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 (O3). This document focuses primarily

on the scientific air quality criteria for O3 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 O3 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 O3 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 O3 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 O3 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 maximum

hourly average ozone concentration and the maximum threemonth SUM06 value and the second highest daily maximum

eight-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 maximum

hourly average ozone concentration and the maximum threemonth SUM06 value and the second highest daily maximum

eight-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 (O3). 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 O3 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 O3 and

related photochemical oxidants and their precursors, whereas Chapter 4 covers environmental

concentrations, patterns, and exposure estimates of O3 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 O3. 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

O3 and nitrogen dioxide (NO2). Other oxidants, such as hydrogen peroxide (H2O2) 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, O3-specific commercial analytical methods

became available, as did information on the concentrations and effects of the related nonO3 photochemical oxidants. As a result, the chemical designation of the standards was

changed in 1979 from photochemical oxidants to O3.

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 O3 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 O3 and related photochemical oxidants in ambient air. The last O3 criteria

1-1
document 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 O3 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 O3 criteria document and to

critically evaluate and assess the more recent scientific data associated with exposure to

O3 and, to a lesser extent, to H2O2 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 O3 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 O3 occurs as the

result of (1) the intrusions of stratospheric O3 into the "free" troposphere and downward

transport into the PBL, and (2) photochemical reactions of methane (CH4), carbon monoxide

(CO), and nitrogen oxides (NOx). These processes contribute to the background O3 near the

surface. The major source of O3 in the PBL is the photochemical process involving

anthropogenic and biogenic emissions of NOx with the many classes of volatile organic

compounds (VOCs).

The topics considered in this section of the document include: tropospheric

O3 chemistry; meteorological influences on O3 formation and transport; precursor VOC and

NOx emissions, ambient concentrations of VOCs and NOx, and source apportionment and

reconciliation of measured VOC ambient concentrations with emission inventories; O3 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, O3 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 CH4, CO, and NOx.

The photochemical production of O3 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) NOx. The only significant initiator of the photochemical production of O3 in the polluted

troposphere is the photolysis of NO2, yielding nitric oxide (NO) and a ground-state oxygen

atom that reacts with molecular oxygen to form O3. The O3 thus formed reacts with NO,

yielding oxygen and NO2. 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 O3. Methane is the chief VOC found in the

free troposphere and in most "clean" areas of the PBL. The VOCs found in polluted ambient

1-2
air are much more complex and more reactive than CH4, but, as with CH4, their atmospheric

oxidative degradation is initiated through attack on the VOCs by hydroxyl (OH) radicals. As

in the CH4 oxidation cycle, the conversion of NO to NO2 during the oxidation of VOCs is

accompanied by the production of O3 and the efficient regeneration of the OH radical. The

O3, PAN, and higher homologues formed in polluted atmospheres increase with the NO2/NO

concentration ratio.

At night, in the absence of photolysis of reactants, the simultaneous presence of

O3 and NO2 results in the formation of the nitrate (NO3) radical. Reactions with NO3 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 O3 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 NO3 radicals and O3, 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 NO3 radicals and

with O3 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-NO2 conversion and of O3 yields, and can present difficulties in representation

of chemical mechanisms, products, and product yields in O3 air quality models. Major

uncertainties in understanding the atmospheric chemistry of the VOCs with NOx 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-O3 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 O3 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 NO2

and sulfur dioxide (SO2) to form nitric acid (HNO3) and sulfuric acid (H2SO4), 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 O3 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

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receptor; the occurrence of inversion layers (layers in which temperature increases with height

above ground level); and the transport of O3 left overnight in layers aloft and subsequent

downward mixing of that O3 to the surface.

The transport of O3 and its precursors beyond the urban scale (≤50 km) to

neighboring rural and urban areas has been well documented. Episodes of high

O3 concentrations in urban areas are often associated with high concentrations of O3 in the

surroundings. Areas of O3 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 O3 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 O3 levels. An association, however, between

tropospheric O3 concentrations and temperature has been demonstrated. Empirical data from

four urban areas, for example, show an apparent upper bound on O3 concentrations that

increases with temperature. A similar qualitative relationship exists at a number of rural

locations.

The relationship between wind speed and O3 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 O3 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.

