion function of atmosphericstability. Horizontal turbulent diffusion function ofatmospheric stability, convective cloud cover andvelocity scale, and the depths of the boundary layerand clouds.RADM Calculated with Community ClimateModel (CCM) and MM4.3-D wind field with vertical transportthrough cumulus clouds. Finitedifference numerical technique.Vertical turbulent diffusion function of atmosphericstability and wind shear. No horizontal turbulentdiffusion.ADOM Constructed through data interpolationin combination with prognostic planetaryboundary-layer model.3-D wind field with vertical transportthrough cumulus clouds. Cubic splinenumerical technique.Vertical turbulent diffusion calculated from planetaryboundary layer model. No horizontal turbulentdiffusion.STEM-IICalculated with dynamic wind model(MASS) or constructed through datainterpolation.3-D wind field with vertical transportthrough clouds. Finite elementnumerical technique.Vertical turbulent diffusion function of atmosphericstability and surface roughness. Horizontal turbulentdiffusion proportional to vertical turbulent diffusion.aSee Appendix A for abbreviations and acronyms.

3-147Table 3-24 summarizes the gas-phase chemical mechanisms incorporated into the six models. Generally three chemical mechanisms are used in the models: (1) CBM-IV used in ROM and UAM; (2) versions of the SAPRC mechanism used in ADOM, STEM-II, and CIT; and (3) the RADM mechanism. Of the three chemical mechanisms, RADM is the largest and CBM-IV is the smallest. Aqueous-phase chemistry is currently treated only in the regional models. Cloud processes are treated in the three regional models, RADM, ADOM, and STEM-II (Table 3-25). Cumulus venting and solar attenuation are treated in ROM. Layer 3 depths also are influenced by cloud thickness. At present, only RADM, ADOM and STEM-II treat wet deposition. The treatment of dry deposition in the models also is summarized in Table 3-
25. Regional-scale modeling is an important contributor to the development of boundary conditions for urban-scale models. In recent years, regional-scale modeling has been receiving increased attention as the need for addressing interlinked air quality problems at broader scales is increasing. At the expanded spatial and temporal scales of regional-scale models, the simulation of certain dynamic processes becomes more critical. For example, in regional-scale models the treatment of biogenic VOC emissions and removal by dry and wet deposition generally require greater attention and accuracy than at the urban scale. On the other hand, the exact mechanistic details of the oxidation of some highly reactive VOCs may be somewhat less important. More detailed descriptions now will be presented for UAM, ROM, and RADM.The UAM is described, as it is specified officially by EPA, as a grid-based model for urbanscale O 3 control strategy determination. The regional-scale O 3 model, ROM, is being used by
EPA to evaluate O 3 control measures for the eastern United States and to provide boundaryconditions for urban area simulations using UAM. Representative of a comprehensive stateof-the-science O 3 /acid deposition model, RADM has been used to evaluate combined O 3 and acid deposition abatement strategies for the northeastern United States and Canada. The EPA is embarking on a project to produce the next generation of photochemical models, termed MODELS 3 (Dennis et al., 1993b). This group of models will be flexible (scalable grid and domain), will be modular (modules with interchangeable data structure), will have uniform input/output across subsystems, and will contain advanced analysis and visualization features. The models will be designed to take advantage of the latest advances in computer architecture and software.

3.6.3.1 The Urban Airshed Model The UAM is the most widely applied and broadly tested grid-based photochemical air quality model. The model is described in a number of sources, including a multi-volume series of documents issued by the U.S. Environmental Protection Agency (1990a,b,c; 1992b) and a comprehensive evaluation by Tesche et al. (1993). Current versions include provisions enabling the user to model transport and dispersion within both the mixed and inversion layers. The computer codes have been structured to allow inclusion of up to 10 vertical layers of cells and any number of cells horizontally. The original UAM developed by Reynolds et al. (1973) simulated the dynamic behavior of six pollutants: (1) reactive and (2) unreactive hydrocarbons, (3) NO, (4) NO 2 , (5)
O 3 , and (6) CO. Since 1977, the UAM has employed various versions of the CBM. Currently, the model utilizes the CBM-IV Mechanism (Gery et al., 1988, 1989), which treats

3-148Table 3-24. Grid-based Urban and Regional Air Pollution Models: Treatment of Chemical ProcessesaModelGas-Phase ChemistryAqueous- Phase ChemistryUAM Eighty-seven reactions among 36 species including NOx, O3,ROG, and SO2(CBM-IV) (Gery et al., 1988, 1989)No treatment of aqueous-phase chemistryCIT One hundred and twelve reactions among 53 speciesincluding NOx, O3, ROG, and SO2(Lurmann et al., 1986)No treatment of aqueous-phase chemistryROM Eighty-seven reactions among 36 species including NOx, O3,ROG, and SO2(CBM-IV)No treatment of aqueous-phase chemistryRADM One hundred and fifty-seven reactions among59 species including NOx, O3, ROG, and SO2(Stockwell et al., 1990)Forty-two equilibria and five reactions for SO2oxidationADOM One hundred and twelve reactions among 53 speciesincluding NOx, O3, ROG, and SO2(Lurmann et al., 1986)Fourteen equilibria and five reactions for SO2oxidationSTEM-IIOne hundred and twelve reactions among 53 speciesincluding NOx, O3, ROG, and SO2(Lurmann et al., 1986)Twenty-six equilibria and about 30 reactions for SO2and NOxoxidation, radical chemistry, and transition metal chemistryaSee Appendix A for abbreviations and acronyms.

3-149Table 3-25. Grid-based Urban and Regional Air Pollution Models: Treatment of Cloud and Deposition ProcessesaModelCloud ProcessesWet DepositionDry DepositionUAM No treatment of cloud processes. No treatment of wet deposition. Dry deposition velocity approach; function of wind speed,friction velocity, land type, and species.CIT No treatment of cloud processes. No treatment of wet deposition. Dry deposition velocity approach; function of atmosphericstability, wind speed, land type, and species.ROM No treatment of cloud processes, exceptvertical transport treatment.No treatment of wet deposition. Resistance transfer approach; function of land type, windspeed, atmospheric stability, and species.RADM Treatment of precipitating cumulus clouds,precipitating stratus clouds, and fairweather cumulus clouds, based onprecipitation amount, temperature, andrelative humidity vertical profiles. Useof cloud-averaged properties for aqueouschemistry.Calculated from precipitation rate andcloud average chemical composition;no below-cloud scavenging.Resistance transfer approach; function of atmosphericstability, wind speed, season, land type, insolation,surface wetness, and species.ADOM Treatment of cumulus clouds and stratusclouds, based on precipitation amount(for stratus clouds), temperature, andrelative humidity vertical profiles.Vertical resolution for cloud chemistry.Calculated from precipitation rate andvertically weighted cloud average chemicalcomposition, below-cloud scavengingincluded.Resistance transfer approach; function of atmosphericstability, wind speed, land type, season, insolation, andspecies.STEM-IITreatment of clouds with the AdvancedScavenging Module based on cloud-baseheight, precipitation rate, and surfacetemperature.Calculated with the AdvancedScavenging Module. Treats cloudwater, rain water, and snow;below-cloud scavenging included.Resistance transfer approach; function of atmosphericstability, land type, wind speed, and species.aSee Appendix A for abbreviations and acronyms.

3-15036 reacting species. Reactive organic compounds include alkanes, alkenes, aromatics, and aldehydes, and nitrogen-bearing species include HNO 2 , HNO 3 , and PAN. Under development at the time of writing of the present document is version V of UAM (Morris et al., 1991, 1992). This version (UAM-V) contains the following features: the ability to treat two-way interactive nested grids; the use of state-of-the-science treatment of atmospheric, meteorological, and chemical processes; the treatment of subgrid-scale plume processes using a plume-in-grid algorithm; and the use of structured programming techniques to take advantage of computational speed enhancement opportunities offered by the current and next generation of computers.

3.6.3.2 The Regional Oxidant Model The ROM was designed to simulate most of the important chemical and physical processes that are responsible for the photochemical production of O 3 over regional domains and for episodes of up to 15 days in duration. These processes include horizontal transport; atmospheric chemistry and subgrid-scale chemical processes; nighttime wind shear and turbulence associated with the low-level nocturnal jet; the effects of cumulus clouds on vertical mass transport and photochemical reaction rates; mesoscale vertical motions induced by terrain and the large-scale flow; terrain effects on advection, diffusion, and deposition; emissions of natural and anthropogenic O 3 precursors; and dry deposition. The processes are simulated mathematically in a three-dimensional Eulerian model with three vertical layers, including the boundary layer and the capping inversion or cloud layer. The ROM geographical domains are summarized in Table 3-26 and illustrated in Figure 3-26. Meteorological data are used to model objectively both regional winds and diffusion. The three model layers of ROM are prognostic (predictive) and are free to expand and contract locally in response to changes in the physical processes occurring within the layers. During an entire simulation period, horizontal advection and diffusion and gas-phase chemistry are modeled in the three layers. Predictions from Layer 1 are used as surrogates for surface concentrations. Layers 1 and 2 model the depth of the well-mixed layer during the day. Some special features of Layer 1 include the modeling of the substantial wind shear that can exist in the lowest few hundred meters above ground in local areas where strong winds exist and the surface heat flux is weak, the thermal internal boundary layer that often exists over large lakes or near sea coasts, and deposition onto terrain features that protrude above the layer. At night, Layer 2 represents what remains of the daytime mixed layer. As stable layers form near the ground and suppress turbulent vertical mixing, a nocturnal jet forms above the stable layer and can transport aged pollutant products and reactants considerable distances. At night, emissions from tall stacks and warm cities are injected directly into Layers 1 and 2. Surface emissions are specified as a mass flux through thebottom of Layer 1. During the day, the top model layer, Layer 3, represents the synopticscale subsidence inversion characteristic of high O 3 -concentration periods; the base of Layer 3 is typically 1 to 2 km above the ground. Relatively clean tropospheric air is assumed to exist above Layer 3 at all times, and stratospheric intrusion of O 3 is assumed to be negligible. If cumulus clouds are present, an upward flux of O 3 and precursor species is injected into the layer by penetrative convection. At night, O 3 and the remnants of other photochemical reaction products may remain in this layer and be transported long distances downwind. These processes are modeled in Layer 3. When cumulus clouds are present in a Layer 3 cell, the upward vertical mass flux from the surface is partially diverted from injection into Layer 1 to injection directly into the

3-151Table 3-26. Regional Oxidant Model Geographical Domains

GENERAL INFORMATION ROM grid cells are 1/4° longitude and 1/6° latitude in size or approximately 18.5 km. Actual domain names are included in parenthesis after the general geographical description. In addition, all domains can be run independently or windowed from the “super” domain.

SUPER DOMAIN (SUPROXA) 99.00 W to 67.00 W Longitude
26.00 N to 47.00 N Latitude 128 × 126 Grid Cells (columns × rows) SOUTHERN DOMAIN (TEXROXA) 99.00 W to 81.00 W Longitude
26.00 N to 37.67 N Latitude 72 × 70 Grid Cells (columns × rows)

NORTHEAST DOMAIN (NEROXA) 89.00 W to 67.00 W Longitude
35.00 N to 47.00 N Latitude 88 × 72 Grid Cells (columns × rows) NORTHEAST DOMAIN (ROMNET) 85.00 W to 69.00 W Longitude
36.33 N to 45.00 N Latitude 64 × 52 Grid Cells (columns × rows)

MIDWEST DOMAIN (MIDROXA) 97.00 W to 78.00 W Longitude
35.00 N to 47.00 N Latitude 76 × 72 Grid Cells (columns × rows) NORTHEAST DOMAIN (NEROS1) 84.00 W to 69.00 W Longitude
38.00 N to 45.00 N Latitude 60 × 42 Grid Cells (columns × rows)

SOUTHEAST DOMAIN (SEROXA) 98.75 W to 76.25 W Longitude
27.67 N to 37.67 N Latitude 90 × 60 Grid Cells (columns × rows) SOUTHEAST DOMAIN (SEROS1) 97.00 W to 82.00 W Longitude
28.00 N to 35.00 N Latitude 60 × 42 Grid Cells (columns × rows)

cumulus cloud of Layer 3. In the atmosphere, strong thermal vertical updrafts, primarilyoriginating near the surface in the lowest portion of the mixed layer, feed growing "fairweather cumulus" clouds with vertical air currents that extend in one steady upward motion from the ground to well above the top of the mixed layer. These types of clouds are termed fair-weather cumulus because atmospheric conditions are such that the clouds do not grow to the extent that precipitation forms. The dynamic effects of this transport process and daytime cloud evolution can have significant effects on the chemical fate of pollutants. Within the ROM system, a submodel parameterizes the above-cloud flux process and the subsequent impact on mass fluxes among all layers of the model. In the current implementation of the chemical kinetics, liquid-phase chemistry is not included, and, thus, part of the effects from the cloud flux processes are not accounted for in the simulations. The magnitude of the mass flux proceeding directly from the surface layer to the cloud layer is modeled as being proportional to the observed amount of cumulus cloud coverage and inversely proportional to the observed depth of the clouds. Horizontal transport within the ROM system is governed by hourly wind fields that are interpolated from periodic wind observations made from upper-air soundings and

3-152Figure 3-26. Regional oxidant model superdomain with modeling domains.

surface measurements. During the nighttime simulation period, the lowest few hundred meters of the atmosphere above the ground may become stable as a radiation inversion forms. Wind speeds increase just above the top of this layer, forming the nocturnal jet. This jet is capable of carrying O 3 , other reaction products, and emissions injected aloft considerable distances downwind. This phenomenon is potentially significant in modeling regional-scale air quality and is implicitly treated by the model, where the definition of Layer 1 attempts to
account for it. The ROM system requires five types of "raw" data inputs: (1) air quality, (2) meteorology, (3) emissions, (4) land use, and (5) topography. Air quality data required by the ROM include initial conditions and boundary conditions. The model usually is initialized 2 to 4 days before the start of the period of interest with clean tropospheric conditions for all species. This period of interest is called an "episode" and usually lasts around 15 days. Ideally, the initial condition field will have been transported out of the model domain in advance of the portion of the episode of greatest interest. Upwind lateral boundary conditions for O 3 are updated every 12 h based on measurements, except for the large superdomain, where tropospheric background values are

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Z ZTGFWEVKQPUQP1 EQPEGPVTCVKQPUXCT[DGECWUG01 KUCPCV[RKECNRTGEWTUQTKGCNVJQWIJKVKU  ZPGEGUUCT[HQT1 HQTOCVKQPHTGUJ01GOKUUKQPUTGOQXG1 CPFJKIJEQPEGPVTCVKQPUQH01  ZTGVCTFVJGTCVGQH1 HQTOCVKQPD[TGOQXKPITCFKECNU%QPVTQNQH01 VGPFUVQCEEGNGTCVGVJG  ZTCVGQH1 HQTOCVKQPJQYGXGTKVUGHHGEVUQPRGCM1 EQPEGPVTCVKQPFGRGPFQPVJGNQECVKQPCPF  VKOKPIQHVJGEQPVTQNCPFQPCODKGPVEQPEGPVTCVKQPUQH81%UCPF01 YJKEJXCT[YKFGN[KP

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### 3.6.6 Conclusions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

+PXGUVKICVQTU 4GIKQP'RKUQFG /QFGN 7UGF 5VTCVGIKGU 'XCNWCVGF
%JW GV CN 
 'CUVGTP 7PKVGF 5VCVGU 41/ #ETQUUVJGDQCTF 01 81% %JW CPF %QZ 
 ,WN[   TGFWEVKQPU 4QUGNNG GV CN 
 /CVJWT CPF 5EJGTG 
 Z

2QUUKGN GV CN 
 0QTVJGCUVGTP 7PKVGF 41/ 'UVKOCVG 1 TGFWEVKQPU RGT  2QUUKGN CPF %QZ 
 5VCVGU %### ,WN[   

/KNHQTF GV CN 
 0QTVJGCUVGTP 7PKVGF 41/ #PCN[UKU QH GHHGEV QH 01 5VCVGU TGFWEVKQPU ,WN[   Z

4CQ 
 0GY ;QTM OGVTQRQNKVCP 7#/41/ 'XCNWCVKQP QH  5+2U CPF 4CQ GV CN 
 CTGC 81%01 UVTCVGIKGU 4CQ CPF 5KUVNC 
  FC[U KP  Z

5EJGHHG CPF /QTTKU 0GY ;QTM 7#/ 7UG QH 7#/ HQT FGOQPUVTCVKPI   5V .QWKU CVVCKPOGPV YKVJ TQWVKPGN[
#VNCPVC CXCKNCDNG FCVC
&CNNCU(V 9QTVJ 2JKNCFGNRJKC 2QUUKGN GV CN 
 0QTVJGCUVGTP 7PKVGF 41/ 1\QPG EQPVTQN UVTCVGIKGU KP 5VCVGU 0QTVJGCUV ,WN[   4QUGNNG CPF 5EJGTG 
 0QTVJGCUVGTP 7PKVGF 41/ 5GPUKVKXKV[ QH 1 KP 0QTVJGCUV VQ 4QUGNNG GV CN 
 5VCVGU DKQIGPKE GOKUUKQPU ,WN[   

&WPMGT GV CN 
CD .QU #PIGNGU 7#/ 'HHGEVU QH CNVGTPCVG HWGNU CPF 0GY ;QTM TGHQTOWNCVGF ICUQNKPGU QP 1
&CNNCU(V 9QTVJ NGXGNU 

/KNHQTF GV CN 
 5QWVJ %QCUV #KT $CUKP %+6 'HHGEVU QH U[UVGOCVKE 81% CPF
01 TGFWEVKQPU

Z

/KFFNGVQP GV CN 
 'CUVGTP 7PKVGF 5VCVGU CPF 4#&/  GOKUUKQPU RTQLGEVKQPU
UQWVJGCUVGTP %CPCFC

5GG #RRGPFKZ # HQT CDDTGXKCVKQPU CPF CETQP[OU C

2TKOCT[KUUWGUCPFNKOKVCVKQPUCUUQEKCVGFYKVJVJGWUGQHRJQVQEJGOKECNCKTSWCNKV[OQFGNUCTGFGUETKDGFDGNQY

i *KIJPQKUGVQUKIPCNTCVKQU/QFGNKORTGEKUKQPHQTQ\QPGRTGFKEVKQPUV[RKECNN[TCPIGUHTQOVQCPFKPCEEWTCE[DKCUTCPIGUHTQOVQ6JGUGWPEGTVCKPVKGUCTGQHVGPQHVJGUCOGQTFGTCUVJGRGTEGPVCIGQHTGFWEVKQPKPVJGRGCM1 EQPEGPVTCVKQPHQTCPCTGCHTQOVQRRD4GCUQPUHQTVJGUG KPCEEWTCEKGUKPENWFGWPEGTVCKPVKGUKPGOKUUKQPUKPXGPVQTKGU

i +PCFGSWCEKGUQHUWRRQTVKPIFCVCDCUGUKPOQUVIGQITCRJKECNCTGCU/QUVCTGCUCTGNCEMKPIQTCTGFGHKEKGPVKPFCVCPGGFGFVQGUVKOCVGDQWPFCT[EQPFKVKQPUCPFOGVGQTQNQIKECNCPFCKTSWCNKV[EQPFKVKQPUCNQHV6JGTGCTGHGYCTGCUYJGTGURGEKCVGF81%EQPEGPVTCVKQPUCTGOGCUWTGFUWTHCEG01 FCVCOC[DG

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CTGFKOKPKUJGF

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### 3.7.4 Analytical Methods for Oxidants and Their Precursors
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7UGQH1\QPG#KT3WCNKV[/QFGNHQT'XCNWCVKPI%QPVTQN5VTCVGIKGU2JQVQEJGOKECNCKTSWCNKV[OQFGNUCTGWUGFHQTEQPVTQNUVTCVGI[GXCNWCVKQPD[HKTUVFGOQPUVTCVKPIVJCVCRCUVGRKUQFGQTGRKUQFGUECPDGUKOWNCVGFCFGSWCVGN[CPFVJGPTGFWEKPIJ[FTQECTDQPQT01 GOKUUKQPUQTDQVJKPVJGOQFGNKPRWVUCPFCUUGUUKPIVJGGHHGEVUQHVJGUG

ZTGFWEVKQPUQP1 KPVJGTGIKQP6JGCFGSWCE[QHEQPVTQNUVTCVGIKGUDCUGFQPITKFDCUGFOQFGNU FGRGPFUKPRCTVQPVJGPCVWTGQHKPRWVFCVCHQTUKOWNCVKQPUCPFOQFGNXCNKFCVKQPQPKPRWVGOKUUKQPUKPXGPVQT[FCVCCPFQPVJGTGNCVKQPUJKRDGVYGGPOQFGNQWVRWVCPFVJGEWTTGPVHQTOQHVJG0##35HQT1  )TKFDCUGFOQFGNUVJCVJCXGDGGPYKFGN[WUGFVQGXCNWCVGEQPVTQNUVTCVGIKGUHQT1 QTCEKFFGRQUKVKQPQTDQVJCTGVJG7#/VJG%+6OQFGNVJG41/VJG#&1/CPFVJG

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### References

#NNGP # 1 *QEJCPCFGN % , )JQTONG[ , # &CXKU 6 9 
 &GEQORQUKVKQP QH YCVGT CPF CSWGQWU UQNWVKQPU WPFGT OKZGF HCUV PGWVTQP CPF ICOOC TCFKCVKQP , 2J[U %JGO  

#NVUJWNNGT # 2 
C /GCUWTGOGPVU QH VJG RTQFWEVU QH CVOQURJGTKE RJQVQEJGOKECN TGCEVKQPU KP NCDQTCVQT[ UVWFKGU CPF KP CODKGPV CKTTGNCVKQPUJKRU DGVYGGP Q\QPG CPF QVJGT RTQFWEVU #VOQU 'PXKTQP
 

#NVUJWNNGT # 2 
D 4GXKGY PCVWTCN XQNCVKNG QTICPKE UWDUVCPEGU CPF VJGKT GHHGEV QP CKT SWCNKV[ KP VJG 7PKVGF
5VCVGU #VOQU 'PXKTQP  

#NVUJWNNGT # 2 
 6JG TQNG QH PKVTQIGP QZKFGU KP PQPWTDCP Q\QPG HQTOCVKQP KP VJG RNCPGVCT[ DQWPFCT[ NC[GT QXGT 0 #OGTKEC 9 'WTQRG CPF CFLCEGPV CTGCU QH QEGCP #VOQU 'PXKTQP  

#NVUJWNNGT # 2 
 'UVKOCVKQP QH VJG PCVWTCN DCEMITQWPF QH Q\QPG RTGUGPV CV UWTHCEG TWTCN NQECVKQPU ,#2%#
 

#NVUJWNNGT # 2 
 5QOG EJCTCEVGTKUVKEU QH Q\QPG HQTOCVKQP KP VJG WTDCP RNWOG QH 5V .QWKU /1 #VOQU
'PXKTQP  

#NVUJWNNGT # 2 
C 5QWTEGU CPF NGXGNU QH DCEMITQWPF Q\QPG CPF KVU RTGEWTUQTU CPF KORCEV CV ITQWPF NGXGN +P 5EJPGKFGT 6 .GG 5 & 9QNVGTU ) , 4 )TCPV . & GFU #VOQURJGTKE Q\QPG TGUGCTEJ CPF KVU RQNKE[ KORNKECVKQPU RTQEGGFKPIU QH VJG TF 75&WVEJ KPVGTPCVKQPCN U[ORQUKWO /C[  0KLOGIGP 6JG 0GVJGTNCPFU #OUVGTFCO 6JG 0GVJGTNCPFU 'NUGXKGT 5EKGPEG 2WDNKUJGTU RR  
5VWFKGU KP GPXKTQPOGPVCN UEKGPEG 

#NVUJWNNGT # 2 
D 0QPOGVJCPG QTICPKE EQORQWPF VQ PKVTQIGP QZKFG TCVKQU CPF QTICPKE EQORQUKVKQP KP EKVKGU
CPF TWTCN CTGCU ,#2%#  

#NVUJWNNGT # 2 
 2TQFWEVKQP QH CNFGJ[FGU CU RTKOCT[ GOKUUKQPU CPF HTQO UGEQPFCT[ CVOQURJGTKE TGCEVKQPU QH CNMGPGU CPF CNMCPGU FWTKPI VJG PKIJV CPF GCTN[ OQTPKPI JQWTU #VOQU 'PXKTQP 2CTV #  

#NVUJWNNGT # 2 $WHCNKPK , , 
 2JQVQEJGOKECN CURGEVU QH CKT RQNNWVKQP C TGXKGY 'PXKTQP 5EK 6GEJPQN
 

#NVUJWNNGT # 2 .GHQJP # 5 
 $CEMITQWPF Q\QPG KP VJG RNCPGVCT[ DQWPFCT[ NC[GT QXGT VJG 7PKVGF 5VCVGU , #KT 9CUVG /CPCIG #UUQE  

#NVUJWNNGT # 2 .GPI . , 
 #RRNKECVKQP QH VJG OGVJ[NDGP\QVJKC\QNQPG J[FTC\QPG OGVJQF HQT CVOQURJGTKE CPCN[UKU QH CNKRJCVKE CNFGJ[FGU #PCN %JGO  

#NVUJWNNGT # 2 /E2JGTUQP 5 2 
 5RGEVTQRJQVQOGVTKE CPCN[UKU QH CNFGJ[FGU KP VJG .QU #PIGNGU CVOQURJGTG , #KT 2QNNWV %QPVTQN #UUQE  

#NVUJWNNGT # 2 /KNNGT & . 5NGXC 5 ( 
 &GVGTOKPCVKQP QH HQTOCNFGJ[FG KP ICU OKZVWTGU D[ VJG EJTQOQVTQRKE CEKF OGVJQF #PCN %JGO  

#PFGTUQP , ) 6QQJG[ & 9 $TWPG 9 * 
 (TGG TCFKECNU YKVJKP VJG #PVCTEVKE XQTVGZ VJG TQNG QH %(%U KP #PVCTEVKE Q\QPG NQUU 5EKGPEG 
9CUJKPIVQP &%  

#PFGTUUQP5MÌNF ; )TGPPHGNV 2 2NGKLGN - 
 2JQVQEJGOKECN Q\QPG ETGCVKQP RQVGPVKCNU C UVWF[ QH FKHHGTGPV EQPEGRVU , #KT 9CUVG /CPCIG #UUQE   #PFTGCG 9 # 
 # UGPUKVKXG OGVJQF HQT VJG GUVKOCVKQP QH J[FTQIGP RGTQZKFG KP DKQNQIKECN OCVGTKCNU 0CVWTG .QPFQP  

#PVJGU 4 # 9CTPGT 6 6 
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#PVJGU 4 # -WQ ;* $CWOJGHPGT & 2 'TTKEQ 4 / $GVVIG 6 9 
 2TGFKEVCDKNKV[ QH OGUQUECNG CVOQURJGTKE OQVKQPU #FX )GQRJ[U $ 

#PVJGU 4 # *UKG ' ; -CQ ; * 
 &GUETKRVKQP QH VJG 2GPP 5VCVG0%#4 OGUQUECNG OQFGN XGTUKQP  
// $QWNFGT %1 0CVKQPCN %GPVGT HQT #VOQURJGTKE 4GUGCTEJ 0%#4 VGEJPKECN PQVG


#Q[CPCIK 5 /KVUWOKUJC * 
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%#  8 

#TG[ , #VMKPUQP 4 #UEJOCPP 5 / 
 2TQFWEV UVWF[ QH VJG ICURJCUG TGCEVKQPU QH OQPQVGTRGPGU YKVJ VJG 1* TCFKECN KP VJG RTGUGPEG QH 01  , )GQRJ[U 4GU =#VOQU?  

Z

#TG[ , 9KPGT # / #VMKPUQP 4 #UEJOCPP 5 / .QPI 9 & /QTTKUQP % . 
C 6JG GOKUUKQP QH 
<JGZGPQN 
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#TG[ , 9KPGT # / #VMKPUQP 4 #UEJOCPP 5 / .QPI 9 & /QTTKUQP % . 1NU\[M & / 
D 6GTRGPGU GOKVVGF HTQO CITKEWNVWTCN URGEKGU HQWPF KP %CNKHQTPKC	U %GPVTCN 8CNNG[ , )GQRJ[U 4GU =#VOQU?  

#TOUVTQPI 9 # *WORJTG[U 9 ) 
 # .'6 KPFGRGPFGPV FQUKOGVGT DCUGF QP VJG EJGOKNWOKPGUEGPV
FGVGTOKPCVKQP Q H*1 %CP , %JGO    

#TPVU 4 4 6GLCFC 5 $ 
 &KPKVTQRJGP[NJ[FTC\KPGEQCVGF UKNKEC IGN ECTVTKFIG OGVJQF HQT FGVGTOKPCVKQP QH HQTOCNFGJ[FG KP CKT KFGPVKHKECVKQP QH CP Q\QPG KPVGTHGTGPEG 'PXKTQP 5EK 6GEJPQN
 

#TQPKCP 2 ( 5EJGHH 2 # 9CFFGP 4 # 
 9KPVGTVKOG UQWTEGTGEQPEKNKCVKQP QH CODKGPV QTICPKEU
#VOQU 'PXKTQP  

#VMKPUQP 4 
 #VOQURJGTKE VTCPUHQTOCVKQPU QH CWVQOQVKXG GOKUUKQPU +P 9CVUQP # ; $CVGU 4 4 -GPPGF[ & GFU #KT RQNNWVKQP VJG CWVQOQDKNG CPF RWDNKE JGCNVJ 9CUJKPIVQP &% 0CVKQPCN #ECFGO[ 2TGUU RR 

#VMKPUQP 4 
 -KPGVKEU CPF OGEJCPKUOU QH VJG ICURJCUG TGCEVKQPU QH VJG J[FTQZ[N TCFKECN YKVJ QTICPKE EQORQWPFU 9CUJKPIVQP &% #OGTKECP %JGOKECN 5QEKGV[ 
, 2J[U %JGO 4GH &CVC /QPQITCRJ PQ 

#VMKPUQP 4 
 )CURJCUG VTQRQURJGTKE EJGOKUVT[ QH QTICPKE EQORQWPFU C TGXKGY #VOQU 'PXKTQP
2CTV #  

#VMKPUQP 4 
 -KPGVKEU CPF OGEJCPKUOU QH VJG ICURJCUG TGCEVKQPU QH VJG 01 TCFKECN YKVJ QTICPKE  EQORQWPFU , 2J[U %JGO 4GH &CVC  

#VMKPUQP 4 
 )CURJCUG VTQRQURJGTKE EJGOKUVT[ QH QTICPKE EQORQWPFU 9CUJKPIVQP &% #OGTKECP %JGOKECN 5QEKGV[ 
, 2J[U %JGO 4GH &CVC /QPQITCRJ PQ  #VMKPUQP 4 #UEJOCPP 5 / 
 1* TCFKECN RTQFWEVKQP HTQO VJG ICURJCUG TGCEVKQPU QH 1 YKVJ C UGTKGU QH  CNMGPGU WPFGT CVOQURJGTKE EQPFKVKQPU 'PXKTQP 5EK 6GEJPQN  

#VMKPUQP 4 #UEJOCPP 5 / 
 2TQFWEVU QH VJG ICURJCUG TGCEVKQPU QH CTQOCVKE J[FTQECTDQPU GHHGEV QH
01 EQPEGPVTCVKQP +PV , %JGO -KPGV   

#VMKPUQP 4 %CTVGT 9 2 . 
 -KPGVKEU CPF OGEJCPKUOU QH VJG ICURJCUG TGCEVKQPU QH Q\QPG YKVJ QTICPKE EQORQWPFU WPFGT CVOQURJGTKE EQPFKVKQPU %JGO 4GX  

#VMKPUQP 4 %CTVGT 9 2 . 
 4GCEVKQPU QH CNMQZ[ TCFKECNU WPFGT CVOQURJGTKE EQPFKVKQPU VJG TGNCVKXG KORQTVCPEG QH FGEQORQUKVKQP XGTUWU TGCEVKQP YKV J 1  , #VOQU %JGO   

#VMKPUQP 4 9KPGT # / 2KVVU , 0 ,T 
 'UVKOCVKQP QH PKIJVVKOG 0 1 EQPEGPVTCVKQPU HTQO CODKGPV   01 CPF 01 TCFKECN EQPEGPVTCVKQPU CPF VJG TQNG QH 0 1 KP PKIJVVKOG EJGOKUVT[ #VOQU 'PXKTQP     

#VMKPUQP 4 9KPGT # / #TG[ , $KGTOCPP * 9 &KPQHH 6 *CTIGT 9 2 /E'NTQ[ 2 # 6WC\QP ' % <KGNKPUMC $ 
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#VMKPUQP 4 $CWNEJ & . %QZ 4 # *CORUQP 4 ( ,T -GTT , # 6TQG , 
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#VMKPUQP 4 #UEJOCPP 5 / #TG[ , 5JQTGGU $ 
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#VYCVGT / # 
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#[GTU ) 2 2GPMGVV 5 # )KNNGVV 4 9 $CPF[ $ )CNDCNN[ + ' /G[GT % 2 'NUYQTVJ % / $GPVNG[ 5 6 (QTICP $ 9 
 'XKFGPEG HQT RJQVQEJGOKECN EQPVTQN QH Q\QPG EQPEGPVTCVKQPU KP WPRQNNWVGF OCTKPG CKT 0CVWTG 
.QPFQP  

$CTPGU + $CUVKCP 8 $GEMGT - * 6QPI < 
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$CWIWGU - 
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Z Z 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$*57

$GEMGT - * $TQEMOCPP - , $GEJCTC , 
 2TQFWEVKQP QH J[FTQIGP RGTQZKFG KP HQTGUV CKT D[ TGCEVKQP QH Q\QPG YKVJ VGTRGPGU 0CVWTG 
.QPFQP  

$GEMGT - * $GEJCTC , $TQEMOCPP - , 
 5VWFKGU QP VJG HQTOCVKQP QH * 1 KP VJG Q\QPQN[UKU QH   CNMGPGU #VOQU 'PXKTQP 2CTV #  

$GPLCOKP 5 ) 5GCOCP 0 . 
 # UKORNG UEJGOG HQT QDLGEVKXG CPCN[UKU KP EWTXGF HNQY /QP 9GCVJGT
4GX  

$GTUKU & 8CUUKNKQW ' 
 # EJGOKNWOKPGUEGPEG OGVJQF HQT FGVGTOKPKPI Q\QPG #PCN[UV 
.QPFQP
  $GVVGTVQP ' # *QHHOCPP / 4 
 *GPT[	U NCY EQPUVCPVU QH UQOG GPXKTQPOGPVCNN[ KORQTVCPV CNFGJ[FGU
'PXKTQP 5EK 6GEJPQN  

$KFNGOCP 6 ( 
 #VOQURJGTKE RTQEGUUGU 'PXKTQP 5EK 6GEJPQN  

$KGTOCPP * 9 6WC\QP ' % 9KPGT # / 9CNNKPIVQP 6 , 2KVVU , 0 ,T 
 5KOWNVCPGQWU CDUQNWVG OGCUWTGOGPVU QH ICUGQWU PKVTQIGP URGEKGU KP WTDCP CODKGPV CKT D[ NQPI RCVJNGPIVJ KPHTCTGF CPF WNVTCXKQNGVXKUKDNG URGEVTQUEQR[ #VOQU 'PXKTQP  

$NCMG 0 , 2GPMGVV 5 # %NGOKVUJCY - % #PY[N 2 .KIJVOCP 2 /CTUJ # 4 9 $WVEJGT ) 
 'UVKOCVGU QH CVOQURJGTKE J[FTQZ[N TCFKECN EQPEGPVTCVKQPU HTQO VJG QDUGTXGF FGEC[ QH OCP[ TGCEVKXG J[FTQECTDQPU KP YGNNFGHKPGF WTDCP RNWOGU , )GQRJ[U 4GU =#VOQU?  

$TCFUJCY , 5O[VJ 5 $CK 4 5CPFJQNO 5 
 4GEGPV CFXCPEGU KP VJG FGUKIP QH 01 RJQVQN[VKE  EQPXGTVGTU +P /GCUWTGOGPV QH VQZKE CPF TGNCVGF CKT RQNNWVCPVU RTQEGGFKPIU QH VJG 75 '2##9/# KPVGTPCVKQPCN U[ORQUKWO /C[ &WTJCO 0% 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP R  
#9/# URGEKCNV[ EQPHGTGPEG 8+2

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$WJT / 2 2CTTKUJ & & 0QTVQP 4 $ (GJUGPHGNF ( % 5KGXGTU 4 ' 4QDGTVU , / 
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$WTPU 9 ( 6KPIG[ & 6 'XCPU 4 % $CVGU ' * 
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 4GCNYQTNF XGJKENG GOKUUKQPU C UWOOCT[ QH VJG VJKTF CPPWCN %4%#24#% QPTQCF XGJKENG GOKUUKQPU YQTMUJQR #KT 9CUVG  

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PQ #

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#9/# RWDNKECVKQP 8+2

%CTOKEJCGN ) 4 2GVGTU . - -KVCFC 6 
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` + CP QXGTXKGY QH OQFGN FGXGNQROGPV CPF CRRNKECVKQPU #VOQU
'PXKTQP 2CTV #  

%CTTQNN / # *CUVKG & 4 4KFNG[ $ # 4QFIGTU / 1 6QTTGU # . &CXKU & & $TCFUJCY , & 5CPFJQNO 5 6 5EJKHH * + -CTGEMK & 4 *CTTKU ) 9 /CEMC[ ) + )TGIQT[ ) . %QPFQP ' 2 6TCKPGT / *WDNGT ) /QPV\MC & & /CFTQPKEJ 5 #NDTKVVQP & . 5KPIJ * $ $GEM 5 / 5JKRJCO / % $CEJOGKGT # 5 
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%CTVGT 9 2 . 
 # FGVCKNGF OGEJCPKUO HQT VJG ICURJCUG CVOQURJGTKE TGCEVKQPU QH QTICPKE EQORQWPFU
#VOQU 'PXKTQP 2CTV #  

%CTVGT 9 2 . 
 &GXGNQROGPV QH Q\QPG TGCEVKXKV[ UECNGU HQT XQNCVKNG QTICPKE EQORQWPFU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$

%CTVGT 9 2 . 
 &GXGNQROGPV QH Q\QPG TGCEVKXKV[ UECNGU HQT XQNCVKNG QTICPKE EQORQWPFU , #KT 9CUVG /CPCIG #UUQE  

%CTVGT 9 2 . #VMKPUQP 4 
 #P GZRGTKOGPVCN UVWF[ QH KPETGOGPVCN J[FTQECTDQP TGCEVKXKV[ 'PXKTQP 5EK
6GEJPQN  

%CTVGT 9 2 . #VMKPUQP 4 
C #NM[N PKVTCVG HQTOCVKQP HTQO VJG CVOQURJGTKE RJQVQQZKFCVKQP QH CNMCPGU C
TGXKUGF GUVKOCVKQP OGVJQF , #VOQU %JGO  

%CTVGT 9 2 . #VMKPUQP 4 
D %QORWVGT OQFGNKPI UVWF[ QH KPETGOGPVCN J[FTQECTDQP TGCEVKXKV[ 'PXKTQP
5EK 6GEJPQN  

%CTVGT 9 2 . .WTOCPP ( 9 
 'XCNWCVKQP QH VJG 4#&/ ICURJCUG EJGOKECN OGEJCPKUO 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$*57

%CUUOCUUK , % /KVUWVQOK 5 5JGRJGTF / 
 6JTGGFKOGPUKQPCN YKPF HKGNFU HQT WUG KP VJG WTDCP CKTUJGF OQFGN +P $GTINWPF 4 . .CYUQP & 4 /E-GG & , GFU 6TQRQURJGTKE Q\QPG CPF VJG GPXKTQPOGPV RCRGTU HTQO CP KPVGTPCVKQPCN EQPHGTGPEG /CTEJ  .QU #PIGNGU %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# VTCPUCEVKQPU UGTKGU PQ 64

%JCOGKFGU 9 . 6CP # 
 6JG VYQFKOGPUKQPCN FKCIPQUVKE OQFGN HQT VTQRQURJGTKE 1* CP WPEGTVCKPV[ CPCN[UKU , )GQRJ[U 4GU % 1EGCPU #VOQU  

%JCOGKFGU 9 . .KPFUC[ 4 9 4KEJCTFUQP , -KCPI % 5 
 6JG TQNG QH DKQIGPKE J[FTQECTDQPU KP WTDCP RJQVQEJGOKECN UOQI #VNCPVC CU C ECUG UVWF[ 5EKGPEG 
9CUJKPIVQP &%   %JCOGKFGU 9 . (GJUGPHGNF ( 4QFIGTU / 1 %CTFGNKPQ % /CTVKPG\ , 2CTTKUJ & .QPPGOCP 9 .CYUQP & 4 4CUOWUUGP 4 # <KOOGTOCP 2 )TGGPDGTI , /KFFNGVQP 2 9CPI 6 
 1\QPG RTGEWTUQT TGNCVKQPUJKRU KP VJG CODKGPV CVOQURJGTG , )GQRJ[U 4GU =#VOQU?  

%JCPFNGT # 5 %JQWNCTVQP 6 9 &QNNCTF ) , 'IINGVQP # ' , )C[ / , *KNN 6 # ,QPGU $ / 4 6[NGT $ , $CPF[ $ , 2GPMGVV 5 # 
 /GCUWTGOGPVU QH * 1 CPF 51   KP ENQWFU CPF GUVKOCVGU QH VJGKT TGCEVKQP TCVG 0CVWTG 
.QPFQP  

%JCPI 6 ; 4WF[ 5 , 
 1\QPGHQTOKPI RQVGPVKCN QH QTICPKE GOKUUKQPU HTQO CNVGTPCVKXGHWGNGF XGJKENGU
#VOQU 'PXKTQP 2CTV #  

%JCPI , 5 $TQUV 4 # +UCMGP + 5 # /CFTQPKEJ 5 /KFFNGVQP 2 5VQEMYGNN 9 4 9CNEGM % ,  # VJTGGFKOGPUKQPCN 'WNGTKCP CEKF FGRQUKVKQP OQFGN RJ[UKECN EQPEGRVU CPF HQTOWNCVKQP , )GQRJ[U 4GU =#VOQU?  

%JCPI 6 ; *COOGTNG 4 * ,CRCT 5 / 5CNOGGP + 6 
C #NVGTPCVKXG VTCPURQTVCVKQP HWGNU CPF CKT SWCNKV[ 'PXKTQP 5EK 6GEJPQN  

%JCPI , 5 /KFFNGVQP 2 $ 5VQEMYGNN 9 4 9CNEGM % , 2NGKO , ' .CPUHQTF * * /CFTQPKEJ 5 $KPMQYUMK ( 5 5GCOCP 0 . 5VCWHHGT & 4 
D 6JG TGIKQPCN CEKF FGRQUKVKQP OQFGN CPF GPIKPGGTKPI OQFGN +P +TXKPI 2 / GF #EKFKE FGRQUKVKQP UVCVG QH UEKGPEG CPF VGEJPQNQI[ XQNWOG + GOKUUKQPU CVOQURJGTKE RTQEGUUGU CPF FGRQUKVKQP 9CUJKPIVQP &% 6JG 75 0CVKQPCN #EKF 2TGEKRKVCVKQP #UUGUUOGPV 2TQITCO 
5VCVG QH UEKGPEG CPF VGEJPQNQI[ TGRQTV PQ 

%JCROCP 5 
 # VJGQT[ QH WRRGTCVOQURJGTKE Q\QPG /GO 4 /GVGQTQN 5QE  

%JQEM & 2 
 # EQORCTKUQP QH PWOGTKECN OGVJQFU HQT UQNXKPI VJG CFXGEVKQP GSWCVKQP

` ++ #VOQU 'PXKTQP
 

%JQEM & 2 
 # EQORCTKUQP QH PWOGTKECN OGVJQFU HQT UQNXKPI VJG CFXGEVKQP RTQDNGO

` +++ #VOQU 'PXKTQP
2CTV #  

%JQEM & 2 -WOCT 5 *GTTOCPP 4 9 
 #P CPCN[UKU QH VTGPFU KP QZKFCPV CKT SWCNKV[ KP VJG UQWVJ EQCUV
CKT DCUKP QH %CNKHQTPKC #VOQU 'PXKTQP  

%JQY , % 9CVUQP , ) .QYGPVJCN & * 5QNQOQP 2 # /CINKCPQ - . <KOCP 5 & 4KEJCTFU . 9 
 2/ CPF 2/ EQORQUKVKQPU KP %CNKHQTPKC	U 5CP ,QCSWKP 8CNNG[ #GTQUQN 5EK 6GEJPQN    

%JW 5* %QZ 9 / 
 &KHHGTGPEGU KP TGIKQPCN Q\QPG TGURQPUGU VQ RTGEWTUQT TGFWEVKQPU CU FGOQPUVTCVGF KP C TGIKQPCN QZKFCPV OQFGN 2TGUGPVGF CV #/5 EQPHGTGPEG QP VJG TQNG QH OGVGQTQNQI[ KP OCPCIKPI VJG GPXKTQPOGPV KP VJG 	U ,CPWCT[ 5EQVVUFCNG #< $QUVQP /# #OGTKECP /GVGQTQNQIKECN 5QEKGV[ %JW 5* /G[GT ' . %QZ 9 / 5EJGHHG 4 & 
 6JG TGURQPUG QH TGIKQPCN Q\QPG VQ 81% CPF 01

Z GOKUUKQPU TGFWEVKQPU CP CPCN[UKU HQT VJG GCUVGTP 7PKVGF 5VCVGU DCUGF QP TGIKQPCN QZKFCPV OQFGNKPI +P 8QUVCN , , GF 6TQRQURJGTKE Q\QPG PQPCVVCKPOGPV CPF FGUKIP XCNWG KUUWGU =RTQEGGFKPIU QH C 75 '2##9/# KPVGTPCVKQPCN URGEKCNV[ EQPHGTGPEG? 1EVQDGT  $QUVQP /# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# VTCPUCEVKQPU UGTKGU PQ 

%KEEKQNK 2 $TCPECNGQPK ' (TCVVQPK / %GEKPCVQ # $TCEJGVVK # 
 7DKSWKVQWU QEEWTTGPEG QH UGOKXQNCVKNG ECTDQP[N EQORQWPFU KP VTQRQURJGTKE UCORNGU CPF VJGKT RQUUKDNG UQWTEGU #VOQU
'PXKTQP 2CTV #  

%KEGTQPG 4 , 
 #PCN[UKU QH UQWTEGU CPF UKPMU QH CVOQURJGTKE PKVTQWU QZKFG 
0 1 , )GQRJ[U 4GU  =#VOQU?  

%NCTM 6 . %NCTMG , ( 
 # .CITCPIKCP UVWF[ QH VJG DQWPFCT[ NC[GT VTCPURQTV QH RQNNWVCPVU KP VJG
PQTVJGCUVGTP 7PKVGF 5VCVGU #VOQU 'PXKTQP  

%NCTM 6 . -CTN 6 4 
 #RRNKECVKQP QH RTQIPQUVKE OGVGQTQNQIKECN XCTKCDNGU VQ HQTGECUVU QH FCKN[ OCZKOWO QPGJQWT Q\QPG EQPEGPVTCVKQPU KP VJG PQTVJGCUVGTP 7PKVGF 5VCVGU , #RRN /GVGQTQN  

%QFG QH (GFGTCN 4GIWNCVKQPU 
 5WDRCTV $

` RTQEGFWTGU HQT VGUVKPI RGTHQTOCPEG EJCTCEVGTKUVKEU QH CWVQOCVGF OGVJQFU 51  %1  1  CPF 01  % ( 4  h  

%QFG QH (GFGTCN 4GIWNCVKQPU 
 #ODKGPV CKT OQPKVQTKPI TGHGTGPEG CPF GSWKXCNGPV OGVJQFU % ( 4  h

%QJGP + 4 2WTEGNN 6 % 
 5RGEVTQRJQVQOGVTKE FGVGTOKPCVKQP QH J[FTQIGP RGTQZKFG YKVJ SWKPQNKPQN
#PCN %JGO  

%QJGP + 4 2WTEGNN 6 % #NVUJWNNGT # 2 
 #PCN[UKU QH VJG QZKFCPV KP RJQVQQZKFCVKQP TGCEVKQPU
'PXKTQP 5EK 6GEJPQN  

%QJP 4 & &GPPKU 4 . 
 6JG GXCNWCVKQP QH CEKF FGRQUKVKQP OQFGNU WUKPI RTKPEKRCN EQORQPGPV URCEGU
#VOQU 'PXKTQP  

%QNDGEM + *CTTKUQP 4 / 
 &T[ FGRQUKVKQP QH Q\QPG UQOG OGCUWTGOGPVU QH FGRQUKVKQP XGNQEKV[ CPF QH XGTVKECN RTQHKNGU VQ  OGVTGU #VOQU 'PXKTQP  

%QOGU ( , #TOGTFKPI 9 )TKIQPKU 4 *GTDGTV # 5RKGMGTOCPP / 9CNVGT , 
 6TQRQURJGTKE 1* NQECN OGCUWTGOGPVU CPF VJGKT KPVGTRTGVCVKQPU $GT $WPUGP)GU 2J[U %JGO  

%QVVQP 9 4 #PVJGU 4 # 
 5VQTO CPF ENQWF F[PCOKEU 0GY ;QTM 0; #ECFGOKE 2TGUU +PE &OQYUMC 4 *QNVQP , 4 GFU +PVGTPCVKQPCN IGQRJ[UKEU UGTKGU X 

%QWVCPV 4 9 
 6JGQTGVKECN GXCNWCVKQP QH UVCDKNKV[ QH XQNCVKNG QTICPKE EJGOKECNU CPF RQNCT XQNCVKNG QTICPKE EJGOKECNU KP ECPKUVGTU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2#4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

%QZ 4 # 
 6JG RJQVQN[UKU QH ICUGQWU PKVTQWU CEKF , 2JQVQEJGO  

%QZ 4 # 4QHHG[ / , 
 6JGTOCN FGEQORQUKVKQP QH RGTQZ[CEGV[NPKVTCVG KP VJG RTGUGPEG QH PKVTKE QZKFG
'PXKTQP 5EK 6GEJPQN  

%QZ 4 & $CNHQWT 9 & .CPING[ ) , 
 3WCNKV[ EQPVTQN HQT CODKGPV NGXGN J[FTQECTDQP UCORNKPI CPF CPCN[UKU 2TGUGPVGF CV VJ CPPWCN OGGVKPI QH VJG #KT 2QNNWVKQP %QPVTQN #UUQEKCVKQP ,WPG 0GY 1TNGCPU .# 2KVVUDWTIJ 2# #KT 2QNNWVKQP %QPVTQN #UUQEKCVKQP RCRGT PQ  %TWV\GP 2 , 
 6JG KPHNWGPEG QH PKVTQIGP QZKFGU QP VJG CVOQURJGTKE Q\QPG EQPVGPV 3 , 4 /GVGQTQN 5QE
 

&CDFWD & 5GKPHGNF , * 
 0WOGTKECN CFXGEVKQP UEJGOGU WUGF KP CKT SWCNKV[ OQFGNU

` UGSWGPVKCN CPF RCTCNNGN KORNGOGPVCVKQP #VOQU 'PXKTQP  

&CTNG[ ' ( -GVVPGT - # 5VGRJGPU ' 4 
 #PCN[UKU QH RGTQZ[CE[N PKVTCVGU D[ ICU EJTQOCVQITCRJ[ YKVJ GNGEVTQP ECRVWTG FGVGEVKQP #PCN %JGO  

&CTPCNN - 4 .NQ[F # % 9KPGT # / 2KVVU , 0 ,T 
 4GCEVKXKV[ UECNG HQT CVOQURJGTKE J[FTQECTDQPU DCUGF QP TGCEVKQP YKVJ J[FTQZ[N TCFKECN 'PXKTQP 5EK 6GEJPQN  

&CU 6 0 /QQTVJ[ 2 0 4CQ - 0 
 %JGOKNWOKPGUEGPV OGVJQF HQT VJG FGVGTOKPCVKQP QH NQY EQPEGPVTCVKQP QH J[FTQIGP RGTQZKFG , +PFKCP %JGO 5QE  

&CUIWRVC 2 - *YCPI * 
 #RRNKECVKQP QH C PGUVGF NQQR U[UVGO HQT VJG HNQY KPLGEVKQP CPCN[UKU QH VTCEG CSWGQWU RGTQZKFG #PCN %JGO  

&CUIWRVC 2 - &QPI 5 *YCPI * ;CPI *% )GPHC < 
 %QPVKPWQWU NKSWKFRJCUG HNWQTQOGVT[ EQWRNGF VQ C FKHHWUKQP UETWDDGT HQT VJG TGCNVKOG FGVGTOKPCVKQP QH CVOQURJGTKE HQTOCNFGJ[FG J[FTQIGP RGTQZKFG CPF UWNHWT FKQZKFG #VOQU 'PXKTQP  

&CUIWRVC 2 - &QPI 5 *YCPI * 
 &KHHWUKQP UETWDDGTDCUGF HKGNF OGCUWTGOGPVU QH CVOQURJGTKE HQTOCNFGJ[FG CPF J[FTQIGP RGTQZKFG #GTQUQN 5EK 6GEJPQN  

&CXKU & & 
 #VOQURJGTKE PKVTQIGP QZKFGU VJGKT FGVGEVKQP CPF EJGOKUVT[ 6JKTF [GCT TGRQTV #VNCPVC )# )GQTIKC +PUVKVWVG QH 6GEJPQNQI[ RR 

&CXKU & & $TCFUJCY , & 4QFIGTU / 1 5CPFJQNO 5 6 -G5JGPI 5 
 (TGG VTQRQURJGTKE CPF DQWPFCT[ NC[GT OGCUWTGOGPVU QH 01 QXGT VJG EGPVTCN CPF GCUVGTP 0QTVJ 2CEKHKE 1EGCP , )GQRJ[U 4GU =#VOQU?  