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Nitrogen Oxides Emissions

Anthropogenic NOx 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 NOx in 1991 in the United

States totaled 21.39 Tg. The two largest single NOx emission sources are electric power

generating plants and highway vehicles. Because a large proportion of anthropogenic NOx

emissions come from distinct point sources, published annual estimates are thought to be

much more reliable than VOC estimates.

Natural NOx sources include stratospheric intrusion, oceans, lightning, soil, and

wildfires. Lightning and soil emissions are the only two significant natural sources of NOx in

the United States. It is estimated that combined natural sources contribute about 2.2 Tg of

NOx to the troposphere over the continental United States; however, uncertainties in natural

NOx emission inventories are much greater than those for anthropogenic NOx 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 NOx made in 22 and 19 U.S. cities in 1984 and 1985,

respectively, showed median 6:00-to-9:00 a.m. NOx concentrations ranging from 0.02 to 0.08

ppm in most of these cities. Nonurban NOx concentrations, reported as average seasonal or

annual NOx, 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 NOx 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/NOx ratios tend to be higher than urban ratios. The NMOC/NOx 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 NOx control within a number of cities. Morning (6:00-to-9:00 a.m.)

NMOC/NOx ratios are used in the EKMA type of trajectory model. The correlation of

NMOC/NOx ratios with maximum 1-h O3 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

1-5
concentrations 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 O3 concentrations change

in response to prescribed changes in source emissions of NOx 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 O3 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 O3.

The EKMA-based method for determining O3 control strategies has limitations, the

most serious of which is that predicted emissions reductions are critically dependent on the

initial NMHC/NOx 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 NOx 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 O3 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 O3 episodes do not occur during

periods of significant clouds or rain.

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Concentration 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 NOx emissions or both are reduced in the model inputs, and the effects of these reductions

on O3 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 O3. Uncertainties in models obviously can affect

their outputs. Uncertainties exist in all components of grid-based O3 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

O3 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

O3 problem. Grid-based O3 air quality modeling is superior to the available alternatives for

O3 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 O3 are chemiluminescence (CL); UV absorption

spectrometry; and newly developed spectroscopic and chemical approaches, including

chemical approaches applied to passive sampling devices (PSDs) for O3.

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 O3 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 O3 methods. A potential disadvantage of UV photometry

is that atmospheric constituents that absorb 254-nm radiation, the wavelength at which O3 is

measured, will cause a positive interference in O3 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.

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Calibration of O3 measurement methods (other than PSDs) is done by UV

spectrometry or by gas-phase titration (GPT) of O3 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 O3 scrubber has been recommended to

prevent interference by O3 in this method in ambient air.

Oxides of Nitrogen

Nitric oxide and NO2 comprise the NOx compounds involved as precursors to

O3 and other photochemical oxidants.

The most common method of NO measurement is the gas-phase CL reaction with

O3, 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 NO2 measurement, even

though they do not measure NO2 directly. Minimum detection levels for NO2 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 NO2 to NO is required for measurement. These analyzers

actually measure NOy (NOx + PAN + HNO3 + other reactive nitrogen species); however, for

most urban atmospheres, NOx is the predominant species measured diurnally.

Several spectroscopic approaches to NO2 detection have been developed but share

the drawbacks of spectroscopic NO methods. Passive samplers for NO2 exist but are still in

the developmental stage for ambient air monitoring.

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Calibration of methods for NO measurement is done using standard cylinders of

NO in nitrogen. Calibration of methods for NO2 measurement include use of cylinders of

NO2 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 O3 from urban area emissions is transported to rural downwind locations,

elevated O3 concentrations can occur at considerable distances from urban centers. Urban

O3 concentration values are often depressed because of titration by NO. Because of the

absence of chemical scavenging, O3 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 O3 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 O3 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 O3 formation.

From 1991 to 1992, the composite mean of the second highest daily maximum

1-h O3 concentrations decreased 7%, and the composite average of the number of estimated

exceedances of the O3 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 O3 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 O3 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, O3 concentrations showed

only moderate peaks but showed multihour levels above 0.1 ppm.

1-9
A small amount of the O3 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 CH4/CO/NOx cycle in the troposphere, and transport of very distant

anthropogenic or biogenic VOCs and NOx. The specific concentrations of this "background"

O3 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 O3 data from isolated monitoring sites, EPA has indicated that a

reasonable estimate of O3 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 O3 contribution from stratospheric

intrusions into the troposphere.