&GNCP[ # % &KEMGTUQP 4 4 /GNEJKQT ( . ,T 9CTVDWTI # ( 
 /QFKHKECVKQP QH C EQOOGTEKCN 01 FGVGEVQT HQT JKIJ UGPUKVKXKV[ 4GX 5EK +PUVTWO  

Z

&GOGTLKCP - . 5EJGTG - . 2GVGTUQP , 6 
 6JGQTGVKECN GUVKOCVGU QH CEVKPKE 
URJGTKECNN[ KPVGITCVGF HNWZ CPF RJQVQN[VKE TCVG EQPUVCPVU QH CVOQURJGTKE URGEKGU KP VJG NQYGT VTQRQURJGTG +P 2KVVU , 0 ,T /GVECNH 4 . )TQULGCP & GFU #FXCPEGU KP GPXKTQPOGPVCN UEKGPEG CPF VGEJPQNQI[ X  0GY ;QTM 0; ,QJP 9KNG[  5QPU RR 

&G/QTG 9 $ 5CPFGT 5 2 )QNFGP & / *CORUQP 4 ( -WT[NQ / , *QYCTF % , 4CXKUJCPMCTC # 4 -QND % ' /QNKPC / , 
 %JGOKECN MKPGVKEU CPF RJQVQEJGOKECN FCVC HQT WUG KP UVTCVQURJGTKE OQFGNKPI 2CUCFGPC %# 0#5# 2CPGN HQT &CVC 'XCNWCVKQP ,GV 2TQRWNUKQP .CDQTCVQT[ RWDNKECVKQP PQ 

&GPPKU 4 . 0QXCM , * 
 '2#	U VJKTF IGPGTCVKQP OQFGNKPI U[UVGO 
/1&'.5 CP QXGTXKGY +P $GTINWPF 4 . GF 6TQRQURJGTKE Q\QPG CPF VJG GPXKTQPOGPV ++ GHHGEVU OQFGNKPI CPF EQPVTQN =RCRGTU HTQO CP KPVGTPCVKQPCN URGEKCNV[ EQPHGTGPEG? 0QXGODGT #VNCPVC )# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/ VTCPUCEVKQPU UGTKGU PQ 

&GPPKU 4 . /E*GPT[ , 0 $CTEJGV 9 4 $KPMQYUMK ( 5 $[WP & 9 
C %QTTGEVKPI 4#&/	U UWNHCVG WPFGTRTGFKEVKQP FKUEQXGT[ CPF EQTTGEVKQP QH OQFGN GTTQTU CPF VGUVKPI VJG EQTTGEVKQPU VJTQWIJ EQORCTKUQPU CICKPUV HKGNF FCVC #VOQU 'PXKTQP 2CTV #   &GPPKU 4 . $[PP & 9 0QXCM , * %QCVGU % , )CNWRRK - , 
D 6JG PGZV IGPGTCVKQP QH KPVGITCVGF CKT SWCNKV[ OQFGNKPI '2#	U /QFGNU 2TGUGPVGF CV VJG KPVGTPCVKQPCN EQPHGTGPEG QP TGIKQPCN RJQVQEJGOKECN OGCUWTGOGPV CPF OQFGNKPI UVWFKGU 0QXGODGT 5CP &KGIQ %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP

&GPVGPGT ( , %TWV\GP 2 , 
 4GCEVKQP QH 0 1 QP VTQRQURJGTKE CGTQUQNU KORCEV QP VJG INQDCN   FKUVTKDWVKQPU QH 01  1  CPF 1* , )GQRJ[U 4GU =#VOQU?  

Z 

>YGPV 4 *QX 1 
 #RRNKECVKQP QH UGPUKVKXKV[ CPF WPEGTVCKPV[ CPCN[UKU VGEJPKSWGU VQ C RJQVQEJGOKECN Q\QPG OQFGN , )GQRJ[U 4GU =#VOQU?  

>YGPV 4 ) ,GPMKP / ' 
 *[FTQECTDQPU CPF VJG NQPITCPIG VTCPURQTV QH Q\QPG CPF 2#0 CETQUU 'WTQRG #VOQU 'PXKTQP 2CTV #  

>YGPV 4 ) -C[ 2 , # 
 (CEVQTU KPHNWGPEKPI VJG ITQWPF NGXGN FKUVTKDWVKQP QH Q\QPG KP 'WTQRG
'PXKTQP 2QNNWV  

&KEMGTUQP 4 4 &GNCP[ # % 9CTVDWTI # ( 
 (WTVJGT OQFKHKECVKQP QH C EQOOGTEKCN 01 FGVGEVQT HQT

Z JKIJ UGPUKVKXKV[ 4GX 5EK +PUVTWO  

&KEMGTUQP 4 4 *WHHOCP ) , .WMG 9 6 0WPPGTOCEMGT . , 2KEMGTKPI - ' .GUNKG # % & .KPFUG[ % ) 5NKPP 9 ) 0 -GNN[ 6 , &CWO 2 * &GNCP[ # % )TGGPDGTI , 2 <KOOGTOCP 2 4 $QCVOCP , ( 4C[ , & 5VGFOCP & * 
 6JWPFGTUVQTOU CP KORQTVCPV OGEJCPKUO KP VJG VTCPURQTV QH CKT RQNNWVCPVU 5EKGPEG 
9CUJKPIVQP &%  

&KGV\ 9 # 
 4GURQPUG HCEVQTU HQT ICU EJTQOCVQITCRJKE CPCN[UGU , )CU %JTQOCVQIT  

&QFIG / % 
 %QODKPGF WUG QH OQFGNKPI VGEJPKSWGU CPF UOQI EJCODGT FCVC VQ FGTKXG Q\QPGRTGEWTUQT TGNCVKQPUJKRU +P &KOKVTKCFGU $ GF +PVGTPCVKQPCN EQPHGTGPEG QP RJQVQEJGOKECN QZKFCPV RQNNWVKQP
CPF KVU EQPVTQN

` RTQEGGFKPIU XQNWOG ++ 5GRVGODGT  4CNGKIJ 0% 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ RR   TGRQTV PQ '2#D #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

&QFIG / % 
 # EQORCTKUQP QH VJTGG RJQVQEJGOKECN QZKFCPV OGEJCPKUOU , )GQRJ[U 4GU =#VOQU?
 

&QFIG / % 
 (QTOCNFGJ[FG RTQFWEVKQP KP RJQVQEJGOKECN UOQI CU RTGFKEVGF D[ VJTGG UVCVGQHVJGUEKGPEG EJGOKECN QZKFCPV OGEJCPKUOU , )GQRJ[U 4GU =#VOQU?  

&QNUMG & # )CV\ & ( 
 # HKGNF KPVGTEQORCTKUQP QH OGVJQFU HQT VJG OGCUWTGOGPV QH RCTVKENG CPF ICU FT[ FGRQUKVKQP , )GQRJ[U 4GU  

&TWOOQPF , 9 8QN\ # 'JJCNV & * 
 #P QRVKOK\GF EJGOKNWOKPGUEGPEG FGVGEVQT HQT VTQRQURJGTKE
01 OGCUWTGOGPVU , #VOQU %JGO  

&TWOOQPF , 5EJKHH * -CTGEMK & /CEMC[ ) 
 /GCUWTGOGPVU QH 0 11 2#0 *0 1  * 1  CPF

    * %1 FWTKPI VJG UQWVJGTP %CNKHQTPKC CKT SWCNKV[ UVWF[ 2TGUGPVGF CV PF CPPWCN OGGVKPI CPF

 GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG #PCJGKO %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP TGRQTV PQ  &TWOOQPF , 9 5JGRUQP 2 $ /CEMC[ ) + 5EJKHH * + 
 /GCUWTGOGPVU QH 01  01  CPF 01 [Z  WUKPI C PGY EQPXGTVGTUGSWGPEGT CPF UGPUKVKXG .WOKPQZs FGVGEVKQP +P /GCUWTGOGPV QH VQZKE CPF TGNCVGF CKT RQNNWVCPVU RTQEGGFKPIU QH VJG  75 '2##9/# KPVGTPCVKQPCN U[ORQUKWO /C[ &WTJCO 0% 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# RWDNKECVKQP 8+2

&WPMGT # / 5EJNG[GT % * /QTTKU 4 ' 2QNNCEM # - 
C 'HHGEVU QH OGVJCPQNICUQNKPG DNGPFU WUGF KP HNGZKDNGXCTKCDNG HWGN XGJKENGU QP WTDCP CKT SWCNKV[ KP [GCT 

` CWVQQKN CKT SWCNKV[ KORTQXGOGPV TGUGCTEJ RTQITCO 2TGUGPVGF CV VJ CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG -CPUCU %KV[ /1 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

&WPMGT # / /QTTKU 4 ' 2QNNCEM # - %QJGP , 2 5EJNG[GT % * %JQEM & 2 
D 'HHGEVU QH CTQOCVKEU /6$' QNGHKPU CPF 6 QP WTDCP CKT SWCNKV[ KP [GCT 

` CWVQQKN CKT SWCNKV[ KORTQXGOGPV TGUGCTEJ RTQITCO 2TGUGPVGF CV VJ CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG -CPUCU %KV[ /1 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

'JJCNV & * &QTP *2 2QRRG & 
 6JG EJGOKUVT[ QH VJG J[FTQZ[N TCFKECN KP VJG VTQRQURJGTG +P .CUV ( 6 9CVNKPI 4 GFU #EKFKE FGRQUKVKQP KVU PCVWTG CPF KORCEVU 2TQEGGFKPIU QH VJG KPVGTPCVKQPCN U[ORQUKWO 5GRVGODGT  )NCUIQY 7PKVGF -KPIFQO 2TQE 4 5QE 'FKPDWTIJ 5GEV $ $KQN
5EK  

'JTGPHGNF , 4 
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'KUGNG ( . 6CPPGT & , 
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'KUGPTGKEJ 5 , .QQPG[ $ $ 6JQTPVQP , & 
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'TTKEQ 4 $CVGU ) 6 
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(CJG[ & 9 'WDCPM % 5 *WDNGT ) (GJUGPHGNF ( % 
 'XCNWCVKQP QH C ECVCN[VKE TGFWEVKQP VGEJPKSWG HQT VJG OGCUWTGOGPV QH VQVCN TGCEVKXG QFFPKVTQIGP 01 KP VJG CVOQURJGTG , #VOQU %JGO   [ 

(GFGTCN 4GIKUVGT 
 0CVKQPCN RTKOCT[ CPF UGEQPFCT[ CODKGPV CKT SWCNKV[ UVCPFCTFU ( 4 
#RTKN   


(GFGTCN 4GIKUVGT 
 #ODKGPV CKT OQPKVQTKPI TGHGTGPEG CPF GSWKXCNGPV OGVJQFU ( 4 
(GDTWCT[   


(GFGTCN 4GIKUVGT 
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(GDTWCT[   

(GFGTCN 4GIKUVGT 
 1HHKEG QH 4GUGCTEJ CPF &GXGNQROGPV CODKGPV CKT OQPKVQTKPI TGHGTGPEG CPF GSWKXCNGPV OGVJQFU GSWKXCNGPV OGVJQF FGUKIPCVKQP ( 4 
5GRVGODGT    (GFGTCN 4GIKUVGT 
 1HHKEG QH 4GUGCTEJ CPF &GXGNQROGPV CODKGPV CKT OQPKVQTKPI TGHGTGPEG CPF GSWKXCNGPV OGVJQFU GSWKXCNGPV OGVJQF CPF TGHGTGPEG OGVJQF FGUKIPCVKQPU ( 4 
5GRVGODGT 
 

(GFGTCN 4GIKUVGT 
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(GDTWCT[   

(GJUGPHGNF ( % &KEMGTUQP 4 4 *ÒDNGT ) .WMG 9 6 0WPPGTOCEMGT . , 9KNNKCOU ' , 4QDGTVU , / %CNXGTV , ) %WTTCP % / &GNCP[ # % 'WDCPM % 5 (CJG[ & 9 (TKGF # )CPFTWF $ 9 .CPIHQTF # 1 /WTRJ[ 2 % 0QTVQP 4 $ 2KEMGTKPI - ' 4KFNG[ $ #  # ITQWPFDCUGF KPVGTEQORCTKUQP QH 01 01  CPF 01 OGCUWTGOGPV VGEJPKSWGU , )GQRJ[U Z [ 4GU =#VOQU?  

(GJUGPHGNF ( % 2CTTKUJ & & (CJG[ & 9 
 6JG OGCUWTGOGPV QH 01 KP VJG PQPWTDCP VTQRQURJGTG

Z +P +UCMUGP + 5 # GF 6TQRQURJGTKE Q\QPG TGIKQPCN CPF INQDCN UECNG KPVGTCEVKQPU &QTFTGEJV 6JG 0GVJGTNCPFU & 4GKFGN 2WDNKUJKPI RR 

(GJUGPHGNF ( % &TWOOQPF , 9 4Q[EJQYFJWT[ 7 - )CNXKP 2 , 9KNNKCOU ' , $WJT / 2 2CTTKUJ & & *WDNGT ) .CPIHQTF # 1 %CNXGTV , ) 4KFNG[ $ # )TCJGM ( *GKMGU $ ) -QM ) . 5JGVVGT , & 9CNGIC , ) 'NUYQTVJ % / 0QTVQP 4 $ (CJG[ & 9 /WTRJ[ 2 % *QXGTOCNG % /QJPGP 8 # &GOGTLKCP - . /CEMC[ ) + 5EJKHH * +  +PVGTEQORCTKUQP QH 01 OGCUWTGOGPV VGEJPKSWGU , )GQRJ[U 4GU =#VOQU?   

(GJUGPHGNF ( %CNXGTV , (CNN 4 )QNFCP 2 )WGPVJGT # $ *GYKVV % 0 .COD $ .KW 5 6TCKPGT / 9GUVDGTI * <KOOGTOCP 2 
 'OKUUKQPU QH XQNCVKNG QTICPKE EQORQWPFU HTQO XGIGVCVKQP CPF VJG KORNKECVKQPU HQT CVOQURJGTKE EJGOKUVT[ )NQDCN $KQIGQEJGO %[ENGU  

(GNVQP % % 5JGRRCTF , % %CORDGNN / , 
 6JG TCFKQEJGOKECN J[FTQZ[N TCFKECN OGCUWTGOGPV OGVJQF
'PXKTQP 5EK 6GEJPQN  

(KPNC[UQP2KVVU $ , 2KVVU , 0 ,T 
 #VOQURJGTKE EJGOKUVT[ HWPFCOGPVCNU CPF GZRGTKOGPVCN VGEJPKSWGU 0GY ;QTM 0; ,QJP 9KNG[  5QPU

(KPNC[UQP2KVVU $ , 2KVVU , 0 ,T 
 #VOQURJGTKE EJGOKUVT[ QH VTQRQURJGTKE Q\QPG HQTOCVKQP UEKGPVKHKE CPF TGIWNCVQT[ KORNKECVKQPU #KT 9CUVG  

(TKGF # *QFIGUQP , 
 .CUGT RJQVQCEQWUVKE FGVGEVKQP QH PKVTQIGP FKQZKFG KP VJG ICURJCUG VKVTCVKQP QH PKVTKE
QZKFG YKVJ Q\QPG #PCN %JGO  

(WLKVC ' / 
 6TGPFU KP GOKUUKQPU CPF CODKGPV EQPEGPVTCVKQPU QH %1 0/*% CPF 01 KP VJG 5QWVJ %QCUV

Z #KT $CUKP +P 5QWVJGTP %CNKHQTPKC #KT 3WCNKV[ 5VWF[ FCVC CPCN[UKU RTQEGGFKPIU QH CP KPVGTPCVKQPCN URGEKCNV[ EQPHGTGPEG ,WN[  .QU #PIGNGU %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# RWDNKECVKQP 8+2

(WLKVC ' / %TQGU $ ' $GPPGVV % . .CYUQP & 4 .WTOCPP ( 9 /CKP * * 
 %QORCTKUQP QH GOKUUKQP KPXGPVQT[ CPF CODKGPV EQPEGPVTCVKQP TCVKQU QH %1 0/1) CPF 01 KP %CNKHQTPKC	U UQWVJ

Z EQCUV CKT DCUKP , #KT 9CUVG /CPCIG #UUQE  

(WPI - 
 %CTDQP[N QDUGTXCVKQPU FWTKPI VJG 5%#35 2TGUGPVGF CV PF CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG #PCJGKO %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

(WPI - )TQULGCP & 
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C 6JTGGFKOGPUKQPCN OQFGN U[PVJGUKU QH VJG INQDCN OGVJCPG E[ENG , )GQRJ[U 4GU =#VOQU?  

(WPI % 5 /KUTC 2 - $NQZCO 4 9QPI 5 
D # PWOGTKECN GZRGTKOGPV QP VJG TGNCVKXG KORQTVCPEG QH * 1 CPF 1 KP CSWGQWU EQPXGTUKQP QH 51 VQ 51  #VOQU 'PXKTQP 2CTV #  

    

)·D 5 *GNNRQKPVPGT ' 6WTPGT 9 8 -QTVG ( 
 *[FTQZ[OGVJ[N J[FTQRGTQZKFG CPF DKU
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)CHHPG[ , 5 (CLGT 4 5GPWO ) + 
 #P KORTQXGF RTQEGFWTG HQT JKIJ RWTKV[ ICUGQWU RGTQZ[CE[N PKVTCVG RTQFWEVKQP WUG QH JGCX[ NKRKF UQNXGPVU #VOQU 'PXKTQP  

)C[ $ 9 ,T $WHCNKPK , , 
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)QWNF 4 ( GF #FXCPEGU KP EJGOKUVT[ UGTKGU X 

)C[ $ 9 ,T $WHCNKPK , , 
D *[FTQIGP RGTQZKFG KP VJG WTDCP CVOQURJGTG 'PXKTQP .GVV  

)C[ $ 9 ,T 0QQPCP 4 % $WHCNKPK , , *CPUV 2 . 
 2JQVQEJGOKECN U[PVJGUKU QH RGTQZ[CE[N PKVTCVGU KP ICU RJCUG XKC EJNQTKPGCNFGJ[FG TGCEVKQP 'PXKTQP 5EK 6GEJPQN  

)C[ $ 9 /GGMU 5 $WHCNKPK , , 
 2GTQZKFG HQTOCVKQP CPF FGVGEVKQP KP XCTKQWU U[UVGOU 2TGUGPVGF CV VJ OGGVKPI QH VJG #OGTKECP %JGOKECN 5QEKGV[ .QU #PIGNGU %#  

)GTVNGT # 9 2KGTUQP 9 4 9KVVQHH & 0 4QDKPUQP 0 ( 
 9JGTG FQ VWPPGN UVWFKGU HKV KP HQT GOKUUKQPU KPXGPVQT[ FGXGNQROGPV! 2TGUGPVGF CV 'OKUUKQPU KPXGPVQT[ YQTMUJQR 0QXGODGT

)GTXCV ) 2 %NCTM 2 # /CTUJ # 4 9 6GCUFCNG + %JCPFNGT # 5 %JQWNCTVQP 6 9 )C[ / , *KNN / - *KNN 6 # 
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.QPFQP  

)GT[ / 9 9JKVVGP ) < -KNNWU , 2 
 &GXGNQROGPV CPF VGUVKPI QH VJG %/$+8 HQT WTDCP CPF TGIKQPCN OQFGNKPI 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

)GT[ / 9 9JKVVGP ) < -KNNWU , 2 &QFIG / % 
 # RJQVQEJGOKECN MKPGVKEU OGEJCPKUO HQT WTDCP CPF TGIKQPCN UECNG EQORWVGT OQFGNKPI , )GQRJ[U 4GU =#VOQU?  

)JQNUQP # 4 ,C[CPV[ 4 - / 5VQTO , ( 
 'XCNWCVKQP QH CNWOKPWO ECPKUVGTU HQT VJG EQNNGEVKQP CPF UVQTCIG QH CKT VQZKEU #PCN %JGO  

)NQDCN 6JKPMKPI 2TQLGEV 
 6JG )NQDCN 6JKPMKPI 2TQLGEV 5WRRQTVGF D[ VJG 'KUGPJQYGT *KIJGT 'FWECVKQP 2TQITCO CPF )GQTIKC 5VCVG 7PKXGTUKV[

)QNFCP 2 & -WUVGT 9 % (GJUGPHGNF ( % /QPV\MC 5 # 
 6JG QDUGTXCVKQP QH C % CNEQJQN  GOKUUKQP KP C 0QTVJ #OGTKECP RKPG HQTGUV )GQRJ[U 4GU .GVV  

)QQFKP 9 4 /E4CG ) , 5GKPHGNF , * 
 #P QDLGEVKXG CPCN[UKU VGEJPKSWG HQT EQPUVTWEVKPI VJTGGFKOGPUKQPCN WTDCPUECNG YKPF HKGNFU , #RRN /GVGQTQN  

)QTFQP ) ' 
 4GEGRVQT OQFGNU 'PXKTQP 5EK 6GEJPQN   )QTFQP 5 / /KNNGT / 
 #PCN[UKU QH CODKGPV RQNCT XQNCVKNG QTICPKE EQORQWPFU WUKPI EJGOKECN KQPK\CVKQPKQP VTCR FGVGEVQT 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

)TCGFGN 6 ' 9GUEJNGT % , 
 %JGOKUVT[ YKVJKP CSWGQWU CVOQURJGTKE CGTQUQNU CPF TCKPFTQRU 4GX )GQRJ[U 5RCEG 2J[U  

)TCGFGN 6 ' *CYMKPU & 6 %NCZVQP . & 
C #VOQURJGTKE EJGOKECN EQORQWPFU UQWTEGU QEEWTTGPEG CPF DKQCUUC[ 1TNCPFQ (. #ECFGOKE 2TGUU +PE

)TCGFGN 6 ' /CPFKEJ / . 9GUEJNGT % , 
D -KPGVKE OQFGN UVWFKGU QH CVOQURJGTKE FTQRNGV EJGOKUVT[  JQOQIGPQWU VTCPUKVKQP OGVCN EJGOKUVT[ KP TCKPFTQRU , )GQRJ[U 4GU =#VOQU?  

)TGGPJWV ) - 
 6TCPURQTV QH Q\QPG DGVYGGP DQWPFCT[ NC[GT CPF ENQWF NC[GT D[ EWOWNWU ENQWFU , )GQRJ[U 4GU =#VOQU?  

)TGIQT[ ) . *QGNN , / ,T 6QTTGU # . %CTTQNN / # 4KFNG[ $ # 4QFIGTU / 1 $TCFUJCY , 5CPFJQNO 5 &CXKU & & 
C #P KPVGTEQORCTKUQP QH CKTDQTPG PKVTKE QZKFG OGCUWTGOGPVU C UGEQPF QRRQTVWPKV[ , )GQRJ[U 4GU =#VOQU?  

)TGIQT[ ) . *QGNN , / ,T %CTTQNN / # 4KFNG[ $ # &CXKU & & $TCFUJCY , 4QFIGTU / 1 5CPFJQNO 5 6 5EJKHH * + *CUVKG & 4 -CTGEMK & 4 /CEMC[ ) + *CTTKU ) 9 6QTTGU # . (TKGF # 
D #P KPVGTEQORCTKUQP QH CKTDQTPG PKVTQIGP FKQZKFG KPUVTWOGPVU , )GQRJ[U 4GU =#VOQU?  

)TKHHKVJ & 9 6 5EJWUVGT ) 
 #VOQURJGTKE VTCEG ICU CPCN[UKU WUKPI OCVTKZ KUQNCVKQP(QWTKGT VTCPUHQTO KPHTCTGF URGEVTQUEQR[ , #VOQU %JGO  

)TQULGCP & 
 (QTOCNFGJ[FG CPF QVJGT ECTDQP[NU KP .QU #PIGNGU CODKGPV CKT 'PXKTQP 5EK 6GEJPQN
 

)TQULGCP & 
 #NFGJ[FGU ECTDQZ[NKE CEKFU CPF KPQTICPKE PKVTCVG FWTKPI 05/%5 #VOQU 'PXKTQP
 

)TQULGCP & 
 #ODKGPV NGXGNU QH HQTOCNFGJ[FG CEGVCNFGJ[FG CPF HQTOKE CEKF KP UQWVJGTP %CNKHQTPKC TGUWNVU QH C QPG[GCT DCUGNKPG UVWF[ 'PXKTQP 5EK 6GEJPQN  

)TQULGCP & (WPI - 
 *[FTQECTDQPU CPF ECTDQP[NU KP .QU #PIGNGU CKT , #KT 2QNNWV %QPVTQN #UUQE
 

)TQULGCP & *CTTKUQP , 
C 2GTQZ[CEGV[N PKVTCVG EQORCTKUQP QH CNMCNKPG J[FTQN[UKU CPF EJGOKNWOKPGUEGPEG
OGVJQFU 'PXKTQP 5EK 6GEJPQN  

)TQULGCP & *CTTKUQP , 
D 4GURQPUG QH EJGOKNWOKPGUEGPEG 01 CPCN[\GTU CPF WNVTCXKQNGV Q\QPG CPCN[\GTU

Z VQ QTICPKE CKT RQNNWVCPVU 'PXKTQP 5EK 6GEJPQN  

)TQULGCP & *KUJCO / 9 / 
 # RCUUKXG UCORNGT HQT CVOQURJGTKE Q\QPG , #KT 9CUVG /CPCIG #UUQE
 

)TQULGCP & 9KNNKCOU ' . ++ 
 (KGNF VGUVU QH C RCUUKXG UCORNGT HQT CVOQURJGTKE Q\QPG CV %CNKHQTPKC
OQWPVCKP HQTGUV NQECVKQPU #VOQU 'PXKTQP 2CTV #  

)TQULGCP & (WPI - %QNNKPU , *CTTKUQP , $TGKVWPI ' 
 2QTVCDNG IGPGTCVQT HQT QPUKVG ECNKDTCVKQP QH RGTQZ[CEGV[N PKVTCVG CPCN[\GTU #PCN %JGO   )TQULGCP & 9KNNKCOU ' . ++ )TQULGCP ' 
C # DKQIGPKE RTGEWTUQT QH RGTQZ[RTQRKQP[N PKVTCVG CVOQURJGTKE QZKFCVKQP QH EKU JGZGPQN 'PXKTQP 5EK 6GEJPQN  

)TQULGCP ' 9KNNKCOU ' . ++ )TQULGCP & 
D #ODKGPV NGXGNU QH HQTOCNFGJ[FG CPF CEGVCNFGJ[FG KP #VNCPVC )GQTIKC #KT 9CUVG  

)WKNDCWNV ) ) $TKIPCE 2 , ,T ,WPGCW / 
 0GY UWDUVTCVGU HQT VJG HNWQTQOGVTKE FGVGTOKPCVKQP QH QZKFCVKXG GP\[OGU #PCN %JGO  

)WKPPWR & 2QUUKGN 0 
 4GIKQPCN Q\QPG OQFGNKPI VQ UWRRQTV VJG .CMG /KEJKICP Q\QPG UVWF[ =FTCHV? 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI
CPF 5VCPFCTFU

)WP\ & 9 *QHHOCPP / 4 
 #VOQURJGTKE EJGOKUVT[ QH RGTQZKFGU C TGXKGY #VOQU 'PXKTQP
2CTV #  

*CMQNC * 5JQTGGU $ #TG[ , #VMKPUQP 4 
 2TQFWEV HQTOCVKQP HTQO VJG ICURJCUG TGCEVKQPU QH
1* TCFKECNU CPF 1 YKVJ RJGNNCPFTGPG 'PXKTQP 5EK 6GEJPQN   

*CMQNC * #TG[ , #UEJOCPP 5 / #VMKPUQP 4 
 2TQFWEV HQTOCVKQP HTQO VJG ICURJCUG TGCEVKQPU QH 1* TCFKECNU CPF 1 YKVJ C UGTKGU QH OQPQVGTRGPGU , #VOQU %JGO   

*CNVKPGT ) , 
 0WOGTKECN YGCVJGT RTGFKEVKQP 0GY ;QTM 0; ,QJP 9KNG[  5QPU +PE

*CNVKPGT ) , 9KNNKCOU 4 6 
 0WOGTKECN RTGFKEVKQP CPF F[PCOKE OGVGQTQNQI[ PF GF 0GY ;QTM 0; ,QJP 9KNG[  5QPU

*CORUQP 4 ( $TCWP 9 $TQYP 4 . )CTXKP & *GTTQP , 6 *WKG 4 ' -WT[NQ / , .CWHGT # * /E-KPNG[ , & 1MCDG * 5EJGGT / & 6UCPI 9 
 5WTXG[ QH RJQVQEJGOKECN CPF TCVG FCVC HQT VYGPV[GKIJV TGCEVKQPU QH KPVGTGUV KP CVOQURJGTKE EJGOKUVT[ , 2J[U
%JGO 4GH &CVC  

*CPUGP & # 
 /GCUWTKPI VTCEG ICUGU YKVJ (/ URGEVTQUEQR[ '24+ , 
 

*CPUV 2 . 9QPI 0 9 $TCIKP , 
 # NQPIRCVJ KPHTCTGF UVWF[ QH .QU #PIGNGU UOQI #VOQU 'PXKTQP
 

*CTF 6 / /GJTCD\CFGJ # # %JCP % ; 1	$TKGP 4 , 
 (#)' OGCUWTGOGPVU QH VTQRQURJGTKE *1 YKVJ OGCUWTGOGPVU CPF OQFGN QH KPVGTHGTGPEGU , )GQRJ[U 4GU =#VOQU?  

*CTNG[ 4 # *CPPKICP / 2 %CUU ) 4 
 4GURGEKCVKQP QH QTICPKE ICU GOKUUKQPU CPF VJG FGVGEVKQP QH GZEGUU WPDWTPGF ICUQNKPG KP VJG CVOQURJGTG 'PXKTQP 5EK 6GEJPQN  

*CTNG[ 4 # 4WUUGNN # ) /E4CG ) , %CUU ) 4 5GKPHGNF , * 
 2JQVQEJGOKECN OQFGNKPI QH VJG 5QWVJGTP %CNKHQTPKC #KT 3WCNKV[ 5VWF[ 'PXKTQP 5EK 6GEJPQN  

*CTOU & ' 4COCP 5 /CFCNC 4 8 
 #P GZCOKPCVKQP QH HQWTFKOGPUKQPCN FCVCCUUKOKNCVKQP VGEJPKSWGU HQT PWOGTKECN YGCVJGT RTGFKEVKQP $WNN #O /GVGQTQN 5QE  

*CTTKU ) 9 %CTVGT 9 2 . 9KPGT # / 2KVVU , 0 ,T 2NCVV 7 2GTPGT & 
 1DUGTXCVKQPU QH PKVTQWU CEKF KP VJG .QU #PIGNGU CVOQURJGTG CPF KORNKECVKQPU HQT RTGFKEVKQPU QH Q\QPGRTGEWTUQT TGNCVKQPUJKRU 'PXKTQP 5EK 6GEJPQN  

*CTTKUQP , 9 6KOOQPU / . &GP[U\[P 4 $ &GEMGT % ' 
 'XCNWCVKQP QH VJG '2# TGHGTGPEG OGVJQF HQT OGCUWTGOGPV QH PQPOGVJCPG J[FTQECTDQPU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 0CVKQPCN 'PXKTQPOGPVCN 4GUGCTEJ %GPVGT TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

*CVCMG[COC 5 +\WOK - (WMW[COC 6 #MKOQVQ * 
 4GCEVKQPU QH Q\QPG YKVJ RKPGPG CPF RKPGPG KP CKT [KGNFU QH ICUGQWU CPF RCTVKEWNCVG RTQFWEVU , )GQRJ[U 4GU =#VOQU?  

*CVCMG[COC 5 +\WOK - (WMW[COC 6 #MKOQVQ * 9CUJKFC 0 
 4GCEVKQPU QH 1* YKVJ RKPGPG CPF RKPGPG KP CKT GUVKOCVG QH INQDCN %1 RTQFWEVKQP HTQO VJG CVOQURJGTKE QZKFCVKQP QH VGTRGPGU , )GQRJ[U 4GU =#VOQU?  

*GKMGU $ ) 
 #SWGQWU * 1 RTQFWEVKQP HTQO 1 KP INCUU KORKPIGTU #VOQU 'PXKTQP    

*GKMGU $ ) .C\TWU # . -QM ) . -WPGP 5 / )CPFTWF $ 9 )KVNKP 5 0 5RGTT[ 2 & 
 'XKFGPEG HQT CSWGQWU RJCUG J[FTQIGP RGTQZKFG U[PVJGUKU KP VJG VTQRQURJGTG , )GQRJ[U 4GU %
1EGCPU #VOQU  

*GKMGU $ ) -QM ) . 9CNGIC , ) .C\TWU # . 
 * 1  1 CPF 51 OGCUWTGOGPVU KP VJG NQYGT    VTQRQURJGTG QXGT VJG GCUVGTP 7PKVGF 5VCVGU FWTKPI HCNN , )GQRJ[U 4GU  

*GNCU ) (NCP\ / 9CTPGEM 2 
 +ORTQXGF 01 OQPKVQT HQT OGCUWTGOGPVU KP VTQRQURJGTKE ENGCP CKT

Z TGIKQPU +PV , 'PXKTQP #PCN %JGO  

*GNNRQKPVPGT ' )·D 5 
 &GVGEVKQP QH OGVJ[N J[FTQZ[OGVJ[N CPF J[FTQZ[GVJ[N J[FTQRGTQZKFGU KP CKT CPF RTGEKRKVCVKQP 0CVWTG 
.QPFQP  

*GNOKI & /WGNNGT , -NGKP 9 
 +ORTQXGOGPVU KP CPCN[UKU QH CVOQURJGTKE RGTQZ[CEGV[N PKVTCVG 
2#0
#VOQU 'PXKTQP  

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E 0WOGTKECN UQNWVKQP QH VJG CVOQURJGTKE FKHHWUKQP GSWCVKQP HQT EJGOKECNN[ TGCEVKPI HNQYU , %QOR 2J[U  

/GCIJGT , ( 1NU\[PC - , 9GCVJGTHQTF ( 2 /QJPGP 8 # 
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/G[GT % 2 'NUYQTVJ % / )CNDCNN[ + ' 
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/G[GT ' . 2QUUKGN 0 % &QNN & % $CWIWGU - # $CNFTKFIG - 9 
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/G[TCJP * /QQTVICV ) - 9CTPGEM 2 
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 3WCPVWO [KGNFU HQT RJQVQFKUUQEKCVKQP QH CEGVQPG KP CKT CPF CP GUVKOCVG HQT VJG NKHG VKOG QH CEGVQPG KP VJG NQYGT VTQRQUJGTG , #VOQU %JGO  

/G[TCJP * *GNCU ) 9CTPGEM 2 
 )CU EJTQOCVQITCRJKE FGVGTOKPCVKQP QH RGTQZ[CEGV[N PKVTCVG VYQ EQPXGPKGPV ECNKDTCVKQP VGEJPKSWGU , #VOQU %JGO  

/KEJKG 4 / ,T 5QMCUJ , # (TKVUEJGN $ 2 /E'NTQ[ ( ( 6JQORUQP 8 . 
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/KFFNGVQP 2 %JCPI , 5 
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/KFFNGVQP 2 %JCPI , 5 FGN %QTTCN , % )GKUU * 4QUKPUMK , / 
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/KFFNGVQP 2 %JCPI , 5 $GCWJCTPQKU / *CUJ . $KPMQYUMK ( 5 
 6JG TQNG QH PKVTQIGP QZKFGU KP QZKFCPV RTQFWEVKQP CU RTGFKEVGF D[ VJG 4GIKQPCN #EKF &GRQUKVKQP /QFGN 
4#&/ +P 8CPCTUFCNG # GF 6JG TQNG CPF KORQTVCPEG QH 01 GOKUUKQP EQPVTQN TGHGTGGF RCRGTU HTQO VJG KPVGTPCVKQPCN

Z U[ORQUKWO /C[,WPG  %CODTKFIG /# 9CVGT #KT 5QKN 2QNNWV  

/KNHQTF , $ 4WUUGNN # ) /E4CG ) , 
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/KNHQTF , $ )CQ & 1FOCP / 6 4WUUGNN # ) 2QUUKGN 0 % 5EJGHHG 4 & 2KGTEG 6 ' 5EJGTG - . 
 #KT SWCNKV[ TGURQPUGU VQ 01 TGFWEVKQPU CPCN[UKU QH 41/0'6 TGUWNVU 2TGUGPVGF CV

Z VJ CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG -CPUCU %KV[ /1 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

/KNHQTF , $ )CQ & 5KNNOCP 5 $NQUUG[ 2 4WUUGNN # ) 
 6QVCN TGCEVKXG PKVTQIGP 
01 [ CU CP KPFKECVQT QH VJG UGPUKVKXKV[ QH Q\QPG VQ TGFWEVKQPU KP J[FTQECTDQP CPF 01 GOKUUKQPU

Z , )GQRJ[U 4GU  

/KNNGT & 2 
 .QYNGXGN FGVGTOKPCVKQP QH PKVTQIGP FKQZKFG KP CODKGPV CKT WUKPI VJG 2CNOGU 6WDG #VOQU
'PXKTQP  

/QQTG ) ' &CN[ % .KW /- *WCPI 5, 
 /QFGNKPI QH OQWPVCKPXCNNG[ YKPF HKGNFU KP VJG UQWVJGTP 5CP ,QCSWKP 8CNNG[ %CNKHQTPKC , %NKO #RRN /GVGQTQN  

/QTTKU 4 ' -GUUNGT 4 % 
 &GXGNQROGPV QH C XCTKCDNG ITKF TGIKQPCN QZKFCPV OQFGN CPF CRRNKECVKQP VQ VJG 5CP ,QCSWKP XCNNG[ +P $GTINWPF 4 . .CYUQP & 4 /E-GG & , GFU 6TQRQURJGTKE Q\QPG CPF VJG GPXKTQPOGPV RCRGTU HTQO CP KPVGTPCVKQPCN EQPHGTGPEG /CTEJ  .QU #PIGNGU %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# VTCPUCEVKQPU UGTKGU PQ 64

/QTTKU 4 ' /[GTU 6 % &QWINCU 5 ) ;QEMG / # /KTCDGNNC 8 
 &GXGNQROGPV QH C PGUVGFITKF WTDCP CKTUJGF OQFGN CPF CRRNKECVKQP VQ UQWVJGTP %CNKHQTPKC 2TGUGPVGF CV VJ CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG 8CPEQWXGT $% %CPCFC 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

/QTTKU 4 ' ;QEMG / # /[GTU 6 % /KTCDGNNC 8 
 1XGTXKGY QH VJG XCTKCDNGITKF 7TDCP #KTUJGF /QFGN 
7#/8 2TGUGPVGF CV VJ CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG -CPUCU %KV[ /1 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT
PQ 

/QVVQNC * # 5KORUQP $ ' )QTKP ) 
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/QWPV ) * 'KUGNG ( . 
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9CUJKPIVQP &%  

/WMCOOCN ' + 0GWOCPP * * )KNNGURKG 6 , 
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/WNKM , & 9KNNKCOU & 
 2CUUKXG UCORNKPI FGXKEGU HQT 01  +P 2TQEGGFKPIU QH VJG  '2##2%#  U[ORQUKWO QP OGCUWTGOGPV QH VQZKE CKT RQNNWVCPVU #RTKN 4CNGKIJ 0% 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN /QPKVQTKPI 5[UVGOU .CDQTCVQT[ RR   TGRQTV PQ '2# 
#2%# RWDNKECVKQP 8+2 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

/WNKM , & 9KNNKCOU & ' 
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#2%# RWDNKECVKQP 8+2

/WNKM , & .GYKU 4 ) /E%NGPP[ 9 # 
 /QFKHKECVKQP QH C JKIJGHHKEKGPE[ RCUUKXG UCORNGT VQ FGVGTOKPG PKVTQIGP FKQZKFG QT HQTOCNFGJ[FG KP CKT #PCN %JGO  

/WNKM , & 8CTPU , . -QWVTCMKU 2 9QNHUQP / $WP[CXKTQEJ # 9KNNKCOU & & -TQPOKNNGT - )  7UKPI RCUUKXG UCORNKPI FGXKEGU VQ OGCUWTG UGNGEVGF CKT XQNCVKNGU HQT CUUGUUKPI GEQNQIKECN EJCPIG +P /GCUWTGOGPV QH VQZKE CPF TGNCVGF CKT RQNNWVCPVU RTQEGGFKPIU QH VJG  75 '2##9/# KPVGTPCVKQPCN U[ORQUKWO X  /C[ &WTJCO 0% 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# RWDNKECVKQP 8+2

0CVKQPCN #EKF 2TGEKRKVCVKQP #UUGUUOGPV 2TQITCO 
 0#2#2 CUUGUUOGPV RTQEGUU EQORGPFKWO 8QNWOG  OQFGNU RNCPPGF HQT WUG KP VJG 0#2#2 KPVGITCVGF CUUGUUOGPV RTQITCO 9CUJKPIVQP &% 0CVKQPCN #EKF 2TGEKRKVCVKQP #UUGUUOGPV 2TQITCO #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$
:#$

0CVKQPCN #GTQPCWVKEU CPF 5RCEG #FOKPKUVTCVKQP 
 #UUGUUOGPV QH VGEJPKSWGU HQT OGCUWTKPI VTQRQURJGTKE 0 1  RTQEGGFKPIU QH C YQTMUJQR #WIWUV  2CNQ #NVQ %# *CORVQP 8# .CPING[ 4GUGCTEJ

Z [ %GPVGT 0#5# EQPHGTGPEG RWDNKECVKQP 0#5#%2 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
0

0CVKQPCN 4GUGCTEJ %QWPEKN 
 4GVJKPMKPI VJG Q\QPG RTQDNGO KP WTDCP CPF TGIKQPCN CKT RQNNWVKQP 9CUJKPIVQP &% 0CVKQPCN #ECFGO[ 2TGUU

0GFGTDTCIV ) 9 XCP FGT *QTUV # XCP &WKLP , 
 4CRKF Q\QPG FGVGTOKPCVKQP PGCT CP CEEGNGTCVQT 0CVWTG 
.QPFQP  

0GNUQP 2 ( 3WKING[ 5 / 5OKVJ / ; 
 5QWTEGU QH CVOQURJGTKE J[FTQECTDQPU KP 5[FPG[ C SWCPVKVCVKXG FGVGTOKPCVKQP WUKPI C UQWTEG TGEQPEKNKCVKQP VGEJPKSWG #VOQU 'PXKTQP  

0KGDQGT * 8CP *CO , 
 2GTQZ[CEGV[N PKVTCVG 
2#0 KP TGNCVKQP VQ Q\QPG CPF UQOG OGVGQTQNQIKECN RCTCOGVGTU CV &GNHV KP 6JG 0GVJGTNCPFU #VOQU 'PXKTQP   0KGNUGP 6 *CPUGP # / 6JQOUGP ' . 
 # EQPXGPKGPV OGVJQF HQT RTGRCTCVKQP QH RWTG UVCPFCTFU QH RGTQZ[CEGV[N PKVTCVG HQT CVOQURJGTKE CPCN[UGU #VOQU 'PXKTQP  

0KMK * /CMGT 2 & 5CXCIG % / $TGKVGPDCEJ . 2 
 #VOQURJGTKE Q\QPGQNGHKP TGCEVKQPU 'PXKTQP
5EK 6GEJPQN  ##

0KMK * /CMGT 2 & 5CXCIG % / $TGKVGPDCEJ . 2 
 #P (6+4 URGEVTQUEQRKE UVWF[ QH VJG TGCEVKQPU
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 %* %1 CPF %* %
111 
 01 %* %
11101 
2#0 +PV , %JGO      -KPGV  

0KMK * /CMGT 2 & 5CXCIG % / $TGKVGPDCEJ . 2 *WTNG[ / & 
 (6+4 URGEVTQUEQRKE UVWF[ QH VJG OGEJCPKUO HQT VJG ICURJCUG TGCEVKQP DGVYGGP Q\QPG CPF VGVTCOGVJ[NGVJ[NGPG , 2J[U %JGO
 

0QPFGM . 4QFNGT & 4 $KTMU , 9 
 /GCUWTGOGPV QH UWDRRX EQPEGPVTCVKQPU QH CNFGJ[FGU KP C HQTGUV CVOQURJGTG WUKPI C PGY *2.% VGEJPKSWG 'PXKTQP 5EK 6GEJPQN  

0QVJQNV , *LQTVJ , 4CGU ( 
C (QTOCVKQP QH *01 QP CGTQUQN UWTHCEGU FWTKPI HQII[ RGTKQFU KP VJG  RTGUGPEG QH 01 CPF 01  #VOQU 'PXKTQP 2CTV #   

0QVJQNV , *LQTVJ , 4CGU ( 5EJTGOU 1 
D 5KOWNVCPGQWU NQPI RCVJ HKGNF OGCUWTGOGPVU QH *01   %* 1 CPF CGTQUQN $GT $WPUGP )GU 2J[U %JGO   

0QZQP , ( 
 01 CPF 01 KP VJG OKF2CEKHKE VTQRQURJGTG , )GQRJ[U 4GU % 1EGCPU #VOQU    

1	5JGC 9 , 5EJGHH 2 # 
 # EJGOKECN OCUU DCNCPEG HQT XQNCVKNG QTICPKEU KP %JKECIQ ,#2%#  


1FOCP / 6 4WUUGNN # ) 
  /WNVKUECNG OQFGNKPI QH RQNNWVCPV VTCPURQTV CPF EJGOKUVT[ , )GQRJ[U 4GU =#VOQU?  

1FOCP / 6 -WOCT 0 4WUUGNN # ) 
 # EQORCTKUQP QH HCUV EJGOKECN MKPGVKE UQNXGTU HQT CKT SWCNKV[ OQFGNKPI #VOQU 'PXKTQP 2CTV #   1ING . & *CNN 4 % %TQY 9 . ,QPGU # ' )KUG , 2 
 &GXGNQROGPV QH RTGEQPEGPVTCVKQP CPF EJTQOCVQITCRJKE RTQEGFWTGU HQT VJG EQPVKPWQWU CPF WPCVVGPFGF OQPKVQTKPI QH J[FTQECTDQPU KP CODKGPV CKT 2TGUGPVGF CV VJ PCVKQPCN OGGVKPI QH VJG #OGTKECP %JGOKECN 5QEKGV[ 5GRVGODGT -CPUCU %KV[ /1 #WUVKP 6: 4CFKCP %QTRQTCVKQP

1ING . & $T[OGT & # ,QPGU % , 0CJCU 2 # 
 /QKUVWTG OCPCIGOGPV VGEJPKSWGU CRRNKECDNG VQ YJQNG CKT UCORNGU CPCN[\GF D[ OGVJQF 61 +P /GCUWTGOGPV QH VQZKE CPF TGNCVGF CKT RQNNWVCPVU RTQEGGFKPIU QH VJG  75 '2##9/# KPVGTPCVKQPCN U[ORQUKWO /C[ &WTJCO 0% 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# RWDNKECVKQP 8+2

1MG 6 4 
 $QWPFCT[ NC[GT ENKOCVGU .QPFQP 7PKVGF -KPIFQO /GVJWGP  %Q .VF

1NKXGT - & 2NGKN , & /E%NGPP[ 9 # 
 5CORNG KPVGITKV[ QH VTCEG NGXGN XQNCVKNG QTICPKE EQORQWPFU KP CODKGPV CKT UVQTGF KP 57//#s RQNKUJGF ECPKUVGTU #VOQU 'PXKTQP  

1NU\[PC - , /GCIJGT , ( $CKNG[ ' / 
 )CURJCUG ENQWF CPF TCKPYCVGT OGCUWTGOGPVU QH J[FTQIGP RGTQZKFG CV C JKIJGNGXCVKQP UKVG #VOQU 'PXKTQP  

1NU\[PC - , $CKNG[ ' / 5KOQPCKVKU 4 /GCIJGT , ) 
 1 CPF 01 TGNCVKQPUJKRU CV C TWTCN UKVG  [ , )GQRJ[U 4GU =#VOQU?  

2CNOGU ' & 6QOE\[M % 
 2GTUQPCN UCORNGT HQT 01  #O +PF *[I #UUQE ,  

Z

2CPFKU 5 0 5GKPHGNF , * 
 5GPUKVKXKV[ CPCN[UKU QH C EJGOKECN OGEJCPKUO HQT CSWGQWURJCUG CVOQURJGTKE EJGOKUVT[ , )GQRJ[U 4GU =#VOQU?  

2CPFKU 5 0 2CWNUQP 5 ' 5GKPHGNF , * (NCICP 4 % 
 #GTQUQN HQTOCVKQP KP VJG RJQVQQZKFCVKQP QH KUQRTGPG CPF RKPGPG #VOQU 'PXKTQP 2CTV #  

2CPFKU 5 0 *CTNG[ 4 # %CUU ) 4 5GKPHGNF , * 
 5GEQPFCT[ QTICPKE CGTQUQN HQTOCVKQP CPF VTCPURQTV #VOQU 'PXKTQP 2CTV #  

2CPMQY , ( $KFNGOCP 6 ( 
 'HHGEVU QH VGORGTCVWTG 652 CPF RGT EGPV PQPGZEJCPIGCDNG OCVGTKCN KP FGVGTOKPKPI VJG ICURCTVKENG RCTVKVKQPKPI QH QTICPKE EQORQWPFU #VOQU 'PXKTQP 2CTV #
 

2CPMQY , ( $KFNGOCP 6 ( 
 +PVGTFGRGPFGPEG QH VJG UNQRGU CPF KPVGTEGRVU HTQO NQINQI EQTTGNCVKQPU QH OGCUWTGF ICURCTVKENG RCTVKVKQPKPI CPF XCRQT RTGUUWTG  + VJGQT[ CPF CPCN[UKU QH CXCKNCDNG FCVC
#VOQU 'PXKTQP 2CTV #  

2CRC . , 6WTPGT . 2 
 %JTQOCVQITCRJKE FGVGTOKPCVKQP QH ECTDQP[N EQORQWPFU CU VJGKT FKPKVTQRJGP[NJ[FTC\QPGU ++ JKIJ RTGUUWTG NKSWKF EJTQOCVQITCRJ[ , %JTQOCVQIT 5EK  


2CTTKUJ & & *CJP % * (CJG[ & 9 9KNNKCOU ' , $QNNKPIGT / , *ÒDNGT ) $WJT / 2 /WTRJ[ 2 % 6TCKPGT / *UKG ' ; .KW 5 % (GJUGPHGNF ( % 
 5[UVGOCVKE XCTKCVKQPU KP VJG EQPEGPVTCVKQP QH 01 
01 RNWU 01  CV 0KYQV 4KFIG %QNQTCFQ , )GQRJ[U 4GU Z  =#VOQU?  

2CTTKUJ & & *CJP % , 9KNNKCOU ' , 0QTVQP 4 $ (GJUGPHGNF ( % 5KPIJ * $ 5JGVVGT , & )CPFTWF $ 9 4KFNG[ $ # 
 +PFKECVKQPU QH RJQVQEJGOKECN JKUVQTKGU QH 2CEKHKE CKT OCUUGU HTQO OGCUWTGOGPVU QH CVOQURJGTKE VTCEG URGEKGU CV 2QKPV #TGPC %CNKHQTPKC , )GQRJ[U 4GU =#VOQU?   2CTTKUJ & & $WJT / 2 6TCKPGT / 0QTVQP 4 $ 5JKOUJQEM , 2 (GJUGPHGNF ( % #PNCWH - ) $QVVGPJGKO , 9 6CPI ; < 9KGDG * # 4QDGTVU , / 6CPPGT 4 . 0GYOCP . $QYGTUQZ 8 % 1NU\[PC - , $CKNG[ ' / 4QFIGTU / 1 9CPI 6 $GTTGUJGKO * 4Q[EJQYFJWT[ 7 - &GOGTLKCP - . 
 6JG VQVCN TGCEVKXG QZKFK\GF PKVTQIGP NGXGNU CPF VJG RCTVKVKQPKPI DGVYGGP VJG KPFKXKFWCN URGEKGU CV UKZ TWTCN UKVGU KP GCUVGTP 0QTVJ #OGTKEC , )GQRJ[U 4GU =#VOQU?  

2CVG $ ,C[CPV[ 4 - / 2GVGTUQP / 4 'XCPU ) ( 
 6GORQTCN UVCDKNKV[ QH RQNCT QTICPKE EQORQWPFU KP UVCKPNGUU UVGGN ECPKUVGTU , #KT 9CUVG /CPCIG #UUQE  

2CWNUQP 5 ' 5GKPHGNF , * 
C &GXGNQROGPV CPF GXCNWCVKQP QH C RJQVQQZKFCVKQP OGEJCPKUO HQT KUQRTGPG , )GQRJ[U 4GU =#VOQU?  

2CWNUQP 5 ' 5GKPHGNF , * 
D #VOQURJGTKE RJQVQEJGOKECN QZKFCVKQP QH QEVGPG 1* 1 CPF 1
 2  
TGCEVKQPU 'PXKTQP 5EK 6GEJPQN  

2CWNUQP 5 ' (NCICP 4 % 5GKPHGNF , * 
C #VOQURJGTKE RJQVQQZKFCVKQP QH KUQRTGPG RCTV + VJG J[FTQZ[N TCFKECN CPF ITQWPF UVCVG CVQOKE QZ[IGP TGCEVKQPU +PV , %JGO -KPGV  

2CWNUQP 5 ' (NCICP 4 % 5GKPHGNF , * 
D #VOQURJGTKE RJQVQQZKFCVKQP QH KUQRTGPG RCTV ++ VJG Q\QPG KUQRTGPG TGCEVKQP +PV , %JGO -KPGV  

2GPMGVV 5 # $NCMG 0 , .KIJVOCP 2 /CTUJ # 4 9 #PY[N 2 $WVEJGT ) 
 6JG UGCUQPCN XCTKCVKQP QH PQPOGVJCPG J[FTQECTDQPU KP VJG HTGG VTQRQURJGTG QXGT VJG 0QTVJ #VNCPVKE 1EGCP RQUUKDNG GXKFGPEG HQT GZVGPUKXG TGCEVKQP QH J[FTQECTDQPU YKVJ VJG PKVTCVG TCFKECN , )GQRJ[U 4GU =#VOQU?  

2GTUEJMG * $TQFC ' 
 &GVGTOKPCVKQP QH XGT[ UOCNN COQWPVU QH J[FTQIGP RGTQZKFG 0CVWTG 
.QPFQP 


2GVGTUQP , 6 
 %CNEWNCVGF CEVKPKE HNWZGU 
 PO HQT CKT RQNNWVKQP RJQVQEJGOKUVT[ CRRNKECVKQPU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$


2KEMGTKPI - ' 6JQORUQP # / &KEMGTUQP 4 4 .WMG 9 6 /CE0COCTC & 2 )TGGPDGTI , 2 <KOOGTOCP 2 4 
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2KGNMG 4 # 
 # VJTGGFKOGPUKQPCN PWOGTKECN OQFGN QH VJG UGC DTGG\G QXGT UQWVJ (NQTKFC /QP 9GCVJGT
4GX  

2KGNMG 4 # 
 /GUQUECNG OGVGQTQNQIKECN OQFGNKPI 1TNCPFQ (. #ECFGOKE 2TGUU

2KGNMG 4 # 
 5VCVWU QH UWDTGIKQPCN CPF OGUQUECNG OQFGNU X  OGUQUECNG OGVGQTQNQIKECN OQFGNU KP VJG 7PKVGF 5VCVGU (QTV %QNNKPU %1 'NGEVTKE 2QYGT 4GUGCTEJ +PUVKVWVG TGRQTV PQ '0 X 

2KGTEG 6 ' 5EJGTG - . &QNN & % *GKNOCP 9 ' 
 'XCNWCVKQP QH VJG TGIKQPCN QZKFCPV OQFGN XGTUKQP  WUKPI CODKGPV CPF FKCIPQUVKE UKOWNCVKQPU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$*57

2KGTQVVK & , 
 #PCN[UKU QH VTCEG QZ[IGPCVGF J[FTQECTDQPU KP VJG GPXKTQPOGPV , #VOQU %JGO
  2KGTQVVK & 9QHU[ 5 % ,CEQD & 4CUOWUUGP 4 # 
 +UQRTGPG CPF KVU QZKFCVKQP RTQFWEVU OGVJCETQNGKP CPF OGVJ[N XKP[N MGVQPG , )GQRJ[U 4GU =#VOQU?  

2KGTUQP 9 4 )GTVNGT # 9 $TCFQY 4 . 
 %QORCTKUQP QH VJG 5%#35 VWPPGN UVWF[ YKVJ QVJGT QPTQCF XGJKENG GOKUUKQP FCVC , #KT 9CUVG /CPCIG #UUQE  

2KVVU , 0 ,T $KGTOCPP * 9 #VMKPUQP 4 9KPGT # / 
C #VOQURJGTKE KORNKECVKQPU QH UKOWNVCPGQWU PKIJVVKOG OGCUWTGOGPVU QH 01 TCFKECNU CPF *101 )GQRJ[U 4GU .GVV   

2KVVU , 0 ,T 5CPJWG\C ' #VMKPUQP 4 %CTVGT 9 2 . 9KPGT # / *CTTKU ) 9 2NWO % 0 D #P KPXGUVKICVKQP QH VJG FCTM HQTOCVKQP QH PKVTQWU CEKF KP GPXKTQPOGPVCN EJCODGTU +PV
, %JGO -KPGV  

2NCEGV / $CVV[G 4 ' (GJUGPHGNF ( % $CUUGVV ) 9 
 'OKUUKQPU KPXQNXGF KP CEKFKE FGRQUKVKQP RTQEGUUGU +P +TXKPI 2 / GF #EKFKE FGRQUKVKQP UVCVG QH UEKGPEG CPF VGEJPQNQI[ XQNWOG + GOKUUKQPU CVOQURJGTKE RTQEGUUGU CPF FGRQUKVKQP 9CUJKPIVQP &% 6JG 75 0CVKQPCN #EKF 2TGEKRKVCVKQP #UUGUUOGPV 2TQITCO 
5VCVG QH UEKGPEG CPF VGEJPQNQI[ TGRQTV PQ 

2NCVV 7 2GTPGT & 
 &KTGEV OGCUWTGOGPV QH CVOQURJGTKE %* 1 *01  1 CPF 51 D[ FKHHGTGPVKCN QRVKECN    CDUQTRVKQP KP VJG PGCT 78 , )GQRJ[U 4GU % 1EGCPU #VOQU  

2NGKN , & 1NKXGT - & /E%NGPP[ 9 # 
 'PJCPEGF RGTHQTOCPEG QH 0CHKQP FT[GTU KP TGOQXKPI YCVGT HTQO CKT UCORNGU RTKQT VQ ICU EJTQOCVQITCRJKE CPCN[UKU ,#2%#  

2NWO % 0 5CPJWG\C ' #VMKPUQP 4 %CTVGT 9 2 . 2KVVU , 0 ,T 
 1* TCFKECN TCVG EQPUVCPVU CPF RJQVQN[UKU TCVGU QH FKECTDQP[NU 'PXKTQP 5EK 6GEJPQN  

2QNNCEM # - 5VQEMGPKWU 6 ' *CPG[ , . 5VQEMKPI 6 5 (KGDGT , . /QG\\K / 
 #PCN[UKU QH JKUVQTKECN Q\QPG EQPEGPVTCVKQPU KP VJG 0QTVJGCUV XQNWOG + 5CP 4CHCGN %# 5[UVGOU #RRNKECVKQPU +PE TGRQTV PQ 5;5#22C

2QUUKGN 0 % %QZ 9 / 
 6JG TGNCVKXG GHHGEVKXGPGUU QH 01 CPF 81% UVTCVGIKGU KP TGFWEKPI PQTVJGCUV

Z 75 Q\QPG EQPEGPVTCVKQPU +P 8CPCTUFCNG # GF 6JG TQNG CPF KORQTVCPEG QH 01Z GOKUUKQP EQPVTQN TGHGTGGF RCRGTU HTQO VJG KPVGTPCVKQPCN U[ORQUKWO /C[,WPG  %CODTKFIG /# 9CVGT
#KT 5QKN 2QNNWV  

2QUUKGN 0 % &QNN & % $CWIWGU - # $CNFTKFIG ' 9 9C[NCPF 4 # 
 +ORCEVU QH TGIKQPCN EQPVTQN UVTCVGIKGU QP Q\QPG KP VJG PQTVJGCUVGTP 7PKVGF 5VCVGU 2TGUGPVGF CV TF CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG 2KVVUDWTIJ 2# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP TGRQTV PQ 

2QUUKGN 0 % 9C[NCPF 4 # 9KNUQP , * ,T .CKEJ ' , /WNNGP / # 
 2TGFKEVGF KORCEVU QH  %### EQPVTQNU QP 0QTVJGCUV 75 Q\QPG NGXGNU =FTCHV? 9CUJKPIVQP &% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU

2TKPP 4 %WPPQNF & 4CUOWUUGP 4 5KOOQPFU 2 #N[GC ( %TCYHQTF # (TCUGT 2 4QUGP 4 
 #VOQURJGTKE GOKUUKQPU CPF VTGPFU QH PKVTQWU QZKFG FGFWEGF HTQO  [GCTU QH #.')#)' FCVC , )GQRJ[U 4GU =#VOQU?  

2TKPP 4 %WPPQNF & 5KOOQPFU 2 #N[GC ( $QNFK 4 %TCYHQTF # (TCUGT 2 )WV\NGT & *CTVNG[ & 4QUGP 4 4CUOWUUGP 4 
 )NQDCN CXGTCIG EQPEGPVTCVKQP CPF VTGPF HQT J[FTQZ[N TCFKECNU FGFWEGF HTQO #.')#)' VTKEJNQTQGVJCPG 
OGVJ[N EJNQTQHQTO FCVC HQT  , )GQRJ[U 4GU =#VOQU?   2WTFWG . , 
 %QPVKPWQWU OQPKVQTKPI QH 81% RTGEWTUQTU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2## #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

2WTFWG . , *CWUGT 6 4 
 4GXKGY QH 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 01 OQPKVQTKPI  OGVJQFQNQI[ TGSWKTGOGPVU +P .GG 5 & GF 0KVTQIGP QZKFGU CPF VJGKT GHHGEVU QP JGCNVJ #PP #TDQT /+ #PP #TDQT 5EKGPEG 2WDNKUJGTU +PE RR 

2WTFWG . , 4GCICP , # .QPPGOCP 9 # .CYNCUU 6 % &TCIQ 4 , <CNCSWGV ) / *QNFTGP / 9 5OKVJ & . 2CVG # & $WZVQP $ ' 5RKEGT % 9 
 #VNCPVC Q\QPG RTGEWTUQT OQPKVQTKPI UVWF[ FCVC TGRQTV 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2#4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$4'$

4CQ 5 6 
 #RRNKECVKQP QH VJG WTDCP CKTUJGF OQFGN VQ VJG 0GY ;QTM OGVTQRQNKVCP CTGC 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$*57

4CQ 5 6 5KUVNC ) 
 'HHKECE[ QH PKVTQIGP QZKFGU CPF J[FTQECTDQPU GOKUUKQPU EQPVTQNU KP Q\QPG CVVCKPOGPV UVTCVGIKGU CU RTGFKEVGF D[ VJG 7TDCP #KTUJGF /QFGN +P 8CPCTUFCNG # GF 6JG TQNG CPF KORQTVCPEG QH 01Z GOKUUKQP EQPVTQN TGHGTGGF RCRGTU HTQO VJG KPVGTPCVKQPCN U[ORQUKWO /C[,WPG  %CODTKFIG /# 9CVGT #KT 5QKN 2QNNWV  

4CQ 5 6 5KUVNC ) 6YCFFGNN 4 
 2JQVQEJGOKECN OQFGNKPI CPCN[UKU QH GOKUUKQP EQPVTQN UVTCVGIKGU KP VJG 0GY ;QTM OGVTQRQNKVCP CTGC 9CUJKPIVQP &% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ TGRQTV PQ
'2#

4CUOWUUGP 2 # 
 5QWVJGTP %CNKHQTPKC #KT 3WCNKV[ 5VWF[ 2CTV + *[FTQECTDQP EQNNGEVKQP CPF CPCN[UGU 2CTV ++ #KT VQZKEU EQNNGEVKQP CPF CPCN[UGU 5CETCOGPVQ %# %CNKHQTPKC #KT 4GUQWTEGU $QCTF HKPCN TGRQTV CITGGOGPV PQ #

4CWJWV / / $QNN[M[ . , 4QDGTVU $ ) .Q[ / 9JKVOCP 4 * +CPPQVVC # 8 5GOUGN # / %NCTMG 4 # 
 %JGOKNWOKPGUEGPEG HTQO TGCEVKQPU QH GNGEVTQPGICVKXGN[ UWDUVKVWVGF CT[N QZCNCVGU YKVJ J[FTQIGP RGTQZKFG CPF HNWQTGUEGPV EQORQWPFU , #O %JGO 5QE  

4GEMPGT . 4 
 5WTXG[ QH WUGTU QH VJG '2#TGHGTGPEG OGVJQF HQT OGCUWTGOGPV QH PQPOGVJCPG J[FTQECTDQPU KP CODKGPV CKT 9CUJKPIVQP &% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 1HHKEG QH 4GUGCTEJ CPF &GXGNQROGPV TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$

4GIGPGT 8 * 
 1P C UGPUKVKXG OGVJQF HQT VJG TGEQTFKPI QH CVOQURJGTKE Q\QPG , )GQRJ[U 4GU  


4GIGPGT 8 * 
 /GCUWTGOGPV QH CVOQURJGTKE Q\QPG YKVJ VJG EJGOKNWOKPGUEGPV OGVJQF , )GQRJ[U 4GU
 

4GJOG - # 2W\CM , % $GCTF / ' 5OKVJ % ( 2CWT 4 , 
 'XCNWCVKQP QH Q\QPG ECNKDTCVKQP RTQEGFWTGU =RTQLGEV UWOOCT[? 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN /QPKVQTKPI 5[UVGOU .CDQTCVQT[ TGRQTV PQ '2#5 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

4G[PQNFU 5 & 4QVJ 2 / 5GKPHGNF , * 
 /CVJGOCVKECN OQFGNKPI QH RJQVQEJGOKECN CKT RQNNWVKQP

` + HQTOWNCVKQP QH VJG OQFGN #VOQU 'PXKTQP   4G[PQNFU 5 & .KW /- *GEJV 6 # 4QVJ 2 / 5GKPHGNF , * 
 /CVJGOCVKECN OQFGNKPI QH RJQVQEJGOKECN CKT RQNNWVKQP

` +++ GXCNWCVKQP QH VJG OQFGN #VOQU 'PXKTQP  

4G[PQNFU 5 & 6GUEJG 6 9 4GKF . ' 
 #P KPVTQFWEVKQP VQ VJG 5#+ CKTUJGF OQFGN CPF KVU WUCIG 5CP 4CHCGN %# 5[UVGOU #RRNKECVKQPU +PE TGRQTV PQ 5#+'(

4KEJOCP ' ' ,T )TGGP # * 9TKIJV 4 5 5KEMNGU , ' ++ 
 .CDQTCVQT[ CPF HKGNF GXCNWCVKQPU QH GZVTCUGPUKVKXG UWNHWT FKQZKFG CPF PKVTQIGP FKQZKFG CPCN[\GTU HQT CEKF FGRQUKVKQP OQPKVQTKPI 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$

4KFNG[ $ # *QYNGVV . % 
 #P KPUVTWOGPV HQT PKVTKE QZKFG OGCUWTGOGPVU KP VJG UVTCVQURJGTG 4GX 5EK
+PUVTWO  

4KFNG[ $ # %CTTQNN / # 6QTTGU # . %QPFQP ' 2 5CEJUG ) 9 *KNN ) ( )TGIQT[ ) . C #P KPVGTEQORCTKUQP QH TGUWNVU HTQO HGTTQWU UWNRJCVG CPF RJQVQN[VKE EQPXGTVGT VGEJPKSWGU HQT OGCUWTGOGPVU QH 01 OCFG FWTKPI VJG 0#5# )6'%+6'  CKTETCHV RTQITCO , )GQRJ[U 4GU

Z =#VOQU?  

4KFNG[ $ # %CTTQNN / # )TGIQT[ ) . 5CEJUG ) 9 
D 01 CPF 01 KP VJG VTQRQURJGTG  VGEJPKSWG CPF OGCUWTGOGPVU KP TGIKQPU QH C HQNFGF VTQRQRCWUG , )GQRJ[U 4GU =#VOQU?  

4KFNG[ $ # %CTTQNN / # &WPNCR & & 6TCKPGT / 5CEJUG ) 9 )TGIQT[ ) . %QPFQP ' 2  /GCUWTGOGPVU QH 01 QXGT VJG GCUVGTP 2CEKHKE QEGCP CPF UQWVJYGUVGTP 7PKVGF 5VCVGU FWTKPI

Z VJG URTKPI  0#5# )6' CKTETCHV RTQITCO , )GQRJ[U 4GU =#VOQU?  

4KRRGTVQP . # 9QTVJ , , $ 8WMQXKEJ ( / &GEMGT % ' 
 4GUGCTEJ 6TKCPING +PUVKVWVG UVWFKGU QH JKIJ Q\QPG EQPEGPVTCVKQPU KP PQPWTDCP CTGCU +P &KOKVTKCFGU $ GF +PVGTPCVKQPCN EQPHGTGPEG QP RJQVQEJGOKECN QZKFCPV RQNNWVKQP CPF KVU EQPVTQN

` RTQEGGFKPIU XQNWOG + 5GRVGODGT  4CNGKIJ 0% 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ RR  TGRQTV PQ '2#C #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

4QDGTVU , / 
 6JG CVOQURJGTKE EJGOKUVT[ QH QTICPKE PKVTCVGU #VOQU 'PXKTQP 2CTV #  

4QDGTVU , / (CLGT 4 9 5RTKPIUVQP 5 4 
 %CRKNNCT[ ICU EJTQOCVQITCRJKE UGRCTCVKQP QH CNM[N PKVTCVGU CPF RGTQZ[ECTDQZ[NKE PKVTKE CPJ[FTKFGU #PCN %JGO  

4QDKPUQP 0 ( 2KGTUQP 9 4 )GTVNGT # 9 5CIGDKGN , % 
 %QORCTKUQP QH /1$+.' CPF /1$+.' RTGFKEVKQPU YKVJ OGCUWTGOGPVU QH XGJKENG GOKUUKQP HCEVQTU KP (QTV /E*GPT[ CPF 6WUECTQTC OQWPVCKP VWPPGNU +P 2CTTKUJ & 6TCKPGT / 4CQ 5 6 5QNQOQP 2 # GFU #9/# URGEKCNV[ EQPHGTGPEG QP TGIKQPCN RJQVQEJGOKECN OGCUWTGOGPVU CPF OQFGNKPI RCTV  0QXGODGT  5CP &KGIQ %# #VOQU 'PXKTQP  

4QFIGTU / 1 &CXKU & & 
 # 78RJQVQHTCIOGPVCVKQPNCUGTKPFWEGF HNWQTGUEGPEG UGPUQT HQT VJG CVOQURJGTKE FGVGEVKQP QH *101 'PXKTQP 5EK 6GEJPQN  

4QIGTU , & 
 7NVTCXKQNGV CDUQTRVKQP ETQUU UGEVKQPU CPF CVOQURJGTKE RJQVQFKUUQEKCVKQP TCVG EQPUVCPVU QH HQTOCNFGJ[FG , 2J[U %JGO  

4QIIG 9 ( *KNFGOCPP . / /C\WTGM / # %CUU ) 4 5KOQPGKV $ 4 6 
 5QWTEGU QH HKPG QTICPKE CGTQUQN  %JCTDTQKNGTU CPF OGCV EQQMKPI QRGTCVKQPU 'PXKTQP 5EK 6GEJPQN   4QIIG 9 ( *KNFGOCPP . / /C\WTGM / # %CUU ) 4 5KOQPGKV $ 4 6 
 5QWTEGU QH HKPG QTICPKE CGTQUQN  0QPECVCN[UV CPF ECVCN[UVGSWKRRGF CWVQOQDKNGU CPF JGCX[FWV[ FKGUGN VTWEMU
'PXKTQP 5EK 6GEJPQN  

4QUGNNG 5 , 5EJGTG - . 
 5GPUKVKXKV[ QH VJG '2# TGIKQPCN QZKFCPV OQFGN VQ DKQIGPKE J[FTQECTDQP GOKUUKQPU 2TGUGPVGF CV TF CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG 2KVVUDWTIJ 2# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT
PQ 

4QUGNNG 5 , 2KGTEG 6 ' 5EJGTG - . 
 6JG UGPUKVKXKV[ QH TGIKQPCN Q\QPG OQFGNKPI VQ DKQIGPKE J[FTQECTDQPU , )GQRJ[U 4GU =#VOQU?  

4QUGNNG 5 , 5EJGTG - . %JW 5* 
 'UVKOCVGU QH Q\QPG TGURQPUG VQ XCTKQWU EQODKPCVKQPU QH 01 CPF

Z 81% GOKUUKQP TGFWEVKQPU KP VJG GCUVGTP 7PKVGF 5VCVGU 2TGUGPVGF CV  SWCFTGPPKCN Q\QPG U[ORQUKWO ,WPG %JCTNQVVGUXKNNG 8#

4QVJ 2 
 7UKPI RJQVQEJGOKECN OQFGNU KP FGXGNQRKPI CVVCKPOGPV UVTCVGIKGU RGTURGEVKXGU CPF RTQDNGOU +P 2TQEGGFKPIU QH VJG 'NGEVTKE 2QYGT 4GUGCTEJ +PUVKVWVG RJQVQEJGOKECN OQFGNKPI YQTMUJQR #WIWUV %CODTKFIG /# 2CNQ #NVQ %# 'NGEVTKE 2QYGT 4GUGCTEJ +PUVKVWVG

4QVJ 2 / $NCPEJCTF % . 4G[PQNFU 5 & 
 6JG TQNG QH ITKFDCUGF TGCEVKXG CKT SWCNKV[ OQFGNKPI KP RQNKE[ CPCN[UKU RGTURGEVKXGU CPF KORNKECVKQPU CU FTCYP HTQO C ECUG UVWF[ 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

4QYNCPF ( 5 
 5VTCVQURJGTKE Q\QPG FGRNGVKQP D[ EJNQTQHNWQTQECTDQPU #ODKQ  

4QYNCPF ( 5 
 5VTCVQURJGTKE Q\QPG FGRNGVKQP #PPW 4GX 2J[U %JGO  

4WUUGNN # ) /E%WG - ( %CUU ) 4 
C /CVJGOCVKECN OQFGNKPI QH VJG HQTOCVKQP QH PKVTQIGPEQPVCKPKPI CKT RQNNWVCPVU  'XCNWCVKQP QH CP 'WNGTKCP RJQVQEJGOKECN OQFGN 'PXKTQP 5EK
6GEJPQN  

4WUUGNN # ) /E%WG - ( %CUU ) 4 
D /CVJGOCVKECN OQFGNKPI QH VJG HQTOCVKQP QH PKVTQIGPEQPVCKPKPI RQNNWVCPVU  'XCNWCVKQP QH VJG GHHGEV QH GOKUUKQP EQPVTQNU 'PXKTQP 5EK
6GEJPQN  

5CMCOCMK ( *CVCMG[COC 5 #MKOQVQ * 
 (QTOCVKQP QH PKVTQWU CEKF CPF PKVTKE QZKFG KP VJG JGVGTQIGPGQWU FCTM TGCEVKQP QH PKVTQIGP FKQZKFG CPF YCVGT XCRQT KP C UOQI EJCODGT +PV , %JGO
-KPGV  

5CMWICYC * -CRNCP + 4 6UCK 9 %QJGP ; 
 #VOQURJGTKE J[FTQIGP RGTQZKFG FQGU KV UJCTG C TQNG YKVJ Q\QPG KP FGITCFKPI CKT SWCNKV[! 'PXKTQP 5EK 6GEJPQN  

5CNCU . , 5KPIJ * $ 
 /GCUWTGOGPVU QH HQTOCNFGJ[FG CPF CEGVCNFGJ[FG KP VJG WTDCP CODKGPV CKT
#VOQU 'PXKTQP  

5COUQP 2 , 4CINCPF - 9 
 1\QPG CPF XKUKDKNKV[ TGFWEVKQP KP VJG /KFYGUV GXKFGPEG HQT NCTIGUECNG VTCPURQTV , #RRN /GVGQTQN  

5COUQP 2 , 5JK $ 
 # OGVGQTQNQIKECN KPXGUVKICVKQP QH JKIJ Q\QPG XCNWGU KP #OGTKECP EKVKGU #PP #TDQT /+ 7PKXGTUKV[ QH /KEJKICP 5RCEG 2J[UKEU 4GUGCTEJ .CDQTCVQT[ &GRCTVOGPV QH #VOQURJGTKE 1EGCPKE CPF 5RCEG 5EKGPEGU 5C[NQT 4 & 2GVGTU . - /CVJWT 4 
 6JG 56'/++ TGIKQPCNUECNG CEKF FGRQUKVKQP CPF RJQVQEJGOKECN
QZKFCPV OQFGN

` +++ C UVWF[ QH OGUQUECNG CEKF FGRQUKVKQP KP VJG NQYGT 1JKQ 4KXGT 8CNNG[ #VOQU
'PXKTQP 2CTV #  

5EJGHH 2 # 9CFFGP 4 # 
 4GEGRVQT OQFGNKPI QH XQNCVKNG QTICPKE EQORQWPFU  'OKUUKQP KPXGPVQT[ CPF
XCNKFCVKQP 'PXKTQP 5EK 6GEJPQN  

5EJGHHG 4 & /QTTKU 4 ' 
 #UUGUUOGPV QH Q\QPG RTGEWTUQT EQPVTQN OGCUWTGOGPVU WUKPI VJG WTDCP CKTUJGF OQFGN KP '2#	U EKV[ 7#/ UVWF[ 2TGUGPVGF CV TF CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG 2KVVUDWTIJ 2# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

5EJGHHG 4 & /QTTKU 4 ' 
 1XGTXKGY QH '2#	U EKV[ 7#/ UVWF[ +P $GTINWPF 4 . .CYUQP & 4 /E-GG & , GFU 6TQRQURJGTKE Q\QPG CPF VJG GPXKTQPOGPV RCRGTU HTQO CP KPVGTPCVKQPCN EQPHGTGPEG /CTEJ  .QU #PIGNGU %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# VTCPUCEVKQPU UGTKGU PQ 64

5EJGHHG 4 & /QTTKU 4 ' 
 # TGXKGY QH VJG FGXGNQROGPV CPF CRRNKECVKQP QH VJG 7TDCP #KTUJGF /QFGN
#VOQU 'PXKTQP 2CTV $  

5EJGTG - . 9C[NCPF 4 # 
C &GXGNQROGPV CPF GXCNWCVKQP QH VJG TGIKQPCN QZKFCPV OQFGN HQT VJG PQTVJGCUVGTP 7PKVGF 5VCVGU +P 5EJPGKFGT 6 .GG 5 & 9QNVGTU ) , 4 )TCPV . & GFU #VOQURJGTKE Q\QPG TGUGCTEJ CPF KVU RQNKE[ KORNKECVKQPU RTQEGGFKPIU QH VJG TF 75&WVEJ KPVGTPCVKQPCN U[ORQUKWO /C[  0KLOGIGP 6JG 0GVJGTNCPFU #OUVGTFCO 6JG 0GVJGTNCPFU 'NUGXKGT 5EKGPEG 2WDNKUJGTU RR  
5VWFKGU KP GPXKTQPOGPVCN UEKGPEG 

5EJGTG - . 9C[NCPF 4 # 
D '2# TGIKQPCN QZKFCPV OQFGN 
41/ GXCNWCVKQP QP  0'415 FCVC DCUGU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$*57

5EJKHH * + *CUVKG & 4 /CEMC[ ) + +IWEJK 6 4KFNG[ $ # 
 6WPCDNG FKQFG NCUGT U[UVGOU HQT OGCUWTKPI VTCEG ICUGU KP VTQRQURJGTKE CKT C FKUEWUUKQP QH VJGKT WUG CPF VJG UCORNKPI CPF ECNKDTCVKQP RTQEGFWTGU HQT 01 01  CPF *01  'PXKTQP 5EK 6GEJPQN  ##  

5EJKHH * + /CEMC[ ) + %CUVNGFKPG % *CTTKU ) 9 6TCP 3 
 # UGPUKVKXG FKTGEV OGCUWTGOGPV 01  KPUVTWOGPV +P 2TQEGGFKPIU QH VJG  '2##2%# U[ORQUKWO QP OGCUWTGOGPV QH VQZKE CKT RQNNWVCPVU #RTKN 4CNGKIJ 0% 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN /QPKVQTKPI 5[UVGOU .CDQTCVQT[ RR  TGRQTV PQ '2# #2%# RWDNKECVKQP 8+2 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

5EJKHH * + *CTTKU ) 9 /CEMC[ ) + 
 /GCUWTGOGPV QH CVOQURJGTKE ICUGU D[ NCUGT CDUQTRVKQP URGEVTQOGVT[ +P ,QJPUQP 4 9 )QTFQP ) ' %CNMKPU 9 'N\GTOCP # < GFU 6JG EJGOKUVT[ QH CEKF TCKP UQWTEGU CPF CVOQURJGTKE RTQEGUUGU =RCRGTU RTGUGPVGF CV UV OGGVKPI QH VJG #OGTKECP %JGOKECN 5QEKGV[ #RTKN  0GY ;QTM 0;? 9CUJKPIVQP &% #OGTKECP %JGOKECN 5QEKGV[ RR  
%QOUVQEM / , GF #%5 U[ORQUKWO UGTKGU X 

5EJYCTV\ 5 ' 
 #EKF FGRQUKVKQP WPTCXGNKPI C TGIKQPCN RJGPQOGPQP 5EKGPEG 
9CUJKPIVQP &%
 

5EQVV 9 ' 5VGRJGPU ' 4 *CPUV 2 . &QGTT 4 % 
 (WTVJGT FGXGNQROGPVU KP VJG EJGOKUVT[ QH VJG CVOQURJGTG 2TQE #O 2GV +PUV 5GEV   

5EQVV ) 5GKV\ 9 4 #ODTQUG , 
 +ORTQXGF FGVGTOKPCVKQP QH J[FTQIGP RGTQZKFG D[ OGCUWTGOGPV QH RGTQZ[QZCNCVG EJGOKNWOKPGUEGPEG #PCN %JKO #EVC   5GCOCP 0 . 
 /GVGQTQNQIKECN OQFGNKPI CRRNKGF VQ TGIKQPCN CKTSWCNKV[ UVWFKGU WUKPI HQWTFKOGPUKQPCN FCVC CUUKOKNCVKQP +P 2TQEGGFKPIU QH VJG +$/ UWOOGT KPUVKVWVG QP GPXKTQPOGPVCN OQFGNKPI ,WN[ 1DGTNGEJ #WUVTKC

5GCOCP 0 . 
 5#4/#2 OGVGQTQNQIKECN OQFGN FGXGNQROGPV CPF VGUVKPI CPPWCN TGRQTV %CNKHQTPKC #KT
4GUQWTEGU $QCTF

5GKNC 4 . .QPPGOCP 9 # /GGMU 5 # 
 &GVGTOKPCVKQP QH % VQ % CODKGPV CKT J[FTQECTDQPU KP    75 EKVKGU HTQO  VJTQWIJ  4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$*57

5GKPHGNF , * 
 #VOQURJGTKE EJGOKUVT[ CPF RJ[UKEU QH CKT RQNNWVKQP 0GY ;QTM 0; ,QJP 9KNG[ CPF 5QPU

5GKPHGNF , * 
 1\QPG CKT SWCNKV[ OQFGNU C ETKVKECN TGXKGY ,#2%#  

5GXEKM , 
 &GVGEVQTU KP ICU EJTQOCVQITCRJ[ 0GY ;QTM 0; #OGTKECP 'NUGXKGT 2WDNKUJKPI %QORCP[ +PE

5GZVQP - 
 'XKFGPEG QH CP CFFKVKXG GHHGEV HQT UOCNN EKV[ RNWOGU 2TGUGPVGF CV VJ CPPWCN OGGVKPI QH VJG #KT 2QNNWVKQP %QPVTQN #UUQEKCVKQP ,WPG 0GY 1TNGCPU .# 2KVVUDWTIJ 2# #KT 2QNNWVKQP %QPVTQN #UUQEKCVKQP RCRGT PQ 

5GZVQP ( 9 /KEJKG 4 # ,T /E'NTQ[ ( ( 6JQORUQP 8 . 
 6GEJPKECN CUUKUVCPEG FQEWOGPV HQT VJG ECNKDTCVKQP CPF QRGTCVKQP QH CWVQOCVGF CODKGPV PQPOGVJCPG QTICPKE EQORQWPF CPCN[\GTU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN /QPKVQTKPI 5[UVGOU .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$

5GZVQP ( 9 /KEJKG 4 / ,T /E'NTQ[ ( ( 6JQORUQP 8 . 
 # EQORCTCVKXG GXCNWCVKQP QH UGXGP CWVQOCVGF CODKGPV PQPOGVJCPG QTICPKE EQORQWPF CPCN[\GTU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN /QPKVQTKPI 5[UVGOU .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

5JGKJ % / 9GUGN[ / . 9CNEGM % , 
 # FT[ FGRQUKVKQP OQFWNG HQT TGIKQPCN CEKF FGRQUKVKQP 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$*57

5JGP , 6CPPGT 4 . -GNN[ 6 , 
 &GXGNQROGPV QH VGEJPKSWGU HQT OGCUWTGOGPV QH ICURJCUG J[FTQIGP RGTQZKFG 7RVQP 0; 75 &GRCTVOGPV QH 'PGTI[ $TQQMJCXGP 0CVKQPCN .CDQTCVQT[ TGRQTV PQ $0. #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# &'

5JGRUQP 2 $ $QVVGPJGKO , 9 *CUVKG & 4 8GPMCVTCO # 
C &GVGTOKPCVKQP QH VJG TGNCVKXG Q\QPG CPF 2#0 FGRQUKVKQP XGNQEKVKGU CV PKIJV )GQRJ[U 4GU .GVV  

5JGRUQP 2 $ *CUVKG & 4 5Q - 9 5EJKHH * + 9QPI 2 
D 4GNCVKQPUJKRU DGVYGGP 2#0 220 CPF
1 CV WTDCP CPF TWTCN UKVGU KP 1PVCTKQ #VOQU 'PXKTQP 2CTV #  



5JGTOCP % # 
 # OCUUEQPUKUVGPV OQFGN HQT YKPF HKGNFU QXGT EQORNGZ VGTTCKP , #RRN /GVGQTQN  


5JTGHHNGT , * 
 # UWTXG[ QH FCVC HTQO VJG EQPVKPWQWU UKVGU QH VJG  #VNCPVC Q\QPG RTGEWTUQT UVWF[ 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2#4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$ 5JTGHHNGT , * 
 %QORCTKUQP QH PQPOGVJCPG QTICPKE EQORQWPF EQPEGPVTCVKQP FCVC EQNNGEVGF D[ VYQ OGVJQFU
KP #VNCPVC #KT 9CUVG  

5KEMNGU , ' ++ /KEJKG 4 / 
 'XCNWCVKQP QH VJG RGTHQTOCPEG QH UWNHCVKQP CPF PKVTCVKQP RNCVGU #VOQU
'PXKTQP  

5KEMNGU , ' ++ 9TKIJV 4 5 
 #VOQURJGTKE EJGOKUVT[ QH UGNGEVGF UWNHWTEQPVCKPKPI EQORQWPFU QWVFQQT UOQI EJCODGT UVWF[

` RJCUG  4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ RR  TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

5KEMNGU , ' ++ )TQJUG 2 / *QFUQP . . 5CNOQPU % # %QZ - 9 6WTPGT # 4 'UVGU ' &  &GXGNQROGPV QH C OGVJQF HQT VJG UCORNKPI CPF CPCN[UKU QH UWNHWT FKQZKFG CPF PKVTQIGP FKQZKFG
HTQO CODKGPV CKT #PCN %JGO  

5KIUD[ , ' ,T 6GLCFC 5 4C[ 9 .CPI , / &WPECP , 9 
 8QNCVKNG QTICPKE EQORQWPF GOKUUKQPU HTQO  KPWUG RCUUGPIGT ECTU 'PXKTQP 5EK 6GEJPQN  

5KNNOCP 5 5COUQP 2 , 
 +ORCEV QH VGORGTCVWTG QP QZKFCPV RJQVQEJGOKUVT[ KP WTDCP RQNNWVGF TWTCN CPF TGOQVG GPXKTQPOGPVU , )GQRJ[U 4GU =#VOQU?  

5KNNOCP 5 .QICP , # 9QHU[ 5 % 
 6JG UGPUKVKXKV[ QH Q\QPG VQ PKVTQIGP QZKFGU CPF J[FTQECTDQPU KP TGIKQPCN Q\QPG GRKUQFGU , )GQRJ[U 4GU =#VOQU?  

5KNNOCP 5 5COUQP 2 , /CUVGTU , / 
 1\QPG RTQFWEVKQP KP WTDCP RNWOGU VTCPURQTVGF QXGT YCVGT RJQVQEJGOKECN OQFGN CPF ECUG UVWFKGU KP VJG PQTVJGCUVGTP CPF OKFYGUVGTP 7PKVGF 5VCVGU , )GQRJ[U 4GU =#VOQU?  

5KOQPCKVKU 4 1NU\[PC - , /GCIJGT , ( 
 2TQFWEVKQP QH J[FTQIGP RGTQZKFG CPF QTICPKE RGTQZKFGU KP VJG ICU RJCUG TGCEVKQPU QH Q\QPG YKVJ PCVWTCN CNMGPGU )GQRJ[U 4GU .GVV  

5KPIJ * $ 
 )WKFCPEG HQT VJG EQNNGEVKQP CPF WUG QH CODKGPV J[FTQECTDQP URGEKGU FCVC KP FGXGNQROGPV QH Q\QPG EQPVTQN UVTCVGIKGU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

5KPIJ * $ -CPCMKFQW / 
 #P KPXGUVKICVKQP QH VJG CVOQURJGTKE UQWTEGU CPF UKPMU QH OGVJ[N DTQOKFG )GQRJ[U 4GU .GVV  

5KPIJ * $ 5CNCU . , 
 /GVJQFQNQI[ HQT VJG CPCN[UKU QH RGTQZ[CEGV[N PKVTCVG 
2#0 KP VJG WPRQNNWVGF CVOQURJGTG #VOQU 'PXKTQP  

5KPIJ * $ 8KG\GG 9 
 'PJCPEGOGPV QH 2#0 CDWPFCPEG KP VJG 2CEKHKE OCTKPG CKT WRQP EQPVCEV YKVJ
UGNGEVGF UWTHCEGU #VOQU 'PXKTQP  

5KPIJ * $ 8KG\GG 9 ,QJPUQP 9 $ .WFYKI ( . 
 6JG KORCEV QH UVTCVQURJGTKE Q\QPG QP VTQRQURJGTKE CKT SWCNKV[ , #KT 2QNNWV %QPVTQN #UUQE  

5MQX * *LQTVJ , .QJUG % ,GPUGP 0 4 4GUVGNNK ) 
 2TQFWEVU CPF OGEJCPKUOU QH VJG TGCEVKQPU QH VJG PKVTCVG TCFKECN 
01  YKVJ KUQRTGPG DWVCFKGPG CPF FKOGVJ[NDWVCFKGPG KP CKT #VOQU  'PXKTQP 2CTV #  

5NGOT ( *CTTKU ) 9 *CUVKG & 4 /CEMC[ ) + 5EJKHH * + 
 /GCUWTGOGPV QH ICU RJCUG J[FTQIGP RGTQZKFG KP CKT D[ VWPCDNG FKQFG NCUGT CDUQTRVKQP URGEVTQUEQR[ , )GQRJ[U 4GU   5OKVJ 4 # &TWOOQPF + 
 6TCEG FGVGTOKPCVKQP QH ECTDQP[N EQORQWPFU KP CKT D[ ICU EJTQOCVQITCRJ[ QH VJGKT FKPKVTQRJGP[NJ[FTC\QPGU #PCN[UV 
.QPFQP  

5OKVJ 1 ( -NGKPFKGPUV 6 ' *WFIGPU ' ' 
 +ORTQXGF JKIJRGTHQTOCPEG NKSWKF EJTQOCVQITCRJKE OGVJQF HQT CTVKHCEVHTGG OGCUWTGOGPVU QH CNFGJ[FGU KP VJG RTGUGPEG QH Q\QPG WUKPI FKPKVTQRJGP[NJ[FTC\KPG , %JTQOCVQIT  

5OKVJ & ( -NGKPFKGPUV 6 ' *WFIGPU ' ' /E+XGT % & $WHCNKPK , , 
C 6JG RJQVQZKFCVKQP QH OGVJ[N VGTVKCT[ DWV[N GVJGT +PV , %JGO -KPGV  

5OKVJ & . *QNFTGP / 9 /E%NGPP[ 9 # 
D &GUKIP CPF QRGTCVKQPCN EJCTCEVGTKUVKEU QH VJG %JTQORCEM OQFGN  CU CP CWVQOCVGF ICU EJTQOCVQITCRJ +P /GCUWTGOGPV QH VQZKE CPF TGNCVGF CKT RQNNWVCPVU RTQEGGFKPIU QH VJG  75 '2##9/# KPVGTPCVKQPCN U[ORQUKWO X  /C[ &WTJCO 0% 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# RWDNKECVKQP 8+2

5OKVJ & ( -NGKPFKGPUV 6 ' *WFIGPU ' ' /E+XGT % & $WHCNKPK , , 
 -KPGVKEU CPF OGEJCPKUO QH VJG CVOQURJGTKE QZKFCVKQP QH GVJ[N VGTVKCT[ DWV[N GVJGT +PV , %JGO -KPGV  

5QNQOQP 2 # (CNN 6 5CNOQP . %CUU ) 4 )TC[ * # &CXKFUQP # 
 %JGOKECN EJCTCEVGTKUVKEU QH 2/ CGTQUQNU EQNNGEVGF KP VJG .QU #PIGNGU CTGC ,#2%#   

5RKEGT % 9 9CTF ) ( -GPP[ & 8 .GUNKG 0 2 $KNNKEM + * 
 /GCUWTGOGPV QH QZKFK\GF PKVTQIGP EQORQWPFU KP KPFQQT CKT +P /GCUWTGOGPV QH VQZKE CPF TGNCVGF CKT RQNNWVCPVU RTQEGGFKPIU QH VJG  75 '2##9/# KPVGTPCVKQPCN U[ORQUKWO X  /C[ &WTJCO 0% 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# RWDNKECVKQP 8+2

5RKEGT % 9 $WZVQP $ ' *QNFTGP / 9 -GNN[ 6 , 4WUV 5 9 4COCOWTVJK / 5OKVJ & . 2CVG # & 5XGTFTWR ) / %JWCPI , % 5JCJ , 
 8CTKCDKNKV[ CPF UQWTEG CVVTKDWVKQP QH JC\CTFQWU WTDCP CKT RQNNWVCPVU %QNWODWU HKGNF UVWF[ 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[
EQPVTCEV PQ &

5VCGJGNKP , *QKIPG , 
 &GEQORQUKVKQP QH Q\QPG KP YCVGT TCVG QH KPKVKCVKQP D[ J[FTQZKFG KQPU CPF J[FTQIGP RGTQZKFG 'PXKTQP 5EK 6GEJPQN  

5VCGJT 9 .CJOCPP 9 9GKVMCOR % 
 4CPIGTGUQNXGF FKHHGTGPVKCN CDUQTRVKQP NKFCT QRVKOK\CVKQP QH TCPIG CPF UGPUKVKXKV[ #RRN 1RV  

5VCUKWM 9 0 ,T %QHHG[ 2 ' 
 4WTCN CPF WTDCP Q\QPG TGNCVKQPUJKRU KP 0GY ;QTM 5VCVG , #KT 2QNNWV
%QPVTQN #UUQE  

5VCWHH , ,CGUEJMG 9 5EJNÌIN ) 
 %JGOKNWOKPGU\GP\ FGU &KQZGVCPFKQPU =%JGOKNWOKPGUEGPEG QH FKQZGVCPGFKQPG? < 0CVWTHQTUEJ $ #PQTI %JGO 1TI %JGO $KQEJGO $KQRJ[U $KQN
 

5VCWHHGT & 4 5GCOCP 0 . 
 7UG QH HQWTFKOGPUKQPCN FCVC CUUKOKNCVKQP KP C NKOKVGFCTGC OGUQUECNG OQFGN 2CTV + 'ZRGTKOGPVU YKVJ U[PQRVKEUECNG FCVC /QP 9GCVJGT 4GX  

5VCWHHGT & 4 9CTPGT 6 6 5GCOCP 0 . 
 # 0GYVQPKCP PWFIKPI CRRTQCEJ VQ HQWT FKOGPUKQPCN FCVC
CUUKOKNCVKQP WUG QH 5'5#/'+8 FCVC KP C OGUQUECNG OQFGN 2TGUGPVGF CV VJ EQPHGTGPEG QP PWOGTKECN YGCVJGT RTGFKEVKQP /QPVTGCN %CPCFC

5VGFOCP & * $KUJQR ) # 2GVGTUQP , ' )WGPVJGT 2 . /E8G[ + ( $GCVQP 5 2 
 1PTQCF ECTDQP OQPQZKFG CPF J[FTQECTDQP TGOQVG UGPUKPI KP VJG %JKECIQ CTGC (KPCN TGRQTV 5RTKPIHKGNF +. +NNKPQKU &GRCTVOGPV QH 'PGTI[ CPF 0CVWTCN 4GUQWTEGU 1HHKEG QH 4GUGCTEJ CPF 2NCPPKPI TGRQTV PQ +.'044'#3

5VGRJGPU ' 4 
 #DUQTRVKXKVKGU HQT KPHTCTGF FGVGTOKPCVKQP QH RGTQZ[CE[N PKVTCVGU #PCN %JGO  

5VGRJGPU ' 4 
 6JG HQTOCVKQP TGCEVKQPU CPF RTQRGTVKGU QH RGTQZ[CE[N PKVTCVGU 
2#0U KP RJQVQEJGOKECN CKT RQNNWVKQP +P 2KVVU , 0 ,T /GVECNH 4 . GFU #FXCPEGU KP GPXKTQPOGPVCN UEKGPEG CPF VGEJPQNQI[ X  0GY ;QTM 0; 9KNG[+PVGTUEKGPEG RR 

5VGRJGPU ' 4 2TKEG / # 
 #PCN[UKU QH CP KORQTVCPV CKT RQNNWVCPV RGTQZ[CEGV[N PKVTCVG , %JGO 'FWE
 

5VGRJGPU ' 4 *CPUV 2 . &QGTT 4 % 5EQVV 9 ' 
C 4GCEVKQPU QH PKVTQIGP FKQZKFG CPF QTICPKE EQORQWPFU KP CKT +PF 'PI %JGO  

5VGRJGPU ' 4 5EQVV 9 ' *CPUV 2 . &QGTT 4 % 
D 4GEGPV FGXGNQROGPVU KP VJG UVWF[ QH VJG QTICPKE EJGOKUVT[ QH VJG CVOQURJGTG 2TQE #O 2GV +PUV 5GEV   

5VGRJGPU ' 4 &CTNG[ ' ( 6C[NQT 1 % 5EQVV 9 ' 
 2JQVQEJGOKECN TGCEVKQP RTQFWEVU KP CKT RQNNWVKQP +PV , #KT 9CVGT 2QNNWV  

5VGRJGPU ' 4 $WTNGUQP ( 4 %CTFKHH ' # 
 6JG RTQFWEVKQP QH RWTG RGTQZ[CE[N PKVTCVGU , #KT 2QNNWV
%QPVTQN #UUQE  

5VGXGPU 4 - *QFIGUQP , # 
 #RRNKECVKQPU QH EJGOKNWOKPGUEGPV TGCEVKQPU VQ VJG OGCUWTGOGPV QH CKT RQNNWVCPVU #PCN %JGO  ##

5VGXGPU 4 - &TCIQ 4 , /COCPG ; 
 # NQPI RCVJ FKHHGTGPVKCN QRVKECN CDUQTRVKQP URGEVTQOGVGT CPF '2#CRRTQXGF HKZGFRQKPV OGVJQFU KPVGTEQORCTKUQP #VOQU 'PXKTQP 2CTV $  

5VQEMDWTIGT . -PCRR - 6 'NNGUVCF 6 ) 
 1XGTXKGY CPF CPCN[UKU QH J[FTQECTDQP UCORNGU FWTKPI VJG UWOOGT 5QWVJGTP %CNKHQTPKC #KT 3WCNKV[ 5VWF[ 2TGUGPVGF CV PF CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG #PCJGKO %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

5VQEMYGNN 9 4 %CNXGTV , ) 
 6JG OGEJCPKUO QH VJG *151 TGCEVKQP #VOQU 'PXKTQP   

5VQEMYGNN 9 4 .WTOCPP ( 9 
 +PVGTEQORCTKUQP QH VJG #&1/ CPF 4#&/ ICURJCUG EJGOKECN OGEJCPKUOU 2CNQ #NVQ %# 'NGEVTKE 2QYGT 4GUGCTEJ +PUVKVWVG

5VQEMYGNN 9 4 /KFFNGVQP 2 %JCPI , 5 6CPI : 
 6JG UGEQPF IGPGTCVKQP 4GIKQPCN #EKF &GRQUKVKQP /QFGN EJGOKECN OGEJCPKUO HQT TGIKQPCN CKT SWCNKV[ OQFGNKPI , )GQRJ[U 4GU =#VOQU?   