Diurnal Variations

Diurnal patterns of O3 may be expected to vary with location, depending on the

balance among the many factors affecting O3 formation, transport, and destruction. Although

they vary with locality, diurnal patterns of O3 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 O3 from layers aloft, (2) destruction of O3 through contact with surfaces and

through reaction with NO at ground level, and (3) in situ photochemical production of O3.

Seasonal Patterns

Seasonal variations in O3 concentrations in urban areas usually show the pattern of

high O3 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 O3 and other oxidants

than during the prior or following year.

Average O3 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 O3. However, for several clean rural sites, the highest exposures

have occurred in the third quarter rather than in the second. For rural O3 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 O3 vary with altitude and with latitude. There appears to be no

consistent conclusion concerning the relationship between O3 exposure and elevation.

Indoor Ozone

Until the early 1970s, very little was known about the O3 concentrations

experienced inside buildings; to date, the database on this subject is not large, and a wide

range of indoor/outdoor O3 concentration relationships can be found in the literature (reported

indoor/outdoor values for O3 are highly variable). Indoor/outdoor O3 concentration ratios

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generally fall in the range from 0.1 to 0.7 and indoor concentrations of O3 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 O3 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 O3 exposure models exist: (1) those that focus

narrowly on predicting indoor O3 levels and (2) those that focus on predicting O3 exposures

on a community-wide basis. These latter models and their distinguishing features are:

1. pNEM/O3 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/O3.

· 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-O3 oxidants in ambient air in the United States, other than the inorganic nitrogenous

oxidants such as NO2, and possibly HNO3. 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 NOx precursors to these compounds are considerably

lower in nonurban areas than in urban areas.

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Co-occurrence

Studies of the joint occurrence of gaseous NO2/O3 and SO2/O3 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 NO2/O3 and SO2/O3 was infrequent. Some researchers have

reported the joint occurrence of O3, nitrogen, and sulfur in forested areas, combining

cumulative exposures of O3 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 O3 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 O3 and SO2 or O3 and NO2. Acid sulfates, which are usually composed of H2SO4,

ammonium bisulfate, and ammonium sulfate, have been measured at a number of locations in

North America. The potential for O3 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

NO2 and O3 was infrequent.

In one study, the relationship between O3 and hydrogen ions in precipitation was

explored using data from sites that monitored both O3 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 O3 exposure were at least

moderately high. With data compiled from all sites, it was found that relatively acidic

precipitation occurred together with relatively high O3 levels approximately 20% of the time,

and highly acidic precipitation occurred together with a high O3 level approximately 6% of

the time. Sites most subject to relatively high levels of both hydrogen ions and O3 were

located in the eastern part of the United States, often in mountainous areas.

The co-occurrence of O3 and acidic cloudwater in high-elevation forests has been

characterized. The frequent O3-only and pH-only single-pollutant episodes, as well as the

simultaneous and sequential co-occurrences of O3 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 O3 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 O3 exposure.

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Effects on Agroecosystems

Methodologies Used in Vegetation Research

Most of the knowledge concerning the effects of O3 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 O3 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 O3 that enters the plant through the stomata (opening in the leaves). An

effect will occur only if sufficient O3 reaches sensitive sites within the leaf cells. The uptake

and movement of O3 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 O3 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 O3 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 O3 can result in mortality,

particularly when plant susceptibility to insects and pathogens is increased.

Factors That Modify Plant Response

Plant response to O3 exposure is influenced by a variety of biological, chemical,

and physical factors. When determining the impact of O3 exposure on plants, both the

influence of environmental factors on plant response and the effects of O3 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 O3. Individuals, varieties, and

cultivars of a species are known to differ greatly in their responses to a given O3 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 O3 can modify plant response to other environmental

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variables. 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 O3 on growth and

yield of crops. However, in the case of crops, drought, per se, much more adversely affects

yield than the effects of O3. Ozone, on the other hand, tends to reduce the water-use

efficiency of well-watered crops. In some plants, O3 exposure reduces cold/winter hardiness.

Although exposure to O3 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 O3. 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 O3 concentration, exposure duration, interval

between exposures, and plant response.

The use of a mean concentration with long averaging times implies that all

concentrations of O3 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 O3 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 O3 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 O3 will inhibit photosynthesis and result in increased reductions in

biomass production.

An index of ambient exposures that relates well to plant response should

incorporate, 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 O3 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 O3 is the primary source of variation in response, year-to-year variations in plant

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response 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 O3 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 O3 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 O3 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

O3 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 O3 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 O3 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 O3 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.

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Plants 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 O3 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 O3 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 O3; 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.

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Injury 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 O3 injury.

Effects on Agriculture, Forestry, and Ecosystems: Economics

A number of economic assessments of the effects of O3 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 O3. 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 O3-crop yield response findings,

if available.

The plant science literature shows that O3 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 O3 is imposing economic costs. The economic effects of O3 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 O3 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 O3 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

O3-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.

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Both the type of dye and the material in which it is incorporated are important

factors in the resistance of a fabric to O3. Some dyed fabrics, such as royal blue and red

rayon-acetate and plum cotton are resistant to O3. On the other hand, anthraquinone dyes on

nylon fibers are sensitive to fading by O3. Field studies and laboratory work show a positive

association between O3 levels and dye fading of nylon materials. At present, the available

research is insufficient to quantify the amount of damaged materials attributable to O3 alone.

The degradation of fibers from exposure to O3 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 O3. 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 O3 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 O3 and other oxidants; in

particular, many organic pigments are subject to fading or other color changes when exposed

to O3. Although most, but not all, modern fine arts paints are more O3 resistant, many older

works of art are at risk of permanent damage due to O3-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 O3 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 O3.

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 O3 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 O3 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 O3-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.

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Generally, O3 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 O3.

Lung Inflammation and Permeability Changes

Elevated concentrations of O3 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 O3 (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 O3 increases.

Long-term exposure effects are discussed under lung morphology.

The impacts of these changes are not fully understood. At higher

O3 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 O3. Ciliated epithelial

cells that move the mucous blanket are altered or destroyed by acute and chronic exposures.

Neonatal sheep exposed to O3 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 O3. Short-term exposure to levels as low as 0.1 ppm O3 (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 O3, 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 O3 on this function. Acute exposures of rabbits to levels

as low as 0.1 ppm O3 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 O3.

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Both the pulmonary and systemic immune system are affected by O3, 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 O3 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 O3, 3 h/day, 5 days; mice) increase viral-induced mortality.

Apparently, O3 does not impact antiviral clearance mechanisms. Although O3 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 O3 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 O3. 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 O3 (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 O3 (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 O3.

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The morphologic lesions described in early publications on laboratory animals

exposed to O3 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 O3 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

O3 (a few hours; rats).

When rats are exposed to O3 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 O3.

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 O3 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 O3 often were

used. Generally, in these studies, O3 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 O3 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 O3-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

O3-exposed mice. At the present time, however, there is inadequate information to provide

1-21
mechanistic 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 O3. 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 O3 itself is not thought to penetrate the lung, these systemic effects are either

secondary to lung alterations or result from reaction products of O3. 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 O3 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 O3. For example, 0.12 ppm O3 (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 O3-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 O3 criteria

document showed that levels up to about 2.0 ppm O3 did not cause birth defects. Rat pups

from females exposed to 1.0 ppm O3 during certain periods of gestation weighed less or had

delays in development of behaviors (e.g., righting, eye opening). No "classical" reproductive

assays with O3 were found.

Other studies have indicated that O3 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 O3 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 O3 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 O3, 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 O3-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 in 1 s (FEV1), 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 FEV1/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 O3-induced lung volume changes and changes in airway responsiveness.

Inflammation and Host Defense Effects

Controlled studies in healthy adult subjects also indicate that O3 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 O3 can be detected within 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 O3 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 O3 have a decreased ability to ingest microorganisms,

indicating some impairment of host defense capability.

Ozone Exposure-Response Relationships

Functional, symptomatic, and inflammatory responses to O3 increase with

increasing exposure dose of O3. The major determinants of the exposure dose are

O3 concentration (C), exposure duration (T), and the amount of ventilation ( E). V ̇

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Exercise increases response to O3 by increasing E (greater mass delivered), tidal V ̇

volume, inspiratory flow (greater percentage delivery), and the intrapulmonary

O3 concentration.