5VWOR ( & -PCRR - 6 4C[ 9 & 5PQY 4 $WTVQP % 
 6JG EQORQUKVKQP QH OQVQT XGJKENG QTICPKE GOKUUKQPU WPFGT GNGXCVGF VGORGTCVWTG UWOOGT FTKXKPI EQPFKVKQPU 
 VQ  ( , #KT 9CUVG /CPCIG #UUQE  

5WP 9; 
 #KT RQNNWVKQP KP C EQPXGEVKXG DQWPFCT[ NC[GT #VOQU 'PXKTQP  

5XGPUUQP 4 .LWPIUVTQGO ' .KPFSXKUV 1 
 -KPGVKEU QH VJG TGCEVKQP DGVYGGP PKVTQIGP FKQZKFG CPF YCVGT XCRQWT #VOQU 'PXKTQP  

5YGGV % 9 8GTOGVVG 5 , 
 6QZKE XQNCVKNG QTICPKE EQORQWPFU KP WTDCP CKT KP +NKPQKU 'PXKTQP 5EK
6GEJPQN   5[UVGOU #RRNKECVKQPU +PVGTPCVKQPCN 
 'XCNWCVKQPU QH VJG '2# TGIKQPCN QZKFCPV OQFGN HQT VJG ,WN[   Q\QPG GRKUQFG 5[UVGOU #RRNKECVKQPU +PVGTPCVKQPCN TGRQTV PQ 5;5#22

6CPPGT 4 . 
 %JGOKECN VTCPUHQTOCVKQPU KP CEKF TCKP X + PGY OGVJQFQNQIKGU HQT UCORNKPI CPF CPCN[UKU QH ICURJCUG RGTQZKFG 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

6CPPGT 4 . /GPI < 
 5GCUQPCN XCTKCVKQPU KP CODKGPV CVOQURJGTKE NGXGNU QH HQTOCNFGJ[FG CPF CEGVCNFGJ[FG 'PXKTQP 5EK 6GEJPQN  

6CPPGT 4 . 5JGP , 
 /GCUWTGOGPV QH J[FTQIGP RGTQZKFG KP CODKGPV CKT D[ KORKPIGT CPF FKHHWUKQP
UETWDDGT #GTQUQN 5EK 6GEJPQN  

6CPPGT 4 . &CWO 2 * -GNN[ 6 , 
 0GY KPUVTWOGPVCVKQP HQT CKTDQTPG CEKF TCKP TGUGCTEJ
+PV , 'PXKTQP #PCN %JGO  

6CPPGT 4 . /CTMQXKVU ) ; (GTTGTK ' / -GNN[ 6 , 
 5CORNKPI CPF FGVGTOKPCVKQP QH ICURJCUG J[FTQIGP RGTQZKFG HQNNQYKPI TGOQXCN QH Q\QPG D[ ICURJCUG TGCEVKQP YKVJ PKVTKE QZKFG #PCN %JGO
 

6GUEJG 6 9 
 2JQVQEJGOKECN FKURGTUKQP OQFGNKPI TGXKGY QH OQFGN EQPEGRVU CPF CRRNKECVKQPU UVWFKGU
'PXKTQP +PV  

6GUEJG 6 9 
 2JQVQEJGOKECN OQFGNKPI QH  5%%%#/2 QZKFCPV GRKUQFGU RTQVQEQN HQT OQFGN UGNGEVKQP CFCRVCVKQP CPF RGTHQTOCPEG GXCNWCVKQP 9CUJKPIVQP &% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[

6GUEJG 6 9 
 'OKUUKQPU OQFGNKPI UVCVWU CPF PGY FKTGEVKQPU +P 2TQEGGFKPIU QH VJG 'NGEVTKE 2QYGT 4GUGCTEJ +PUVKVWVG RJQVQEJGOKECN OQFGNKPI YQTMUJQR #WIWUV %CODTKFIG /# 2CNQ #NVQ %#
'NGEVTKE 2QYGT 4GUGCTEJ +PUVKVWVG

6GUEJG 6 9 
 9QTMRNCP HQT VJG FGXGNQROGPV CPF CRRNKECVKQP QH VJG 5#4/#2 OQFGNKPI U[UVGO 5CETCOGPVQ %# %CNKHQTPKC #KT 4GUQWTEGU $QCTF TGRQTV PQ #)65

6GUEJG 6 9 *CPG[ , . /QTTKU 4 ' 
 2GTHQTOCPEG GXCNWCVKQP QH HQWT ITKFDCUGF FKURGTUKQP OQFGNU KP EQORNGZ VGTTCKP #VOQU 'PXKTQP  

6GUEJG 6 9 )GQTIQRQWNQU 2 .WTOCP ( . 4QVJ 2 / 5GKPHGNF , * %CUU ) 
 +ORTQXGOGPV QH RTQEGFWTGU HQT GXCNWCVKPI RJQVQEJGOKECN OQFGNU 5CETCOGPVQ %# %CNKHQTPKC #KT 4GUQWTEGU $QCTF TGRQTV PQ #4$4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

6GUEJG 6 9 4QVJ 2 / 4G[PQNFU 5 & .WTOCPP ( 9 
 5EKGPVKHKE CUUGUUOGPV QH VJG WTDCP CKTUJGF OQFGN 
7#/+8 9CUJKPIVQP &% #OGTKECP 2GVTQNGWO +PUVKVWVG #2+ RWDNKECVKQP PQ 

6JQORUQP # / 
 6JG QZKFK\KPI ECRCEKV[ QH VJG 'CTVJ	U CVOQURJGTG RTQDCDNG RCUV CPF HWVWTG EJCPIGU 5EKGPEG 
9CUJKPIVQP &%  

6JQORUQP # / 5VGYCTV 4 9 1YGPU / # *GTYGJG , # 
 5GPUKVKXKV[ QH VTQRQURJGTKE QZKFCPVU VQ INQDCN EJGOKECN CPF ENKOCVG EJCPIG #VOQU 'PXKTQP  

6KNFGP , 9 5GKPHGNF , * 
 5GPUKVKXKV[ CPCN[UKU QH C OCVJGOCVKECN OQFGN HQT RJQVQEJGOKECN CKT RQNNWVKQP
#VOQU 'PXKTQP   6QRJCO . # /CEMC[ ) + 5EJKHH * + 
 2GTHQTOCPEG CUUGUUOGPV QH VJG RQTVCDNG CPF NKIJVYGKIJV .1< EJGOKNWOKPGUEGPEG V[RG Q\QPG OQPKVQT +P /GCUWTGOGPV QH VQZKE CPF TGNCVGF CKT RQNNWVCPVU RTQEGGFKPIU QH VJG  75 '2##9/# KPVGTPCVKQPCN U[ORQUKWO /C[ &WTJCO 0% 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RR  
#9/# RWDNKECVKQP 8+2 

6QTTGU # . 
 0KVTKE QZKFG OGCUWTGOGPVU CV C PQPWTDCP GCUVGTP 7PKVGF 5VCVGU UKVG 9CNNQRU KPUVTWOGPV TGUWNVU HTQO ,WN[  )6'%+6' OKUUKQP , )GQRJ[U 4GU =#VOQU?  

6TCKPGT / 9KNNKCOU ' , 2CTTKUJ & & $WJT / 2 #NNYKPG ' , 9GUVDGTI * * (GJUGPHGNF ( % .KW 5 % 
 /QFGNU CPF QDUGTXCVKQPU QH VJG KORCEV QH PCVWTCN J[FTQECTDQPU QP TWTCN Q\QPG 0CVWTG 
.QPFQP  

6TCKPGT / $WJT / 2 %WTTCP % / (GJUGPHGNF ( % *UKG ' ; .KW 5 % 0QTVQP 4 $ 2CTTKUJ & & 9KNNKCOU ' , 
 1DUGTXCVKQPU CPF OQFGNKPI QH VJG TGCEVKXG PKVTQIGP RJQVQEJGOKUVT[ CV C TWTCN UKVG , )GQRJ[U 4GU =#VOQU?  

6TCKPGT / 2CTTKUJ & & $WJT / 2 0QTVQP 4 $ (GJUGPHGNF ( % #PNCWH - ) $QVVGPJGKO , 9 6CPI ; < 9KGDG * # 4QDGTVU , / 6CPPGT 4 . 0GYOCP . $QYGTUQZ 8 % /GCIJGT , ( 1NU\[PC - , 4QFIGTU / 1 9CPI 6 $GTTGUJGKO * &GOGTLKCP - . 4Q[EJQYFJWT[ 7 - 
 %QTTGNCVKQP QH Q\QPG YKVJ 01 KP RJQVQEJGOKECNN[ CIGF CKT , [ )GQRJ[U 4GU =#VOQU?  

6TKRQNK ) , %QVVQP 9 4 
 6JG %QNQTCFQ 5VCVG 7PKXGTUKV[ VJTGGFKOGPUKQPCN ENQWFOGUQUECNG OQFGN

`  2CTV + )GPGTCN VJGQTGVKECN HTCOGYQTM CPF UGPUKVKXKV[ GZRGTKOGPVU , 4GEJ #VOQU
 

6UCNMCPK 0 6QWRCPEG ) 
 +PHTCTGF CDUQTRVKXKVKGU CPF KPVGITCVGF DCPF KPVGPUKVKGU HQT ICUGQWU RGTQZ[CEGV[N PKVTCVG 
2#0 #VOQU 'PXKTQP  

6WC\QP ' % #VMKPUQP 4 
 # RTQFWEV UVWF[ QH VJG ICURJCUG TGCEVKQP QH OGVJ[N XKP[N MGVQPG YKVJ VJG 1* TCFKECN KP VJG RTGUGPEG QH 01  +PV , %JGO -KPGV  

Z

6WC\QP ' % #VMKPUQP 4 
C # RTQFWEV UVWF[ QH VJG ICURJCUG TGCEVKQP QH KUQRTGPG YKVJ VJG 1* TCFKECN KP VJG RTGUGPEG QH 01  +PV , %JGO -KPGV  

Z

6WC\QP ' % #VMKPUQP 4 
D # RTQFWEV UVWF[ QH VJG ICURJCUG TGCEVKQP QH OGVJCETQNGKP YKVJ VJG 1* TCFKECN KP VJG RTGUGPEG QH 01  +PV , %JGO -KPGV  

Z

6WC\QP ' % )TCJCO 4 # 9KPGT # / 'CUVQP 4 4 2KVVU , 0 ,T *CPUV 2 . 
 # MKNQOGVGT RCVJNGPIVJ (QWTKGTVTCPUHQTO KPHTCTGF U[UVGO HQT VJG UVWF[ QH VTCEG RQNNWVCPVU KP CODKGPV CPF U[PVJGVKE CVOQURJGTGU #VOQU 'PXKTQP  

6WC\QP ' % 9KPGT # / )TCJCO 4 # 2KVVU , 0 ,T 
 #VOQURJGTKE OGCUWTGOGPVU QH VTCEG RQNNWVCPVU D[ MKNQOGVGTRCVJNGPIVJ (6+4 URGEVTQUEQR[ #FX 'PXKTQP 5EK 6GEJPQN  

6WC\QP ' % 9KPGT # / )TCJCO 4 # 2KVVU , 0 ,T 
C #VOQURJGTKE OGCUWTGOGPVU QH VTCEG RQNNWVCPVU NQPI RCVJ (QWTKGT VTCPUHQTO KPHTCTGF URGEVTQUEQR[ 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

6WC\QP ' % 9KPGT # / 2KVVU , 0 ,T 
D 6TCEG RQNNWVCPV EQPEGPVTCVKQPU KP C OWNVKFC[ UOQI GRKUQFG KP VJG %CNKHQTPKC 5QWVJ %QCUV #KT $CUKP D[ NQPI RCVJ NGPIVJ (QWTKGT VTCPUHQTO KPHTCTGF URGEVTQUEQR[
'PXKTQP 5EK 6GEJPQN   6WC\QP ' % %CTVGT 9 2 . #UEJOCPP 5 / #VMKPUQP 4 
 2TQFWEVU QH VJG ICURJCUG TGCEVKQP QH OGVJ[N VGTV DWV[N GVJGT YKVJ VJG 1* TCFKECN KP VJG RTGUGPEG QH 01  +PV , %JGO -KPGV

Z  

6WTRKP $ , *WPV\KEMGT , , 
 5GEQPFCT[ HQTOCVKQP QH QTICPKE CGTQUQN KP VJG .QU #PIGNGU DCUKP C FGUETKRVKXG CPCN[UKU QH QTICPKE CPF GNGOGPVCN ECTDQP EQPEGPVTCVKQPU #VOQU 'PXKTQP 2CTV #
 

6[PFCNN ) 5 4CXKUJCPMCTC # 4 
 #VOQURJGTKE QZKFCVKQP QH TGFWEGF UWNHWT URGEKGU +PV , %JGO -KPGV
 

7PKVGF -KPIFQO 2JQVQEJGOKECN 1ZKFCPVU 4GXKGY )TQWR 
 1\QPG KP VJG 7PKVGF -KPIFQO  .QPFQP 7PKVGF -KPIFQO &GRCTVOGPV QH 'PXKTQPOGPV 6GEJPKECN 2QNKE[ $TCPEJ

75 %QPITGUU 
 %NGCP #KT #EV COGPFOGPVU QH  EQPHGTGPEG TGRQTV VQ CEEQORCP[ 5  9CUJKPIVQP &% 75 )QXGTPOGPV 2TKPVKPI 1HHKEG TGRQTV 

75 &GRCTVOGPV QH *GCNVJ 'FWECVKQP CPF 9GNHCTG 
 #KT SWCNKV[ ETKVGTKC HQT RJQVQEJGOKECN QZKFCPVU 9CUJKPIVQP &% 0CVKQPCN #KT 2QNNWVKQP %QPVTQN #FOKPKUVTCVKQP RWDNKECVKQP PQ #2 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$$#

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
 #KT SWCNKV[ ETKVGTKC HQT Q\QPG CPF QVJGT RJQVQEJGOKECN QZKFCPVU 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH *GCNVJ CPF 'PXKTQPOGPVCN #UUGUUOGPV 'PXKTQPOGPVCN %TKVGTKC CPF #UUGUUOGPV 1HHKEG TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
 %QORKNCVKQP QH CKT RQNNWVCPV GOKUUKQP HCEVQTU 8QNWOG + UVCVKQPCT[ RQKPV CPF CTGC UQWTEGU 8QNWOG ++ OQDKNG UQWTEGU VJ GF 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU #PP #TDQT /+ 1HHKEG QH /QDKNG 5QWTEGU TGRQTV PQU #2 '&81. CPF #2'&81. #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$ CPF
2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
C #KT SWCNKV[ ETKVGTKC HQT Q\QPG CPF QVJGT RJQVQEJGOKECN QZKFCPVU 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH *GCNVJ CPF 'PXKTQPOGPVCN #UUGUUOGPV 'PXKTQPOGPVCN %TKVGTKC CPF #UUGUUOGPV 1HHKEG TGRQTV PQU '2#C(G( X #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
D )WKFGNKPG QP CKT SWCNKV[ OQFGNU 
TGXKUGF 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
 6JG  0#2#2 GOKUUKQPU KPXGPVQT[ 
XGTUKQP  FGXGNQROGPV QH VJG PCVKQPCN WVKNKV[ TGHGTGPEG HKNG 4GUGCTEJ 6TKCPING 2CTM 0% #KT CPF 'PGTI[ 'PIKPGGTKPI 4GUGCTEJ .CDQTCVQT[ TGRQTV PQ '2## #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
C 7UGT	U IWKFG HQT VJG WTDCP CKTUJGF OQFGN XQNWOG + WUGT	U OCPWCN HQT 7#/ 
%$+8 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2## #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
D 7UGT	U IWKFG HQT VJG WTDCP CKTUJGF OQFGN XQNWOG ++ WUGT	U OCPWCN HQT VJG 7#/ 
%$+8 OQFGNKPI U[UVGO 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#$ #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$ 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
E 7UGT	U IWKFG HQT VJG WTDCP CKTUJGF OQFGN X +++ WUGT	U OCPWCN HQT VJG FKCIPQUVKE YKPF OQFGN 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#% #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
F 7UGT	U IWKFG HQT VJG WTDCP CKTUJGF OQFGN XQNWOG 8 FGUETKRVKQP CPF QRGTCVKQP QH VJG 41/7#/ KPVGTHCEG RTQITCO U[UVGO 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#' #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
C 0CVKQPCN CKT SWCNKV[ CPF GOKUUKQPU VTGPFU TGRQTV  4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$:#$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
D )WKFGNKPG HQT TGIWNCVQT[ CRRNKECVKQP QH VJG WTDCP CKTUJGF OQFGN 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#  #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$*57

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
E 6GEJPKECN CUUKUVCPEG FQEWOGPV HQT UCORNKPI CPF CPCN[UKU Q\QPG RTGEWTUQTU 4GUGCTEJ 6TKCPING 2CTM 0% #VOQURJGTKE 4GUGCTEJ CPF 'ZRQUWTG #UUGUUOGPV .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
C 0CVKQPCN CKT RQNNWVCPV GOKUUKQP GUVKOCVGU  4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$:#$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
D 7UGT	U IWKFG HQT VJG WTDCP CKTUJGF OQFGN XQNWOG +8 WUGT	U OCPWCN HQT VJG GOKUUKQPU RTGRTQEGUUQT U[UVGO  2CTV # EQTG (1464#0 U[UVGO 2CTV $ KPVGTHCEG CPF GOKUUKQP FKURNC[ U[UVGO 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#&
4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$


75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
C 4GIKQPCN KPVGTKO GOKUUKQP KPXGPVQTKGU 
 X + FGXGNQROGPV OGVJQFQNQIKGU 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#4C #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8#
2$:#$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
D 4GIKQPCN KPVGTKO GOKUUKQP KPXGPVQTKGU 
 X ++ GOKUUKQP UWOOCTKGU 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#4D #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
E #KT SWCNKV[ ETKVGTKC HQT QZKFGU QH PKVTQIGP 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH *GCNVJ CPF 'PXKTQPOGPVCN #UUGUUOGPV 'PXKTQPOGPVCN %TKVGTKC CPF #UUGUUOGPV 1HHKEG TGRQTV PQU '2#C(E( X #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$ 2$ CPF 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
F 8QNCVKNG QTICPKE EQORQWPFRCTVKEWNCVG OCVVGT URGEKCVKQP FCVCDCUG OCPCIGOGPV U[UVGO 
52'%+#6' XGTUKQP  
HQT OKETQEQORWVGTU 4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#59&- #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$

75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 
 0CVKQPCN CKT RQNNWVCPV GOKUUKQP VTGPFU  4GUGCTEJ 6TKCPING 2CTM 0% 1HHKEG QH #KT 3WCNKV[ 2NCPPKPI CPF 5VCPFCTFU TGRQTV PQ '2#4 #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$4'$ 7NTKEMUQP $ . 
 /GUQUECNG EKTEWNCVKQPU KP VJG .QU #PIGNGU $CUKP C PWOGTKECN OQFGNKPI UVWF[ =2J& FKUUGTVCVKQP? 5GCVVNG 9# 7PKXGTUKV[ QH 9CUJKPIVQP

8CNGPVG 4 , 6JQTPVQP ( % 
 'OKUUKQPU QH 01 HTQO UQKN CV C TWTCN UKVG KP EGPVTCN 6GPPGUUGG , )GQRJ[U 4GU =#VOQU?  

8CP 8CNKP % % 4C[ , & $QCVOCP , ( )WPVGT 4 . 
 *[FTQIGP RGTQZKFG KP CKT FWTKPI YKPVGT QXGT VJG UQWVJEGPVTCN 7PKVGF 5VCVGU )GQRJ[U 4GU .GVV  

8GPMCVTCO # -CTCOEJCPFCPK 2 - /KUTC 2 - 
 6GUVKPI C EQORTGJGPUKXG CEKF FGRQUKVKQP OQFGN
#VOQU 'PXKTQP  

8KGTMQTP4WFQNRJ $ 4WFQNRJ , &KGFGTKEJ 5 
 &GVGTOKPCVKQP QH RGTQZ[CEGV[NPKVTCVG 
2#0 KP WPRQNNWVGF CTGCU +PV , 'PXKTQP #PCN %JGO  

8KG\GG 9 5KPIJ * $ 
 %QPVTKDWVKQP QH UVTCVQURJGTKE Q\QPG VQ ITQWPFNGXGN Q\QPG EQPEGPVTCVKQPU

` C UEKGPVKHKE TGXKGY QH GZKUVKPI GXKFGPEG 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ 'PXKTQPOGPVCN 5EKGPEG 4GUGCTEJ .CDQTCVQT[ ITCPV %4

8KG\GG 9 ,QJPUQP 9 $ 5KPIJ * $ 
 #KTDQTPG OGCUWTGOGPVU QH UVTCVQURJGTKE Q\QPG KPVTWUKQPU KPVQ VJG VTQRQURJGTG QXGT VJG 7PKVGF 5VCVGU =HKPCN TGRQTV? /GPNQ 2CTM %# 54+ +PVGTPCVKQPCN 54+ RTQLGEV


8KG\GG 9 ,QJPUQP 9 $ 5KPIJ * $ 
 5VTCVQURJGTKE Q\QPG KP VJG NQYGT VTQRQURJGTG

` ++ CUUGUUOGPV QH FQYPYCTF HNWZ CPF ITQWPFNGXGN KORCEV #VOQU 'PXKTQP  

9CEMVGT & , $C[N[ 2 8 
 6JG GHHGEVKXGPGUU QH GOKUUKQP EQPVTQNU QP TGFWEKPI Q\QPG NGXGNU KP %QPPGEVKEWV HTQO  VJTQWIJ  +P 9QNHH ) 6 *CPKUEJ , . 5EJGTG - GFU 6JG UEKGPVKHKE CPF VGEJPKECN KUUWGU HCEKPI RQUV Q\QPG EQPVTQN UVTCVGIKGU VTCPUCEVKQPU QH CP #2%# KPVGTPCVKQPCN URGEKCNV[ EQPHGTGPEG 0QXGODGT  *CTVHQTF %6 2KVVUDWTIJ 2# #KT 2QNNWVKQP %QPVTQN #UUQEKCVKQP RR 

9CFFGP 4 # 7PQ + 9CMCOCVUW 5 
 5QWTEG FKUETKOKPCVKQP QH UJQTVVGTO J[FTQECTDQP UCORNGU
OGCUWTGF CNQHV 'PXKTQP 5EK 6GEJPQN  

9CMKO 2 ) 
 6GORGTCVWTGCFLWUVGF Q\QPG VTGPFU HQT *QWUVQP 0GY ;QTM CPF 9CUJKPIVQP  2TGUGPVGF CV PF CPPWCN OGGVKPI CPF GZJKDKVKQP QH VJG #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP ,WPG #PCJGKO %# 2KVVUDWTIJ 2# #KT  9CUVG /CPCIGOGPV #UUQEKCVKQP RCRGT PQ 

9CNGIC , ) 5VGFOCP & * 5JGVVGT 4 ' /CEMC[ ) + +IWEJK 6 5EJKHH * + 
 %QORCTKUQP QH C EJGOKNWOKPGUEGPV CPF C VWPCDNG FKQFG NCUGT CDUQTRVKQP VGEJPKSWG HQT VJG OGCUWTGOGPV QH PKVTQIGP QZKFG PKVTQIGP FKQZKFG CPF PKVTKE CEKF 'PXKTQP 5EK 6GEJPQN  

9CNNCEG . # 1VV 9 4 
 2GTUQPCN OQPKVQTU C UVCVGQHVJGCTV UWTXG[ , #KT 2QNNWV %QPVTQN #UUQE
 

9CNNKPIVQP 6 , ,CRCT 5 / 
 #VOQURJGTKE EJGOKUVT[ QH FKGVJ[N GVJGT CPF GVJ[N VGTV DWV[N GVJGT 'PXKTQP
5EK 6GEJPQN  

9CPI 9 9CTPGT 6 6 
 7UG QH HQWTFKOGPUKQPCN FCVC CUUKOKNCVKQP D[ 0GYVQPKCP TGNCZCVKQP CPF NCVGPVJGCV HQTEKPI VQ KORTQXG C OGUQUECNGOQFGN RTGEKRKVCVKQP HQTGECUV C ECUG UVWF[ /QP 9GCVJGT
4GX   9CPI 5% 2CWNUQP 5 ' )TQULGCP & (NCICP 4 % 5GKPHGNF , * 
 #GTQUQN HQTOCVKQP CPF ITQYVJ KP CVOQURJGTKE QTICPKE01 U[UVGOU+ 1WVFQQT UOQI EJCODGT UVWFKGU Q H %  CPF % J[FTQECTDQPU

Z   #VOQU 'PXKTQP 2CTV #  

9CTPGEM 2 
 %JGOKECN TGCEVKQPU KP ENQWFU (TGUGPKWU	 , #PCN %JGO  

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# 4

Environmental Concentrations, Patterns, and Exposure Estimates

4.1 Introduction

The effects of ozone (O 3 ) on humans, animals, and vegetation have received
extensive examination and are discussed elsewhere in this document. As indicated in the previous O 3 criteria document (U.S. Environmental Protection Agency, 1986), most of the human and welfare effects research has focused on evaluating those impacts on health or vegetation of exposure to O 3 that simulate ambient O 3 exposures (e.g., matching the occurrence of hourly average concentrations or more prolonged times of exposure). This information on concentrations obtained from extensive monitoring in the United States can be useful both for linking anthropogenic emissions of O 3 precursors with the protection of health and welfare (i.e., determining compliance with air standards) and for augmenting exposure assessment and epidemiology studies. The major emphasis in this chapter, however, will be on characterizing and summarizing the extensive O 3 -monitoring data collected under ambient conditions. Although most of the O 3 air quality data summarized were gathered for compliance and enforcement purposes, the hourly averaged O 3 information can be used for determining patterns and trends and as inputs to exposure and health assessments (e.g., U.S. Environmental Protection Agency, 1992a; Lefohn et al., 1990a). In the sections that follow, the hourly averaged ambient O 3 data have been summarized in different ways to reflect the interests of those who wish to know more about the potential for O 3 to affect humans and the environment. This chapter is not an exposure assessment for ambient O 3 ; rather, this chapter
elucidates the features of O 3 concentration patterns and exposure possibilities. Trend patterns for O 3 over several periods of time are described in Section 4.2.
The trends for O 3 have been summarized by the U.S. Environmental Protection Agency (1994) for 1983 to 1993. In addition, trends analysis for specific regions of the United States have been performed by several investigators. In some cases, attempts have been made to adjust for meteorological variation. In Section 4.3, the hourly averaged concentration information from several monitoring networks has been characterized for urban and rural areas. The diurnal variation (Section 4.4) occurring at urban and rural locations, as well as seasonal patterns, also are described. Specific focus is provided on O 3 monitoring sites that experience low maximum hourly average concentrations because these locations form the "basis for comparison" for O 3 concentrations and exposures. In Section 4.5, the seasonal patterns of hourly average concentrations are discussed. The hourly average concentration information is used in Section 4.6 to compare the spatial variations that occur in urban areas with those in nonurban areas, as well as with those in high-elevation locations. For

4-1comparing indoor to outdoor O 3 exposures or concentrations, information is provided in Section 4.7 on the latest data on indoor/outdoor (I/O) ratios. Section 4.8 describes efforts to estimate both human and vegetation exposure to O 3 . Examples are provided on how both fixed-site monitoring information and human exposure models are used to estimate risks
associated with O 3 exposure. A short discussion is provided on the importance of hourly average concentrations, which are used in human health and vegetation experiments that simulate "real world" exposures. As indicated in the previous O 3 criteria document (U.S. Environmental Protection Agency, 1986), O 3 is the only photochemical oxidant other than nitrogen dioxide (NO 2 ) that is routinely monitored and for which a comprehensive aerometric database exists. Data for peroxyacetyl nitrate (PAN) and hydrogen peroxide (H 2 O 2 ) have been obtained only as part of special research investigations. Consequently, no data on nationwide patterns of occurrence
are available for these non-O 3 oxidants; nor are extensive data available on the correlations of levels and patterns of these oxidants with those of O 3 . Sections 4.9 and 4.10 summarize the available data for these other oxidants. Section 4.11 describes the co-occurrence patterns of
O 3 with NO 2 ; sulfur dioxide (SO 2 ); and acidic aerosols, precipitation, and cloudwater.



### 4.1.1 Characterizing Ambient Ozone Concentrations It is important to distinguish among concentration, exposure, and dose when using air quality data to assess human health and vegetation effects. For this document, the following definitions apply: 1. The "concentration" of a specific air pollutant is the amount of that material per unit volume of air. Air pollution monitors measure pollutant concentrations, which may or may not provide accurate exposure estimates. 2. The term "exposure" is defined as the concentration of a pollutant encountered by the subject (animal, human, or plant) for a duration of time. Exposure implies that such an encounter leads to intake (i.e., through the respiratory tract or stomata). 3. The term "dose" is defined as that mass of pollutant delivered to an inner target. This term has numerous quantitative descriptions (e.g., micrograms of
O 3 per square centimeter of lung epithelium per minute), so the context of the use of this term within the document must be considered. Human dosimetry is discussed in Chapter 8. The dose incurred by an organism (e.g., plant, animal, or human) is a more complicated measure involving the concentration, the exposure duration, and the concurrent state of the organism’s susceptibility. These distinctions become important because the concentration of an airborne contaminant that is measured in an empty room or at a stationary outdoor monitor is not in fact an exposure. A measured concentration functions as an alternative to an exposure only to the degree to which it represents concentrations actually experienced by individuals. Concentrations of airborne contaminants for vegetation are considered to represent an exposure when a plant is subjected to them over a specified time period. As indicated in Chapter 5 (see Section 5.5), dose has been defined historically by air pollution vegetation researchers as ambient air quality concentration multiplied by time (O’Gara, 1922). However, a more rigorous definition was required. Runeckles (1974) introduced the concept of "effective dose" as the amount or concentration of pollutant that is adsorbed by vegetation, in

4-2contrast to that which is present in the ambient air. Fowler and Cape (1982) developed this concept further and proposed that the "pollutant adsorbed dose" be defined in units of grams per square meter (of ground or leaf area) and could be obtained as the product of concentration, time, and stomatal (or canopy) conductance for the gas in question. Taylor et al. (1982) suggested internal flux (milligrams per square meter per hour) as a measure of the dose to which plants respond. In this chapter, dose will be taken to signify, for the purposes of vegetation, that amount of pollutant absorbed by the plant. In order to characterize the specific doses responsible for affecting human health and vegetation, there has to be a linkage between exposure and actual dose. Unfortunately, it is difficult to predict this relationship, even with the available models. For example, the sensitivity of vegetation to O 3 as a function of time of day, period of growth, or edaphic conditions can determine the severity of response. For example, high O 3 concentrations may cause minimum injury or damage to plants, whereas more moderate O 3 concentrations may cause a greater degree of injury or damage (Showman, 1991). Because not enough is known to quantify the links between exposure and dosage, and routine monitoring for O 3 is summarized as hourly average concentrations (i.e., potential exposure), most of the information provided in this chapter is characterized in terms of concentration and exposure. As indicated in Chapter 5, for many years, air pollution specialists have explored alternative mathematical approaches for summarizing ambient air quality information in biologically meaningful forms that can serve as alternatives for characterizing dose. For vegetation, as indicated in Chapter 5 (Section 5.5), extensive research has focused on identifying indicators of concentration and duration (exposure) that are firmly founded on biological principles. Many of these indicators have been based on research results indicating that the magnitude of vegetation responses to air pollution is determined more as a function of the magnitude of the concentration than of the length of the exposure (U.S. Environmental Protection Agency, 1992b). Short-term (1- to 8-h), high
O 3 concentrations (>0.1 ppm) have been identified by many researchers as being more important than long-term, low O 3 concentrations for induction of visible injury to vegetation (see Chapter 5 for further discussion). Long-term, average concentrations were used initially as an exposure indicator to
describe O 3 concentrations over time when assessing vegetation effects (Heck et al., 1982). Based on the view presented in the previous criteria document (U.S. Environmental Protection Agency, 1986) that higher concentrations of O 3 should be given more weight than lower concentrations (see Section 5.5 for further details), the following specific concerns about the use of a long-term average to summarize exposures of O 3 began appearing in the literature: the use of a long-term average failed to consider the impact of peak concentrations and of duration; a large number of hourly data sets within the commonly used 7-h window (0900 to 1559 hours), although diversely distributed and implying potentially diverse exposure potentials, were characterized by the same 7-h seasonal mean; and high hourly average concentrations (e.g., values greater than 0.1 ppm) occurred outside of a fixed 7-h window. In summarizing the hourly average concentrations in this chapter, specific attention is given to the relevance of the exposure indicators used. For example, for human health considerations, concentration (or exposure) indicators such as the daily maximum 1-h average concentrations, as well as the number of daily maximum 4-h or 8-h average concentrations, are used to characterize information in the population-oriented locations. For vegetation, several different types of exposure indicators are used. For example, much of the National Crop Loss Assessment Network (NCLAN) exposure information is summarized in terms of

4-3the 7-h average concentrations. However, because peak-weighted, cumulative indicators (i.e., exposure parameters that sum the products of hourly average concentrations multiplied by time over an exposure period) have shown considerable promise in relating exposure and vegetation response (see Section 5.5), several exposure indicators that use either a threshold or a sigmoidal weighting scheme are discussed in this chapter to provide insight concerning
the O 3 exposures that are experienced at a select number of rural monitoring sites in the United States. The peak-weighted, cumulative exposure indicators used in this chapter are SUM06 and SUM08 (the sums of all hourly average concentrations equal to or greater than 0.06 and 0.08 ppm, respectively) and W126 (the sum of the hourly average concentrations that have been weighted according to a sigmoid function [see Lefohn and Runeckles, 1987] that theoretically is based on a hypothetical vegetation response). The exposure indicators used for human health considerations are in concentration units (i.e., parts per million), whereas the indicators used for vegetation are in both parts per million (e.g., 7-h seasonal average concentrations) and parts per million per hour (e.g., SUM06, SUM08, W126). The magnitude of the peak-weighted, cumulative indicators at specific sites can be compared with those values experienced at areas that experience low hourly average maximum concentrations. In some cases, to provide more detailed information about the distribution patterns for a specific O 3 exposure regime, the percentile distribution of the hourly average concentrations (in parts per million) is given. For further clarification of the determination and rationale for the exposure indicators that are used for assessing human health and vegetation effects, the reader is encouraged to read Chapters 5 (Section 5.5) and 7.



### 4.1.2 The Identification and Use of Existing Ambient Ozone Data Information is readily available from the database supported by a network of monitoring stations that were established to determine compliance with the National Ambient Air Quality Standards (NAAQS) for O 3 . Most of the data presented in this chapter were obtained from data stored in the U.S. Environmental Protection Agency’s (EPA’s) computerized Aerometric Information Retrieval System (AIRS) and were collected after 1978. As pointed out in the previous criteria document for O 3 and other photochemical oxidants (U.S. Environmental Protection Agency, 1986), there was some difficulty in interpreting the
O 3 data obtained at most sites across the United States prior to 1979 because of calibration problems. In the United States, O 3 hourly average concentrations are monitored routinely through the National Air Monitoring Network, consisting of three types of sites. The National Air Monitoring Station (NAMS) sites are located in areas where the concentrations
of O 3 and subsequent potential human exposures are expected to be high. Criteria for these sites have been established by regulation to meet uniform standards of siting, quality assurance, equivalent analytical methodology, sampling intervals, and instrument selection to assure consistency among the reporting agencies. For O 3 , NAMS sites are located only in urban areas with populations exceeding 200,000. The other two types of sites are State and Local Air Monitoring Stations and Special Purpose Monitors, which meet the same rigid criteria for the NAMS sites but may be located in areas that do not necessarily experience high concentrations in populated areas.
For O 3 , the reporting interval is 1 h, with the instruments operating continuously and producing an integrated hourly average measurement. In many cases, EPA summarizes

4-4air quality data by an O 3 "season". Table 4-1 summarizes the O 3 season for the District of
Columbia and each of the states in the United States.

Table 4-1. Ozone Monitoring Season by State

State Begin End State Begin End

Alabama March November Montana June September

Alaska April October Nebraska April October

Arizona January December Nevada January December

Arkansas March November New Hampshire April October

California January December New Jersey April October

Colorado March September New Mexico January December

Connecticut April October New York April October

Delaware April October North Carolina April October

D.C. April October North Dakota May September

Florida January December Ohio April October

Georgia March November Oklahoma March November

Hawaii January December Oregon April October

Idaho April October Pennsylvania April October

Illinois April October Rhode Island April October

Indiana April October South Carolina April October

Iowa April October South Dakota June September

Kansas April October Tennessee April October

Kentucky April October Texas a January December

Louisiana January December Texas b March October

Maine April October Utah May September

Maryland April October Vermont April October

Massachusetts April October Virginia April October

Michigan April October Washington April October

Minnesota April October West Virginia April October

Mississippi March November Wisconsin April October

Missouri April October Wyoming April October

a Air Quality Control Region (AQCR) Numbers 4, 5, 7, 10, and 11.

b AQCR Numbers 1, 2, 3, 6, 8, 9, and 12.

Source: Code of Federal Regulations (1991).

4-5In this chapter, data are analyzed for the purpose of providing focus on specific issues of exposure-response relationships that are considered in the later effects chapters. The analyses proceed from a national picture of peak annual averages in Metropolitan Statistical Areas (MSAs), through national 10- and 3-year trends, to characteristic seasonal and diurnal patterns at selected stations, and then a brief examination of the incidence of episodic 1-h levels. Although there are O 3 data collected from monitoring stations not listed in AIRS, the major source of information was derived from ambient air concentrations from monitoring sites operated by the State and local air pollution agencies who report their data to AIRS. Because meteorology affects the identification of trends, methodologies that adjust for meteorology are described below. To obtain a better understanding of the potential effect of ambient
O 3 concentrations on human health and vegetation, hourly average concentration information was summarized for urban versus rural (forested and agricultural) areas in the United States. A land use characterization of "rural" does not imply that any specific location is isolated from anthropogenic influences. For example, Logan (1989) has noted that hourly average
O 3 concentrations above 0.08 ppm are common in rural areas of the eastern United States in spring and summer, but are unusual in remote western sites. Consequently, for the purposes of comparing exposure regimes that may be characteristic of clean locations in the United States with those that are urban influenced (i.e., located in either urban or rural locations), this chapter characterizes data collected from those stations whose locations appear to be isolated from large-scale anthropogenic influences. Long-term (multiyear) patterns and trends are available only from stationary ambient monitors; data on indoor concentrations are collected predominantly in selected settings during comparatively short-term studies. Data from the indoor and outdoor environments are reviewed here separately.



## 4.2 Trends in Ambient Ozone Concentrations

Ozone concentrations and, thus, exposure change from year to year. High
O 3 levels occurred in 1983 and 1988 in some areas of the United States. These levels more than likely were attributable, in part, to hot, dry, stagnant conditions. However, O 3 levels in 1992 were the lowest of the 1983 to 1992 period (U.S. Environmental Protection Agency, 1993). These low levels may have been due to meteorological conditions that were less
favorable for O 3 formation and to recently implemented control measures. Nationally, the summer of 1992 was the third coolest summer on record (U.S. Environmental Protection Agency, 1993). The U.S. Environmental Protection Agency (1993) has reported a 21% improvement in O 3 levels between 1983 and 1992, which, in part, may be attributed to relatively high O 3 levels in 1983, compared to the low O 3 exposure years from the period 1989 through 1992. However, new statistical techniques accounting for meteorological influences have been used by EPA and they appear to suggest an improvement (independent of meteorological considerations) of 10% for the 10-year period, 1983 to 1992 (U.S. Environmental Protection Agency, 1993). The EPA summarizes trends for the NAAQS for the most current 3- and 10-year periods. In order to be included in the 10-year trend analysis in the annual National Air Quality and Emissions Trend Report (U.S. Environmental Protection Agency, 1993), a

4-6station must report valid data for at least 8 of the last 10 years. A companion analysis of the most recent 3 years requires valid data in all 3 years. Analysis in the above report covers the periods 1983 to 1992 and 1990 to 1992, respectively; 509 sites met the 10-year period criteria, and 672 sites are included in the 1990 to 1992 database. The NAMS sites comprise 196 of the long-term trends sites and 222 of the sites in the 3-year database. Figure 4-1 displays the 10-year composite average trend for the second highest daily maximum hourly average concentration during the O 3 season for the 509 trend sites and the subset of 196 NAMS sites. The 1992 composite average for the 509 trend sites is 21% lower than the 1983 average and 20% lower for the subset of 196 NAMS sites. The 1992 value is the lowest composite average of the past 10 years (U.S. Environmental Protection Agency, 1993). The 1992 composite average is significantly less than all the previous nine years, 1983 to 1991. As discussed in U.S. Environmental Protection Agency (1992a), the relatively high O 3 concentrations in 1983 and 1988 likely were attributable in part to hot, dry stagnant conditions in some areas of the country that were especially conducive to
O 3 formation.

Figure 4-1. National trend in the composite average of the second highest maximum 1-h ozone concentration at both National Air Monitoring Stations (NAMS) and all sites with 95% confidence intervals, 1983 to 1992.

Source: U.S. Environmental Protection Agency (1993).

From 1991 to 1992, the composite mean of the second highest daily maximum 1-h
O 3 concentrations decreased 7% at the 672 sites and 6% at the subset of 222 NAMS sites.

4-7Also, from 1991 to 1992, the composite average of the number of estimated instances of
O 3 exceeding the standard decreased by 23% at the 672 sites, and by 19% at the 222 NAMS sites. Nationwide volatile organic compound (VOC) emissions decreased 3% from 1991 to 1992 (U.S. Environmental Protection Agency, 1993). The composite average of the second daily maximum concentrations decreased in 8 of the 10 EPA regions from 1991 to 1992, and remained unchanged in Region VII. Except for Region VII, the 1992 regional composite means are lower than the corresponding 1990 levels. Although meteorological conditions in the east during 1993 were more conducive to
O 3 formation than those in 1992, the composite mean level for 1993 was the second lowest composite average for the decade (1984 to 1993) (U.S. Environmental Protection Agency, 1994). Investigators have explored methods for investigating techniques for adjusting
O 3 trends for meteorological influences (Stoeckenius and Hudischewskyj, 1990; Wakim, 1990; Shively, 1991; Korsog and Wolff, 1991; Lloyd et al., 1989; Davidson, 1993; Cox and Chu, 1993). Stoeckenius and Hudischewskyj (1990) used a classification method to group days into categories according to the magnitude of O 3 and the similarity of meteorological conditions within each defined group. Adjusted O 3 statistics for each year were computed from the meteorologically grouped data, and the yearly frequency of occurrence of each group relative to its long-term frequency was described. Wakim (1990) used standard regression analysis to quantify the effect of daily meteorology on O 3 . Adjusted O 3 statistics were calculated by adding the expected O 3 statistic for a year with typical meteorology to the average of the regression residuals obtained for the adjusted year. Shively (1991) described a model in which the frequency of exceedance of various O 3 thresholds was modeled as a nonhomogeneous Poisson process where the parameter is a function of time and meteorological variables. Kolaz and Swinford (1990) categorized O 3 days as "conducive" or "nonconducive", based on selected meteorological conditions within the Chicago, IL, area. Within these categories, the meteorological intensity of days conducive to daily exceedances of the NAAQS for O 3 was calculated and used to establish long-term trends in the annual
exceedance rate. Cox and Chu (1993) modeled the daily maximum O 3 concentration using a Weibull distribution with fixed-shape and scale parameters, the logarithm of which varies as a linear function of several meteorological variables and a yearly index. The authors tested for a statistically significant trend term to determine if an underlying meteorologically adjusted trend could be detected. Overall, the measured and modeled predicted percentiles tracked closely in the northern latitudes but performed less adequately in southern coastal and desert areas. The results suggested that meteorologically adjusted upper percentiles of the distribution of daily maximum 1-h O 3 are decreasing in most urban areas over the period 1981 through 1991. The median rate of change was −1.1% per year, indicating that O 3 levels have decreased approximately 11% over this time period. The authors reported that trends estimated by ignoring the meteorological component appear to underestimate the rate of improvement in O 3 primarily because of the uneven year-to-year distribution of meteorological conditions favorable to O 3 formation. Lefohn et al. (1993a) focused on a potentially useful method for identifying monitoring sites whose improvement in the level of O 3 concentrations may be attributed more to the implementation of abatement control strategies than meteorological changes. As has been pointed out previously, meteorology plays an important role in affecting the
O 3 concentrations that are contained in the tail of the 1-h distributions, as indicated by the

4-8successful predictive application of the exponential-tail model to distributions (California Air Resources Board, 1992). Because meteorology plays such an important role in affecting the tail of the 1-h distribution at a specific site, changes in "attainment" status are not expected to affect changes in the entire distribution pattern and, thus, the average diurnal pattern. Lefohn et al. (1993b) investigated the change in the annual average diurnal pattern as changes in
O 3 levels occurred. The authors reported that, although the amplitude of the diurnal patterns changed, there was little evidence for consistent changes in the shape of the annual diurnal patterns (Figure 4-2). In a follow-up to this analysis, Lefohn et al. (1993a) reported that 25 of the 36 sites that changed compliance status across years showed no statistically significant change in the shape of the average diurnal profile (averaged by O 3 season). In addition, the authors reported that for 71% (10 of 14) of the sites in Southern California and Dallas-Fort Worth, TX, that showed improvement in O 3 levels (i.e., reductions in the number of exceedances over the years), but still remained in "nonattainment," a statistically significant change in the shape of the seasonally averaged diurnal profile occurred (e.g., Figure 4-3). Thus, the authors noted that, for the Southern California and Dallas-Fort Worth sites, changes were observed in the seasonally averaged diurnal profiles, whereas for the sites moving between attainment and nonattainment status, such a change in shape generally was not observed. Lefohn et al. (1993a) pointed out that it was possible that meteorology played a more important role in affecting attainment status than did changes in emission levels. Historically, the long-term O 3 trends in the United States characterized by EPA have emphasized air quality statistics that are closely related to the NAAQS. A report by the National Academy of Sciences (NAS) (National Research Council, 1991) stated that the principal measure currently used to assess O 3 trends is highly sensitive to meteorological fluctuations and is not a reliable measure of progress in reducing O 3 over several years for a given area. The NAS report recommended that "more statistically robust methods be developed to assist in tracking progress in reducing ozone." The NAS report also points out that most of the trends analyses are developed from violations of standards based on lower concentration cutoffs or using percentile distributions. Because of the interest by EPA in tracking trends in the quality of the air that people breathe when outdoors, most of the above measures have some association with the existing NAAQS, in the form of either threshold
violations or O 3 concentrations. Several of the alternative examples provided in the NAS report were described previously by Curran and Frank (1991). Several of the examples mentioned in the NAS report involved threshold violations: the number of days on which the maximum
O 3 concentration was above 0.12 ppm (Jones et al., 1989; Kolaz and Swinford, 1990; Wakim, 1990); the number of times during the year that the daily summary statistics exceeded 0.080 or 0.105 ppm (Stoeckenius, 1991), or the number of days in California when the
O 3 concentration exceeded 0.2 ppm (Zeldin et al., 1991). Several other O 3 concentration measures are described in this report. As an alternative to the way in which EPA historically has implemented its trends analysis, U.S. Environmental Protection Agency (1992a) used percentiles in the range of the 50th percentile (or median) to the 95th percentile. The U.S. Environmental Protection Agency (1992a) reported that the pattern for the 10-year trends (1982 to 1991), using the
various alternative O 3 summary statistics, were somewhat similar. There was a tendency for the curves to become flatter in the lower percentiles. The peak years of 1983 and 1988 were
still evident in the trend lines for each indicator. The increase of 8% recorded in the annual

4-9Figure 4-2. The annually averaged composite diurnal curves for the following sites that changed from nonattainment to attainment status: (a) Montgomery County, AL; (b) Concord, CA; (c) Louisville, KY; and (d) Dade County, FL; for the period 1987 to 1990. The darkened curve in each figure identified the year in which the greatest number of daily maximum 4-h average concentrations

≥ 0.08 ppm occurred.

Source: Lefohn et al. (1993b).

second-highest daily maximum 1-h concentration from 1987 to 1988 also was seen in the 95th and 90th percentile concentrations. The lower percentile indicators had smaller increases of 3 to 4%. The percent change between 1982 and 1991 for each of the summary statistics follows: annual daily maximum 1-h concentration, −11%; annual second daily maximum 1-h concentration, −8%; 95th percentile of the daily maximum 1-h concentrations, −5%; 90th percentile, −4%; 70th percentile, −1%; 50th percentile, or median of the daily maximum 1-h concentrations, +1%; and the annual mean of the daily maximum 1-h concentrations, −1%. Besides EPA, additional investigators have assessed trends at several locations in the United States (e.g., Kuntasai and Chang, 1987; Gallopoulos et al., 1988; Korsog and Wolff, 1991; Lloyd et al., 1989; Rao et al., 1992; Davidson, 1993). For example, Kuntasai and Chang (1987) performed a basin-wide air quality trend analysis for the South Coast Air

4-10Figure 4-3. A summary of the (a) seasonal (January to December) averaged composite ozone diurnal curve and (b) integrated exposure W126 index for the Los Angeles, CA, site for the period 1980 to 1991.

Source: Lefohn et al. (1993a)

.

Basin of California using multistation composite daily maximum 1-h average ambient concentrations for the third quarter from 1968 to 1985. Basin-wide ambient O 3 concentrations appeared to show downward trends for the period 1970 to 1985, but because of high fluctuations, it was difficult to delineate trends for shorter periods. The meteorology-adjusted
O 3 showed a more consistent downward trend than did unadjusted O 3 . Korsog and Wolff (1991) examined trends from 1973 to 1983 at eight major population centers in the northeastern United States, using a robust statistical method. The 75th percentile was used by the authors in determining trends. The data were collected over a 3-mo (June through August) period. The surface temperature and upper air temperature variables were found to be the best predictors of O 3 behavior. Two regression procedures were performed to remove the variability of meteorological conditions conducive to high O 3 (i.e., O 3 concentrations >0.08 ppm). The results of the analysis showed that there had been a decrease of a few ppb on a yearly basis for the majority of the sites investigated by the authors. Lloyd et al. (1989) investigated the improvement in O 3 air quality from 1976 to 1987 in the South Coast Air Basin. The authors reported that when the trend in total exceedance hours of a consistent set of basin air monitoring stations was considered, the improvement over the period of investigation was substantial. The authors reported that the number of station hours at or above the Stage I Episode Level (0.2 ppm, 1-h average) had decreased by about two-thirds over the period 1976 to 1987. Davidson (1993) reported on the number of days on which O 3 concentrations at one or more stations in the South Coast Air Basin exceeded the federal standard and the number of days reaching Stage I episode levels, for the months of May through October in the years 1976 to 1991. The author reported that the number of basin days exceeding the federal standard declined at an average annual rate of

4-112.27 days/year over the period. In addition, the number of basin days with Stage I episodes declined at an average annual rate of 4.70 days/year over the period 1976 to 1991. Rao et al. (1992) demonstrated the use of some statistical methods for examining trends in ambient
O 3 air quality downwind of major urban areas. The authors examined daily maximum 1-h
O 3 concentrations measured over New Jersey, metropolitan New York City, and Connecticut for the period 1980 to 1989. The analyses indicated that although there has been an improvement in O 3 air quality downwind of New York City, there has been little change in
O 3 levels upwind of New York City during this 10-year period. Lefohn and Runeckles (1987) proposed a sigmoidal weighting function that was used in developing a cumulative integrated exposure index (W126):

(4-1)

where: w i = weighting factor for concentration i, M and A are positive arbitrary constants, and

c i = concentration i.

Lefohn et al. (1988b) reported the use of the sigmoidally weighted index with constants, M and A, 4,403 and 126 ppm -1 , respectively. The authors referred to the index as W126. The values were subjectively determined to develop a weighting function that (1) included hourly average concentrations as low as 0.04 ppm, (2) had an inflection point near 0.065 ppm, and (3) had an equal weighting of one for hourly average concentrations at approximately 0.10 ppm and above. To determine the value of the index, the sigmoidal weighting function at c i was multiplied by the hourly average concentration, c i , and summed over all relevant hours. The index included the lower, less biologically effective concentrations in the integrated exposure summation. The weighting function has been used to describe the relationship between O 3 exposure and vegetation response (e.g., Lefohn et al., 1988b, 1992a). Lefohn and Shadwick (1991), using the W126 sigmoidally weighted exposure index, assessed trends in O 3 exposures at rural sites in the United States over 5- and 10-year periods (1984 to 1988 and 1979 to 1988, respectively) for forestry and agricultural regions of the United States. Although the statistical analysis did not explore the effects on trends of the
lower O 3 exposure period 1989 to 1992, the analysis did reflect the effect of the higher
O 3 exposure years (1983 and 1988). The hot, dry summer of 1988 was associated with the highest O 3 exposures in both the forest and agricultural regions of the eastern United States. To compare the exposure index values across years, a correction for missing data was applied for each pollutant. The corrections were determined for each site on a monthly basis. The Kendall’s K statistic (Mann-Kendall test) was used to identify linear trends. Estimates of the rate of change (slope) for the index were calculated. Table 4-2 summarizes the results of the analysis. For sites distributed by forestry regions, there were more positive than negative slope estimates for the 5-year analysis of sites in the southern, midwestern, and Mid-Atlantic regions. For the 10-year analysis, the above was true except for the Mid-Atlantic seasonal analysis, where there was one positive and one negative significant trend. In the southern region, 38% of the sites showed significant trends. For the sites in Table 4-2. Summary by Forestry and Agricultural Regions for

4-12Ozone Trends Using the W126 Exposure Parameter Accumulated on a Seasonal Basis a

Forestry

5-Year Trends 10-Year Trends

Significant Significant
Region Not Significant b

− + Not Significant

− +

South 53 (16) 0 14 13 1 7

Midwest 38 (1) 0 7 20 1 6

West 10 (0) 0 3 4 2 1

Pacific

Northwest 4 (2) 0 0 2 0 0

Plains 3 (0) 0 0 2 0 0

Northeast 14 (0) 1 0 7 1 1

Mid-Atlantic 12 (0) 0 3 4 1 1
Rocky

Mountains 5 (2) 0 1 2 0 1

All 139 (21) 1 28 54 6 17

Agricultural

5-Year Trends 10-Year Trends

Significant Significant
Region Not Significant b

− + Not Significant

− +

Pacific 14 (2) 0 3 6 2 1

Mountain 5 (2) 0 1 2 0 1

Northern

Plains 3 (0) 0 0 2 0 0

Lake States 10 (0) 0 1 5 0 1

Corn Belt 20 (1) 0 3 11 1 2

Northeast 26 (0) 1 3 11 2 2
Appalachian 27 (9) 0 14 8 0 8

Southeast 16 (5) 0 1 4 1 0

Delta State 9 (0) 0 2 4 0 1

Southeastern

Plains 9 (2) 0 0 1 0 1

All 139 (21) 1 28 54 6 17

a See Appendix A for abbreviations and acronyms.

b Numbers in parentheses in the "Not Significant" column under "5-Year Trends" are the number of sites with exactly 3 years of data.

Source: Lefohn and Shadwick (1991).

4-13the northeastern region, few sites showed a significant trend. There were considerably fewer sites in the remaining regions than in the four forestry regions above. Hence, for these regions, no significance was assigned to the differences in the number of negative and positive slope estimates in the tables. Similar to the results reported for the forestry regions, most of the sites in the agricultural regions showed no O 3 trends. However, in the Appalachian agricultural region, as many as 50% of the sites showed a pronounced indication of a trend. A predominance of positive significant trends for both the 5- and 10-year analyses was observed. In the other agricultural regions, there were approximately an equal number of positive and negative significant 5- and 10-year trends. The O 3 results produced patterns that were not pronounced enough to draw more than tentative conclusions for the 10-year analysis. For the 5-year analysis, there was still not a strong indication of an O 3 trend. However, when significant trends were observed, they were almost always positive. This can be attributed to
eastern O 3 levels that were generally higher in 1988 than in previous years.



## 4.3 Surface Ozone Concentrations

4.3.1 Introduction Ozone is measured at levels above the minimum detectable level at all monitoring locations in the world (Lefohn et al., 1990a). As dicussed earlier in Chapter 3, the concept of a "natural" background of O 3 is complex. Concentrations of background O 3 can vary with temperature, wind speed and direction, vertical motion, geographic location including latitude and altitude, and season of the year. This background O 3 can be attributed the following sources: (1) downward transport of stratospheric O 3 through the free troposphere to near ground level; (2) in situ O 3 production from methane emitted from swamps and wetlands reacting with natural NO

x emitted from soils, lighting strikes, and from downward transport of NO from the stratosphere into the troposphere; and (3) in situ production of O 3 from the reactions of biogenic VOCs with natural NO

x (National Research Council, 1991). A fourth
source to be considered is the O 3 production resulting from long range transport of O 3 from distant pollutant sources (see Chapter 3). The occasional occurrence of stratospheric injection of O 3 , at specific times and in certain locations, is accepted and may be responsible for some of the rare occurrences of elevated levels that have been observed at some high- and low-elevation remote sites. A summer season average contribution of approximately 5 to 10 ppb for surface-level
O 3 concentration from stratospheric intrusion has been estimated (Altshuller, 1989). For purposes of comparing how O 3 levels have changed over time, it would be interesting to know how current levels compare to previous, historical natural background levels. However, estimations of background O 3 concentrations are difficult to make. The definition of background and the use of O 3 measurements are subject to much uncertainty. It is difficult, if not impossible, to determine whether any geographic location on earth is free from human influence (Finlayson-Pitts and Pitts, 1986). The natural precursor emissions can be responsible for the production of the O 3 concentrations observed at remote sites (Chameides et al., 1988; Zimmerman, 1979; Trainer et al., 1987). Citing indirect evidence for the possible importance of natural emissions, Lindsay et al. (1989) have emphasized that additional research is required to assess the role that natural hydrocarbons might play in urban and regional O 3 episodes.

4-14It is possible for urban emissions, as well as O 3 produced from urban area emissions, to be transported to more rural downwind locations. This can result in elevated
O 3 concentrations at considerable distances from urban centers (Wolff et al., 1977; Husar et al., 1977; Wight et al., 1978; Vukovich et al., 1977; Wolff and Lioy, 1980; Pratt et al. 1983; Logan, 1985; Altshuller, 1986; U.S. Environmental Protection Agency, 1986; Kelly et al., 1986; Pinkerton and Lefohn, 1986; Lefohn et al., 1987a; Logan, 1989; Lefohn and Lucier, 1991; Taylor and Hanson, 1992). For example, on over 40% of the 98 days that the
maximum 1-h O 3 concentrations exceeded 0.12 ppm, the highest value was measured downwind of St. Louis at one of the rural sites, which was located approximately 50 km from downtown St. Louis (Altshuller, 1986). Urban O 3 concentration values often are depressed because of titration by NO

x (Stasiuk and Coffey, 1974). Reagan (1984) and Lefohn et al. (1987a) have observed this phenomenon where O 3 concentrations at center-city sites were lower than some rural locations. Because of the absence of chemical scavenging, O 3 tends to persist longer in nonurban than in urban areas (U.S. Environmental Protection Agency, 1986; Coffey et al., 1977; Wolff et al., 1977; Isaksen et al., 1978).
The distribution of O 3 or its precursors at a rural site near an urban source is affected by wind direction (i.e., whether the rural site is located up- or downwind from the source) (Kelly et al., 1986; Lindsay and Chameides, 1988). Thus, it may be difficult to apply land-use designations to the generalization of exposure regimes that may be experienced in urban versus rural areas. Because of this, it is difficult to identify a set of unique
O 3 distribution patterns that adequately describe the hourly average concentrations experienced at monitoring sites in rural locations (Lefohn et al., 1991).



### 4.3.2 Urban Area Concentrations Figure 4-4 shows the highest second daily maximum 1-h average O 3 concentrations in 1991 across the United States. The highest second daily maximum 1-h O 3 concentrations by MSA for the years 1989 to 1991 are summarized in Table 4-3. The highest
O 3 concentrations are observed in Southern California, but high levels of O 3 also occur in the Texas Gulf Coast, the Northeast Corridor, and other heavily populated regions of the United States, but with a much lower frequency. Lefohn (1992a) reported that, for many urban sites that experience high second daily maximum 1-h average values (i.e, >0.125 ppm), most are associated with only a few episodes. Monitoring sites in polluted regions tend to experience frequent hourly average
O 3 concentrations at or near minimum detectable levels. The percentile summary information for some of these sites shows that, although some of the highest hourly average concentrations occur at these locations, their occurrence is infrequent (Table 4-4). For example, O 3 monitoring sites at Delmar, CA; Stratford and Madison, CT; Baton Rouge, LA; Bayonne, NJ; New York City and Babylon, NY; Harris County, TX; and Bayside, WI, exhibit maximum hourly average concentrations above 0.125 ppm; however, only 1% of the hourly average concentrations generally exceed 0.100 ppm. Although for human health considerations, the occurrence of a second daily maximum hourly average concentration >0.125 ppm is important, Table 4-4 illustrates that, for most of the sites listed (except for several sites in California), such high hourly average concentrations occur less than 1% of the time and are associated with occasional episodes. As indicated in Section 4.1, interest has been expressed in characterizing
O 3 exposure regimes for sites experiencing daily maximum 8-h concentrations above specific

4-15Figure 4-4. United States map of the highest second daily maximum 1-h average ozone concentration by Metropolitan Statistical Area, 1991.

Source: U.S. Environmental Protection Agency (1992a).

thresholds (e.g., 0.08 or 0.10 ppm). Table 4-5 summarizes the highest second daily maximum 8-h average O 3 concentrations by MSA for the years 1989 to 1991. The data have been reported for the O 3 season as summarized in Table 4-1. In some cases, high concentrations occur in the fall and winter periods as well as in the summertime. Analyses documented the occurrence, at some sites, of multihour periods within a day of O 3 at levels of potential health effects. Although most of these analyses were made using monitoring data collected from sites in or near nonattainment areas, the analysis of Berglund et al. (1988) showed that at five sites, two in New York state, two in rural California, and one in rural Oklahoma, an
alternative O 3 standard of an 8-h average of 0.10 ppm would be exceeded even though the existing 1-h standard would not be. Berglund et al. (1988) described the occurrence at these five sites (none of which was in or near a nonattainment area) of O 3 concentrations showing only moderate peaks but exhibiting multihour levels above 0.10 ppm. Lefohn et al. (1993b) identified those areas in the United States for the period 1987 to 1989 where more than one occurrence of an 8-h daily maximum average concentration of 0.08 ppm was experienced, but an hourly average concentration equal to or greater than 0.12 ppm never occurred. A follow-up to the points made above is whether an improvement in O 3 levels may produce distributions of 1-h O 3 that result in a broader diurnal profile than those seen in

4-16Table 4-3. The Highest Second Daily Maximum One-Hour Ozone Concentration (ppm) by Metropolitan Statistical Area (MSA) for the Years 1989 to 1991

MSA 1989 1990 1991 MSA 1989 1990 1991

Akron, OH 0.14 0.11 0.13 Decatur, IL 0.09 0.09 0.10 Albany-Schenectady-Troy, NY 0.10 0.11 0.10 Denver, CO 0.11 0.11 0.11 Albuquerque, NM 0.10 0.10 0.09 Des Moines, IA 0.08 0.07 0.07 Allentown-Bethlehem, PA-NJ 0.10 0.11 0.12 Detroit, MI 0.14 0.12 0.13 Altoona, PA 0.10 0.10 0.11 Duluth, MN-WI 0.06 Anaheim-Santa Ana, CA 0.24 0.21 0.20 Eau Claire, WI 0.06 Anderson, IN 0.10 El Paso, TX 0.14 0.14 0.13 Anderson, SC 0.09 Elmira, NY 0.09 0.10 0.10 Ann Arbor, MI 0.10 0.09 0.11 Erie, PA 0.12 0.10 0.11 Appleton-Oshkosh-Neenah, WI 0.10 0.08 0.09 Eugene-Springfield, OR 0.08 0.09 0.09 Asheville, NC 0.08 0.09 0.08 Evansville, IN-KY 0.12 0.11 0.12 Atlanta, GA 0.12 0.15 0.13 Fayetteville, NC 0.11 0.10 0.10 Atlantic City, NJ 0.12 0.16 0.14 Flint, MI 0.10 0.10 0.10 Augusta, GA-SC 0.10 0.11 0.10 Fort Collins, CO 0.09 0.10 0.09 Aurora-Elgin, IL 0.11 0.09 0.13 Ft. Lauderdale-Hollywood-Pompano, FL 0.12 0.10 0.10 Austin, TX 0.11 0.11 0.10 Fort Myers-Cape Coral, FL 0.10 0.08 0.08 Bakersfield, CA 0.16 0.16 0.16 Fort Wayne, IN 0.12 0.09 0.10 Baltimore, MD 0.13 0.14 0.16 Fort Worth-Arlington, TX 0.13 0.14 0.15 Baton Rouge, LA 0.16 0.18 0.14 Fresno, CA 0.15 0.15 0.16 Beaumont-Port Arthur, TX 0.15 0.15 0.13 Galveston-Texas City, TX 0.14 0.15 0.15 Beaver County, PA 0.10 0.10 0.11 Gary-Hammond, IN 0.11 0.12 0.12 Bellingham, WA 0.05 0.08 0.07 Grand Rapids, MI 0.13 0.14 0.15 Benton Harbor, MI 0.12 Greeley, CO 0.10 0.11 0.10 Bergen-Passaic, NJ 0.12 0.13 0.14 Green Bay, WI 0.09 0.09 0.10 Billings, MT 0.08 Greensboro-Winston Salem-High Point, NC 0.10 0.12 0.11 Birmingham, AL 0.12 0.13 0.11 Greenville-Spartanburg, SC 0.10 0.11 0.11 Boston, MA 0.12 0.11 0.13 Hamilton-Middletown, OH 0.11 0.13 0.12 Boulder-Longmont, CO 0.11 0.10 0.10 Harrisburg-Lebanon-Carlisle, PA 0.11 0.12 0.11 Bradenton, FL 0.10 0.10 0.10 Hartford, CT 0.14 0.15 0.15 Brazoria, TX 0.15 0.13 Hickory, NC 0.09 Bridgeport-Milford, CT 0.18 0.16 0.15 Honolulu, HI 0.05 0.05 0.05 Brockton, MA 0.13 0.12 0.15 Houma-Thibodaux, LA 0.11 0.12 0.10 Buffalo, NY 0.11 0.11 0.11 Houston, TX 0.23 0.22 0.20 Canton, OH 0.12 0.11 0.12 Huntington-Ashland, WV-KY-OH 0.12 0.14 0.14 Cedar Rapids, IA 0.08 0.07 0.08 Huntsville, AL 0.09 0.09 0.11 Champaign-Urbana-Rantoul, IL 0.09 0.09 0.08 Indianapolis, IN 0.12 0.11 0.11 Charleston, SC 0.09 0.10 0.09 Iowa City, IA 0.09 0.09 0.06 Charleston, WV 0.10 0.12 0.12 Jackson, MS 0.09 0.10 0.09 Charlotte-Gastonia-Rock Hill, NC-SC 0.13 0.12 0.12 Jacksonville, FL 0.11 0.11 0.10 Chattanooga, TN-GA 0.11 0.12 0.10 Jamestown-Dunkirk, NY 0.08 0.10 Chicago, IL 0.12 0.11 0.13 Janesville-Beloit, WI 0.12 0.09 0.11 Chico, CA 0.10 0.12 0.09 Jersey City, NJ 0.12 0.18 0.14 Cincinnati, OH-KY-IN 0.12 0.15 0.14 Johnson City-Kingsport-Bristol, TN-WV 0.11 0.12 0.12 Cleveland, OH 0.12 0.12 0.13 Johnstown, PA 0.10 0.10 0.11 Colorado Springs, CO 0.09 0.09 0.09 Joliet, IL 0.10 0.09 0.12 Columbia, SC 0.10 0.11 0.11 Kalamazoo, MI 0.08 Columbus, GA-AL 0.09 0.11 0.10 Kansas City, MO-KS 0.11 0.11 0.12 Columbus, OH 0.11 0.11 0.12 Kenosha, WI 0.13 0.11 0.15 Corpus Christi, TX 0.10 0.10 0.11 Knoxville, TN 0.10 0.12 0.11 Cumberland, MD-WV 0.09 0.10 Lafayette, LA 0.10 0.11 0.08 Dallas, TX 0.13 0.14 0.12 Lafayette, IN 0.09 0.10 Danbury, CT 0.13 0.15 0.14 Lake Charles, LA 0.13 0.13 0.12 Davenport-Rock Island-Moline, IA-IL 0.11 0.10 0.10 Lake County, IL 0.13 0.10 0.12 Dayton-Springfield, OH 0.15 0.12 0.12 Lancaster, PA 0.10 0.10 0.12

4-17Table 4-3 (cont’d). The Highest Second Daily Maximum One-Hour Ozone Concentration (ppm) by Metropolitan Statistical Area (MSA) for the Years 1989 to 1991

MSA 1989 1990 1991 MSA 1989 1990 1991
Lansing-East Lansing, MI 0.10 0.10 0.11 Portland, OR-WA 0.09 0.15 0.11 Las Cruces, NM 0.11 0.10 0.10 Portsmouth-Dover-Rochester, NH-ME 0.11 0.10 0.13 Las Vegas, NV 0.11 0.11 0.09 Poughkeepsie, NY 0.08 0.12 0.13 Lawrence-Haverhill, MA-NH 0.12 0.10 0.13 Providence, RI 0.13 0.14 0.16 Lexington-Fayette, KY 0.11 0.11 0.10 Provo-Orem, UT 0.11 0.09 0.08 Lima, OH 0.10 0.10 0.10 Racine, WI 0.14 0.11 0.14 Lincoln, NE 0.06 0.07 0.07 Raleigh-Durham, NC 0.11 0.12 0.11 Little Rock-North Little Rock, AR 0.09 0.10 0.10 Reading, PA 0.11 0.11 0.12 Longview-Marshall, TX 0.10 0.13 0.11 Redding, CA 0.09 0.09 0.08 Lorain-Elyria, OH 0.12 0.09 0.10 Reno, NV 0.10 0.14 0.09 Los Angeles-Long Beach, CA 0.33 0.27 0.31 Richmond-Petersburg, VA 0.11 0.12 0.12 Louisville, KY-IN 0.11 0.13 0.13 Riverside-San Bernardino, CA 0.28 0.30 0.25 Lynchburg, VA 0.10 0.09 Roanoke, VA 0.10 0.09 0.10 Madison, WI 0.10 0.08 0.11 Rochester, NY 0.11 0.11 0.11 Manchester, NH 0.10 0.10 0.10 Rockford, IL 0.10 0.09 0.09 Medford, OR 0.09 0.10 0.07 Sacramento, CA 0.14 0.16 0.16 Melbourne-Titusville-Palm Bay, FL 0.10 0.09 0.09 St. Louis, MO-IL 0.13 0.13 0.12 Memphis, TN-AR-MS 0.12 0.12 0.11 Salinas-Seaside-Monterey, CA 0.11 0.09 0.09 Miami-Hialeah, FL 0.12 0.11 0.12 Salt Lake City-Ogden, UT 0.15 0.12 0.11 Middlesex-Somerset-Hunterdon, NJ 0.13 0.15 0.13 San Antonio, TX 0.11 0.10 0.11 Middletown, CT 0.17 0.16 0.17 San Diego, CA 0.19 0.17 0.18 Milwaukee, WI 0.15 0.13 0.18 San Francisco, CA 0.09 0.06 0.07 Minneapolis-St. Paul, MN-WI 0.10 0.10 0.09 San Jose, CA 0.13 0.12 0.12 Mobile, AL 0.10 0.11 0.09 San Juan, PR 0.06 0.07 0.08 Modesto, CA 0.13 0.12 0.11 Santa Barbara-Santa Maria-Lompoc, CA 0.16 0.13 0.10 Monmouth-Ocean, NJ 0.14 0.14 0.15 Santa Cruz, CA 0.08 0.08 0.10 Montgomery, AL 0.08 0.10 0.09 Santa Fe, NM 0.05 0.08 0.08 Muskegon, MI 0.14 0.13 0.15 Santa Rosa-Petaluma, CA 0.10 0.08 0.10 Nashua, NH 0.09 0.10 0.11 Sarasota, FL 0.10 0.10 0.10 Nashville, TN 0.14 0.13 0.12 Scranton-Wilkes-Barre, PA 0.11 0.11 0.13 Nassau-Suffolk, NY 0.15 0.14 0.18 Seattle, WA 0.09 0.13 0.11 New Bedford, MA 0.12 0.13 0.13 Sharon, PA 0.11 0.10 0.11 New Haven-Meriden, CT 0.15 0.16 0.18 Sheboygan, WI 0.11 0.11 0.16 New London-Norwich, CT-RI 0.14 0.16 0.14 Shreveport, LA 0.12 0.12 0.11 New Orleans, LA 0.11 0.11 0.11 South Bend-Mishawaka, IN 0.10 0.10 0.11 New York, NY 0.13 0.16 0.18 Spokane, WA 0.07 0.08 Newark, NJ 0.13 0.13 0.14 Springfield, IL 0.11 0.10 0.10 Niagara Falls, NY 0.10 0.10 0.10 Springfield, MO 0.09 0.08 0.08 Norfolk-Virginia Beach-Newport News, VA 0.10 0.11 0.11 Springfield, MA 0.13 0.12 0.13 Oakland, CA 0.13 0.12 0.12 Stamford, CT 0.16 0.14 0.15 Oklahoma City, OK 0.11 0.11 0.11 Steubenville-Weirton, OH-WV 0.11 0.09 0.12 Omaha, NE-IA 0.10 0.08 0.08 Stockton, CA 0.11 0.12 0.11 Orlando, FL 0.11 0.12 0.10 Syracuse, NY 0.10 0.11 0.11 Owensboro, KY 0.10 0.11 0.09 Tacoma, WA 0.09 0.13 0.09 Oxnard-Ventura, CA 0.17 0.15 0.16 Tallahassee, FL 0.07 0.05 Parkerburg-Marietta, WV-OH 0.12 0.11 0.12 Tampa-St. Petersburg-Clearwater, FL 0.10 0.11 0.11 Pascogoula, MS 0.10 0.11 0.10 Terre Haute, IN 0.11 0.11 0.10 Pensacola, FL 0.09 0.12 0.11 Toledo, OH 0.11 0.10 0.12 Peoria, IL 0.11 0.09 0.10 Trenton, NJ 0.14 0.14 0.15 Philadelphia, PA-NJ 0.16 0.14 0.16 Tucson, AZ 0.10 0.10 0.09 Phoenix, AZ 0.11 0.14 0.12 Tulsa, OK 0.12 0.12 0.12 Pittsburgh, PA 0.13 0.11 0.12 Utica-Rome, NY 0.09 0.10 0.10 Pittsfield, MA 0.09 0.11 0.10 Vallejo-Fairfield-Napa, CA 0.11 0.10 0.11 Portland, ME 0.13 0.13 0.14 Vancouver, WA 0.09 0.11 0.10

4-18Table 4-3 (cont’d). The Highest Second Daily Maximum One-Hour Ozone Concentration (ppm) by Metropolitan Statistical Area (MSA) for the Years 1989 to 1991

MSA 1989 1990 1991 MSA 1989 1990 1991
Victoria, TX 0.10 0.07 0.10 Wilmington, DE-NJ-MD 0.13 0.14 0.15 Vineland-Millville-Bridgeton, NJ 0.13 0.13 0.12 Wilmington, NC 0.09 Visalia-Tulare-Porterville, CA 0.15 0.14 0.12 Worcester, MA 0.10 0.12 0.14 Washington, DC-MD-VA 0.13 0.13 0.14 York, PA 0.10 0.12 0.11 W. Palm Beach-Boca Raton-Delray, FL 0.11 0.09 0.09 Youngstown-Warren, OH 0.11 0.10 0.12 Wheeling, WV-OH 0.11 0.11 0.11 Yuba City, CA 0.01 0.09 0.10 Wichita, KS 0.09 0.10 0.10 Yuma, AZ 0.09 0.09 Williamsport, PA 0.08 0.09 0.10

high-oxidant urban areas where O 3 regimes contain hourly average concentrations with sharper peaks. The result would be an increase in the number of exceedances of daily maximum 8-h average concentrations ≥ 0.08 ppm, when compared to those sites experiencing sharper peaks. Lefohn et al. (1993b), using aerometric data at specific sites, observed how
O 3 concentrations change when the sites change compliance status. One of the parameters examined was 4-h daily maxima. The number of exceedances for a specific daily maximum average concentration tended to decrease as fewer exceedances of the current 1-h standard were observed at a given site. The number of occurrences of the daily maximum 4-h average
concentration ≥ 0.08 ppm and the number of exceedances of the current form of the standard had a positive, weak correlation (r = 0.51). Lefohn et al. (1993a,b) reported few changes in the shape of the average diurnal patterns as sites changed attainment status; this may have explained why Lefohn et al. (1993b) could not find evidence that the number of occurrences of the daily maximum 4-h average concentration ≥ 0.08 ppm increased when the sites experienced few high hourly average concentrations. There has been considerable interest in possibly substituting one index for another when attempting to relate O 3 exposure with an effect. For example, using O 3 ambient air quality data, McCurdy (1988) compared the number of exceedances of 0.12 ppm and the number of occurrences of the daily maximum 8-h average concentrations ≥ 0.08 ppm and reported that a positive correlation (r = 0.79) existed between the second-highest 1-h daily maximum in a year and the expected number of days with an 8-h daily maximum average concentration >0.08 ppm O 3 . In this case, the predictive strength of using one O 3 exposure index to predict another is not strong. Similar to analysis performed by McCurdy (1988), all of the hourly averaged data from rural agricultural and forested sites in the AIRS database were summarized into maximum 3-mo SUM06, second highest daily maximum hourly average concentration, and second highest daily maximum 8-h average concentration exposure indices per year for the period 1980 to 1991. For the rural agricultural sites, the correlation coefficients between the 3-mo SUM06 and the second highest daily maximum hourly average concentration and the second highest daily maximum 8-h average concentration were 0.650 and 0.739, respectively (Figure 4-5). For the rural forested sites, the correlation coefficients between the 3-mo SUM06 and the second highest daily maximum hourly average concentration and the second highest daily maximum 8-h average concentration were 0.585 and 0.683, respectively (Figure 4-6).

4-19Table 4-4. Summary of Percentiles of Hourly Average Concentrations (ppm)for the April-to-October PeriodaAIRS SiteNameYearMin.10305070909599MaxNumber of Observations060370016Glendora, CA1989199019910.0000.0000.0000.0000.0000.0000.0200.0100.0100.0300.0300.0200.0600.0500.0500.1200.1100.1000.1500.1400.1400.2200.2000.2000.3400.2900.3204,8744,8884,907060595001 La Habra, CA 1989199019910.0000.0000.0000.0000.0000.0000.0100.0100.0100.0300.0200.0200.0400.0400.0400.0700.0700.0700.0900.0900.0900.1400.1400.1300.2600.2100.2104,8754,8874,899060710005 San Bernardino County, CA 1989199019910.0000.0000.0000.0200.0200.0200.0500.0400.0400.0600.0600.0600.0900.0800.0800.1400.1200.1200.1600.1500.1400.2000.1800.1900.2700.3300.2704,8714,8994,905060731001 Del Mar, CA 1989199019910.0000.0000.0000.0200.0200.0200.0400.0300.0400.0400.0400.0500.0500.0500.0500.0700.0600.0600.0800.0700.0700.1200.1000.1000.2500.1700.1504,8145,0605,017090013007 Stratford, CT 1989199019910.0010.0010.0000.0080.0100.0070.0240.0230.0190.0360.0330.0300.0460.0440.0420.0640.0590.0600.0770.0680.0740.1150.1000.1100.2020.1760.1574,6733,8534,794090093002 Madison, CT 1989199019910.0010.0000.0000.0080.0080.0070.0220.0230.0230.0330.0330.0340.0430.0430.0450.0590.0630.0650.0700.0750.0820.1030.1070.1230.1490.1970.1934,2724,4774,814220330003 Baton Rouge, LA 1989199019910.0000.0000.0000.0010.0000.0020.0090.0110.0100.0210.0230.0200.0340.0380.0310.0590.0630.0540.0690.0790.0670.0940.1090.0920.1680.1870.1344,9645,0004,905340170006 Bayonne, NJ 1989199019910.0010.0010.0010.0010.0010.0020.0080.0090.0110.0210.0220.0240.0360.0360.0380.0590.0580.0650.0740.0730.0820.0990.1060.1100.1470.1850.1674,8154,9394,943360610063 New York, NY 1989199019910.0000.0000.0020.0150.0140.0150.0280.0290.0290.0400.0390.0410.0510.0510.0560.0730.0740.0820.0860.0900.0960.1100.1160.1230.1340.1750.1774,8254,7074,910361030002Babylon, NY1989199019910.0010.0000.0010.0040.0060.0050.0150.0170.0180.0270.0270.0300.0390.0400.0440.0600.0600.0670.0730.0750.0810.1010.1050.1110.1560.1460.2174,4074,8764,873

4-20Table 4-4 (cont’d). Summary of Percentiles of Hourly Average Concentrations (ppm)for the April-to-October PeriodaAIRS SiteNameYearMin.10305070909599MaxNumber of Observations482010024Harris County, TX1989199019910.0000.0000.0000.0000.0000.0000.0100.0100.0000.0200.0200.0200.0300.0400.0300.0600.0700.0600.0700.0900.0800.1100.1300.1100.2300.2200.1704,7284,2744,322550790085Bayside, WI1989199019910.0020.0020.0020.0060.0090.0080.0240.0250.0250.0350.0340.0350.0460.0440.0470.0660.0610.0700.0770.0710.0810.1010.0940.1130.1510.1300.1894,3764,3954,303aSee Appendix A for abbreviations and acronyms.

4-21Table 4-5. The Highest Second Daily Maximum Eight-Hour Average Ozone Concentration (ppm) by Metropolitan Statistical Area (MSA) for the Years 1989 to 1991

MSA 1989 1990 1991 MSA 1989 1990 1991
Akron, OH 0.109 0.097 0.102 Davenport-Rock Island-Moline, IA-IL 0.102 0.071 0.086 Albany-Schenectady-Troy, NY 0.087 0.091 0.089 Dayton-Springfield, OH 0.122 0.096 0.107 Albuquerque, NM 0.078 0.089 0.077 Decatur, IL 0.084 0.077 0.087 Alexandria, LA 0.077 0.076 0.074 Denver, CO 0.089 0.086 0.080 Allentown-Bethlehem, PA-NJ 0.091 0.098 0.112 Des Moines, IA 0.073 0.051 0.056 Altoona, PA 0.077 0.090 0.094 Detroit, MI 0.103 0.091 0.111 Anaheim-Santa Ana, CA 0.146 0.135 0.110 Duluth, MN-WI 0.073 0.051 Anderson, IN 0.084 Eau Claire, WI 0.049 Anderson, SC 0.081 El Paso, TX 0.083 0.087 0.080 Ann Arbor, MI 0.093 0.087 0.096 Elmira, NY 0.075 0.080 0.094 Appleton-Oshkosh-Neenah, WI 0.091 0.078 0.082 Erie, PA 0.092 0.088 0.093 Asheville, NC 0.083 0.074 0.064 Eugene-Springfield, OR 0.061 0.077 0.070 Atlanta, GA 0.096 0.125 0.102 Evansville, IN-KY 0.097 0.094 0.107 Atlantic City, NJ 0.104 0.135 0.112 Fayetteville, NC 0.089 0.088 0.085 Augusta, GA-SC 0.078 0.092 0.081 Flint, MI 0.093 0.086 0.090 Aurora-Elgin, IL 0.088 0.077 0.100 Fort Collins, CO 0.076 0.076 0.077 Austin, TX 0.099 0.103 0.084 Ft. Lauderdale-Hollywood-Pompano, FL 0.089 0.078 0.064 Bakersfield, CA 0.124 0.120 0.118 Fort Myers-Cape Coral, FL 0.084 0.070 0.062 Baltimore, MD 0.103 0.111 0.127 Fort Wayne, IN 0.105 0.091 0.096 Baton Rouge, LA 0.095 0.134 0.100 Fort Worth-Arlington, TX 0.098 0.116 0.116 Beaumont-Port Arthur, TX 0.110 0.100 0.101 Fresno, CA 0.116 0.105 0.119 Beaver County, PA 0.095 0.085 0.095 Galveston-Texas City, TX 0.102 0.096 0.094 Bellingham, WA 0.038 0.068 0.059 Gary-Hammond, IN 0.102 0.122 0.101 Benton Harbor, MI 0.098 Grand Rapids, MI 0.119 0.107 0.124 Bergen-Passaic, NJ 0.093 0.097 0.106 Greeley, CO 0.080 0.080 0.081 Billings, MT 0.56 Green Bay, WI 0.095 0.074 0.079 Biloxi-Gulfport, TX 0.089 Greensboro-Winston Salem-High Point, NC 0.083 0.100 0.087 Birmingham, AL 0.088 0.105 0.088 Greenville-Spartanburg, SC 0.088 0.091 0.085 Bismark, ND 0.086 0.062 0.061 Hamilton-Middletown, OH 0.095 0.111 0.105 Bloomington-Normal, IL 0.081 0.071 0.095 Harrisburg-Lebanon-Carlisle, PA 0.091 0.108 0.100 Boston, MA 0.109 0.105 0.118 Hartford, CT 0.114 0.109 0.112 Boulder-Longmont, CO 0.082 0.084 0.083 Hickory, NC 0.080 Bradenton, FL 0.086 0.075 0.074 Honolulu, HI 0.020 0.037 0.042 Brazoria, TX 0.101 0.107 Houma-Thibodaux, LA 0.082 0.084 0.077 Bridgeport-Milford, CT 0.139 0.114 0.121 Houston, TX 0.121 0.141 0.115 Brockton, MA 0.110 0.106 0.107 Huntington-Ashland, WV-KY-OH 0.102 0.109 0.124 Buffalo, NY 0.085 0.096 0.097 Huntsville, AL 0.072 0.080 0.082 Canton, OH 0.098 0.098 0.099 Indianapolis, IN 0.097 0.099 0.100 Carson City, NV 0.070 Iowa City, IA 0.078 0.084 0.060 Cedar Rapids, IA 0.078 0.057 0.066 Jackson, MS 0.086 0.083 0.075 Champaign-Urbana-Rantoul, IL 0.084 0.080 0.077 Jacksonville, FL 0.090 0.084 0.077 Charleston, SC 0.094 0.084 0.074 Jamestown-Dunkirk, NY 0.068 0.082 Charleston, WV 0.087 0.083 0.099 Janesville-Beloit, WI 0.097 0.081 0.090 Charlotte-Gastonia-Rock Hill, NC-SC 0.089 0.100 0.094 Jersey City, NJ 0.105 0.128 0.117 Charlottesville, VA 0.076 0.089 0.091 Johnson City-Kingsport-Bristol, TN-WV 0.083 0.100 0.080 Chattanooga, TN-GA 0.091 0.094 0.083 Johnstown, PA 0.082 0.090 0.099 Chicago, IL 0.101 0.084 0.106 Joliet, IL 0.082 0.070 0.091 Chico, CA 0.081 0.083 0.074 Kalamazoo, MI 0.071 Cincinnati, OH-KY-IN 0.106 0.119 0.115 Kansas City, MO-KS 0.090 0.089 0.089 Cleveland, OH 0.099 0.096 0.101 Kenosha, WI 0.113 0.093 0.118 Colorado Springs, CO 0.072 0.065 0.068 Knoxville, TN 0.088 0.105 0.091 Columbia, SC 0.079 0.094 0.083 Lafayette, LA 0.080 0.086 0.075 Columbus, GA-AL 0.068 0.075 0.083 Lafayette, IN 0.077 0.092 0.090 Columbus, OH 0.097 0.098 0.112 Lake Charles, LA 0.088 0.084 0.096 Corpus Christi, TX 0.083 0.085 0.075 Lake County, IL 0.092 0.082 0.102 Cumberland, MD-WV 0.070 0.076 Lancaster, PA 0.085 0.089 0.096 Dallas, TX 0.101 0.115 0.095 Lansing-East Lansing, MI 0.093 0.083 0.087 Danbury, CT 0.098 0.105 0.116 Las Cruces, NM 0.074 0.082 0.074

4-22Table 4-5 (cont’d). The Highest Second Daily Maximum Eight-Hour Average Ozone Concentration (ppm) by Metropolitan Statistical Area (MSA) for the Years 1989 to 1991

MSA 1989 1990 1991 MSA 1989 1990 1991
Las Vegas, NV 0.084 0.082 0.075 Provo-Orem, UT 0.094 0.070 0.071 Lawrence-Haverhill, MA-NH 0.104 0.077 0.106 Racine, WI 0.110 0.090 0.118 Lewiston-Auburn, ME 0.089 0.090 0.101 Raleigh-Durham, NC 0.099 0.094 0.091 Lexington-Fayette, KY 0.097 0.097 0.088 Reading, PA 0.095 0.101 0.109 Lima, OH 0.088 0.086 0.091 Redding, CA 0.080 0.100 0.093 Lincoln, NE 0.057 0.060 0.060 Reno, NV 0.081 0.109 0.075 Little Rock-North Little Rock, AR 0.077 0.083 0.089 Richmond-Petersburg, VA 0.094 0.101 0.097 Longview-Marshall, TX 0.076 0.089 0.086 Riverside-San Bernardino, CA 0.196 0.193 0.189 Lorain-Elyria, OH 0.096 0.082 0.091 Roanoke, VA 0.077 0.075 0.078 Los Angeles-Long Beach, CA 0.188 0.170 0.178 Rochester, NY 0.094 0.097 0.103 Louisville, KY-IN 0.096 0.093 0.119 Rockford, IL 0.085 0.073 0.081 Lynchburg, VA 0.083 0.079 Sacramento, CA 0.105 0.125 0.124 Madison, WI 0.089 0.079 0.089 St. Louis, MO-IL 0.105 0.098 0.107 Manchester, NH 0.084 0.098 0.087 Salinas-Seaside-Monterey, CA 0.082 0.074 0.071 Medford, OR 0.063 0.076 0.055 Salt Lake City-Ogden, UT 0.114 0.086 0.086 Melbourne-Titusville-Palm Bay, FL 0.082 0.082 0.069 San Antonio, TX 0.100 0.080 0.085 Memphis, TN-AR-MS 0.099 0.100 0.093 San Diego, CA 0.139 0.135 0.128 Miami-Hialeah, FL 0.087 0.076 0.072 San Francisco, CA 0.064 0.056 0.054 Middlesex-Somerset-Hunterdon, NJ 0.108 0.111 0.111 San Jose, CA 0.094 0.075 0.086 Middletown, CT 0.119 0.117 0.125 San Juan, PR 0.043 0.042 0.044 Milwaukee, WI 0.115 0.100 0.118 Santa Barbara-Santa Maria-Lompoc, CA 0.129 0.129 0.075 Minneapolis-St. Paul, MN-WI 0.090 0.077 0.079 Santa Cruz, CA 0.066 0.058 0.067 Mobile, AL 0.079 0.098 0.062 Santa Fe, NM 0.049 0.069 0.076 Modesto, CA 0.101 0.106 0.091 Santa Rosa-Petaluma, CA 0.083 0.061 0.076 Monmouth-Ocean, NJ 0.118 0.107 0.122 Sarasota, FL 0.085 0.083 0.080 Montgomery, AL 0.066 0.081 0.071 Scranton-Wilkes-Barre, PA 0.088 0.096 0.111 Muskegon, MI 0.139 0.100 0.119 Seattle, WA 0.078 0.099 0.087 Nashua, NH 0.072 0.095 0.110 Sharon, PA 0.098 0.095 0.094 Nashville, TN 0.093 0.102 0.107 Sheboygan, WI 0.103 0.088 0.103 Nassau-Suffolk, NY 0.099 0.115 0.121 Shreveport, LA 0.098 0.102 0.087 New Bedford, MA 0.104 0.101 0.106 South Bend-Mishawaka, IN 0.089 0.089 0.093 New Haven-Meriden, CT 0.108 0.122 0.128 Spokane, WA 0.060 0.060 New London-Norwich, CT-RI 0.128 0.127 0.115 Springfield, IL 0.085 0.082 0.087 New Orleans, LA 0.075 0.086 0.079 Springfield, MO 0.075 0.061 0.069 New York, NY 0.111 0.119 0.133 Springfield, MA 0.123 0.113 0.117 Newark, NJ 0.108 0.107 0.119 Stamford, CT 0.113 0.112 0.115 Niagara Falls, NY 0.082 0.092 0.095 Steubenville-Weirton, OH-WV 0.094 0.075 0.098 Norfolk-Virginia Beach-Newport News, VA 0.089 0.095 0.089 Stockton, CA 0.086 0.093 0.090 Oakland, CA 0.091 0.091 0.083 Syracuse, NY 0.090 0.093 0.098 Oklahoma City, OK 0.089 0.090 0.089 Tacoma, WA 0.077 0.094 0.077 Omaha, NE-IA 0.075 0.075 0.073 Tallahassee, FL 0.072 Orlando, FL 0.096 0.082 0.075 Tampa-St. Petersburg-Clearwater, FL 0.088 0.085 0.083 Owensboro, KY 0.096 0.104 0.077 Terre Haute, IN 0.087 0.095 0.089 Oxnard-Ventura, CA 0.147 0.119 0.129 Toledo, OH 0.093 0.084 0.107 Parkersburg-Marietta, WV-OH 0.094 0.088 0.104 Trenton, NJ 0.119 0.112 0.131 Pascagoula, MS 0.082 0.092 0.077 Tucson, AZ 0.074 0.084 0.080 Pensacola, FL 0.080 0.098 0.082 Tulsa, OK 0.093 0.094 0.097 Peoria, IL 0.087 0.075 0.088 Utica-Rome, NY 0.082 0.094 0.091 Philadelphia, PA-NJ 0.118 0.110 0.123 Vallejo-Fairfield-Napa, CA 0.076 0.074 0.078 Phoenix, AZ 0.086 0.096 0.094 Vancouver, WA 0.058 0.080 0.042 Pittsburgh, PA 0.107 0.100 0.106 Victoria, TX 0.093 0.056 0.086 Pittsfield, MA 0.075 0.094 0.095 Vineland-Millvile-Bridgeton, NJ 0.122 0.106 0.108 Portland, ME 0.124 0.109 0.134 Visalia-Tulare-Porterville, CA 0.114 0.103 0.104 Portland, OR-WA 0.071 0.111 0.092 Washington, DC-MD-VA 0.106 0.110 0.114 Portsmouth-Dover-Rochester, NH-ME 0.107 0.086 0.123 W. Palm Beach-Boca Raton-Delray, FL 0.081 0.067 0.059 Poughkeepsie, NY 0.079 0.085 0.101 Wheeling, WV-OH 0.086 0.089 0.093 Providence, RI 0.107 0.112 0.127 Wichita, KS 0.079 0.089 0.081

4-23Table 4-5 (cont’d). The Highest Second Daily Maximum Eight-Hour Average Ozone Concentration (ppm) by Metropolitan Statistical Area (MSA) for the Years 1989 to 1991

MSA 1989 1990 1991 MSA 1989 1990 1991
Williamsport, PA 0.065 0.072 0.087 York, PA 0.091 0.108 0.103 Wilmington, DE-NJ-MD 0.105 0.110 0.118 Youngstown-Warren, OH 0.088 0.085 0.101 Wilmington, NC 0.086 Yuba City, CA 0.084 0.076 0.084 Worcester, MA 0.097 0.089 0.107 Yuma, AZ 0.080 0.075 0.070

Figure 4-5. The relationship between (a) the second highest daily maximum hourly average ozone (O 3 ) concentration and the maximum 3-mo SUM06 value and (b) the second highest daily maximum 8-h average O 3 concentration and the maximum 3-mo SUM06 value for specific site years at rural agricultural sites for the 1980-to-1991 period.

4-24Figure 4-6. The relationship between (a) the second highest daily maximum hourly average ozone (O 3 ) concentration and the maximum 3-mo SUM06 value and (b) the second highest daily maximum 8-h average O 3 concentration and the maximum 3-mo SUM06 value for specific site years at rural forested sites for the 1980-to-1991 period.

One of the difficulties in attempting to use correlation analysis between indices for rationalizing the substitution of one exposure index for another to predicting an effect (e.g., SUM06 versus the second highest daily maximum hourly average concentration) is the introduction of the error associated with estimating levels of one index from another. Lefohn et al. (1989) have recommended that if a different exposure index (e.g., second highest daily maximum hourly average concentration) is to be compared to, for example, the SUM06 for

4-25adequacy in predicting crop loss, then the focus should be on how well the two exposure indices predict crop loss using the effects model that is a function of the most relevant index, and not on how well the indices predict one another. Using data from both urban and rural
O 3 monitoring sites in the midwestern United States that were located near agricultural or forested areas, Lefohn et al. (1989) reported a large amount of scatter between the second highest daily maximum hourly average concentration and the SUM06 indices. This large scatter indicated considerable uncertainty when attempting to predict a value for SUM06, given a specific second highest daily maximum hourly average concentration value. The authors reported that for a given second highest daily maximum hourly average concentration, the SUM06 values varied over a large range. Lefohn et al. (1989) concluded that such large uncertainty would introduce additional uncertainty when attempting to use the predicted exposure index to estimate an effect. The authors concluded that less error would be introduced if either of the two indices were used directly in the development of an exposure-response model. As pointed out by the U.S. Environmental Protection Agency (1986), a familiar
measure of O 3 air quality is the number or percentage of days on which some specific concentration is equalled or exceeded. This measure, however, does not shed light on one of the more important questions regarding the effects of O 3 on both people and plants: what is the possible significance of high concentrations lastin g 1 h or longer and then recurring on 2 or more successive days? The recurrence of high O 3 concentrations on consecutive days was examined in four cities (one site in each city) by the U.S. Environmental Protection Agency (1986). The numbers of multiday events were tallied by length of event (i.e., how many consecutive days) using data for the daylight hours (0600 to 2000 hours) in the second and third quarters of 1979 through 1981. These sites were selected because they included areas known to experience high O 3 concentrations (e.g., California), and because they represent different geographic regions of the country (west, southwest, and east). Because of the importance of episodes and respites, the U.S. Environmental Protection Agency (1986) commented on the occurrences of the length of episodes and the time between episodes. The agency concluded that its analysis showed variations among sites in the lengths of episodes as well as the respite periods. In its discussion, the U.S. Environmental Protection Agency (1986) defined a day or series of days on which the daily 1-h maximum reached or exceeded the specified level as an "exposure"; the intervening day or days when that level was not reached was called a "respite". Four O 3 concentrations were selected: 0.06, 0.12, 0.18, and 0.24 ppm. At the Dallas site, for example, the value equalled or exceeded 0.06 ppm for more than 7 days in a row. The Pasadena site experienced 10 such exposures, but these 10 exposure events spanned 443 days; in Dallas, the 11 exposures involved only 168 days. At the lowest concentration ( ≥ 0.06 ppm), the Dallas station recorded more short-term ( ≤ 7 days) exposures (45) involving more days (159) than the Pasadena station (14 exposures over 45 days) because the daily 1-h maximum statistic in Pasadena remained above 0.06 ppm for such protracted periods. At concentrations

≥ 0.12 ppm, the lengthy exposures at the Pasadena site resolved into numerous shorter exposures, whereas in Dallas the exposures markedly dwindled in number and duration.

4-26

### 4.3.3 Nonurban Area Concentrations 4.3.3.1 Sites That Experience Low Maximum Hourly Average Concentrations It is important to establish reference points that can be used to describe the distribution of hourly average concentrations at monitoring sites experiencing low maximum concentrations. Doing so will make it possible to confirm that the hourly average concentrations in control chambers utilized for research experiments associated with human health and vegetation effects are similar to those experienced under ambient conditions. For example, there has been concern expressed that O 3 concentrations in charcoal-filtration chambers used by NCLAN did not simulate the levels in those areas of the United States that experience low maximum O 3 concentrations (see Chapter 5, Section 5.5). Heuss (1982) expressed concern that the O 3 levels in the charcoal-filtration chambers were lower than those at sites experiencing low maximum hourly average concentrations, and that the resulting agricultural loss estimates derived from the NCLAN models may have been too high. Two possible approaches for establishing reference points have been discussed (Lefohn et al., 1990a). One method is to estimate, using mathematical models and historical data, unpolluted background levels prior to disturbance by human influence. However, there are difficulties with this approach. Background can be defined as the unpolluted conditions in preindustrial times (i.e., absolutely unpolluted air in which there was no human interference). Alternatively, a background also can be defined as the condition currently existing at any location that is presently free from human influence. However, almost all geographic locations on the earth have been impacted by human influences (e.g., Finlayson-Pitts and Pitts, 1986; Hong et al., 1994) (see Section 4.3.1). It is unlikely that a single value or even a fixed range of O 3 background values can apply uniformly across North America or elsewhere in the northern or southern hemispheres. Any attempt to quantify the historical background is subject to much uncertainty for the following reasons: 1. Little is known with certainty about the nature of past unpolluted
conditions. 2. Even if all anthropogenic emissions of O 3 precursors were eliminated, it is unlikely that O 3 in, for example, eastern North America, would return to preindustrial levels. Since preindustrial times, major land use changes have occurred.
Because substantial amounts of natural emissions of
O 3 precursors are derived from soils and vegetation, especially during the warmer months, it is probable that these land use changes have modified the emissions of O 3 precursors and, thus, changed the concentrations of O 3 . Although not representing natural O 3 background, attempts have been made, using historical data, to estimate O 3 concentrations in the late 1800s and early 1900s. Model simulations and limited observations suggest that tropospheric O 3 has increased in the northern hemisphere since the preindustrial times and future increases may be possible (Bojkov, 1986; Volz and Kley, 1988; Thompson, 1992). However, several investigators have discussed the possible confounding influences that led to a great deal of uncertainty associated with characterizing the O 3 concentrations measured in the late 1800s and early 1900s (Lisac and Grubisic, 1991; Lefohn et al., 1992c; Cartalis and Varotsos, 1994). Using data collected over the past 30 years, consistent trends in tropospheric O 3 have not been observed across the

4-27northern hemisphere (World Meteorological Organization/United Nations Environment Program, 1994). Using ozonesonde data, observations show that tropospheric O 3 has increased above some locations in the northern hemisphere. However, in the 1980s, the trends were variable and either small or nonexistent (Logan, 1994). European measurements
at some surface sites indicate an increase in O 3 concentration since earlier this century. Because of the uncertainty associated with measurements taken in the late 1880s and early 1990s, it is difficult to compare O 3 concentration levels experienced in the United States with today’s levels. However, it can be concluded that O 3 levels measured around 100 years ago were lower than the values observed today at most sites in the United States, and that consistently increasing trends in O 3 concentration measured at surface levels have not been observed annually at sites monitoring O 3 in the United States. An alternative approach (adopted by Lefohn et al., 1990a, to establish reference points) is to examine O 3 hourly average concentration data from those monitoring sites in the world that experienced low maximum hourly average values. When data from these sites, including several in North America, were characterized, and the range of hourly average concentrations compared, it was found that the distributions of hourly average concentrations for sites with the lowest hourly average concentrations were similar. The authors believed the data from these sites could be used for establishing reference points that could be compared with more polluted areas. Is it appropriate to use sites that experience the lowest hourly average concentrations in the United States today as reference levels, or should O 3 background levels that may have existed 100 years ago be used? Although it might be argued that all sites in the United States have been affected to such a level that today’s values are not relevant
because an increase in O 3 concentrations has occurred since the late 1800s, two key points argue against this line of reasoning. First, as indicated above, although O 3 levels have increased since the last century, consistent trends in tropospheric O 3 have not been observed across the northern hemisphere. Thus, at some monitoring sites, O 3 levels may not show increasing trends. Second, all the increases in background levels of O 3 may not necessarily be associated with changes in anthropogenic emissions. Since preindustrial times, major land use changes, resulting from human-induced activities, have occurred. Changes in natural
emissions of O 3 precursors associated with soils and vegetation in the last 100 years may be associated with these land use changes, with the result that some of the increased levels in
O 3 concentrations may be attributed to sources other than anthropogenic emissions. Thus, using historical data that contain large uncertainties in the estimation of O 3 concentrations as a reference point, may yield unrealistically low hourly average concentrations. Based on a review of available data, the U.S. Environmental Protection Agency (1989) has indicated that a reasonable estimate of O 3 background concentration near sea level in the United States today, for an annual average, is from 0.020 to 0.035 ppm. This estimate included a 0.005- to 0.015-ppm contribution from stratospheric intrusions and a 0.01-ppm contribution from photochemically affected biogenic nonmethane hydrocarbons. In addition, the U.S. Environmental Protection Agency (1989) estimated that an additional 0.010 ppm is possible from the photochemical reaction of biogenic methane. A more conservative approach would be to associate the sum of O 3 concentrations from these two processes with the differences between 0.020 to 0.035 ppb and the stratospheric intrusion contribution. For calculating annual average concentrations, the estimate made by the U.S. Environmental Protection Agency (1989) may be valid. Pratt et al. (1983), using data from low-elevation rural sites in Minnesota and North Dakota, reported that annual average

4-28concentrations for an O 3 monitoring site in LaMoure County, ND (400 m), for 1978 through 1981, ranged from 0.030 to 0.035 ppm, whereas an O 3 monitoring site in Traverse County, MN (311 m), had a range of 0.029 to 0.035 ppm. Bower et al. (1989) reported that the remote northern Scotland site, Strath Vaich (270 m), had a 1987 to 1988 annual average
O 3 concentration of 0.031 ppm. The U.S. Environmental Protection Agency (1989) has estimated that background
O 3 concentrations for a 1-h daily maximum at sea level in the United States during the summer are in the range of 0.03 to 0.05 ppm. However, the actual value may vary from site to site. Using measurements at a remote site in South Dakota, Kelly et al. (1982) estimated the background O 3 in air masses entering the midwestern and eastern United States to be 0.03 to 0.05 ppm. Lefohn and Foley (1992) reported that hourly average O 3 concentration data available for sites that experience low maximum hourly average values in the western United States indicate that, in almost all cases, the maximum hourly average concentrations were in the range of 0.060 to 0.075 ppm, and, at some of the sites, there were infrequent occurrences of hourly average concentrations below 0.02 ppm (i.e., lack of scavenging). These observations were similar to those reported for several O 3 monitoring sites experiencing low maximum hourly average O 3 concentrations for other locations in the world (Lefohn et al., 1990a; Pedersen and Lefohn, 1994). Some vegetation researchers have used the seasonal average of the daily 7-h (0900 to 1559 hours) average as the exposure parameter in exposure-response models (Heck et al., 1982). For quantifying the effects of air pollution on crops and trees, some investigators have used controlled environment and field methods with charcoal-filtration systems (Olszyk et al., 1989). In both the design of the experiments and the analysis of the data, the 7-h (0900 to 1559 hours) seasonal mean reference point for O 3 was assumed to be 0.025 ppm. The 0.025 ppm concentration was used to estimate crop loss across the United States (Adams et al., 1985, 1989). For sites experiencing low maximum hourly average concentrations in the western United States, except for several years of O 3 measurements at Olympic National Park (Table 4-6), the 7-mo (April to October) average of the 7-h daily average concentrations ranged from 0.025 to 0.045 ppm (Altshuller and Lefohn, 1996). Ozone hourly average concentrations were characterized at several sites located in both the western and south-central United States that experienced low maximum hourly average concentrations (Table 4-6). Redwood National Park, CA; Olympic National Park, WA; Glacier National Park, MT; Denali National Park, AK; Badlands, SD; Great Sand Dunes National Monument, CO; Theodore Roosevelt National Park, ND; and Quachita National Forest, AR, experienced no hourly average concentration ≥ 0.08 ppm for the period April to October (Altshuller and Lefohn, 1996). Except for 1988, the year in which Yellowstone National Park, WY, experienced a major forest fire, the Wyoming site experienced no hourly average concentrations ≥ 0.08 ppm. Logan (1989) has noted that O 3 hourly average concentrations above 0.08 ppm rarely are exceeded at remote western sites. In almost all cases for the above sites, the maximum hourly average concentration was ≤ 0.075 ppm. There have been some questions raised to whether the distributions experienced at those sites exhibiting low maximum hourly average concentrations in the western United States were representative of sites in the eastern and midwestern United States because of differences in biogenic precursors. The O 3 monitoring site in the

4-29Table 4-6. Seasonal (April to October) Percentile Distribution of HourlyOzone Concentrations, Number of Hourly Mean Ozone Occurrences≥0.08 and≥0.10,Seasonal Seven-Hour Average Concentrations, W126, and SUM06 Values for SitesExperiencing Low Hourly Average Concentrations with Data Capture≥75%(Concentrations in ppm)aLocationSite/AIRS IDYearMin.1030Percentiles50 70909599MaxNo. ofObs.Hours≥0.08≥0.10Seasonal7-hW126(ppm-h)SUMO6(ppm-h)Redwood, CA Redwood NP 1988 0.002 0.011 0.018 0.023 0.029 0.038 0.041 0.046 0.060 4,825 0 0 0.026 1.8 0.1060150002 1989 0.000 0.010 0.017 0.022 0.027 0.034 0.038 0.042 0.047 4,624 0 0 0.024 1.0 0.01990 0.000 0.011 0.018 0.023 0.028 0.035 0.038 0.043 0.053 4,742 0 0 0.025 1.2 0.01991 0.001 0.012 0.019 0.025 0.031 0.038 0.041 0.045 0.054 4,666 0 0 0.027 1.7 0.01992 0.000 0.010 0.017 0.021 0.026 0.035 0.039 0.045 0.055 4,679 0 0 0.023 1.1 0.01993 0.000 0.010 0.017 0.022 0.027 0.035 0.038 0.042 0.054 4,666 0 0 0.025 1.1 0.0Olympic, WA Olympic NP 1982 0.000 0.000 0.010 0.010 0.020 0.030 0.030 0.040 0.060 4,704 0 0 0.020 7.4 0.1530090012 1984 0.000 0.000 0.010 0.010 0.020 0.020 0.020 0.030 0.050 4,872 0 0 0.015 1.6 0.01986 0.000 0.000 0.010 0.020 0.020 0.040 0.040 0.040 0.060 4,776 0 0 0.025 13.7 0.11989 0.000 0.003 0.010 0.015 0.022 0.030 0.035 0.046 0.065 4,220 0 0 0.021 0.7 0.11990 0.000 0.005 0.012 0.018 0.023 0.030 0.034 0.043 0.064 4,584 0 0 0.022 0.8 0.31991 0.000 0.006 0.014 0.019 0.024 0.033 0.036 0.044 0.056 4,677 0 0 0.025 0.9 0.01993 0.000 0.004 0.010 0.016 0.021 0.029 0.034 0.041 0.064 4,595 0 0 0.022 0.7 0.3Glacier, MT Glacier NP 1989 0.000 0.003 0.015 0.026 0.036 0.046 0.050 0.058 0.067 4,770 0 0 0.036 5.9 1.8300298001 1990 0.000 0.003 0.014 0.026 0.035 0.044 0.047 0.052 0.066 5,092 0 0 0.036 4.1 1.31991 0.000 0.001 0.014 0.027 0.036 0.046 0.049 0.056 0.062 5,060 0 0 0.036 5.3 0.71992 0.000 0.001 0.013 0.025 0.033 0.043 0.048 0.055 0.077 4,909 0 0 0.033 4.1 1.0Yellowstone, WY Yellowstone NP 1988 0.002 0.020 0.029 0.037 0.044 0.054 0.058 0.070 0.098 4,257 17 0 0.043 14.0 8.9560391010 1989 0.002 0.018 0.027 0.036 0.044 0.052 0.057 0.063 0.071 4,079 0 0 0.042 11.0 6.71990 0.000 0.015 0.023 0.029 0.036 0.043 0.046 0.053 0.061 4,663 0 0 0.034 3.8 0.51991 0.004 0.020 0.030 0.037 0.042 0.048 0.051 0.057 0.064 4,453 0 0 0.042 7.7 1.21992 0.001 0.018 0.029 0.036 0.042 0.051 0.056 0.064 0.075 4,384 0 0 0.042 10.7 6.31993 0.000 0.018 0.028 0.036 0.042 0.047 0.050 0.054 0.060 4,399 0 0 0.041 6.5 0.2022900003 1990 0.003 0.017 0.024 0.029 0.034 0.040 0.043 0.048 0.050 3,978 0 0 0.030 2.1 0.01991 0.005 0.018 0.024 0.028 0.034 0.041 0.043 0.047 0.057 4,809 0 0 0.030 2.7 0.01992 0.003 0.016 0.023 0.028 0.034 0.044 0.047 0.050 0.054 4,800 0 0 0.031 3.7 0.019930.0020.0170.0230.0280.0330.0410.0430.0480.0554,773000.0302.60.0

4-30Table 4-6 (con’t). Seasonal (April to October) Percentile Distribution of HourlyOzone Concentrations, Number of Hourly Mean Ozone Occurrences≥00.08 and≥0.10,Seasonal Seven-Hour Average Concentrations, W126, and SUM06 Values for SitesExperiencing Low Hourly Average Concentrations with Data Capture≥75%(Concentrations in ppm)aLocationSite/AIRS IDYearMin.1030Percentiles50 70909599MaxNo. ofObs.Hours≥00.08≥0.10Seasonal7-hW126(ppm-h)SUMO6(ppm-h)Badlands, SD Badlands NP 1988 0.005 0.022 0.032 0.038 0.045 0.053 0.056 0.061 0.072 4,791 0 0 0.043 13.3 5.2460711010 1989 0.007 0.020 0.028 0.034 0.041 0.049 0.053 0.060 0.071 4,840 0 0 0.040 9.2 3.11990 0.006 0.019 0.027 0.032 0.037 0.044 0.048 0.054 0.063 4,783 0 0 0.037 4.8 0.81991 0.005 0.020 0.028 0.034 0.040 0.047 0.050 0.056 0.066 4,584 0 0 0.038 6.2 0.7Great Sand Dunes, CO Sand Dunes NM 1988 0.010 0.028 0.035 0.039 0.044 0.050 0.053 0.058 0.076 4,827 0 0 0.043 11.1 1.9080030002 1989 0.011 0.031 0.037 0.041 0.045 0.050 0.052 0.057 0.063 4,436 0 0 0.044 10.8 1.21990 0.010 0.030 0.037 0.041 0.045 0.052 0.055 0.062 0.070 4,624 0 0 0.044 13.6 5.11991 0.008 0.029 0.037 0.043 0.048 0.055 0.058 0.065 0.077 4,130 0 0 0.046 17.0 9.0Theodore Roosevelt,ND, North UnitTheodoreRoosevelt NP3805300021984 0.000 0.017 0.025 0.032 0.039 0.047 0.050 0.059 0.068 4,923 0 0 0.038 7.0 2.81985 0.000 0.019 0.026 0.032 0.038 0.046 0.049 0.054 0.061 4,211 0 0 0.038 5.0 0.11986 0.004 0.017 0.027 0.033 0.039 0.047 0.050 0.056 0.062 4,332 0 0 0.039 5.5 0.41989 0.004 0.023 0.032 0.039 0.045 0.054 0.058 0.065 0.073 4,206 0 0 0.046 14.2 11.01992 0.005 0.019 0.027 0.033 0.039 0.047 0.050 0.056 0.063 4,332 0 0 0.040 6.1 0.81993 0.004 0.018 0.025 0.031 0.037 0.045 0.048 0.055 0.064 4,281 0 0 0.038 4.6 0.7Point Reyes, CA Point Reyes NP0604100021989 0.007 0.021 0.026 0.031 0.036 0.042 0.045 0.058 0.080 4,577 1 0 0.033 4.8 2.51990 0.006 0.017 0.022 0.025 0.029 0.036 0.040 0.046 0.063 4,856 0 0 0.028 1.8 0.41991 0.006 0.019 0.025 0.030 0.034 0.040 0.043 0.048 0.072 4,588 0 0 0.031 3.0 0.61992 0.007 0.018 0.024 0.028 0.033 0.041 0.045 0.050 0.066 4,794 0 0 0.031 3.0 0.3Arches, UT Arches NP4901901011989 0.000 0.031 0.040 0.045 0.050 0.057 0.059 0.065 0.084 4,260 2 0 0.048 21.2 12.61990 0.000 0.020 0.025 0.028 0.031 0.036 0.039 0.045 0.056 4,639 0 0 0.030 1.7 0.0Montgomery Co., AR Quachita NF 1991 0.000 0.002 0.010 0.016 0.023 0.035 0.041 0.051 0.064 4,835 0 0 0.027 1.6 0.1050970001 1992 0.000 0.005 0.015 0.024 0.030 0.041 0.045 0.053 0.067 4,902 0 0 0.033 3.0 0.919930.0000.0080.0190.0270.0350.0470.0520.0610.0704,844000.0366.73.7aSee Appendix A for abbreviations and acronyms.

4-31Quachita National Forest in Arkansas experienced distributions of hourly average
concentrations similar to some of the western sites. Evans et al. (1983) summarized O 3 hourly averaged data collected at eight stations located in eight national forests across the United States. The first three stations began operations in 1976 (Green Mountain National Forest, VT; Kisatchie National Forest, LA; and Custer National Forest, MT); the second three in 1978 (Chequamegon National Forest, WI; Mark Twain National Forest, MO; and Croatan National Forest, NC); and the last two in 1979 (Apache National Forest, AZ; and Ochoco National Forest, OR). For the period 1979 to 1983, hourly maximum average concentrations at the sites (Custer, Ochoco, and Apache national forests) were similar to the hourly average concentrations determined for six of the seven sites characterized by Lefohn and Foley (1992). In almost all cases, none of the sites experienced hourly average concentrations ≥ 0.08 ppm, and the maximum hourly average concentrations were in the range of 0.060 to 0.075 ppm. Table 4-7 summarizes the percentile
distributions for the three national forest sites. Several sites experiencing low maximum hourly average concentrations were characterized by Lefohn et al. (1990a), using various exposure indices. One of the indices used was W126 (see Section 4.1); the W126 values, calculated over an annual period, are provided in Table 4-8. The W126 values for Theodore Roosevelt National Park were in the range of 6.48 to 8.03 ppm-h. The maximum hourly average concentration reported at the site was 0.068 ppm. The W126 values at the Custer and Ochoco national forests sites ranged from 5.79 to 22.67 ppm-h. The maximum hourly average concentrations measured at each site were 0.075 and 0.080 ppm, respectively. The W126 values calculated for the Custer and Ochoco national forest sites showed greater variability from year to year than the values calculated for the South Pole, Barrow, and Theodore Roosevelt National Park sites. As the W126 values increased, the magnitude of the year-to-year variability also increased. For 2 years of data, the W126 values calculated for the White River U-4 Oil Shale, UT, site were 19.98 and 32.10 ppm-h. The maximum hourly concentration recorded was 0.079 ppm. The W126 values calculated for the Apache National Forest site ranged from 10.24 to 81.39 ppm-h. The highest hourly average concentration was 0.090 ppm. The 7-h (0900 to 1559 hours) average concentration has been used by vegetation
researchers to characterize O 3 exposures experienced in plant chamber experiments (see Chapter 5). Because O 3 concentrations are highest during the warm-season months and, at many low-elevation sites, during daylight hours, the 7-mo seasonal, 7-h (0900 to 1559 hours) average concentration is higher than annual average values. Most remote sites outside North America experience seasonal 7-h averages of 0.025 ppm (Table 4-9) (Lefohn et al., 1990a). The seasonal average of the daily 7-h average values for the South Pole, Antarctica, range from 0.024 to 0.027 ppm. The values range from 0.022 to 0.026 ppm at Barrow, AK. In the continental United States and southern Canada, values range from approximately 0.028 to 0.050 ppm (Lefohn et al., 1990a). At an O 3 monitoring site at the Theodore Roosevelt National Park, the range of the 7-mo (April to October) average of the 7- h daily average concentrations was from 0.038 to 0.046 ppm (Table 4-6). These 7-mo seasonal averages appear to be representative of values that may occur at other sites located in the United States and other locations in the northern hemisphere. In earlier investigations, Lefohn (1984) reported 3-mo (June to August), 7-h averages of 0.048, 0.044, and 0.059 ppm at remote sites at Custer, Ochoco, and Apache national forests, respectively.

4-32Table 4-7. Seasonal (April to October) Percentile Distribution of Hourly OzoneConcentrations, Number of Hourly Mean Ozone Occurrences≥0.08 and≥0.10,Seasonal Seven-Hour Average Concentrations, and W126 Values for Three "Clean"National Forest Sites with Data Capture≥75%(Concentrations in ppm)aSiteAIRS IDYearMin.1030Percentiles50 70909599MaxNo. ofObs.Hours≥0.08≥0.10Seasonal7-h (ppm)W126(ppm-h)Custer NF, MT 300870101 19781979198019830.0000.0100.0100.0100.0100.0250.0250.0250.0200.0350.0350.0350.0350.0400.0400.0400.0400.0450.0500.0450.0500.0500.0550.0500.0550.0550.0600.0550.0600.0600.0650.0600.0700.0750.0700.0654,7595,0144,5744,835000000000.0330.0430.0430.0428.313.219.710.7Ochoco NF, OR 410130111 19801981198219830.0100.0100.0100.0100.0300.0250.0250.0250.0350.0300.0300.0350.0400.0350.0350.0350.0450.0400.0400.0400.0550.0450.0450.0450.0550.0450.0500.0500.0650.0550.0550.0550.0800.0750.0650.0604,7594,4594,6974,423500000000.0440.0350.0380.03916.54.77.66.8Apache NF, AZ0401101101981198219830.0100.0150.0040.0250.0300.0250.0300.0400.0350.0350.0450.0400.0400.0500.0450.0450.0550.0550.0500.0600.0550.0550.0650.0650.0650.0750.0704,8064,7144,7880000000.0390.0470.0427.621.914.6aSee Appendix A for abbreviations and acronyms.

4-33Table 4-8. The Value of the W126 Sigmoidal Exposure ParameterCalculated Over the Annual Period(Units in ppm-h)aSiteElevation (m)197619771978197919801981198219831984198519861987South Pole, Antarctica2,8352.653.723.012.413.542.764.09Bitumount, Alberta, Canada 350 2.99Barrow, AK 11 2.60 2.60 3.15 2.36 2.79 2.03 2.46 3.69Theodore Roosevelt NP, ND 727 8.03 6.69 6.48bCuster NF, MT 1,006 14.08 22.67 12.18Ochoco NF, OR 1,364 19.54 5.79 9.10 8.02Birch Mountain, Alberta, Canada 850 19.73White River Oil Shale Project, UT 1,600 19.98 32.10Fortress Mountain, Alberta, Canada 2,10325.04 83.89Apache NF, AZ 2,424 81.39 10.24 27.18 17.48Mauna Loa, HI 3,397 27.48 45.68 33.68 48.90 19.18 32.66 24.48Whiteface Mountain, NY 1,483 86.50 68.30 33.75 32.03 37.82 42.94 41.36 32.07 58.33Hohenpeissenberg, FRG 975 61.28 25.04 35.64 21.76 18.53 29.53 49.00 19.85 40.43American Samoa820.280.240.250.280.300.260.300.32aSee Appendix A for abbreviations and acronyms.bCollection did not occur during the months of October, November, and December.Source: Lefohn et al. (1990a).

4-34Table 4-9. The Value of the Ozone Season (Seven-Month) Average ofthe Daily Seven-Hour (0900 to 1559 Hours) Concentration (ppm)aSiteElevation (m)197619771978197919801981198219831984198519861987South Pole, Antarctica2,8350.0250.0270.0260.0270.0240.025Bitumount, Alberta, Canada 350 0.028Barrow, AK 11 0.022 0.025 0.024 0.024 0.022 0.026 0.022 0.026Theodore Roosevelt NP, ND 727 0.038 0.039 0.039bCuster NF, MT 1,006 0.043 0.044 0.042Ochoco NF, OR 1,364 0.043 0.035 0.038 0.038Birch Mountain, Alberta, Canada 850 0.036White River Oil Shale Project, UT 1,600 0.045 0.045Fortress Mountain, Alberta, Canada 2,1030.041 0.050Apache NF, AZ 2,424 0.054 0.039 0.047 0.040Mauna Loa, HI 3,397 0.035 0.039 0.034 0.038 0.035 0.035 0.034Whiteface Mountain, NY 1,483 0.049 0.046 0.040 0.034 0.041 0.044 0.043 0.043 0.045Hohenpeissenberg, FRG 975 0.047 0.040 0.044 0.040 0.037 0.043 0.047 0.040 0.043American Samoa820.0100.0100.0110.0090.0120.0100.011aSee Appendix A for abbreviations and acronyms.bCollection did not occur during the months of October, November, and December.Source: Lefohn et al. (1990a).

4-354.3.3.2 Urban-Influenced Nonurban Areas It is difficult to identify a set of unique O 3 distribution patterns that adequately describes the hourly average concentrations experienced at monitoring sites in nonurban locations because, as indicated earlier, many nonurban sites in the United States are influenced by local sources of pollution or long-range transport of O 3 or its precursors. Unlike the clean sites characterized by Lefohn and Jones (1986), Lefohn et al. (1990a), and Lefohn and Foley (1992), urban-influenced nonurban sites sometimes show frequent hourly average concentrations near the minimum detectable level, but almost always show occurrences of hourly average concentrations above 0.1 ppm. The frequent occurrence of hourly average concentrations near the minimum detectable level is indicative of scavenging processes (i.e., NO

x ); the presence of high hourly average concentrations can be attributable to the influence of either local generation or the long-range transport of O 3 . For example, Evans et al. (1983) reported that the Green Mountain (VT) and Mark Twain national forest (MO) sites were influenced by long-range transport of O 3 . The U.S. Environmental Protection Agency (1986) reported that the maximum hourly average concentrations at Green Mountain (for the period 1977 to 1981) and Mark Twain (for the period 1979 to 1983) were 0.145 and 0.155 ppm, respectively. Using hourly averaged data from the AIRS database for a select number of rural monitoring sites, Table 4-10 summarizes the percentiles of the hourly average
O 3 concentrations, the number of occurrences of the hourly average concentration ≥ 0.10 ppm, and the 3-mo sum of all hourly average concentrations ≥ 0.06 ppm. As part of a comprehensive air monitoring project sponsored by the Electric Power Research Institute (EPRI), O 3 measurements were made by the chemiluminescence method from 1977 through 1979 at nine "nonurban" Sulfate Regional Experiment (SURE) Program sites and Eastern Regional Air Quality Study sites in the eastern United States. On the basis
of diurnal NO

x patterns that indicated the influence of traffic emissions, five of the sites were classed as "suburban", and the other four were classed as "rural". The O 3 data from these nine stations are summarized in Table 4-11. The sites are influenced either by local sources or by transport of O 3 or its precursors. The maximum hourly average concentrations generally are higher than 0.125 ppm, and the occurrence of hourly average concentrations
near minimum detectable levels indicates NO

x scavenging processes. As part of its effort to provide long-term estimates of dry acidic deposition across the United States, the National Dry Deposition Network (NDDN) operated more than 50 sites, which included 41 in the eastern United States and 9 in the western United States, that routinely recorded hourly average O 3 concentrations. Figure 4-7 shows the locations of the NDDN sites. Edgerton and Lavery (1992) have summarized the O 3 concentrations at some of the sites for the period 1988 to 1990. Table 4-12 summarizes the 7-h (0900 to 1559 hours) growing season average concentration (May to September) for selected sites in the Midwest and the East. Fifty-nine percent of the monitoring sites listed in the table have been classified as agricultural and 36% as forested; one site was classified as commercial. As noted by the U.S. Environmental Protection Agency (1992a), 1988 was an exceptionally high
O 3 concentration year, when compared with 1989 and 1990. The number of hourly
O 3 concentrations ≥ 0.08 ppm is presented in Table 4-12. Edgerton and Lavery (1992) have
summarized O 3 hourly average concentration data for several sites using the cumulative

4-36Table 4-10. Summary of Percentiles, Number of Hourly Occurrences≥0.10 ppm,and Three-Month SUM06 Values for Selected Rural Ozone Monitoring Sitesin 1989 (April to October)(Concentrations in ppm)aAIRS SiteNameMin.103050Percentiles (ppm)70 909599MaxNumber ofHourlyValuesNumber ofOccurrences≥0.10Max Uncorrected3-mo SUM06 Value(ppm-h)RURAL AGRICULTURAL170491001 Effingham County, IL180970042 Indianapolis, IN240030014 Anne Arundel, MD310550032 Omaha, NE420070003 New Brighton, PA510610002 Fauquier County, VA0.0000.0010.0000.0020.0000.0000.0090.0060.0060.0210.0080.0090.0230.0210.0210.0300.0210.0210.0360.0340.0320.0370.0320.0330.0460.0460.0450.0470.0430.0450.0630.0630.0640.0620.0620.0610.0700.0720.0730.067.0.0700.0690.0810.0850.0900.0750.0870.0840.1040.1030.1200.0980.1020.1224,6004,5924,3604,1605,0555,050131004525.325.425.524.929.424.6RURAL FOREST060430004360310002470090101511870002Yosemite NP, CAEssex County, NYSmoky Mountain NP, TNShenandoah NP (Dickey Ridge), VA0.0000.0160.0000.0040.0080.0310.0250.0270.0220.0400.0360.0370.0350.0490.0440.0450.0490.0560.0530.0540.0650.0660.0650.0650.0720.0720.0700.0710.0830.0860.0810.0820.1110.1060.0980.1004,8534,7924,7644,454340137.645.635.933.5RURAL OTHER040132004350431001370810011371470099550270001551390007Scottsdale, AZSandoval County, NMGuilford County, NCFarmville, NCHoricon, WIOshkosh, WI0.0000.0000.0040.0000.0020.0020.0060.0100.0100.0100.0190.0160.0180.0200.0230.0230.0290.0280.0310.0300.0340.0340.0370.0380.0450.0400.0460.0440.0470.0480.0620.0600.0630.0620.0620.0630.0710.0600.0700.0700.0700.0700.0840.0700.0830.0830.0880.0840.1070.0900.1130.1000.1110.1215,0705,0594,8534,8334,1424,206402211331.725.127.726.424.627.9aSee Appendix A for abbreviations and acronyms.

4-37Table 4-11. Summary of Percentiles of Hourly Average Concentrations for Electric PowerResearch Institute Sulfate Regional Experiment (SURE) Program Sites/Eastern Regional AirQuality Study (ERAQS) Ozone Monitoring Sites(Units in ppm)SURE/ERAQS NameYearMin.1030Percentiles50 70909599MaxNumber ofObservationsMontague, MA197819790.0000.0000.0020.0000.0180.0130.0320.0250.0430.0350.0610.0560.0750.0700.1190.1030.2020.1497,1388,485Scranton, PA 197819790.0000.0000.0150.0110.0310.0220.0400.0300.0480.0400.0620.0610.0730.0740.0940.0970.1260.1325,4618,313Indian River, DE 197819790.0000.0000.0100.0080.0240.0200.0350.0310.0490.0420.0720.0630.0850.0730.1030.0920.1340.1386,8748,527Duncan Falls, OH 197819790.0000.0000.0050.0100.0220.0210.0340.0290.0490.0420.0710.0600.0810.0690.1100.0860.1440.1105,1257,595Rockport, IN 197819790.0000.0000.0080.0080.0210.0190.0320.0280.0440.0380.0660.0550.0780.0640.1010.0830.1450.1046,8498,391Giles County, TN 197819790.0000.0000.0000.0000.0180.0140.0320.0240.0460.0360.0660.0550.0750.0650.0870.0810.1100.1306,0348,439Roanoke, IN 197819790.0000.0000.0040.0040.0190.0170.0320.0260.0440.0380.0670.0610.0790.0740.1060.0980.1600.1335,8748,001Research Triangle Park, NC 197819790.0000.0000.0010.0010.0170.0120.0320.0240.0490.0370.0760.0580.0870.0680.1080.0840.1420.1317,0818,652Lewisburg, WV197819790.0020.0000.0200.0130.0340.0220.0450.0290.0560.0390.0720.0560.0790.0650.0910.0800.1150.0997,0197,849

4-38Figure 4-7. The location of National Dry Deposition Network monitoring sites as of
December 1990.

Source: Edgerton and Lavery (1992).

integrated exposure index, W126, as proposed by Lefohn and Runeckles (1987). Based on evidence presented in the literature relating O 3 exposure with agricultural yield reduction, the index was proposed as a way to weight the higher hourly average concentrations greater than the lower values. The data in the table illustrate the large differences in cumulative exposure between those that occurred in 1988 and those that were experienced in 1989 and 1990. The percentile of the hourly average concentrations is summarized in Table 4-13. Although several of the monitoring sites are located in fairly remote locations in the eastern United States (based on land use characterization) the maximum hourly average concentrations reflect the transport of O 3 or its precursors into the area. Taylor et al. (1992) have summarized the O 3 concentrations that were experienced at 10 EPRI Integrated Forest Study sites in North America. The authors reported that in 1988 all sites experienced maximum hourly average concentrations ≥ 0.08 ppm. In almost all cases, the sites experienced multiple occurrences above 0.08 ppm. This implies that,

4-39Table 4-12. Seven-Hour Growing Season Mean, W126 Values, and Number of Hourly OzoneConcentrations≥80 ppb for Selected Eastern National Dry Deposition Network Sitesa7-h Mean (ppb)W126 (ppm-h)SUM06 (ppm-h)SUM08 (ppm-h)Subregion State Site Land Classb1988 1989 1990 1988 1989 1990 1988 1989 1990 1988 1989 1990NORTHEASTConnecticut HillWashington’s CrossingPennsylvania State UniversityLaurel Hill State ParkBeltsvilleCedar Creek State ParkNYNJPAPAMDWV110144106117116119RFRARARFRARA55.0—59.062.7—59.848.352.846.048.454.644.945.352.451.048.655.548.275.5—63.568.8—50.440.346.025.429.145.419.636.843.742.731.045.724.386.8—65.675.5—56.347.252.128.130.848.619.035.848.445.032.149.423.244.3—32.341.6—27.05.521.25.07.022.94.13.321.311.68.021.96.5UPPER NORTHEASTWhiteface MountainAshlandNYME105135RFRA43.5—45.737.942.335.337.8—25.39.131.28.740.9—25.25.429.05.817.0—2.60.68.40.8MIDWESTArgonne National LabVincennesOxfordILINOH146140122RARARA61.162.065.351.451.153.546.350.951.759.168.191.829.636.448.421.635.846.469.478.5103.235.040.355.325.741.251.732.336.756.810.48.715.83.611.817.2UPPER MIDWESTUnionvillePerkinstownMIWI124134RARA——51.544.247.438.8——35.419.030.711.6——41.618.331.67.6——9.00.27.10.0SOUTH CENTRALSand MountainGeorgia StationPerryvilleResearch Triangle ParkCoweetaEdgar Evins State ParkHorton StationALGAKYNCNCTNVA152153129101137127120RARARARCRFRFRF——65.262.355.6—62.352.648.150.850.841.047.251.463.662.6——47.956.154.4——103.662.344.3—127.640.628.139.731.716.126.961.268.769.7——21.344.570.6——99.671.0—150.733.221.838.935.524.464.083.477.7——49.282.8——39.720.5—60.23.44.65.26.61.58.524.028.4——8.29.2SOUTHERN PERIPHERYCaddo ValleySumatraARFL150156RFRF——46.239.849.546.3——18.517.821.020.0——15.616.525.217.4——0.21.02.30.9aSee Appendix A abbreviations for and acronyms.bRA = Rural agricultural; RF = Rural forest; RC = Rural commercial; — = Nodata or insufficient data.Source: Edgerton and Lavery (1992).

4-40Table 4-13. Summary of Percentiles for National Dry Deposition Network Monitoring Sites(Units in ppm)PercentilesSite No. Name Year Min. 10 30 50 70 90 95 99 Max Number of ObservationsRURAL AGRICULTURAL SITES106 Pennsylvania State University, PA 1988198919900.0000.0000.0000.0130.0100.0150.0260.0220.0270.0360.0330.0380.0490.0430.0480.0730.0590.0650.0860.0660.0740.1140.0820.0900.1430.1040.1204,7165,0895,056116 Beltsville, MD 198919900.0020.0000.0030.0010.0140.0150.0290.0270.0440.0410.0680.0670.0810.0800.0960.1030.1310.1375,0624,597119 Cedar Creek, WV 1988198919900.0000.0010.0010.0080.0060.0070.0170.0130.0140.0290.0240.0240.0440.0370.0380.0690.0560.0570.0820.0650.0670.1080.0820.0850.1340.1720.1164,9385,0445,025122 Oxford, OH 1988198919900.0010.0010.0000.0190.0170.0150.0320.0290.0280.0440.0390.0370.0580.0500.0480.0830.0690.0670.0960.0770.0770.1170.0920.0920.2210.1090.1164,7465,0735,077124 Unionville, MI 198919900.0030.0040.0210.0200.0310.0290.0380.0360.0470.0440.0630.0610.0710.0690.0860.0840.1130.1055,0415,065129 Perryville, KY 198819890.0020.0010.0240.0200.0380.0330.0490.0430.0620.0520.0800.0660.0940.0720.1100.0860.1430.1024,0614,787134 Perkinstown, WI 198919900.0070.0060.0230.0200.0320.0280.0380.0350.0460.0410.0570.0500.0620.0560.0710.0650.0850.0745,0295,063135 Loring AFB/Ashland, ME 198919900.0020.0020.0170.0140.0260.0230.0320.0290.0390.0360.0490.0460.0550.0510.0630.0680.1030.0885,0675,080140 Vincennes, IN 1988198919900.0000.0000.0000.0070.0090.0090.0240.0250.0250.0360.0360.0350.0520.0470.0450.0760.0640.0620.0890.0720.0730.1040.0850.0890.1200.1120.1104,9085,0655,084144 Washington Crossing, NJ 198919900.0000.0010.0060.0080.0210.0210.0330.0320.0460.0430.0670.0650.0780.0790.1000.1040.1590.1485,0535,058146Argonne National Laboratory, IL1988198919900.0000.0000.0000.0040.0050.0040.0190.0190.0170.0320.0290.0280.0460.0410.0390.0730.0610.0570.0850.0700.0650.1030.0880.0770.1460.1260.0975,0375,0555,033

4-41Table 4-13 (cont’d). Summary of Percentiles for National Dry Deposition Network Monitoring Sites(Units in ppm)PercentilesSite No. Name Year Min. 10 30 50 70 90 95 99 Max Number of ObservationsRURAL AGRICULTURAL SITES (cont’d)152 Sand Mountain, AL 198919900.0000.0000.0200.0210.0310.0350.0410.0450.0510.0570.0650.0740.0720.0800.0820.0930.0970.1174,5095,068153 Georgia Station, GA 198919900.0020.0020.0140.0210.0250.0340.0340.0440.0450.0560.0620.0730.0690.0840.0820.1020.1180.1443,5404,814RURAL FOREST SITES105 Whiteface Mountain, NY 1988198919900.0000.0030.0050.0160.0220.0180.0260.0300.0280.0340.0380.0360.0440.0470.0460.0620.0590.0600.0740.0660.0690.0980.0780.0860.1290.0930.1155,0514,6985,016110 Ithaca, NY 1988198919900.0050.0020.0010.0250.0250.0220.0340.0360.0330.0430.0440.0410.0550.0520.0490.0800.0650.0630.0900.0710.0690.1030.0810.0810.1260.1010.0934,8275,0645,075117 Laurel Hill, PA 1988198919900.0010.0000.0010.0120.0090.0090.0250.0200.0200.0360.0310.0300.0500.0430.0420.0760.0610.0600.0920.0690.0710.1190.0870.0860.1560.1100.1095,0074,6975,032120 Horton Station, VA 1988198919900.0100.0020.0040.0310.0320.0320.0450.0430.0440.0570.0500.0520.0670.0590.0590.0840.0700.0710.0960.0760.0750.1140.0850.0840.1450.1030.0975,0124,9765,066127 Edgar Evins State Park, TN 198919900.0000.0010.0170.0190.0280.0320.0370.0410.0470.0520.0620.0670.0670.0730.0770.0850.0900.1095,0605,027137 Coweeta, NC 1988198919900.0010.0010.0000.0100.0070.0080.0220.0160.0180.0340.0250.0290.0470.0370.0430.0650.0550.0590.0720.0610.0640.0940.0710.0720.1450.0940.0854,1824,2755,046150 Caddo Valley, AR 198919900.0020.0020.0050.0040.0160.0150.0280.0290.0410.0410.0570.0570.0630.0650.0750.0770.1020.0945,0465,078156 Sumatra, FL 198919900.0010.0000.0120.0110.0220.0230.0300.0330.0400.0430.0570.0570.0650.0630.0750.0720.0980.1184,7004,444RURAL COMMERCIAL SITE101Research Triangle Park, NC198819890.0000.0000.0040.0040.0200.0190.0350.0300.0500.0420.0720.0630.0840.0710.1110.0830.1370.1215,0304,893

4-42although the sites were located in remote forested areas, the sites experienced elevated
O 3 concentrations that were more than likely due to long-range transport of O 3 or its

precursors. Ozone concentrations on a seasonal basis in the Shenandoah National Park exhibit some features in common with both urban and rural areas. During some years, maximum hourly average concentrations exceed 0.12 ppm, although some sites in the park exhibit a lack of hourly average concentrations near minimum detectable level. Taylor and Norby (1985)
have characterized O 3 episodes, which they defined as any day in which a 1-h mean
O 3 concentration was >0.08 ppm. Based on a 4-year monitoring period in the park, the probability was 80% that any given episode during the growing season would last 2 or more days, whereas the probabilities of episodes lasting for periods greater than 3, 4, and 5 days were 30, 10, and 2%, respectively. Single-day O 3 episodes were infrequent. Taylor and Norby (1985) noted that, given the frequency of respites, there was a 50% probability that a second episode would occur within 2 weeks. Because of a lack of air quality data collected at rural and remote locations, it has been necessary to use interpolation techniques to estimate O 3 exposures in nonurban areas. In
the absence of actual O 3 data, interpolation techniques have been applied to the estimation of
O 3 exposures across the United States (Reagan, 1984; Lefohn et al., 1987a; Knudsen and Lefohn, 1988). "Kriging", a mathematical interpolation technique, has been used to provide
estimates of seasonal O 3 values for the NCLAN for 1978 through 1982 (May to September ofeach year) (Reagan, 1984). These values, along with updated values, coupled with exposureresponse models were used to predict agriculturally related economic benefits anticipated by
lower O 3 levels in the United States (Adams et al., 1985; Adams et al., 1989). Kriging is a statistical tool developed by Matheron (1963) and named in honor of D.G. Krige. Although originally developed specifically for ore reserve estimation, kriging has been used for other spatial estimation applications, such as analyzing and modeling air quality data (Grivet, 1980; Faith and Sheshinski, 1979). At its simplest, kriging can be thought of as a way to interpolate spatial data much as an automatic contouring program would. In a more precise manner, kriging can be defined as a best, linear unbiased estimator of a spatial variable at a particular site or geographic area. Kriging assigns low weights to distant samples and vice versa, but also takes into account the relative position of the samples to each other and the site or area being estimated. Figure 4-8 shows the average for the 1985 through 1987 period for the seasonal (April to October) average of the daily maximum 7- and 12-h values across the United States. The estimates made for the Rocky Mountain region had large uncertainties associated with them because of a lack of monitoring sites. Because of the importance of the higher hourly average concentrations in eliciting injury and yield reduction for agricultural crops (U.S. Environmental Protection Agency, 1992b), kriging was used to predict O 3 exposures in the eastern United States. The sigmoidally weighted W126 exposure index was used as described earlier in this section. Lefohn et al. (1992b) used the W126 index in its kriging to characterize the O 3 exposures that occurred during the period 1985 to 1989. Figure 4-9 illustrates the integrated O 3 exposure for the 1988 and 1989 periods (data derived from work described in Lefohn et al., 1992b). Using the kriged data in the East, the 1988 exposures were the highest for the 5-year period, whereas 1989 exhibited the lowest exposures. The O 3 gradient pattern analyses described by Lefohn et al. (1992b) identified contiguous areas of persistent,

4-43Figure 4-8. The kriged 1985 to 1986 maximum (a) 7-h and (b) 12-h average concentrations of ozone across the United States.

Source: Lefohn et al. (1991).

4-44Figure 4-9. The kriged estimates of the W126 integrated ozone exposure index for the eastern United States for (a) 1988 and (b) 1989.

Source: Lefohn et al. (1992b).

4-45relatively high seasonal O 3 values. The largest area extended from New Jersey south to northern Georgia and South Carolina. This area was roughly bounded on the west by the Appalachian Mountains. A second area, which exhibited persistent relatively high seasonal
O 3 exposures, was centered over the Ohio River Valley in the region near the KentuckyIndiana-Ohio borders. Relatively low O 3 exposures were found in Minnesota, Iowa, Wisconsin, Maine, Vermont, New Hampshire, and Florida. On a year-to-year basis, the analysis by Lefohn et al. (1992b) showed that regions that tended to be high for a specific year continued to experience O 3 exposures that were higher when compared to other regions.



## 4.4 Diurnal Variations in Ozone Concentrations

4.4.1 Introduction By definition, diurnal variations are those that occur during a 24-h period. Diurnal patterns of O 3 may be expected to vary with location, depending on the balance among the many factors affecting O 3 formation, transport, and destruction. Although they vary with locality, diurnal patterns for O 3 typically show a rise in concentration from low (or levels near minimum detectable amounts) to an early afternoon peak. The 1978 criteria document (U.S. Environmental Protection Agency, 1978) ascribed the diurnal pattern of concentrations to three simultaneous processes: (1) downward transport of O 3 from layers aloft; (2) destruction
of O 3 through contact with surfaces and through reaction with nitric oxide (NO) at ground level; and (3) in situ photochemical production of O 3 (U.S. Environmental Protection Agency, 1978; Coffey et al., 1977; Mohnen et al., 1977; Reiter, 1977a). The form of an average diurnal pattern may provide information on sources, transport, and chemical formation and destruction effects at various sites (Lefohn, 1992b). Nontransport conditions will produce early afternoon peaks. However, long-range transport processes will influence the actual timing of a peak from afternoon to evening or early morning hours. Investigators have utilized diagrams that illustrate composite diurnal patterns as a means to describe qualitatively the differences in O 3 exposures between sites (Lefohn and Jones, 1986; Böhm et al., 1991). Although it might appear that composite diurnal pattern diagrams could be used to quantify the differences in O 3 exposures between sites, Lefohn et al. (1991) cautioned their use for this purpose. The average diurnal patterns are derived from long-term calculations of the hourly average concentrations, and the resulting diagram cannot identify adequately, at most sites, the presence of high hourly average concentrations and, thus, may not adequately distinguish O 3 exposure differences among sites. Logan (1989)
noted that diurnal variation of O 3 did not reflect the presence of high hourly average
concentrations. Unique families of diurnal average profiles exist, and it is possible to distinguish between two types of O 3 monitoring sites. A seasonal diurnal diagram provides the investigator with the opportunity to identify whether a specific O 3 monitoring site has more scavenging than any other site. Ozone is rapidly depleted near the surface below the nocturnal inversion layer (Berry, 1964). Mountainous sites, which are above the nocturnal inversion layer, do not necessarily experience this depletion (Stasiuk and Coffey, 1974). Taylor and Hanson (1992) reported similar findings using data from the Integrated Forest Study. For the low-elevation sites, the authors reported that intraday variability was most significant due to the pronounced daily amplitude in O 3 concentration between the predawn minimum and midafternoon-to-early evening maximum. The authors reported that the

4-46interday variation was more significant in the high-elevation sites. Ozone trapped below the inversion layer is depleted by dry deposition and chemical reactions if other reactants are present in sufficient quantities (Kelly et al., 1984). Above the nocturnal inversion layer, dry deposition generally does not occur, and the concentration of O 3 scavengers generally is lower, so O 3 concentration remains fairly constant (Wolff et al., 1987). A flat diurnal pattern is usually interpreted as indicating a lack of efficient scavenging of O 3 or a lack of photochemical precursors, whereas a varying diurnal pattern is taken to indicate the opposite. With the composite diagrams alone, it is difficult to quantify the daily or long-term exposures
of O 3 . For example, the diurnal patterns for two such sites are illustrated in Figure 4-10. The Jefferson County, KY, site is urban-influenced and experiences elevated levels of O 3 and NO

x . The Oliver County, ND, site is fairly isolated from urban-influenced sources and hourly average O 3 concentrations are mostly below 0.09 ppm. The flat diurnal pattern observed for the Oliver County site is usually interpreted as indicating a lack of efficient scavenging of
O 3 or a lack of photochemical precursors, whereas the varying diurnal pattern observed at the Jefferson County site may be interpreted to indicate the opposite. Logan (1989) has described the diurnal pattern for several rural sites in the United States (Figure 4-11) and noted that average daily profiles showed a broad maximum from about 1200 hours until about 1800 hours at all the eastern sites, except for the peak of Whiteface Mountain, NY. Logan (1989) noted that the maximum concentrations were higher at the SURE sites than at the Western National Air Pollution Background Network sites in the East, because the latter were situated
in more remote or coastal locations. There is concern that the highest hourly average concentrations observed at rural agricultural and forested sites occur outside the most biologically active period of the day. To address this concern, a review of the hourly average data collected at all rural agricultural
and forested sites in EPA’s AIRS database for 1990 to 1992 was undertaken to evaluate the percentage of time hourly average concentrations ≥ 0.1 ppm occurred during the period 0900 to 1559 hours in comparison with the 24-h period. Each rural site for each of the 3 years that experienced a 3-mo SUMO6 ≥ 26.4 ppm-h (see Chapter 5, Section 5.6 for more details concerning the use of a 3-mo SUMO6 exposure index) was characterized. It was found that 70% of the rural agricultural and forested sites used in the analysis experienced at least 50%
of the occurrences ≥ 0.1 ppm during the period 0900 to 1559 hours when compared to the 24- h period (Figure 4-12). When O 3 monitoring sites in California were eliminated, approximately 73% of the remaining sites experienced at least 50% of the occurrences ≥ 0.10 ppm during the daylight 7-h period when compared with the 24-h period (Figure 4-13). Reviewing Figures 4-12 and 4-13, for most rural agricultural and forested sites that experience 3-mo SUMO6 ≥ 26.4 ppm in the United States, most of the hourly average
concentrations ≥ 0.1 ppm occur during the 0900 to 1559 hours period.



### 4.4.2 Urban Area Diurnal Patterns The U.S. Environmental Protection Agency (1986) has discussed diurnal patterns for urban sites. Figure 4-14, reproduced from the previous document, shows the diurnal pattern of O 3 concentrations on July 13, 1979, in Philadelphia, PA. On this day a peak 1-h average concentration of 0.20 ppm, the highest for the month, was reached at 1400 hours, presumably as the result of meteorological factors, such as atmospheric mixing and local

4-47Figure 4-10. The comparison of the seasonal diurnal patterns using 1988 data for Jefferson County, KY, and Oliver County, ND.

photochemical processes. The severe depression of concentrations to below detection limits (less than 0.005 ppm) between 0300 and 0600 hours usually is explained as resulting from the scavenging of O 3 by local NO emissions. In this regard, this station is typical of most urban
locations. Diurnal profiles of O 3 concentrations can vary from day to day at a specific site, however, because of changes in the various factors that influence concentrations. Composite diurnal data (that is, concentrations for each hour of the day averaged over multiple days or months) often differ markedly from the diurnal cycle shown by concentrations for a specific day. In Figures 4-15 through 4-17 (reproduced from the previous document), diurnal data for 2 consecutive days are compared with composite diurnal data (1-mo averages of hour-by-hour measurements) at three different kinds of sites: (1) center city-commercial (Washington, DC), (2) rural-near urban (St. Louis, MO), and (3) suburban-residential (Alton, IL). Several obvious points of interest present themselves in these figures: at some sites, at least, peaks can occur at virtually any hour of the day or night, but these peaks may not show up strongly in the longer term average data; some sites may be exposed to multiple peaks during a 24-h period; and disparities, some of them large, can exist between peaks (the diurnal data) and the 1-mo average (the composite diurnal data) of hourly O 3 concentrations.

4-48Figure 4-11. Diurnal behavior of ozone at rural sites in the United States in July. Sites are identified by the state in which they are located. (a) Western National Air Pollution Background Network (NAPBN); (b) Whiteface Mountain (WFM) located at 1.5 km above sea level; (c) eastern NAPBN sites; and (d) sites selected from the Electric Power Research Institute’s Sulfate Regional Air Quality Study. IN(R) refers to Rockport.

Source: Logan (1989).

4-49Figure 4-12. Percent of time hourly average concentrations ≥ 0.1 ppm occurred between 0900 and 1559 hours in comparison to 24-h period for all rural agricultural and forested sites with 3-mo SUM06 ≥ 26.4 ppm-h.

Figure 4-13. Percent of time hourly average concentrations ≥ 0.1 ppm occurred between 0900 and 1559 hours in comparison to 24-h period for all non-California rural agricultural and forested sites with 3-mo SUM06 ≥ 26.4 ppm-h.

4-50Figure 4-14. Diurnal pattern of 1-h ozone concentrations on July 13, 1979, Philadelphia,
PA.

When diurnal or short-term composite diurnal O 3 concentrations are compared with longer term composite diurnal O 3 concentrations, the peaks are smoothed as the averaging period is lengthened. Figure 4-1 demonstrates the effects of lengthening the period of time over which values are averaged. This figure shows a composite diurnal pattern calculated on the basis of 3 mo. Although seasonal differences are observed, the comparison of 3-mo (Figure 4-18) with 1-mo composite diurnal concentrations (Figure 4-17) at the Alton, IL, site readily demonstrates the smoothing out of peak concentrations as the averaging period is lengthened. As indicated in the previous version of the document (U.S. Environmental Protection Agency, 1986), although this is an obvious and familiar result in the statistical treatment of monitoring data, it is highly pertinent to the protection of human health and
welfare from the effects of O 3 .



### 4.4.3 Nonurban Area Diurnal Patterns Nonurban areas only marginally affected by transported O 3 usually have a flatter diurnal profile than sites located in urban areas. Nonurban O 3 monitoring sites experience differing types of diurnal patterns (Böhm et al., 1991; Lefohn, 1992b). As indicated earlier,
O 3 concentrations at a specific location are influenced by local emissions and by long-range transport from both natural and anthropogenic sources. Thus, considerable variation of
O 3 exposures among sites characterized as agricultural or forested is found and there is no preference for maximum diurnal patterns to occur in either the second or third quarter.

4-51Figure 4-15. Diurnal and 1-mo composite diurnal variations in ozone concentrations, Washington, DC, July 1981.

Source: U.S. Environmental Protection Agency (1986).

The diurnal patterns for several agricultural sites have been characterized (U.S. Environmental Protection Agency, 1986). Figures 4-19 and 4-20 show some typical patterns of exposure. As discussed by U.S. Environmental Protection Agency (1986), the six sites, whose diurnal patterns are illustrated in Figure 4-18, represent counties with high soybean, wheat, or hay production. The figures show a distinct afternoon maximum with the lowest concentrations occurring in the early morning and evening hours. Quarterly composite diurnal patterns clearly show the division of the afternoon O 3 concentrations into two seasonal patterns, the low, "winter" levels in the first and fourth quarters and the high, "summer" levels in the second and third quarters of the year. Remote forested sites experience unique patterns of O 3 concentrations (Evans et al., 1983; Lefohn, 1984). These sites tend to experience a weak diurnal pattern, with

4-52Figure 4-16. Diurnal and 1-mo composite diurnal variations in ozone concentrations, St. Louis County, MO, September 1981.

Source: U.S. Environmental Protection Agency (1986).

hourly average O 3 concentrations that occur frequently in the range of 0.04 to 0.05 ppm. Figure 4-21 shows diurnal patterns for several sites in the NDDN network that are located in forested areas. Several of the NDDN sites analyzed by Edgerton and Lavery (1992) exhibit fairly flat average diurnal patterns. Such a pattern is based on average concentrations calculated over an extended period. On a daily basis, some variation in O 3 concentration does occur from hour to hour, and, in some cases, high hourly average concentrations are experienced either during daytime or nighttime periods (Lefohn and Mohnen, 1986; Lefohn and Jones, 1986; Logan, 1989; Lefohn et al., 1990b; Taylor et al., 1992). Lefohn et al. (1990b) characterized O 3 concentrations at high-elevation monitoring sites. The authors reported that a fairly flat diurnal pattern for the Whiteface Mountain summit site (WF1) was observed (Figure 4-22a), with the maximum hourly average concentrations occurring in the late evening or early morning hours. A similar pattern was observed for the mid-elevation site at Whiteface Mountain (WF3). The site at the base of Whiteface Mountain (WF4) showed the typical diurnal pattern expected from sites that

4-53Figure 4-17. Diurnal and 1-mo composite diurnal variations in ozone concentrations, Alton, IL, October 1981 (fourth quarter).

Source: U.S. Environmental Protection Agency (1986).

experience some degree of O 3 scavenging. More variation in the diurnal pattern for the highest Shenandoah National Park sites occurred than for the higher elevation Whiteface Mountain sites, with the typical variation for urban-influenced sites in diurnal pattern at the lower elevation Shenandoah National Park site (Figure 4-22b). Aneja and Li (1992), in their analysis of the five high-elevation Mountain Cloud Chemistry Program (MCCP) sites (see Section 4.6.2 for site descriptions), noted the flat diurnal pattern typical of high-elevation sites that has been described previously in the literature. Aneja and Li (1992) noted that the peak of the diurnal patterns over the period May to October (1986 to 1988) for the five sites occurred between 1800 and 2400 hours, whereas the minimum was observed between 0900 and 1200 hours. However, it is important to note that, as indicated by Lefohn et al. (1990b), the flat diurnal pattern is not observed for all high-elevation sites.

4-54Figure 4-18. Composite diurnal patterns of ozone concentrations by quarter, Alton, IL,
1981.



## 4.5 Seasonal Patterns in Ozone Concentrations

4.5.1 Urban Area Seasonal Patterns

Seasonal variations in O 3 concentrations in 1981 were described by the U.S. Environmental Protection Agency (1986). The current form of the standard focuses on the highest hourly average concentrations. The description that follows uses the highest hourly average concentration as an indication of exposure. Figure 4-23 shows the 1-mo averages and the single 1-h maximum concentrations within the month for eight sites across the nation. The data from most of these sites exhibit the expected pattern of high O 3 in late spring or in summer and low levels in the winter. Data from Pomona, CA (Figure 4-23c), and Denver, CO (Figure 4-23d), show summer maxima. Tampa, FL, shows a late spring maximum but with concentrations in the fall (i.e., October) approaching those of spring (June) (Figure 4-23f). Dallas data also tend to be skewed toward higher spring concentrations; but note that November concentrations are also relatively high (Figure 4-23h). Because of seasonal humidity and storm tracks from year to year, the general weather conditions in a

4-55Figure 4-19. Quarterly composite diurnal patterns of ozone concentrations at selected sites representing potential for exposure of major crops, 1981.

Source: U.S. Environmental Protection Agency (1986).

4-56Figure 4-20. Composite diurnal ozone pattern at a rural National Crop Loss Assessment Network site in Argonne, IL, August 6 through September 30, 1980.

Source: U.S. Environmental Protection Agency (1986).

given year may be more favorable for the formation of O 3 and other oxidants than during the prior or following year. For example, 1988 was a hot and dry year during which some of the highest O 3 concentrations of the last decade occurred, whereas 1989 was a cold and wet year in which some of the lowest concentrations occurred (U.S. Environmental Protection Agency, 1992a).



### 4.5.2 Nonurban Area Seasonal Patterns In the literature, several investigators have reported on the tendency for average
O 3 concentrations to be higher in the second versus the third quarter of the year for many isolated rural sites (Evans et al., 1983; Singh et al., 1978). This observation has been attributed either to stratospheric intrusions or to an increasing frequency of slow-moving, high-pressure systems that promote the formation of O 3 . Lefohn et al. (1990a) reported that for several clean sites, the highest values of exposure indices occurred in the third quarter rather than in the second. The results of this analysis will be discussed in the Section 4.5.3. Taylor et al. (1992) reported that for 10 forest sites in North America, the temporal patterns
of O 3 on quarterly or annual periods exhibited less definitive patterns. Based on the exposure index selected, different patterns were reported. The different patterns may be

4-57Figure 4-21. Composite diurnal ozone pattern at selected National Dry Deposition Network
sites.

Source: Edgerton and Lavery (1992).

4-58Figure 4-22. Composite diurnal pattern at (a) Whiteface Mountain, NY, and the (b) Mountain Cloud Chemistry Program Shenandoah National Park site for May to September 1987.

Source: Lefohn et al. (1990b).

4-59Figure 4-23. Seasonal variations in ozone concentrations as indicated by monthly averages and the 1-h maximum in each month at selected sites, 1981.

Source: U.S. Environmental Protection Agency (1986).

4-60associated with the observations by Logan (1989) that rural O 3 in the eastern United States in the spring and summer is severely impacted by anthropogenic and possibly natural emissions
of NO

x and hydrocarbons, and that O 3 episodes occur when the weather is particularly conducive to photochemical formation of O 3 . Meagher et al. (1987) reported for rural
O 3 sites in the southeastern United States that the daily maximum 1-h average concentration was found to peak during the summer months. Taylor and Norby (1985) reported that, in the Shenandoah National Park, the probability of a day occurring in which a 1-h mean
O 3 concentration was >0.08 ppm was the same during the months of May, June, and July, whereas the probability was nearly 40% less in August. The probability of an episode during each of the remaining months of the growing season was <5%. The month of July experienced both the highest frequency of episodes and the highest mean duration of exposure

events. Aneja and Li (1992) reported that the maximum monthly ozone levels occurred in either the spring or the summer (May to August), and the minimum occurred in the fall (September and October). The timing of the maximum monthly values differed across sites and years. However, in 1988, an exceptionally high O 3 concentration year, for almost all of the five sites, June was the month in which the highest monthly average concentration occurred. This was the month in which the greatest number of O 3 episodes occurred in the
eastern United States.



### 4.5.3 Seasonal Pattern Comparisons with Sites Experiencing Low Exposures Lefohn et al. (1990a) have characterized the O 3 concentrations that occurred at several sites in the United States that experience low maximum hourly average concentrations. The Theodore Roosevelt National Park site experienced its maximum in July for 1984 and 1985 and in May for 1986. Of the three western national forest sites evaluated by Lefohn et al. (1990a), only Apache National Forest experienced its maximum monthly mean concentration in the spring. The Apache National Forest site was above mean nocturnal inversion height, and no decrease of concentrations occurred during the evening hours. This site also experienced the highest hourly maximum concentration, as well as the highest W126
O 3 exposures. The Custer and Ochoco national forest sites experienced most of their maximum monthly mean concentrations in the summer. The White River Oil Shale site in Colorado experienced its maximum monthly mean during the spring and summer months. The W126 sigmoidal weighting function index was also used to identify the month of highest O 3 exposure. A somewhat more variable pattern was observed than when the maximum monthly average concentration was used. For some sites, the winter/spring pattern was represented; for others, it was not. In some cases, the highest W126 exposures occurred earlier in the year than was indicated by the maximum monthly concentration. For example, in 1979, the Custer National Forest site experienced its highest W126 exposure in April, although the maximum monthly mean occurred in August. In 1980, the reverse occurred. There was no consistent pattern for those sites located in the continental United States. The Theodore Roosevelt and Ochoco national park sites, the Custer National Forest site, and the White River Oil Shale site experienced their maximum O 3 exposures during the spring and summer months. The sites experiencing their highest O 3 exposures in the fall-to-spring period did not necessarily experience the lowest O 3 exposures.

4-61

## 4.6 Spatial Variations in Ozone Concentrations

4.6.1 Urban-Nonurban Area Concentration Differences Diurnal concentration data presented earlier indicate that peak O 3 concentrations can occur later in the day in rural areas than in urban, with the distances downwind from urban centers generally determining how much later the peaks occur. Meagher et al. (1987) reported that for five rural sites in the Tennessee Valley region of the southeastern United States, O 3 levels were found to equal or exceed urban values for the same region. Data presented in the 1978 criteria document demonstrated that peak concentrations of O 3 in rural areas generally are lower than those in urban areas, but that average concentrations in rural areas are comparable to or even higher than those in urban areas (U.S. Environmental Protection Agency, 1978). Reagan (1984) noted that O 3 concentrations measured near population-oriented areas were depressed in comparison with data collected in more isolated areas. As noted earlier, urban O 3 values are often depressed because of titration by NO (Stasiuk and Coffey, 1974). In reviewing the NCLAN’s use of kriging to estimate the 7-h seasonal average O 3 levels, Lefohn et al. (1987a) found that the 7-h values derived from kriging for sites located in rural areas tended to be lower than the actual values because of the effect of using data from urban areas to estimate rural values. In addition to the occurrence of higher average concentrations and occasionally higher peak concentrations of O 3 in nonurban than in urban areas, it is well documented that O 3 persists longer in nonurban than in urban areas (Coffey et al., 1977; Wolff et al., 1977; Isaksen et al., 1978). The absence of chemical scavengers appears to be the main reason.



### 4.6.2 Concentrations Experienced at High-Elevation Sites The distributions of hourly average concentrations experienced at high-elevation cities are similar to those experienced in low-elevation cities. For example, the distribution of hourly average concentrations for several O 3 sites located in Denver were similar to distributions observed at many low-elevation sites in the United States. However, as will be discussed in Section 4.6.3, for assessing the possible impacts of O 3 at high-elevation sites, the use of absolute concentrations (e.g., in units of micrograms per cubic meter) instead of mixing ratios (e.g., parts per million) may be an important consideration. Lefohn et al. (1990b) summarized the characterization of gaseous exposures at rural sites in 1986 and 1987 at several MCCP high-elevation sites. Aneja and Li (1992) have
summarized the ozone concentrations for 1986 to 1988. Table 4-14 summarizes the sites characterized by Lefohn et al. (1990b). Table 4-15 summarizes the concentrations and exposures that occurred at several of the sites for the period 1987 to 1988. In 1987, the
7- and 12-h seasonal means were similar at the Whiteface Mountain WF1 and WF3 sites (Figure 4-24a). The 7-h mean values were 0.0449 and 0.0444 ppm, respectively, and the 12-h mean values were 0.0454 and 0.0444 ppm, respectively. Note that, in some cases, the 12-h mean was slightly higher than the 7-h mean value. This resulted when the 7-h mean period (0900 to 1559 hours) did not capture the period of the day when the highest hourly mean
O 3 concentrations were experienced. A similar observation was made, using the 1987

4-62Table 4-14. Description of Mountain Cloud Chemistry Program Sites a

Site Elevation (m) Latitude Longitude

Howland Forest (HF1), ME 65 45° 11 ′ 68° 46 ′

Mt. Moosilauke (MS1), NH 1,000 43° 59 ′ 18 ′′ 71° 48 ′ 28 ′′

Whiteface Mountain (WF1), NY 1,483 44° 23 ′ 26 ′′ 73° 51 ′ 34 ′′

Shenandoah NP (SH1), VA 1,015 38° 37 ′ 12 ′′ 78° 20 ′ 48 ′′

Shenandoah NP (SH2), VA 716 38° 37 ′ 30 ′′ 78° 21 ′ 13 ′′

Shenandoah NP (SH3), VA 524 38° 37 ′ 45 ′′ 78° 21 ′ 28 ′′

Whitetop Mountain (WT1), VA 1,689 36° 38 ′ 20 ′′ 81° 36 ′ 21 ′′

Mt. Mitchell (MM1), NC 2,006 35° 44 ′ 15 ′′ 82° 17 ′ 15 ′′

Mt. Mitchell (MM2), NC 1,760 35° 45 ′ 82° 15 ′

a See Appendix A for abbreviations and acronyms.

data, for the MCCP Shenandoah National Park sites. The 7- and 12-h seasonal means were similar for the SH1 and SH2 sites (Figure 4-24b). Based on cumulative indices, the Whiteface Mountain summit (1,483-m) site (WF1) experienced a higher exposure than the WF3 (1,026-m) site (Figure 4-24c). Both the sum of the concentrations ≥ 0.07 ppm (SUM07) and the number of hourly concentrations ≥ 0.07 ppm were higher at the WF1 site than at the WF3 site. The site at the base of the mountain (WF4) experienced the lowest exposure of
the three O 3 sites. Among the MCCP Shenandoah National Park sites, the SH2 site experienced marginally higher O 3 exposures, based on the index that sums all of the hourly average concentrations (i.e., referred to as "total dose" in the figure) and sigmoidal values, than the high-elevation site (SH1; Figure 4-24d). The reverse was true for the sums of the
concentrations ≥ 0.07 ppm and the number of hourly concentrations ≥ 0.07 ppm. When the Big Meadows, Dickey Ridge, and Sawmill Run, Shenandoah National Park, data for 1983 to 1987 were compared, it again was found that the 7- and 12-h seasonal
means were insensitive to the different O 3 exposure patterns. A better resolution of the differences was observed when the cumulative indices were used (Figure 4-25). There was no evidence that the highest elevation, Big Meadows, site consistently had experienced higher
O 3 exposures than the other sites. In 2 of the 5 years, the highest elevation site experienced lower exposures than the Dickey Ridge and Sawmill Run sites, based on the sum of all concentration or sigmoidal indices. For 4 of the 5 years, the SUM07 index yielded the same
result. Taylor et al. (1992) indicate that the forests they monitored experienced differences
in O 3 exposure. The principal spatial factors underlying this variation were elevation, proximity to anthropogenic sources of oxidant precursors, regional-scale meteorological conditions, and airshed dynamics between the lower free troposphere and the surface boundary layer. Table 4-16 summarizes the exposure values for the 10 EPRI Integrated Forest Study sites located in North America.

4-63Table 4-15. Seasonal (April to October) Percentiles, SUM06, SUM08, and W126 Valuesfor the Mountain Cloud Chemistry Program SitesaSiteElev.(m)YearMin.10305070909599MaxNo. Obs.SUM06SUM08W126Howland Forest, ME(HF1)65198719880.0000.0000.0130.0120.0210.0210.0280.0280.0350.0360.0460.0470.0520.0540.0650.0760.0760.1064,7664,7865.910.90.02.97.711.6Mt. Moosilauke, NH(MS1)1,000 198719880.0060.0100.0270.0260.0360.0330.0450.0430.0530.0550.0650.0760.0740.0870.0860.1130.1020.1274,0772,83545.051.99.521.240.143.4Whiteface Mountain, NY(WF1) (36-031-0002)1,483 198719880.0110.0140.0290.0250.0370.0330.0460.0430.0530.0560.0670.0780.0740.0890.0870.1100.1040.1354,7044,67362.065.812.240.849.556.5Whiteface Mountain, NY(WF3)1,026 1987 0.010 0.025 0.033 0.039 0.047 0.064 0.075 0.091 0.117 4,755 45.4 14.4 40.3Whiteface Mountain, NY(WF4)604 1987 0.000 0.011 0.023 0.031 0.041 0.056 0.065 0.081 0.117 4,463 23.8 5.1 21.3Mt. Mitchell, NC(MM1)2,006 19871988198919920.0080.0110.0100.0050.0340.0380.0380.0360.0440.0540.0470.0430.0510.0650.0540.0480.0580.0750.0590.0530.0670.0950.0680.0630.0740.1060.0720.0690.0850.1260.0810.0810.1050.1450.1470.0963,5392,9892,7883,97159.4145.154.837.87.869.73.54.446.5116.640.736.7Mt. Mitchell, NC(MM2)1,760 198719880.0170.0090.0320.0290.0420.0410.0490.0500.0560.0600.0670.0800.0730.0920.0830.1100.0960.1623,1182,99247.068.75.128.137.457.7Shenandoah Park, VA(SH1)1,015 198719880.0000.0060.0230.0240.0360.0360.0440.0470.0540.0580.0690.0770.0760.0870.0850.1030.1350.1403,6363,95954.280.98.529.642.067.2Shenandoah Park, VA(SH2)716 1987b0.003 0.027 0.040 0.049 0.059 0.071 0.077 0.086 0.145 2,908 55.7 7.855.841.8Shenandoah Park, VA(SH3)524 198719880.0000.0060.0180.0200.0290.0310.0370.0400.0470.0510.0610.0670.0680.0760.0800.0970.1080.1353,0304,27823.152.32.615.619.244.2Whitetop Mountain, VA(WT1)1,689198719880.0110.0000.0380.0300.0510.0460.0590.0580.0660.0680.0780.0840.0850.0940.0960.1190.1110.1634,3263,788147.7133.832.451.0105.7102.8aSee Appendix A for abbreviations and acronyms.bCalculations based on a May to September season.

4-64Figure 4-24. Seven- and 12-h means at (a) Whiteface Mountain and (b) Shenandoah National Park for May to September 1987 and integrated exposures at (c) Whiteface Mountain and (d) Shenandoah National Park for May to September 1987.

Source: Lefohn et al. (1990b).



### 4.6.3 Other Spatial Variations in Ozone Concentrations Despite relative intraregional homogeneity, evidence exists for intracity variations in concentrations that are pertinent to potential exposures of human populations and to the assessment of actual exposures sustained in epidemiologic studies. Two illustrative pieces of data are presented in this section: (1) a case of relative homogeneity in a city with a population under 500,000 (New Haven, CT) and (2) a case of relative inhomogeneity of concentrations in a city of greater than 9 million population (New York City). As described in the previous version of the criteria document (U.S. Environmental Protection Agency, 1986), the percentiles of the hourly average concentrations for a New Haven site and two other monitoring stations that were operating at the time in the same county, one in Derby, 9 mi west of New Haven, and one in Hamden, 6 mi north of New Haven, generally are similar. Table 4-17 shows the monitoirng data and time of the maximum hourly concentrations by quarter at these three sites.

4-65Figure 4-25. Integrated exposures for three non-Mountain Cloud Chemistry Program Shenandoah National Park sites, 1983 to 1987.

Source: Lefohn et al. (1990b).

4-66Table 4-16. Summary Statistics for 11 Integrated Forest Study SitesaSiteYearQuarter24-h(ppb)12-h(ppb)7-h(ppb)1-h Max(ppb)SUM06(ppm-h)SUM08(ppm-h)HIGH ELEVATION SITESWhiteface Mountain, NY 1987198719881988232342454944434450434243494310411413111913.230.133.522.62.511.813.910.4Great Smoky Mountain NP 19871987198819882323545371595251705749496855999511912057.134.3126.374.710.98.861.222.2Coweeta Hydrologic Lab, NC 19871987198819882323504761574844595447425951859510410032.424.181.663.62.62.418.519.8LOW ELEVATION SITESHuntington Forest, NY 198719871988198823233624403742324646423346488876106919.85.419.218.60.90.26.12.7Howland, MA 19871987198819882323342636243932413039314130697690711.93.88.11.70.00.02.90.0Oak Ridge, TN1987198719881988232342294032534457475041585111210510412239.524.326.419.713.59.09.87.7

4-67Table 4-16 (cont’d). Summary Statistics for 11 Integrated Forest Study SitesaSiteYearQuarter24-h(ppb)12-h(ppb)7-h(ppb)1-h Max(ppb)SUM06(ppm-h)SUM08(ppm-h)LOW ELEVATION SITES (cont’d)Thompson Forest, WA 198719871988198823233630323243363939413437361039410314010.710.38.113.53.62.12.36.7B.F. Grant Forest, GA 198719871988198823233233473246526347485464489910212711626.131.353.124.15.110.321.97.4Gainesville, FL 19871987198819882323422935205344482950415130bb8470bb23.41.9bb0.50.1Duke Forest, NC 1987198719881988232338525438485969515250755410012411514129.2bb52.97.8bb23.4Nordmoen, Norway19871987198819882323321422114018281541202916753253302.40.00.00.00.00.00.00.0aSee Appendix A for abbreviations and acronyms.bData were insufficient to calculate statistic.Source: Adapted from Taylor et al. (1992).

4-68Table 4-17. Quarterly Maximum One-Hour Ozone Values at Sites in and Around New Haven, Connecticut, 1976 (Chemiluminescence Method, Hourly Values in ppm)

Quarter of Year

1 2 3 4

New Haven, CT

No. measurements Max 1-h, ppm Time of day
Date 10
0.045
1100 hours
March 29 1,964
0.274
1400 hours

June 24 2,079
0.235
1400 hours August 12 66
0.066
1000 hours
October 3

Derby, CT

No. measurements Max 1-h, ppm Time of day
Date 11
0.015
2300 hours
March 31 2,140
0.280
1400 hours

June 24 2,187
0.290
1400 hours August 12 1,360
0.060
1900 hours
December 20

Hamden, CT

No. measurements Max 1-h, ppm Time of day
Date 56
0.050
2400 hours
March 29 2,065
0.240
1500 hours

June 24 1,446
0.240
1300 hours

July 20 286
0.065
1500 hours
October 7

Source: U.S. Environmental Protection Agency (1986).

The source of much of the O 3 experienced in the New Haven area is the greater New York area (e.g., Wolff et al., 1975; Cleveland et al., 1976a,b). An urban plume transported over the distance from New York City to New Haven would tend to be relatively well-mixed and uniform, such that intracity variations in New Haven probably would be
minimal. As indicated in the previous version of the criteria document (U.S. Environmental Protection Agency, 1986), intracity differences in O 3 concentrations also have been reported by Kelly et al. (1986) for a 1981 study in Detroit, MI. Ozone concentrations were measured for about 3 mo at 16 sites in the metropolitan Detroit area and in nearby Ontario, Canada. Values at 15 sites were correlated with those at a site adjacent to the Detroit Science Center, about 3 km north of the central business district in Detroit. In general, the correlation decreased as distance from the Science Center site increased; and, in general, the actual concentrations increased with distance from that site toward the north-northeast. The highest
O 3 concentrations were recorded at sites about 10 to 70 km north-northeast of the urban core. At greater distances or in other directions, O 3 maxima decreased. Chicago is an example where O 3 concentrations increase as the distance from the inner city increases. Figure 4-26 shows, for a number of O 3 monitoring sites, the number of days in 1991 that the maximum hourly average concentration was greater than 0.1 ppm. The greatest number of exceedances of the daily maximum 1-h concentration of 0.1 ppm was to the north of the city.

4-69Figure 4-26. Number of days in 1991 for which the maximum hourly average ozone concentration was greater than 0.1 ppm at Chicago, IL.

4-70Concentrations of O 3 vary with altitude and with latitude. Although a number of reports contain data on O 3 concentrations at high altitudes (e.g., Coffey et al., 1977; Reiter, 1977b; Singh et al., 1977; Evans et al., 1985; Lefohn and Jones, 1986), fewer reports are available that present data for different elevations in the same locality. There appears to be no consistent conclusion concerning the relationship between O 3 exposure and elevation. Wolff et al. (1987) reported, for a short-term study at High Point Mountain in northwestern New Jersey, that both the daily maximum and midday O 3 concentrations were similar at different altitudes, but that the O 3 exposures increased with elevation. Wolff et al. (1987) conducted a study of the effects of altitude on O 3 concentrations at three sites located at three separate elevations on High Point Mountain in northwestern New Jersey. Data for several days indicate that in mid-July, when atmospheric mixing was good, vertical profiles were nearly constant, with concentrations increasing only slightly with elevation. Likewise, the daily O 3 maxima were similar at different elevations. At night, however,
O 3 concentrations were nearly zero in the valley (i.e., the lowest-elevation site) and increased with elevation. Comparison of the O 3 exposures at the three sites (number of hours >0.08 ppm) showed that greater cumulative exposures were sustained at the higher elevations. Comparable data from an urban area (Bayonne) about 80 km southeast of High Point Mountain showed that the cumulative exposures were higher at all three of the mountain sites than in the urban area (Wolff et al., 1987). The investigators concluded from their concentration and meteorological data that elevated, mountainous sites in the eastern United States may be expected to be exposed to higher O 3 concentrations than valley sites throughout the year. Winner et al. (1989) reported that, for three Shenandoah National Park sites (i.e., Big Meadows, Dickey Ridge, and Sawmill Run), the 24-h monthly mean O 3 concentrations tended to increase with elevation, but that the number of elevated hourly occurrences equal to or above selected thresholds did not. The authors reported that the highest elevation site (Big Meadows) experienced a smaller number of concentrations at or below the minimum detectable level than did the other two sites. The larger number of hourly average concentrations that occurred at or below the minimum detectable level at both Dickey Ridge and Sawmill Run resulted in lower 24-h averages at these sites. Lefohn et al. (1990b), characterizing the O 3 exposures at several high-elevation sites, reported that, based on cumulative indices, the Whiteface Mountain summit site (WF1) experienced a slightly higher exposure than the lower elevation Whiteface Mountain (WF3) site. The site at the base of Whiteface Mountain (WF4) experienced the lowest exposure of
the three O 3 sites. Among the MCCP Shenandoah National Park sites, the SH2 site experienced higher O 3 exposures than the high-elevation site (SH1). The "total dose" (correctly referred to as the sum of all hourly average concentrations) and sigmoidal (W126) indices were slightly higher at the SH2 than at the SH1 site. The data capture at the two sites for the 5-mo period was similar. However, the sum of the concentrations ≥ 0.07 ppm and the number of hourly concentrations ≥ 0.07 ppm were slightly higher at the SH1 than at the SH2 site. For the Whiteface Mountain sites, both the sum of the concentrations ≥ 0.07 ppm (SUM07) and the number of hourly concentrations ≥ 0.07 ppm were higher at the WF1 site
than at the WF3 site. When the Big Meadows, Dickey Ridge, and Sawmill Run, Shenandoah National Park, data for 1983 to 1987 were compared, a higher resolution of the differences among the regimes was observed when the cumulative indices were used. No specific trend could be identified that showed the highest elevation site, Big Meadows, had consistently experienced

4-71higher O 3 exposures than the lower elevation sites. In 2 of the 5 years, the highest elevation site experienced lower exposures than the Dickey Ridge and Sawmill Run sites, based on the sum of all concentrations or sigmoidal indices. For 4 of the 5 years, the SUM07 index yielded the same result. An important issue for assessing possible impacts of O 3 at high-elevation sites that requires further attention is the use of mixing ratios (e.g., parts per million) instead of absolute concentration (e.g., in units of micrograms per cubic meter) to describe
O 3 concentration. In most cases, mixing ratios or mole fractions are used to describe
O 3 concentrations. Lefohn et al. (1990b) have pointed out that the manner in which concentration is reported may be important when assessing the potential impacts of air pollution on high-elevation forests. Concentration varies as a function of altitude. Although the change in concentration is small when the elevational difference between sea level and themonitoring site is small, it becomes substantial at high-elevation sites. Given the same partper-million value experienced at both a high- and low-elevation site, the absolute concentrations (i.e., micrograms per cubic meter) at the two elevations will be different.
Because both O 3 and ambient air are gases, changes in pressure directly affect their volume. According to Boyle’s law, if the temperature of a gas is held constant, the volume occupied by the gas varies inversely with the pressure (i.e., as pressure decreases, volume increases). This pressure effect must be considered when measuring absolute pollutant concentrations. At any given sampling location, normal atmospheric pressure variations have very little effect on air pollutant measurements. However, when mass/volume units of concentration are used and pollutant concentrations measured at significantly different altitudes are compared, pressure (and, hence, volume) adjustments are necessary. In practice, the summit site at Whiteface Mountain had a slightly higher O 3 exposure than the two low-elevation sites (Lefohn et al., 1991). However, at Shenandoah National Park sites, the higher elevation site experienced lower exposures than lower elevation sites in some years. These exposure considerations are trivial at low-elevation sites. However, whenone compares exposure-effects results obtained at high-elevation sites with those from lowelevation sites, the differences may become significant (Lefohn et al., 1990b). In particular, assuming that the sensitivity of the biological target is identical at both low and high elevations, some adjustment will be necessary when attempting to link experimental data obtained at low-elevation sites with air quality data monitored at the high-elevation stations.



## 4.7 Indoor Ozone Concentrations

Most people in the United States spend a large proportion of their time indoors. A knowledge of actual exposures of populations to indoor levels of O 3 is essential for the interpretation and use of results associated with epidemiological studies. However, essentially all routine air pollution monitoring is done on outdoor air. Until the early 1970s, very little
was known about the O 3 concentrations experienced inside buildings. The ratio of the indoor/outdoor (I/O) O 3 concentrations is a parameter that has been widely used for studying the indoor and outdoor relationships, sources, and exposure patterns of O 3 . However, the database on this subject is not large, and a wide range of I/O O 3 concentration relationships can be found in the literature. The only significant source of O 3 in indoor residential air is
infiltration of outdoor O 3 , with ventilation rates affecting the flow of air between indoor and outdoor (Zhang and Lioy, 1994).

4-72Reported I/O values for O 3 are highly variable (U.S. Environmental Protection Agency, 1986) and range from <0.1 to 0.8 for various indoor environments and ventilation rates (Weschler et al., 1989). Unfortunately, the number of experiments and kinds of structures examined to date provide only limited data for use in modeling indoor exposures. Data were summarized by Yocom (1982) describing studies of indoor-outdoor gradients in buildings and residences for either O 3 or photochemical oxidant. The results were highly variable. A relatively large number of factors can affect the difference in O 3 concentrations between the inside of a structure and the outside air. In general, outside air infiltration or exchange rates, interior air circulation rates, and interior surface composition (e.g., rugs, draperies, furniture, walls) affect the balance between replenishment and decomposition of O 3 within buildings (U.S. Environmental Protection Agency, 1986). Although indoor
concentrations of O 3 will almost invariably be less than outdoors, the fact that people spend more time indoors than outdoors may result in greater overall indoor exposures. Cass et al. (1991) have discussed the importance of protecting works of art from damage due to O 3 . Experiments show that the fading of artists’ pigments in the presence of
O 3 is directly related to the product of concentration times duration of exposure. Druzik et al. (1990) reported that, in a survey of 11 museums, galleries, historical houses, and libraries in Southern California, facilities with a high air exchange with the outdoors and no pollutant removal system have indoor O 3 concentrations more than two-thirds those of outdoor concentrations. The author reported that museums with conventional air-conditioning systems
showed indoor O 3 concentrations about 30 to 40% of those outside, whereas museums with no forced ventilation system, where slow air infiltration provides the only means of air exchange, have indoor O 3 levels typically 10 to 20% of those outdoors. Several other studies have been reported in the literature and Table 4-18 lists the I/O ratios reported from these efforts as well as those from earlier years.
Automobiles and other vehicles constitute another indoor environment in which people may spend appreciable amounts of time. As with buildings, the mode of ventilation and cooling helps determine the inside concentrations. The U.S. Environmental Protection Agency (1986) describes studies for the I/O ratios. In one study reported by Contant et al. (1985), the I/O ratios from 49 measurements inside vehicles were 0.44 for the mean, 0.33 for the median, and 0.56 for maximum concentrations measured. Chan et al. (1991) reported anI/O ratio of 0.20 for median in-vehicle concentrations (0.011 ppm) and time-matched fixedsite measurements (0.051 ppm). At present, there are no long-term monitoring data on indoor air pollutant concentrations comparable to the concentration data available for outdoor locations. Thus, for estimates of the exposure of building or vehicle occupants to O 3 and other photochemical oxidants, it is necessary to rely on extrapolations of very limited I/O data.



## 4.8 Estimating Exposure to Ozone

4.8.1 Introduction Human exposure represents the joint occurrence of an individual being located at point ( x,y,z ) during time t , with the simultaneous presence of an air pollutant at concentration
C

x,y,z ( t ) (U.S. Environmental Protection Agency, 1991). Consequently, an individual’s exposure to an air pollutant is a function of location as well as time. If a volume at a location can be defined such that air pollutant concentrations within it are homogeneous yet

4-73Table 4-18. Summary of Reported Indoor-Outdoor Ozone Ratios

Structure Indoor-Outdoor Ratio Reference

Hospital 0.67 a Thompson (1971)

Residence (with evaporative cooler) 0.60 a Thompson et al. (1973)

Office

(air-conditioned; 100% outside air intake) 0.80 ± 0.10 Sabersky et al. (1973)

(air-conditioned; 70% outside air intake) 0.65 ± 0.10 Sabersky et al. (1973)

Office 0.66
0.54 Shair and Heitner (1974) Shair and Heitner (1974)

Office/Lab 0.62 Hales et al. (1974)

Residence 0.70 Sabersky et al. (1973)

Residence 0.50-0.70 Moschandreas et al. (1978)

Two offices 0.30 Moschandreas et al. (1978)

Residence

(gas stoves) (all electric) 0.19
0.20 Moschandreas et al. (1981)

Office 0.29 Moschandreas et al. (1978)

School room 0.19 (max concentration) Berk et al. (1980)

Residence 0.10-0.25 Berk et al. (1981)

Residences (1 each)

(air-conditioned) (100% outside air; no air-conditioning) 0.00-0.09
1.00 Stock et al. (1983)

Residences (12)

(air-conditioned) 0.21 (mean concentration) 0.12 (med. concentration) 0.59 (max concentration) Contant et al. (1985)

Residences (41) 0.30 Lebowitz et al. (1984)

Residences (6)

(window open) (window closed) (air-conditioning) 0.59 ± 0.16
0.26 ± 0.12
0.28 ± 0.12 Zhang and Lioy (1994)

Art gallery 0.50 Shaver et al. (1983)

Art gallery

(three modes of ventilation in each 24-h period: recirculation,
mixture of recirculated and outside air, and 100% outside air) 0.70 ± 0.10 (mean concentration) Davies et al. (1984)

Museums <0.10 Shaver et al. (1983)

4-74Table 4-18 (cont’d). Summary of Reported Indoor-Outdoor Ozone Ratios

Structure Indoor-Outdoor Ratio Reference

Museum 0.45 Nazaroff and Cass (1986)

Museums

(with high air exchange, but no air-conditioning)

(with no air-conditioning and with low air exchange rate)

(with natural convection-induced air-exchange system)

(with conventional air-conditioning system but with no activated carbon air filtration)

(with activated carbon air-filtration system) 0.69-0.84 (1 h) 0.50-0.87 (8 h)

0.10-0.59 (1 h) 0.10-0.58 (8 h)

0.33-0.49 (1 h) 0.28-0.40 (8 h)

0.24-0.40 (1 h) 0.25-0.41 (8 h)

0.03-0.37 (1 h) 0.03-0.31 (8 h) Druzik et al. (1990)

a Measured as total oxidants.

potentially different from other locations, the volume may be considered a "microenvironment" (Duan, 1982). Microenvironments may be aggregated by location (i.e., indoor or outdoor) or activity performed at a location (i.e., residential, commercial) to form microenvironment types. Also, activity has two major dimensions: location and
exertion. Various microenvironments can have different levels of ventilation that will significantly influence the delivered dose. Air Quality Criteria for Carbon Monoxide (U.S. Environmental Protection Agency, 1991) discusses the difference between individual and population exposures. The document notes that Sexton and Ryan (1988) define the pollutant concentrations experienced by a specific individual during normal daily activities as "personal" or "individual" exposures. A personal exposure depends on the air pollutant concentrations that are present in the location through which the person moves, as well as on the time spent at each location. Because time-activity patterns can vary substantially from person to person, individual exposures exhibit wide variability (U.S. Environmental Protection Agency, 1991). Thus, although it is a relatively straightforward procedure to measure any one person’s exposure, many such measurements may be needed to quantify exposures for a defined group. The daily activities of a person in time and space define the individual’s activity pattern. Accurate estimates of air pollution exposure generally require that an exposure model account for the activity patterns of the population of interest. From a public health perspective, it is important to determine the "population exposure", which is the aggregate exposure for a specified group of people (e.g., a community or an identified occupational cohort). Because exposures are likely to vary substantially between individuals, specification of the distribution of personal exposures within a

4-75population, including the average value and the associated variance, is often the focus of exposure assessment studies. In many cases, the upper tail of the distribution, which represents those individuals exposed to the highest concentrations, is frequently of special interest because the determination of the number of individuals who experience elevated pollutant levels can be critical for health risk assessments. This is especially true for pollutants for which the relationship between dose and response is highly nonlinear. Runeckles and Bates (1991) have pointed out the importance of peak concentrations in eliciting adverse human effects. As indicated in Section 4.1, results using controlled human exposures have shown the possible importance of concentration in relation to duration of exposure and inhalation rate. The implication of the importance of concentration can be translated into the conclusion that the simple definition of exposure (i.e., equal to concentration multiplied by time) may be too simplified. Several human exposure models have been developed for most cases, because it is not possible to estimate population exposure solely from fixed-station data. Some of these models include information on human activity patterns (i.e., the microenvironments people visit and the time they spend there). These models also contain submodels depicting the sources and concentrations likely to be found in each microenvironment, including indoor, outdoor, and in-transit settings.



### 4.8.2 Fixed-Site Monitoring Information Used To Estimate Population and Vegetation Exposure Based on the information provided in earlier sections in this chapter, fixed-site monitors alone cannot accurately depict population exposures for most cases, because indoor
and in-transit concentrations of O 3 may be significantly different from ambient
O 3 concentrations, and ambient outdoor concentrations of O 3 that people come in contact with may vary significantly from O 3 concentrations measured at fixed-site monitors. Fixed-site monitors measure concentrations of pollutants in ambient air. Ambient air as noted by the U.S. Environmental Protection Agency (1991) is defined in the U.S. Code of Federal Regulations (1991) as air that is "external to buildings, to which the general public has access." But the nature of modern urban lifestyles in many countries, including the United States, is that people spend an average of over 20 h per day indoors (Meyer, 1983). Reviews
of studies summarized in Section 4.7 show that indoor O 3 concentration measurements vary significantly from simultaneous measurements in ambient air. The difference between indoor and outdoor air quality and the amount of time people spend indoors reinforce the conclusion that using ambient air quality measurements alone does not provide accurate estimates of population exposure in most cases. It is assumed that exposure for vegetation is the same as the concentration information provided at fixed monitors in the field (see Sections 5.5 and 5.6). In some cases, because of foliar scavenging and height differences between the vegetation canopy and the pollutant monitor, the measured concentration is not equivalent to the vegetation exposure. A subgroup, children attending summer camp, has been studied by several investigators to evaluate the influence of ambient air pollution on respiratory health and function. Because children are predominantly outdoors and relatively active while at camp, they provide a unique opportunity to examine the relationships between respiratory health and

4-76function and concurrent air pollution levels. Children may be at potentially increased risk from air pollution by virtue of their lifestyle patterns, which often involve several hours of outdoor exercise, regardless of air quality, during daylight hours. For campers, attempts have been made to estimate human exposure to O 3 using types of activity patterns (Mage et al., 1985; Paul et al., 1987). Mage et al. (1985) developed an objective approach to estimate the dose delivered to the lung of a 12-year-old camper by using pulmonary minute volume associated with a specific activity, the fractional penetration beyond the trachea, and infiltration of ozone indoors. Lioy and Dyba (1989) have applied the parameters used by Mage et al. (1985) to predict the delivered O 3 dose over a 4-day episodic period. The schedule of a hypothetical camper was matched to the actual O 3 concentrations, and the predicted doses were estimated. Several studies involving children attending summer camp have been summarized in Chapter 7. In one study, Avol et al. (1990) reported that O 3 levels at a Southern California summer camp, located 190 km southeast of Los Angeles, CA, rose gradually throughout each day, displaying a "broad peak" between 1000 and 2000 hours each day. Daily maxima typically occurred in late afternoon (1500 to 1700 hours); subsequently, concentrations gradually declined to overnight O 3 levels of 0.025 to 0.050 ppm. Spektor et al. (1991) investigated the pulmonary function of 46 healthy children on at least 7 days for each child during a 4-week period at a northwestern New Jersey residential summer camp in 1988. The daily levels of 1-h peak O 3 and the 12-h average hygroden ion (H + ) concentrations are shown in Figure 4-27. On 5 of these days, the current NAAQS of 0.12 ppm was exceeded. The maximum hourly concentration attained during the study was 0.15 ppm. The year 1984 was a
milder O 3 exposure year and Figure 4-28 summarizes the maximal 1-h O 3 concentrations at Fairview Lake during a 1984 study period (Spektor et al., 1988).



### 4.8.3 Personal Monitors A personal exposure profile can be identified by using a personal exposure monitor. McCurdy (1994) has described the development of personal exposure monitors by several companies. However, few data are available describing personal exposures for individuals using these monitors. An example of a pilot study using a personal exposure monitor was described for assessing O 3 exposure in 23 children by Liu et al. (1993). The accuracy of the monitor was within 20% of the actual value. The authors collected indoor, outdoor, and personal O 3 concentration data as well as time-activity data in State College, PA. Results from the pilot study demonstrated that fixed-site ambient measurements may not adequately represent individual exposures. Outdoor O 3 concentrations showed substantial spatial variation between rural and residential regions. In addition, Liu et al. (1993) reported that models based on time-weighted indoor and outdoor concentrations explained only 40% of the variability in personal exposures. When the model used included observations for only those participants who spent the majority of their day in or near their homes, an R 2 of 0.76 resulted when estimates were regressed on measured personal exposures. The authors
concluded that contributions from diverse indoor and outdoor microenvironments should be considered to estimate personal O 3 exposure accurately. From these results, it is clear that additional data are needed to better quantify the O 3 exposures to which populations are exposed. Figure 4-27. Maximum 1-h ozone (O 3 ) concentrations (in parts per billion) and average 0800 to 2000 hours strong acid concentrations (expressed as micrograms per

4-77cubic meter of sulfuric acid [H 2 SO 4 ]) for each day that pulmonary function data were collected at Fairview Lake camp in 1988. The correlation coefficient between O 3 and the hydrogen ion (H + ) was 0.56.

Source: Spektor et al. (1991).



### 4.8.4 Population Exposure Models McCurdy (1994) has reviewed the current status of human exposure modeling. The author describes two distinct types of O 3 exposure models: (1) those that focus narrowly on predicting indoor O 3 levels and (2) those that focus on predicting O 3 exposures on a community-wide basis. The models that predict indoor O 3 levels have been described by Sabersky et al. (1973), Shair and Heitner (1974), Nazaroff and Cass (1986), and Hayes (1989, 1991). McCurdy (1994) discusses four distinct models that predict O 3 exposure on a community-wide basis. These models and their distinguishing features are: 1. pNEM/O 3 based on the National Air Quality Standards Exposure Model (NEM) series of models (Paul et al., 1987; Johnson et al., 1990; McCurdy et al., 1991). · Uses mass-balance approach and seasonal considerations for I/O ratio
estimation. · Variables affecting indoor exposure obtained by Monte Carlo sampling from empirical distributions of measured data.

4-78Figure 4-28. Maximal 1-h ozone concentrations at Fairview Lake during the study period.

Source: Spektor et al. (1988).

2. Systems Applications International (SAI)/NEM (Hayes et al., 1984; Hayes and Lundberg, 1985; Austin et al., 1986; Hayes et al., 1988; Hayes and Rosenbaum, 1988).
· More districts and microenvironments and more detailed mass-balance model than pNEM/O 3 . · Human activity data outdated and inflexible. 3. Regional Human Exposure Model (REHEX) (Lurmann and Colome, 1991; Winer et al., 1989; Lurmann et al., 1989; Lurmann et al., 1990). · More detailed geographic resolution than NEM. · Uses California-specific activity data and emphasizes in-transit and outdoor
microenvironments. 4. Event probability exposure model (EPEM) (Johnson et al., 1992). · Estimates probability that a randomly selected person will experience a particular exposure regime. · Lacks multiday continuity. McCurdy (1994) points out that all four models are related to the NEM. The NEM is an EPA exposure model developed in the 1980s (Biller et al., 1981). Outdoor air quality data are obtained from monitoring or modeling data. In most applications of NEM, fixed-site monitoring data are used. The hourly average values are transformed by a suitable relationship so that they better represent air quality outside of the various microenvironments of interest. McCurdy (1994) points out that the important point of the NEM spatial dimension is that people can be assigned to a monitor using U.S. census data. In addition,

4-79community trips can be assigned among the districts, grid cells, or neighborhood types using census data. Thus, the NEM model simulates the movement of people through space for work-trip purposes. Interested readers are referred to McCurdy (1994) for further discussion of the pNEM model.



### 4.8.5 Concentration and Exposures Used in Research Experiments It is important to adequately characterize the exposure patterns that result in vegetation and human health effects. Hourly average concentrations used in many of the high treatment experimental studies did not necessarily mimic those concentrations observed under ambient conditions. Although the ramifications of this observation on the effects observed are not clear, it was pointed out that the highest treatments used in many of the open-top chamber experiments were bimodal in the distribution of the hourly average concentrations. In other experiments designed to assess the effects of O 3 on vegetation, constant concentration (i.e., square wave) exposures were implemented. As has been discussed in earlier sections of this chapter, hourly average concentrations change by the hour and square wave exposure regimes do not normally occur under ambient conditions. In addition to the exposures used at the highest treatment levels, there is concern that the hourly average concentrations used in the control treatments may be lower than those experienced at isolated sites in the United States or in other parts of the world. Although the ramifications of using such exposure regimes are unclear, there is some concern that the use of atypically low control levels may result in an overestimation of vegetation yield losses when used as the baseline for evaluating the effects of treatments at higher concentrations (Lefohn and Foley, 1992). For assessing the human health effects of O 3 exposure, a series of studies has explored prolonged 6.6-h O 3 exposures at low levels (i.e., 0.08 to 0.12 ppm) (Horstman et al., 1990). McDonnell et al. (1991), using similar hourly average concentration regimes, have confirmed the findings reported by Horstman et al. (1990). All the research investigations using 6.6-h durations have applied constant concentrations during the exposure period. If, as indicated in the introduction of this chapter, concentration is more important than duration and ventilation rate, different human health effects may occur as a result of different exposure regimes that have identical 6.6-h average concentrations. Because of this, it is important to explore the different types of exposure regimes that occur under ambient conditions during an 8-h episode. Lefohn and Foley (1993) reported on an analysis of hourly average data for
O 3 monitoring sites that never experienced an exceedance of an hourly average concentration

≥ 0.12 ppm and that experienced 8-h daily maximum average concentrations >0.08 ppm. For those monitoring sites that met the above two criteria, they identified the number of times the 8-h daily maximum average concentration exceeded 0.08 ppm during the monitoring year. For the period 1987 to 1989, there were 925 exposure regimes identified from 166 site-years of data that met the above criteria. The data were then organized into the following seven categories: I. The occurrence of 8-h daily maximum averages >0.08 ppm and <0.09 ppm; II. The occurrence of 8-h daily maximum averages >0.08 ppm but ≤ 0.082 ppm, which contained only hourly average concentrations >0.08 ppm but

≥ 0.082 ppm; III. 8-h daily maximum averages >0.08 ppm, which contained hourly average concentrations <0.09 ppm;

4-80IV. 8-h daily maximum averages >0.08 ppm and <0.09 ppm, which contained at least one hourly average concentration ≥ 0.09 ppm but <0.10 ppm; V. 8-h daily maximum averages >0.08 ppm and <0.09 ppm, which contained at least one hourly average concentration ≥ 0.10 ppm; VI. 8-h daily maximum averages <0.08 ppm, which contained at least one hourly average concentration ≥ 0.09 ppm but <0.10 ppm; and VII. 8-h daily maximum averages <0.08 ppm, which contained at least one hourly average concentration ≥ 0.10 ppm. Figure 4-29 summarizes the results of the analysis. The results indicated that there was a poor relationship between the value of the 8-h daily maximum average concentration and the frequency of occurrence of hourly average concentrations within specific ranges (e.g., between 0.09 and 0.10 ppm). In no case could the authors identify a monitoring site that experienced the square-wave type of exposure that was described in Category II (i.e., the occurrence of 8- h daily maximum averages >0.08 ppm but ≤ 0.082 ppm). Lefohn and Foley (1993) concluded that the square wave exposures used in the 6.6-h human health effects experiments were not found under ambient conditions. The authors identified 453 additional exposure regimes, where the 8-h daily maximum average was <0.08 ppm but experienced maximum hourly average concentrations ≥ 0.09 ppm. Thus, if hourly average concentrations ≥ 0.08 ppm are of concern for affecting human health, there will be instances where occurrences above this threshold are evident, but the 8-h average value is below 0.08 ppm.



## 4.9 Concentrations of Peroxyacetyl Nitrates in Ambient Atmospheres

4.9.1 Introduction The biological effects of PAN in human exposures, toxicological studies of animals, and plant response and yield have been considered previously (U.S. Environmental Protection Agency, 1986). Controlled human exposure studies involving O 3 and O 3 + PAN are discussed elsewhere in this document (Chapter 7, Section 7.2.6.3). Some effects on respiratory parameters have been reported in one study, but not in others. However, the PAN
concentrations used in these studies have been well above the maximum ambient concentrations usually experienced many years ago within the Los Angeles Basin (U.S. Environmental Protection Agency, 1986) and, more importantly, above the maximum ambient
concentrations in the more recent measurements considered in this section. The PANs are of importance as reservoirs for NO 2 as NO

x is depleted relative to VOCs in plumes moving downwind into less polluted areas (Chapter 3, Section 3.2.4). In performance evaluation of ozone air quality models, measured concentrations of PANs are useful in model evaluation (Chapter 3, Section 3.6.4.2).

4-81Figure 4-29. The number of occurrences for each of the seven categories described in text.

In the previous air quality criteria for O 3 and other photochemical oxidants (U.S. Environmental Protection Agency, 1986), extensive tabulations of PAN and peroxypropionyl nitrate (PPN, CH3CH2C(O)OONO2) concentrations were given based on measurements made between 1965 and 1981 from references up to 1983. In the present work, references from 1983 to the present are used for measurements of PANs in urban and rural locations. The urban area measurements are from the United States, Canada, France, Greece, and Brazil. The use of measurements from aboard serve to illustrate or support certain U.S. results as well as to demonstrate the widespread presence of PANs in the atmosphere. These PAN measurements usually were of limited duration, and the results should not be assumed to be comparable to those obtained at the O 3 monitoring sites discussed earlier in this chapter (see also Section 4.10).



### 4.9.2 Urban Area Peroxyacetyl Nitrate Concentrations For urban sites, the prior criteria document for ozone and other photochemical oxidants contains a number of tables tabulating measurements of PAN, PPN, and PPN to
PAN and PAN to O 3 ratios (Altshuller, 1983; U.S. Environmental Protection Agency, 1986). Based on comparisons of PAN measurements in Los Angeles in 1980 with those made in the 1960s, it was uncertain whether PAN concentrations had decreased. In the Los Angeles area, the average and maximum PAN concentrations reported ranged from 1.6 to 31 ppb and from 6 to 214 ppb, respectively. The wide variations, at least in part, were associated with the range of years, different seasons, and differing average times among studies. On average, the

4-82PPN to PAN ratios among studies in Los Angeles ranged from 0.15 to 0.20, whereas the PAN
to O 3 ratios among studies ranged from 0.04 to 0.20. In the earlier PAN measurement results, studies conducted in the South Coast Air Basin predominated. The average PAN concentrations measured in other cities usually were lower than in the Los Angeles area, whereas the maximum PAN concentrations overlapped with the lower end of range in Los Angeles. The PPN and PAN ratios in other cities ranged from 0.1 to 0.4, whereas the PAN to O 3 ratios were in the 0.01 to 0.05 range. Seasonally, PAN to O 3 ratios tended to be somewhat higher in the winter. The
diurnal characteristics of O 3 and of PAN were similar, but not identical.
The urban area measurement results are tabulated in Table 4-19. The earlier maximum PAN concentrations reported usually were substantially higher than those given in Table 4-19. A possible exception occurs for the Claremont, CA, results. Measurements of PAN and PPN were made in 1989 and 1990 at sites downwind of Los Angeles: Perrin, 90 km to the east-southeast, and Palm Springs, 120 km to the east (Grosjean and Williams, 1992). The concentrations of PAN and PPN were high, and the concentration maxima occurred during the evening hours, which was consistent with downwind transport from the Los Angeles area rather than from local sources. In Southern California, the maximum PAN concentrations appear to be more evenly distributed spatially during the fall than during the summer (Williams and Grosjean, 1990). At coastal and central locations, the PAN maxima during the fall were comparable to those observed at inland locations during the summer. As observed previously, PAN concentrations in other U.S. cities and in cities in other countries tend to be substantially lower than in Los Angeles and its surrounding urban areas (Table 4-19). An exception occurs for the measurements from Paris (Tsalkani et al., 1991). Maximum PAN concentrations in the 20 to 35 ppb range were observed. In measurements made in 1992 in Atlanta, GA, at the Georgia Institute of Technology campus site, not only were PAN and PPN measured, but very occasionally peroxymethacryloyl nitrate (MPAN, CH 2 =C(CH 3 ) C(O)OONO 2 ) (a product of the atmospheric photooxidation of local biogenic sources of isoprene) was observed (Williams et al., 1993). Maximum diurnal concentrations of peroxyacyl nitrates and O 3 occur in late afternoon and early evening. The average MPAN concentration was 0.3 ppb, and the maximum value was 0.5 ppb and constituted about 15% of the concurrent PAN concentrations. In a study in Rio de Janeiro performed to investigate the effects of the use of ethanol or ethanol-containing fuel on PAN concentrations, the maximum PAN concentration reached 5.4 ppb (Tanner et al., 1988). However, this maximum concentration is well below the maximum concentrations reported in and around Los Angeles, and it falls within the maximum PAN values reported for a number of other cities (Table 4-19).



### 4.9.3 Concentration of Peroxyacetyl Nitrate and Peroxypropionyl Nitrate in Rural Areas

Prior measurements of nonurban PAN and PPN concentrations and PAN to
O 3 ratios are available (Altshuller, 1983; U.S. Environmental Protection Agency, 1986). At nonurban sites that are not impacted by urban plumes, PAN and PPN concentrations are

4-83Table 4-19. Summary of Measurements of Peroxyacetyl Nitrateand Peroxypropionyl Nitrate in Urban AreasaSiteMonth/YearNumberof DaysSampledPANConcentration(ppb)Average/MeanMaxPPNConcentration(ppb)Average/MeanMaxReferenceLong Beach, CA6-12/198716NA16NANAWilliams and Grosjean (1990)Anaheim, CA 6-12/1987 14 NA 19 NA NA Williams and Grosjean (1990)Los Angeles, CA 6-12/1987 16 NA 13 NA NA Williams and Grosjean (1990)Burbank, CA 6-12/1987 16 NA 19 NA NA Williams and Grosjean (1990)Azusa, CA 6-9/1987 11 NA 13 NA NA Williams and Grosjean (1990)Claremont, CA 6-9/1987 10 NA 30 NA NA Williams and Grosjean (1990)Perrin, CA 6/1989 to 6/1990 NA 1.6 9.1 NA 0.73 Grosjean and Williams (1992)Palm Springs, CA 6/1989 to 6/1990 NA 1.6 7.6 NA 0.42 Grosjean and Williams (1992)Downey, CA 2/1984 10 1.2 6.7 0.06 0.40 Singh and Salas (1989)Boulder, CO 5, 6 and8, 9/198712450.630.592.03.80.080.070.30.6Ridley et al. (1990)Denver, CO 3/1984 9 0.64 2.0 0.02 0.09 Singh and Salas (1989)Houston, TX 3/1984 9 0.75 7.9 0.045 0.54 Singh and Salas (1989)Philadelphia, PA 4/1983 19 1.1 3.7 0.14 0.50 Singh and Salas (1989)Staten Island, NY 4/1983 7 1.6 5.5 0.21 0.90 Singh and Salas (1989)Atlanta, GA 7, 8/1992 36 0.71 2.9 0.14 0.37 Williams et al. (1993)Edmonton, Alberta,Canada12/1983 to 4/1984 66 7.5 NA NA Peake et al. (1988)Calgary, Alberta,Canada7/1981 to 2/1982 213 0.14 6.6 NA NA Peake and Sandhu (1983)University ofCalgary, Alberta,Canada10/1980 to 8/1981 175 0.22 2.4 NA NA Peake and Sandhu (1983)Simcoe, Ontario,Canada6/1980 to 3/19811911.35.6NANACorkum et al. (1986)

4-84Table 4-19 (cont’d). Summary of Measurements of Peroxyacetyl Nitrateand Peroxypropionyl Nitrate in Urban AreasaSiteMonth/YearNumberof DaysSampledPANConcentration(ppb)Average/MeanMaxPPNConcentration(ppb)Average/MeanMaxReferenceRio de JaneiroVila IsabelPUC/RJ7/19857/198584NANA5.43.3NANA1.00.6Tanner et al. (1988)Tanner et al. (1988)Athens, Greece 2-11/1985 113 NA 3.7 NA NA Tsani-Bazaca et al. (1988)Paris, France11/1985 to 11/1986NA1.120.5NANATsalkani et al. (1991)aSee Appendix A for abbreviations and acronyms.

4-85much lower than those in urban areas. Average PAN concentrations ranged between 0.1 and 1.0 ppb, whereas the PAN to O 3 ratios were at or below 1. Concentrations of PAN, PPN, and other peroxyacyl nitrates have been reported (Table 4-20) at Tanbark Flat, CA, 35 km northeast of Los Angeles, during 1989, 1990, and 1991 and at Franklin Canyon, CA, 25 km west of Los Angeles, during 1991 (Grosjean and Williams, 1992; Grosjean et al., 1993). As indicated by the results tabulated in Table 4-20, the concentrations were high at these mountain sites, the PPN to PAN ratios were relatively high, and the concentration maxima occurred during the afternoon hours. These concentration levels of PAN and PPN are attributed to downwind transport from the Los Angeles urban area. The MPAN was occasionally detected with average concentrations of 1.2 ppb at Tanbark Flat and 1.0 ppb at Franklin Canyon in 1991. At Tanbark Flat, the O 3 and PAN diurnal concentration patterns were similar to those in upwind urban areas. The PAN to O 3 ratios at the O 3 maximum were as follows: 1989, 0.05; 1990, 0.08; 1991, 0.05; all the ratios are within the same range as at sites in urban areas in and around Los Angeles. Additional measurements of PAN and PPN or other peroxyacyl nitrates are available over a period of years at Niwot Ridge, CO, just west of the Denver-Boulder area; at Point Arena, CA; and at a forest site, Scotia, PA (Ridley et al., 1990). The concentrations reported at all of these sites are much lower than the mountain sites in California. The Niwot Ridge site concentrations, which show the effects of easterly upslope flow of air parcels from Denver-Boulder, are still low compared to the sites downwind of the urban Los Angeles area (Table 4-20).
The PAN concentrations at the Scotia rural site in the eastern United States tend to be somewhat higher than the Niwot Ridge or Point Arena sites (Table 4-20). This difference may relate to higher regional precursor concentration levels.



## 4.10 Concentration and Patterns of Hydrogen Peroxide in the Ambient Atmosphere

Efforts to measure H 2 O 2 began in the 1970s, but the early reports of H 2 O 2 concentrations above 10 ppb and even 100 ppb appear to be in error because of the artifact
H 2 O 2 generated within the presence of O 3 (Chapter 3, Section 3.5.1.3). Subsequent
measurements of H 2 O 2 in the 1980s resulted in maximum H 2 O 2 concentrations at or below 5 ppb and mean concentrations at or below 1 ppb (Sakugawa et al., 1990). Studies comparing more recent methods for measuring H 2 O 2 , which were conducted in North Carolina, indicated differences among measurement methods in synthetic
mixtures of H 2 O 2 , including possible interferences, and in the ambient atmosphere of up to about ±25% (Kleindienst et al., 1988). However, results from the same study from mixtures irradiated in a smog chamber produced larger differences among methods, especially for the luminol technique compared to the fluorescence technique and with tunable-diode laser absorption spectroscopy. Another comparison study conducted in California resulted in differences between methods for measuring H 2 O 2 that varyied by a factor or two (Lawson et al., 1988). In the measurements of H 2 O 2 discussed below, the cryogenic fluorescence method or the scrubber-coil fluorescence method generally was used.

4-86Table 4-20. Summary of Measurements of Peroxyacetyl Nitrateand Peroxypropionyl Nitrate in Rural AreasaSiteMonth/YearNumberof DaysSampledPANConcentration(ppb)Average/MeanMaxPPNConcentration(ppb)Average/MeanMaxReferenceTanbark Flat, CA8-10/1989692.9>16.10.755.1Williams and Grosjean (1991)8,9/1990 34 4.8 22.0 0.76 4.3 Grosjean et al. (1993)8/1991 22 2.8 12.8 0.43 2.66 Grosjean et al. (1993)Franklin Canyon, CA 9/1991 9 1.6 7.0 0.18 1.15 Grosjean et al. (1993)Niwot Ridge, CO 7/1984 16 0.28 2.3 0.016 0.17 Singh and Salas (1989)6, 7/1984 23≈0.25 NA NA NA Fahey et al. (1986)8, 9/1984 21≈0.25 NA NA NA Fahey et al. (1986)6, 7/1987 46 0.81 (b)0.21 (c)3.2 0.08 (b)0.01 (c)0.45 Ridley et al. (1990)Point Arena, CA 1/1984 14 0.12 1.1 0.005 0.07 Singh and Salas (1989)Spring 1985 NA 0.05 NA NA NA Ridley (1991)Scotia, PA Summer 1986 NA≈0.6 NA NA NA Ridley (1991)6-8/1988 47 1.0 NA NA NA Buhr et al. (1990)Kananaskis Valley, Alberta,Canada9/1979,4/1982,6-8/1982NA≈0.5 2.3 NA NA Peake et al. (1983)Frijoles Mesa, NM10/1987 to1/1989NA0.261.9NANAGaffney et al. (1993)aSee Appendix A for abbreviations and acronyms.bFlow from Boulder-Denver area.cFlow across the Rockies.

4-87Based on interpretation of a compilation of H 2 O 2 measurements made between 1984 and 1988 at a number of urban locations, at rural/remote locations, and on aircraft flights, it was concluded that the higher H 2 O 2 concentrations were associated with the following measurement conditions: in afternoon hours, during summer months, at rural locations, and at lower latitudes (Sakugawa et al., 1990; Van Valin et al., 1987). The H 2 O 2 concentrations increase from the surface to the top of the boundary layer (Daum et al., 1990).
Available values for mean H 2 O 2 concentrations at three U.S. locations were (1) at the summit of Whitetop Mountain, VA: summer, 0.80 ppb; winter, 0.15 ppb (Olszyna et al., 1988); (2) at the summit of Whiteface Mountain: 1986, 0.6 ppb; 1987; 0.8 ppb (Mohnen and Kadlecek, 1989); and (3) at Westwood, CA: summer, ≈ 1.0 ppb; winter, 0.2 ppb (Sakugawa and Kaplan, 1989). At Westwood, the highest correlation with various parameters was found for solar radiation consistent with the higher H 2 O 2 concentrations being observed in the afternoon during the late spring and early summer months (Sakugawa and Kaplan, 1989). In the same study, the average H 2 O 2 concentrations were observed to increase from Westwood, near the coast in the Los Angeles Basin, to Duarte, inland; at Daggett in the Mohave Desert; and at Sky Mountain and Lake Gregory in the San Bernadino Mountains. The ratios of O 3 to H 2 O 2 concentrations at the these sites were ≥ 100. In subsequent measurements, the same relationship in H 2 O 2 concentrations between Westwood and the other California sites listed above was observed (Sakugawa and Kaplan, 1993). Unlike the results at several urban sites and other mountain sites, it was reported that the highest diurnal H 2 O 2 concentrations at Lake Gregory in the San Bernardino Mountains were observed during the nighttime hours (Sakugawa and Kaplan, 1993).



## 4.11 Co-occurrence of Ozone

4.11.1 Introduction There have been several attempts to characterize air pollutant mixtures (Lefohn and Tingey, 1984; Lefohn et al., 1987b). Pollutant combinations can occur at or above a threshold concentration either together or temporally separated from one another. For example, for characterizing the different types of co-occurrence patterns, Lefohn et al. (1987b) grouped air quality data within a 24-h period starting at 0000 hours and ending at 2359 hours. Patterns that showed air pollutant pairs appearing at the same hour of the day at concentrations equal to or greater than a minimum hourly mean value were defined as "simultaneous-only" daily co-occurrences. When pollutant pairs occurred at or above a minimum concentration during the 24-h period, without occurring during the same hour, a "sequential-only" co-occurrence was defined. During a 24-h period, if the pollutant pair occurred at or above the minimum level at the same hour of the day and at different hours during the period, the co-occurrence pattern was defined as "complex-sequential". A co-occurrence was not indicated if one pollutant exceeded the minimum concentration just before midnight and the other pollutant exceeded the minimum concentration just after midnight. As will be discussed below, studies of the joint occurrence of gaseous NO 2 /O 3 and
SO 2 /O 3 reached two conclusions: (1) the co-occurrence of two-pollutant mixtures lasted only a few hours per episode, where an episode was defined by the threshold concentration used, and (2) the time between episodes is generally long (i.e., weeks, sometimes months) (Lefohn and Tingey, 1984; Lefohn et al., 1987b).

4-88For exploring the co-occurrence of O 3 and other pollutants (e.g., acid precipitation, acidic cloudwater, and acidic sulfate aerosols), there are limited data available. In most cases, routine monitoring data are not available from which to draw general conclusions. However, published results are reviewed and summarized for the purpose of assessing an estimate of the possible importance of co-occurrence patterns of exposure.



### 4.11.2 Nitrogen Oxides Ozone occurs frequently at concentrations ≥ 0.03 ppm at many rural and remote monitoring sites in the United States (Evans et al., 1983; Lefohn, 1984; Lefohn and Jones, 1986). Therefore, for many rural locations in the United States, the co-occurrence patterns observed by Lefohn and Tingey (1984) for O 3 and NO 2 were defined by the presence or
absence of NO 2 . As anticipated, Lefohn and Tingey (1984) reported that most of the sites analyzed experienced fewer than 10 co-occurrences (when both pollutants were present at an hourly average concentration ≥ 0.05 ppm). However, the authors did note that several urban monitoring sites in the South Coast Air Basin experienced more than 450 co-occurrences. The rural sites of Riverside, Fontana, and Rubidoux, CA, had more than 100 co-occurrences. Denver and San Jose, CA, also experienced more than 100 co-occurrences of O 3 /NO 2 . Lefohn and Tingey (1984) reported that for Rubidoux, because NO 2 concentration maxima tended to peak in the evenings or early morning, the co-occurrences were present at these times. For more moderate areas of the country, Lefohn et al. (1987b) reported that even with a threshold of 0.03 ppm O 3 , the number of co-occurrences with NO 2 was small.



### 4.11.3 Sulfur Dioxide

Because elevated SO 2 concentrations are mostly associated with industrial activities (U.S. Environmental Protection Agency, 1992a), co-occurrence observations are usually associated with monitors located near these types of sources. Lefohn and Tingey (1984) reported that, for the rural and nonrural monitoring sites investigated, most sites experienced
fewer than 10 co-occurrences of SO 2 and O 3 . Only Rockport, IN, and Paradise No. 21 (KY) had more than 40 co-occurrences during the monitoring period (48 and 45, respectively). The monitors at these two sites were influenced by the local sources. The authors noted that at
Fontana there were numerous O 3 episodes above 0.05 ppm, and there was a high probability
that when the SO 2 hourly average concentrations rose above 0.05 ppm, both pollutants would be present at levels equal to or greater than 0.05 ppm. Meagher et al. (1987) reported that several documented O 3 episodes at specific rural locations appeared to be associated with elevated SO 2 levels. The investigators defined
the co-occurrence of O 3 and SO 2 to be when hourly mean concentrations were equal to or greater than 0.10 and 0.01 ppm, respectively. On reviewing the hourly mean O 3 and SO 2 data used by Lefohn et al. (1987b) in 1980 (using a threshold of 0.05 ppm for both pollutants), the Paradise No. 23 (KY); Giles County, TN; Murphy Hill (reported as Marshall County by Meagher et al., 1987), AL; and Saltillo (reported as Hardin Co. by Meagher et al., 1987), TN, sites experienced fewer than 7 days over a 153-day period for a co-occurrence of any form (i.e., simultaneous only, sequential, and complex co-occurrence). Thus, as reported by Lefohn et al. (1987b), the co-occurrence pattern of O 3 and SO 2 was infrequent. The above discussion was based on the co-occurrence patterns associated with the presence or absence of hourly average concentrations of pollutant pairs. Taylor et al. (1992) have discussed the joint occurrence of O 3 , nitrogen, and sulfur in forested areas using

4-89cumulative exposures of O 3 with data on dry deposition of sulfur and nitrogen. The authors concluded in their study that the forest landscapes with the highest loadings of sulfur and nitrogen via dry deposition tended to be the same forests with the highest average
O 3 concentrations and largest cumulative exposure. Although the authors concluded that the joint occurrences of multiple pollutants in forest landscapes were important, nothing was mentioned about the hourly co-occurrences of O 3 and SO 2 or O 3 and NO 2 .



### 4.11.4 Acidic Sulfate Aerosols Acid sulfates, which are usually composed of sulfuric acid (H 2 SO 4 ), ammonium bisulfate, and ammonium sulfate, have been measured at a number of locations in North America. Acidic sulfate and neutralized species can accumulate and range in concentration from 0 to 50 μg/m 3 at a specific location or a number of locations simultaneously (Lioy, 1989). For many summertime studies, peaks of H 2 SO 4 or H + appear to be associated with the presence of a slow-moving high pressure system (Lioy and Waldman, 1989). Acid sulfates are found primarily in the fine particle size range (<2.5 μm in diameter). Lioy (1989) reports that the acidic sulfate concentrations measured in the summertime can be found at 20 μg/m 3 for over an hour and can be found at high concentrations of 10 to 20 μg/m 3 for 6 to 24 h at one or more sites (Lioy, 1989). Acidic sulfate aerosol concentrations can occur at concentrations in the summertime above 10 μg/m 3 for periods longer tha n 5 h (Lioy, 1989). As has been discussed earlier in this chapter, the highest O 3 exposures for sites affected by anthropogenically derived photooxidant precursors are expected to occur during the late spring and summer months. Thus, the potential for O 3 and acidic sulfate aerosols to co-occur at some locations in some form (i.e., simultaneously, sequentially, or complex-sequentially) is real. Our knowledge of the potential exposure of the co-occurrence of acidic sulfate aerosols
and O 3 is limited because routine monitoring data for acidic aerosols are not available. Information on the co-occurrence patterns is limited to research studies and some of the results of these studies are provided in this section. Spektor et al. (1991) investigated the effects of single- and multiday O 3 exposures on respiratory function in active normal children aged 8 to 14 years at a northwestern New Jersey residential summer camp in 1988. During the investigation, the authors measured daily levels of 1-h peak O 3 and the 12-h average H + concentrations. On 7 days, the acid aerosol concentrations (reported as H 2 SO 4 ) were higher than 10 μg/m 3 , reaching a 12-h maximum of 18.6 μg/m 3 . Figure 4-27 shows the relationship between daily maximum O 3 and daily 12-h average H + concentrations. Thurston et al. (1992) reported occurrences in 1988 of maximum 24-h average concentrations of H + as high as 18.7 μg/m 3 (Buffalo, NY) and a maximum daily hourly average concentration of 0.164 ppm. Although lower than Buffalo, high O 3 or H + values were reported by the investigators for Albany and White Plains, NY. It is unclear
whether the O 3 or H + maximum concentrations occurred simultaneously; however, it is clear that high concentrations could occur either sequentially, complex-sequentially, or simultaneously. Evidence exists in the literature indicating that hourly co-occurrences are experienced. Raizenne and Spengler (1989) described an episodic co-occurrence pattern in 1986 of high hourly averaged concentrations of O 3 and H 2 SO 4 that occurred at a residential summer camp located on the north shore of Lake Erie, Ontario, (Figure 4-30). Thurston et al. (1994) conducted a study of ambient acidic aerosols in the Toronto, Ontario, metropolitan area in July and August of 1986, 1987, and 1988, and reported on the fine particle (aerodynamic equivalent diameter <2.5 μm) samples collected twice per day. The authors

4-90reported that their results indicated that acidic aerosol episodes (i.e., H + ≥ 100 nmol/m 3 ) occurred routinely during the summer months and that H + peaks were correlated with sulfate (SO 4 ) episodes. Figure 4-31 illustrates the relationship among SO 4 , H + , and O 3 .

Figure 4-30. The co-occurrence pattern of ozone and sulfuric acid (H 2 SO 4 ) for July 25, 1986, at a summer camp on the north shore of Lake Erie, Ontario, Canada.

Source: Raizenne and Spengler (1989).



### 4.11.5 Acid Precipitation Concern has been expressed about the possible effects on vegetation from co-occurring exposures of O 3 and acid precipitation (Prinz et al., 1985; National Acid Precipitation Assessment Program, 1987; Prinz and Krause, 1988). Little information has been published concerning the co-occurrence patterns associated with the joint distribution of
O 3 and acidic deposition (i.e., H + ). Lefohn and Benedict (1983) reviewed EPA’s SAROAD monitoring data for 1977 through 1980 and, using National Atmospheric Deposition Program (NADP) and EPRI wet deposition data, evaluated the frequency distribution of pH events for 34 NADP and 8 EPRI chemistry monitoring sites located across the United States. Unfortunately, there were few sites where O 3 and acidic deposition were comonitored. Figure 4-31. Sulfate (SO 4 - ), hydrogen ion (H + ), and ozone (O 3 ) measured at Breadalbane Street (Site 3) in Toronto during July and August 1986, 1987, and 1988.

Source: Thurston et al. (1994).

4-91As a result, Lefohn and Benedict (1983) focused their attention on O 3 and acidic deposition monitoring sites that were closest to one another. In some cases, the sites were as far apart as 144 km. Using hourly O 3 monitoring data and weekly and event acidic deposition data from the NADP and EPRI databases, the authors identified specific locations where the hourly mean O 3 concentrations were ≥ 0.1 ppm and 20% of the wetfall daily or weekly samples were below pH 4.0. Elevated levels of O 3 were defined as hourly mean concentrations equal to or greater than 0.1 ppm. Although for many cases, experimental research results of acidic deposition on agricultural crops show few effects at pH levels above 3.5 (National Acid Precipitation Assessment Program, 1987), it was decided to use a pH threshold of 4.0 to take into consideration the possibility of synergistic effects of O 3 and acidic deposition. Based on their analysis, Lefohn and Benedict (1983) reported five sites where there may be the potential for agricultural crops to experience additive, less than additive, or synergistic (i.e., greater than additive) effects from elevated O 3 and H + concentrations. The authors stated that they believed, based on the available data, the greatest potential for
interaction between acid rain and O 3 concentrations in the United States, with possible effects on crop yields, may be in the most industrial areas (e.g., Ohio and Pennsylvania). However,they cautioned that, because no documented evidence existed to show that pollutant interaction had occurred under field growth conditions and ambient exposures, their conclusions should only be used as a guide for further research. In their analysis, Lefohn and Benedict (1983) found no colocated sites. The authors rationalized that data from non-co-monitoring sites (i.e., O 3 and acidic deposition)
could be used because O 3 exposures are regional in nature. However, work by Lefohn et al. (1988a) has shown that hourly mean O 3 concentrations vary from location to location within a region, and that cumulative indices, such as the percent of hourly mean concentrations ≥ 0.07 ppm, do not form a uniform pattern over a region. Thus, extrapolating hourly mean
O 3 concentrations from known locations to other areas within a region may provide only qualitative indications of actual O 3 exposure patterns.

4-92In the late 1970s and the 1980s, both the private sector and the government funded research efforts to better characterize gaseous air pollutant concentrations and wet deposition. The event-oriented wet deposition network, EPRI/Utility Acid Precipitation Study Program, and the weekly oriented sampling network, NADP, provided information that can be compared with hourly mean concentrations of O 3 collected at several comonitored locations. No attempt was made to include H + cloud deposition information. In some cases, for mountaintop locations (e.g., Clingman’s Peak, Shenandoah, Whiteface Mountain, and Whitetop Mountain), the H + cloud water deposition is greater than the H + deposition in precipitation (Mohnen, 1989), and the co-occurrence patterns associated with O 3 and cloud deposition will be different than those patterns associated with O 3 and deposition in precipitation. Smith and Lefohn (1991) explored the relationship between O 3 and H + in precipitation, using data from sites that monitored both O 3 and wet deposition simultaneously and within one minute latitude and longitude of each other. The authors reported that individual sites experienced years in which both H + deposition and total O 3 exposure were at least moderately high (i.e., annual H + deposition ≥ 0.5 kg ha -1 and an annual O 3 cumulative, sigmoidally weighted exposure (W126) value ≥ 50 ppm-h). With data compiled from all sites, it was found that relatively acidic precipitation (pH ≤ 4.31 on a weekly basis or pH ≤ 4.23 on a daily basis) occurred together with relatively high O 3 levels (i.e., W126 values ≥ 0.66 ppmh for the same week or W126 values ≥ 0.18 ppm-h immediately before or after a rainfall event) approximately 20% of the time, and highly acidic precipitation (i.e, pH ≤ 4.10 on a weekly basis or pH ≤ 4.01 on a daily basis) occurred together with a high O 3 level (i.e.,
W126 values ≥ 1.46 ppm-h for the same week or W126 values ≥ 0.90 ppm-h immediately before or after a rainfall event) approximately 6% of the time. Whether during the same week or before, during, or after a precipitation event, correlations between O 3 level and pH (or H + deposition) were weak to nonexistent. Sites most subject to relatively high levels of both hydrogen ion and O 3 were located in the eastern portion of the United States, often in
mountainous areas.



### 4.11.6 Acid Cloudwater

In addition to the co-occurrence of O 3 and acid precipitation, results have been reported on the co-occurrence of O 3 and acidic cloudwater in high-elevation forests. Vong and Guttorp (1991) characterized the frequent O 3 -only and pH-only, single-pollutant episodes, as well as the simultaneous and sequential co-occurrences of O 3 and acidic cloudwater. The authors reported that both simultaneous and sequential co-occurrences were observed a few times each month above the cloud base. Episodes were classified by considering hourly
O 3 average concentrations ≥ 0.07 ppm and cloudwater events with pH ≤ 3.2. The authors reported that simultaneous occurrences of O 3 and pH episodes occurred two to three times per month at two southern sites (Mitchell, NC, and Whitetop, VA) and the two northern sites (Whiteface Mountain, NY, and Moosilauke, NH) averaged one episode per month. No co-occurrences were observed at the central Appalachian site (Shenandoah, VA), due to a much lower cloud frequency. Vong and Guttorp (1991) reported that the simultaneous occurrences were usually of short duration (mean = 1.5 h/episode) and were followed by an
O 3 -only episode. As would be expected, O 3 -only episodes were longer than co-occurrences and pH episodes, averaging an 8-h duration.

4-93

## 4.12 Summary

Ozone is a pervasive compound that is detected at all monitoring locations throughout the world. To obtain a better understanding of the potential for ambient
O 3 exposures affecting human health and vegetation, hourly average concentration information is summarized for urban, rural forested, and rural agricultural areas in the United States.
The distribution of O 3 or its precursors at a rural site near an urban source is affected by wind direction (i.e., whether the rural site is located up- or downwind from the source). It is difficult to apply land-use designations to the generalization of exposure regimes that may be experienced in urban versus rural areas, because the land use characterization of "rural" does not imply that a specific location is isolated from anthropogenic influences. Rather, the characterization implies only the current use of the land. Because it is possible for urban emissions, as well as O 3 produced from urban area emissions, to be transported to more rural downwind locations, elevated O 3 concentrations can
occur at considerable distances from urban centers. Urban O 3 concentration values often are depressed because of titration by NO. Because of the absence of chemical scavenging, O 3 tends to persist longer in nonurban than in urban areas, and exposures may be higher in
nonurban than in urban locations. For vegetation, as indicated in Chapter 5 (Section 5.5), extensive research has focused on identifying exposure indices with a firm foundation on biological principles. Many of these exposure indices have been based on research results indicating that the magnitude of vegetation responses to air pollution is more an effect of the magnitude of the concentration than the length of the exposure. For O 3 , the short-term (1- to 8-h), high concentration exposures (>0.1 ppm) have been identified by many researchers as being more important than long-term, low concentration exposures in producing visible injury to plants (see Chapter 5 for further discussion). Similarly, for human health considerations, results using controlled human exposures have shown the possible importance of concentration in relation to duration of exposure and inhalation rate. In summarizing the hourly average concentrations in this chapter, specific attention is given to the relevance of the exposure indices used. For example, for human health considerations, concentration (or exposure) indices such as the daily maximum 1-h average concentrations, as well as the number of daily maximum 4- or 8-h average concentrations above a specified threshold, are used to characterize information in the population-oriented locations. For vegetation, several different types of exposure indices are used. Because much of the NCLAN exposure information is summarized in terms of 7-h average concentrations, this exposure index is used. However, because peak-weighted, cumulative indices (i.e., exposure parameters that sum the products of hourly average concentrations multiplied by time over an exposure period have shown considerable promise in relating exposure and vegetation response (see Chapter 5, Section 5.5), several exposure indices that use either a threshold or a sigmoidally weighted scheme are used in this chapter to provide insight concerning the O 3 exposures that are experienced at a select number of rural monitoring sites in the United States. The peak-weighted cumulative exposure indices such as SUM06, SUM08, and W126 are used. Ozone hourly average concentrations have been recorded for many years by the State and local air pollution agencies who report their data to EPA. The 10-year (1983 to 1992) composite average trend for the second highest daily maximum hourly average concentration during the O 3 season for 509 trend sites and a subset of 196 NAMS sites,

4-94shows that the 1992 composite average for the trend sites is 21% lower than the 1983 average and 20% lower for the subset of NAMS sites. The 1992 value is the lowest composite average of the past 10 years. The 1992 composite average is significantly less than all the previous 9 years, 1983 to 1991. The relatively high O 3 concentrations in 1983 and 1988 were attributable, in part, to hot, dry, stagnant conditions in some areas of the country that were especially conducive to O 3 formation. From 1991 to 1992, the composite mean of the second highest daily maximum 1-h
O 3 concentrations decreased 7% at the 672 sites and 6% at the subset of 222 NAMS sites. Also, from 1991 to 1992, the composite average of the number of estimated exceedances of
the O 3 standard decreased by 23% at the 672 sites, and 19% at the 222 NAMS sites. Nationwide VOC emissions decreased 3% from 1991 to 1992 (U.S. Environmental Protection Agency, 1993). The composite average of the second daily maximum concentrations decreased in 8 of the 10 EPA regions from 1991 to 1992, and remained unchanged in Region VII. Except for Region VII, the 1992 regional composite means are lower than the corresponding 1990 levels. Although meteorological conditions in the east during 1993 were
more conducive to O 3 formation than those in 1992, the composite mean level for 1993 was the second lowest composite average for the decade (1984 to 1993). Information is provided in this chapter on methods used for investigating techniques for adjusting O 3 trends for meteorological influences. Historically, the long-term
O 3 trends in the United States characterized by EPA have emphasized air quality statistics that are closely related to the NAAQS. Information is provided on the use of alternative indices. Besides EPA, additional investigators have assessed trends at several locations in the United States, and information is provided for both urban and rural areas. Interest has been expressed in characterizing O 3 exposure regimes for sites experiencing daily maximum 8-h concentrations above specific thresholds (e.g., 0.08 or 0.10 ppm). Documented evidence has been published showing the occurrence, at some sites, of multihour periods within a day of O 3 at levels of potential health effects. Although most of these analyses were made using monitoring data collected from sites in or near nonattainment areas, one analysis showed that at five sites, two in New York state, two in rural California, and one in rural Oklahoma, an alternative O 3 standard of an 8-h average of 0.10 ppm would be exceeded even though the existing 1-h standard would not be. The study indicated the occurrence at these five sites, none of which was in or near a nonattainment area, of O 3 concentrations showing only moderate peaks but showing multihour levels above 0.10 ppm. An important question is whether an improvement in O 3 levels would produce
distributions of 1-h O 3 concentrations that result in a broader diurnal profile than those seen in high-oxidant urban areas where O 3 regimes contain hourly average concentrations with sharper peaks. The result would be an increase in the number of exceedances of daily maximum 8-h average concentrations ≥ 0.08 ppm, when compared to those sites experiencing sharper peaks. One research effort observed, using aerometric data at specific sites, how O 3 concentrations change when the sites change compliance status. One of the parameters examined was 4-h daily maxima. The number of exceedances for a specific daily maximum average concentration tended to decrease as fewer exceedances of the current 1-h standard were observed at a given site. The number of occurrences of the daily maximum 4-h average
concentration ≥ 0.08 ppm and the number of exceedances of the current form of the standard had a positive, weak correlation (r = 0.51). The investigators reported few changes in the shape of the average diurnal patterns as sites changed attainment status. The lack of a change

4-95in shape may have explained why the investigators could not find evidence that the number of occurrences of the daily maximum 4-h average concentration ≥ 0.08 ppm increased when the sites experienced few high hourly average concentrations. There has been considerable interest in possibly substituting one index for another when attempting to relate O 3 exposure with an effect. For example, using O 3 ambient air quality data, the number of exceedances of 0.125 ppm and the number of occurrences of the daily maximum 8-h average concentrations ≥ 0.08 ppm have been compared with the result that a positive correlation (r = 0.79) existed between the second-highest 1-h daily maximum in a year and the expected number of days with an 8-h daily maximum average concentration >0.08 ppm O 3 . However, there was not much predictive strength in using one O 3 exposure index to predict another. Similarly, the maximum 3-mo SUM06, second highest daily maximum hourly average concentration, and second highest daily maximum 8-h average concentration exposure indices were compared. For the rural agricultural and forest sites, the correlations among the indices were not strong. One of the difficulties in attempting to use correlation analysis between indices for rationalizing the substitution of one exposure index for another for predicting an effect (e.g., SUM06 versus the second highest daily maximum hourly average concentration) is the introduction of the error associated with estimating levels of one index from those of another. Evidence has been presented in the literature for recommending that, if a different exposure index (e.g., second highest daily maximum hourly average concentration) is to be compared to, for example, the SUM06 for adequacy in predicting crop loss, then the focus should be on how well the two exposure indices predict crop loss using the effects model that is a function of the most relevant index and not on how well the indices predict one another. Less error would be introduced if either of the two indices were used directly in the development of an exposure-response model. The EPA has indicated that a reasonable estimate, as an annual average, of
O 3 background concentration near sea level in the United States today is from 0.020 to 0.035 ppm; this estimate included a 0.005 to 0.015 ppm contribution from the stratosphere.
The EPA concluded that a reasonable estimate of natural O 3 background concentration for a 1-h daily maximum at sea level in the United States during the summer is on the order of 0.03 to 0.05 ppm. Reviewing data from sites that appear to be isolated from anthropogenic sources, it has been reported that, in almost all cases, none of the sites experienced hourly average concentrations ≥ 0.08 ppm and that the maximum hourly average concentrations were in the range of 0.060 to 0.075 ppm. Using data from these sites, in the continental United States, the 7-mo (April to October) average of the 7-h daily average concentrations range from approximately 0.025 to 0.045 ppm. At an O 3 monitoring site at the Theodore Roosevelt National Park, 7-mo (April to October) averages of the 7-h daily average concentrations of 0.038, 0.039, and 0.039 ppm, respectively, were experienced in 1984, 1985, and 1986. These 7-h seasonal averages appear to be representative of the 8-h daily average O 3 concentrations that may occur at other fairly clean sites in the United States and other locations in the northern hemisphere. Diurnal variations are those that occur during a 24-h period. Diurnal patterns of
O 3 may be expected to vary with location, depending on the balance among the many factors affecting O 3 formation, transport, and destruction. Although they vary with locality, diurnal patterns for O 3 typically show a rise in concentration from low levels or levels near minimum detectable amounts to an early afternoon peak. The diurnal pattern of concentrations can be ascribed to four simultaneous processes: (1) downward transport of O 3 from layers aloft,

4-96(2) destruction of O 3 through contact with surfaces and through reaction with NO at ground level, (3) in situ photochemical production of O 3 , and (4) horizontal transport of O 3 and its

precursors. Although it might appear that composite diurnal pattern diagrams could be used to quantify the differences in O 3 exposures among sites, caution has been expressed in their use for this purpose. The average diurnal patterns are derived from long-term calculations of the hourly average concentrations, and the resulting diagram cannot adequately identify, at most sites, the presence of high hourly average concentrations; thus, they may not be adequate to distinguish O 3 exposure differences among sites. Unique families of diurnal average profiles exist, and it is possible to distinguish between two types of O 3 monitoring sites. A seasonal diurnal diagram provides the investigator with the opportunity to identify whether a specific
O 3 monitoring site has more scavenging than any other site. For low-elevation sites, intraday variability is most significant due to the pronounced daily amplitude in O 3 concentration between the predawn minimum and midafternoon to early-evening maximum, whereas interday variation is more significant in the high-elevation sites.
Seasonal variations in O 3 concentrations in urban areas usually show the pattern of high O 3 in late spring or in summer and low levels in the winter. Because of temperature, relative humidity, and seasonal changes in storm tracks from year to year, the general weather conditions in a given year may be more favorable for the formation of O 3 and other oxidants than during the prior or following year. For example, 1988 was a hot and dry year in which some of the highest O 3 concentrations of the last decade occurred, whereas 1989 was a cold and wet year in which some of the lowest concentrations occurred. Several investigators have reported on the tendency for average O 3 concentrations to be higher in the second than in the third quarter of the year for many isolated rural sites. This observation has been attributed to either stratospheric intrusions or an increasing frequency of slow-moving, high-pressure systems that promote the formation of O 3 . However, for several clean rural sites, the highest exposures have occurred in the third quarter
rather than in the second. For rural O 3 sites in the southeastern United States, the daily maximum 1-h average concentration was found to peak during the summer months. For sites located in rural areas, but not isolated from anthropogenic sources of pollution, the different patterns may be associated with anthropogenic emissions of NO

x and hydrocarbons.
Concentrations of O 3 vary with altitude and with latitude. There appears to be no generalizable conclusion concerning the relationship between O 3 exposure and elevation. The differences in exposure occur when one site is above the natural inversion and the other is not. An important issue for assessing possible impacts of O 3 at high-elevation sites that requires further attention is the use of mixing ratios (e.g., parts per million) instead of absolute concentration (e.g., in units of micrograms per cubic meter) to describe
O 3 concentration. In most cases, mixing ratios, or mole fractions, are used to describe
O 3 concentrations. The manner in which concentration is reported may be important when assessing the potential impacts of air pollution on high-elevation forests. Concentration varies as a function of altitude. Although the change in concentration is small when the elevational difference between sea level and the monitoring site is small, it becomes substantial at high-elevation sites. Given the same part-per-million value registering at both a high- and low-elevation site, the absolute concentrations (i.e., micrograms per cubic meter) at the two elevations will be different. Because both pollutants and ambient air are gases, changes in pressure directly affect their volume. This pressure effect must be considered when measuring absolute pollutant concentrations. Although these exposure considerations

4-97are trivial at low-elevation sites, when one compares exposure-effects results obtained at highelevation sites with those from low-elevation sites, the differences may become significant. Most people in the United States spend a large proportion of their time indoors. Until the early 1970s, very little was known about the O 3 concentrations experienced inside buildings. Even to date, the database on this subject is not extensive, and a wide range of I/O
O 3 concentration relationships can be found in the literature. Reported I/O values for O 3 are highly variable. A relatively large number of factors can affect the difference in
O 3 concentrations between the inside of a structure and the outside air. In general, outside air infiltration or exchange rates, interior air circulation rates, and interior surface composition (e.g., rugs, draperies, furniture, walls) affect the balance between replenishment and decomposition of O 3 within buildings. The I/O O 3 concentration ratios generally fall in the range of 0.1 to 0.7, and indoor concentrations of O 3 will almost invariably be less than those
outdoors. It is important that accurate estimates of both human and vegetation exposure to O 3 be available for assessing the risks posed by the pollutant. Examples are provided on how both fixed-site monitoring information and human exposure models are used to estimate risks
associated with O 3 exposure. In many cases, the upper tail of the distribution, which represents those individuals exposed to the highest concentrations, often generates special interest because the determination of the number of individuals who experience elevated pollutant levels can be critical for health risk assessments. This is especially true for pollutants for which the relationship between dose and response is highly nonlinear. Because it is not possible to estimate population exposure, in most cases, solely from fixed-station data, several human exposure models have been developed. Some of these models include information on human activity patterns (i.e., the microenvironments people visit and the times they spend there). These models also contain submodels depicting the sources and concentrations likely to be found in each microenvironment, including indoor, outdoor, and in-transit settings. A subgroup that has been studied by several investigators to assess the influence of ambient air pollution on their respiratory health and function is children attending summer camp. Because children are predominantly outdoors and relatively active while at camp, they provide a unique opportunity to assess the relationships between respiratory health and function and concurrent air pollution levels. Examples are provided on the type of exposure patterns that children experience. A personal exposure profile can be identified by using a personal exposure monitor. Few data are available for individuals using personal exposure monitors. Results from a pilot study demonstrated that fixed-site ambient measurements may not adequately represent individual exposures. Outdoor O 3 concentrations showed substantial spatial variation between rural and residential regions. The study showed that the use of fixed-site measurements could result in an error as high as 127%. In addition, the study showed that models based on time-weighted I/O concentrations explained only 40% of the variability in personal exposures. The investigators concluded that contributions from diverse indoor and outdoor microenvironments could estimate personal O 3 exposure accurately. The field of human exposure modeling is relatively young, with the first rigorous exposure modeling analyses appearing in the mid-1970s and the theoretical constructs regarding human exposure to environmental pollution being published in the early 1980s. Two distinct types of O 3 exposure models exist: (1) those that focus narrowly on predicting

4-98indoor O 3 levels and (2) those that focus on predicting O 3 exposures on a community-wide basis. The following four distinct models address the prediction of O 3 exposures on a community-wide basis: (1) pNEM/O 3 , (2) SAI/NEM, (3) REHEX, and (4) EPEM. All four
O 3 exposure models are derived from the NEM (NAAQS exposure model), which was first developed in the early 1980s. Most applications of NEM use fixed-site monitoring data, U.S. census data, and human-activity data. The calculations result in an estimate of the
O 3 concentration experienced by an individual in each microenvironment that the person
inhabits. The hourly average concentrations used in many of the high-treatment experimental vegetation and human health effects studies did not necessarily simulate those concentrations observed under ambient conditions. Although the ramifications of this observation on the effects observed are not clear, it has been pointed out that the highest treatments used in many of the vegetation open-top chamber experiments were bimodal in the distribution of the hourly average concentrations. In other experiments designed to assess the
effects of O 3 on vegetation, constant concentration (i.e., square wave) exposures were implemented. As discussed in earlier sections of this Chapter, square wave exposure regimes do not normally occur under ambient conditions. Similar square wave exposures have been used in human health effects studies. In addition to the exposures used at the highest treatment levels for vegetation experiments, there is concern that the hourly average concentrations used in the charcoal-filtered control treatments may be lower than those experienced at isolated sites in the United States and in other parts of the world. Although the ramifications of using such exposure regimes is unclear, there is some concern that the use of atypically low control levels may result in an overestimation of vegetation yield losses when used as the baseline for evaluating the effects of treatments at higher concentrations. Published data on the concentrations of photochemical oxidants other than O 3 in ambient air are neither comprehensive nor abundant. A review of the data shows that PAN
and PPN are the most abundant of the non-O 3 oxidants in ambient air in the United States, other than the inorganic nitrogenous oxidants such as NO 2 and possibly nitric acid. At least one study has reported that a higher homologue of the series, peroxybenzoyl nitrate (PBzN), like PAN, is a lachrymator. No unambiguous identification of PBzN in the ambient air of the
United States has been made. Given the information available on PAN, the concentrations of PAN that are of most concern are those to which vegetation could potentially be exposed, especially during daylight hours in agricultural areas. These are followed in importance by concentrations both indoors and outdoors, in urban and nonurban areas, to which human populations potentially could be exposed. Most of the available data on concentrations of PAN and PPN in ambient air are from urban areas. The levels to be found in nonurban areas will be highly dependent on the transport of PAN and PPN or their precursors from urban areas, because the
concentrations of the NO

x precursors to these compounds are considerably lower in nonurban
than in urban areas. There have been several attempts to characterize air pollutant mixtures. Pollutant combinations can occur at or above a threshold concentration either together or temporally separated from one another. Studies of the joint occurrence of gaseous NO 2 /O 3 and SO 2 /O 3 have concluded that the co-occurrence of two-pollutant mixtures lasted only a few hours per episode, and that the time between episodes is generally long (i.e, weeks, sometimes months). Using hourly averaged data collected at rural sites for vegetation considerations, the periods of co-occurrence represent a small portion of the potential plant growing period. For human

4-99ambient exposure considerations, the simultaneous co-occurrence of NO 2 /O 3 was infrequent in most cases. However, for several sites located in the South Coast Air Basin, more than 450 simultaneous co-occurrences of each pollutant, at hourly average concentrations ≥ 0.05 ppm, were present. Although the focus of co-occurrence research has been on patterns associated with the presence or absence of hourly average concentrations of pollutant pairs, some researchers have discussed the joint occurrence of O 3 , nitrogen, and sulfur in forested areas, combining cumulative exposures of O 3 with data on dry deposition of sulfur and nitrogen. One study reported that several forest landscapes with the highest dry deposition loadings of sulfur and nitrogen tended to experience the highest average O 3 concentrations and the largest cumulative exposure. Although the investigators concluded that the joint occurrences of multiple pollutants in forest landscapes were important, nothing was mentioned about hourly
co-occurrences of O 3 and SO 2 or O 3 and NO 2 . Knowledge of the potential exposure of the co-occurrence of acidic sulfate aerosols
and O 3 is limited because routine monitoring data for acidic aerosols are not available. Information on the co-occurrence patterns is limited to research studies; some of the results are provided in this chapter. Acid sulfates, which are composed of H 2 SO 4 , ammonium bisulfate, and ammonium sulfate, have been measured at a number of locations in North America. Acidic sulfate and neutralized species can accumulate and range in concentration from 0 to 50 μg/m 3 at a specific location or a number of locations simultaneously. For many summertime studies, peaks of H 2 SO 4 or H + appear to be associated with the presence of a slow-moving high pressure system. Acid sulfates are found primarily in the fine particle size range (<2.5 μm in diameter). The acidic sulfate concentrations measured in the summertime can be found at 20 μg/m 3 for over an hour and at high concentrations of 10 to 20 μg/m 3 for 6
to 24 h at one or more sites. Acidic sulfate aerosol concentrations can occur at concentrations in the summertime above 10 μg/m 3 for periods longer than 5 h. The highest
O 3 exposures for sites affected by anthropogenically derived photooxidant precursors are expected to occur during the late spring and summer months. Thus, the potential for O 3 and acidic sulfate aerosols to co-occur at some locations in some form (i.e., simultaneously, sequentially, or complex-sequentially) is real and requires further characterization. Concern has been expressed about the possible effects on vegetation from co-occurring exposures of O 3 and acid precipitation. One study explored the relationship
between O 3 and H + in precipitation, using data from sites that monitored both O 3 and wet deposition simultaneously and within one minute latitude and longitude of each other. The investigators reported that individual sites experienced years in which both H + deposition and
total O 3 exposure were at least moderately high (i.e., annual H + deposition ≥ 0.5 kg ha -1 and an
annual O 3 cumulative sigmoidally weighted exposure (W126) value ≥ 50 ppm-h). Based on data compiled from all sites, relatively acidic precipitation (pH ≤ 4.31 on a weekly basis or pH ≤ 4.23 on a daily basis) occurred together with relatively high O 3 levels (i.e., W126 values

≥ 0.66 ppm-h for the same week or W126 values ≥ 0.18 ppm-h immediately before or after a rainfall event) approximately 20% of the time, and highly acidic precipitation (i.e, pH ≤ 4.10 on a weekly basis or pH ≤ 4.01 on a daily basis) occurred together with a high O 3 level (i.e.,
W126 values ≥ 1.46 ppm-h for the same week or W126 values ≥ 0.90 ppm-h immediately before or after a rainfall event) approximately 6% of the time. Whether during the same week or before, during, or after a precipitation event, correlations between O 3 level and pH (or H + deposition) were weak to nonexistent. Sites most subject to relatively high levels of
both H + and O 3 were located in the eastern portion of the United States, often in mountainous

areas.

4-100The co-occurrence of O 3 and acidic cloudwater in high-elevation forests has been characterized. The frequent O 3 -only and pH-only, single-pollutant episodes, as well as the simultaneous and sequential co-occurrences of O 3 and acidic cloudwater, have been reported. Both simultaneous and sequential co-occurrences were observed a few times each month
above the cloud base.

4-101

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4-117# 

# Appendix A

Abbreviations and Acronyms

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