Repeated daily exposures to relatively high levels of O3 doses (C×T× E) V ̇

causing substantial reductions in FEV1 (≥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 O3 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 O3 exposure. There is V ̇

clear evidence of a response plateau in terms of lung volume response to prolonged

O3 exposure. This evidence suggests that for a given combination of exercise and

O3 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 O3 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

O3 delivery to the tissue (i.e., the inhaled concentration, breathing pattern, airway geometry;

O3 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 E2 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 O3 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 O3; however,

most are addressed inadequately in the available clinical data to make definitive conclusions.

Active smokers are less responsive to O3 exposure, which may reverse following

smoking cessation, but these results should be interpreted with caution.

The possibility of age-related differences in response to O3 has been explored,

although young adults historically have provided the subject population for controlled human

studies. Children and adolescents have lung volume responses to O3 similar to those of

young adults, but lack respiratory symptoms. Pulmonary function responsiveness in adults

1-24
appears 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 O3. 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 O3, 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 O3.

Seasonal and ambient factors may vary responsiveness to O3, but further research

is needed to determine how they affect individual subjects. Individual sensitivity to O3 may

vary throughout the year, related to seasonal variations in ambient O3 concentrations.

The specific inhalation route appears to be of minor importance in exercising

adults. Exposure to O3 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 O3 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 O3 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 O3. 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 O3 mixed with SO2, NO2, H2SO4, HNO3,

particulate aerosols, or combinations of these pollutants. It is fairly well established that

simultaneous exposure of healthy adults and asthmatics to mixtures of O3 and other pollutants

for short periods of time (<2 h) induces pulmonary function responses not significantly

different from those following O3 alone when studies are conducted at the same

O3 concentration. Exposure to PAN has been reported to induce greater pulmonary function

responses than exposure to O3 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 O3 and the complex mix of pollutants found in the ambient air.

1-25
Prior exposure to O3 in asthmatics may cause an increase in response to other

pollutant gases, especially SO2. Likewise, prior exposure to other pollutants can enhance

responses to O3 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 O3. A series of these studies from

Los Angeles has demonstrated pulmonary function decrements at mean ambient

O3 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 O3 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 O3 on lung function decrements and increased morbidity and mortality in

human populations exposed to real-world conditions of O3 exposure.

Camp and exercise studies of lung function provide quantitative information on

exposure-response relationships linking lung function declines with O3 exposure occurring in

ambient air. Combined statistical analysis of six recent camp studies in children yields an

average relationship between decrements in FEV1 and previous-hour O3 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 O3 exposure is consistent

with the results of controlled human studies.

Daily life studies support a consistent relationship between O3 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 O3,

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 O3,

accounting for approximately one to three excess respiratory hospital admissions per hundred

parts per billion O3 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 O3.

Many of the time-series epidemiology studies looking for associations between

O3 exposure and daily human mortality have been difficult to interpret because of

1-26
methodological or statistical weaknesses, including the a failure to account for other pollutant

and environmental effects. One of the two most useful new studies on O3-mortality found a

small but statistically significant association in Los Angeles when peak 1-h maximum

O3 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 O3 concentrations

(St. Louis, MO, and Kingston-Harriman, TN) did not detect a significant O3 association with

mortality.

Only suggestive epidemiologic evidence exists for health effects of chronic

ambient O3 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 O3 from those of other

pollutants, especially particles.

1.8 Extrapolation of Animal Toxicological Data to

Humans

There have been significant advances in O3 dosimetry since 1986 that better enable

quantitative extrapolation with marked reductions in uncertainty. Experiments and models

describing the uptake efficiency and delivered dose of O3 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 O3 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 O3 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 O3 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 O3 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 O3 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

O3 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

O3 responses in the context of intra- and interspecies comparisons. In the case of intraspecies

comparisons, for example, the distribution of predicted O3 tissue dose to a ventilatory unit in

a rat as a function of distance from the bronchoalveolar duct junction is very consistent with

1-27
the distribution of alveolar wall thickening. In the case of interspecies comparisons (using the

delivered O3 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, with

humans 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 O3 dosimetry

data sets and between the experimental data and theoretical predictions of O3 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 O3 dose. The use of O3 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 O3 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 O3 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 O3 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 O3 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 O3 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

O3-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 O3-exposed asthmatics in addition to the other

demonstrated symptoms of airway irritation. Airway resistance, however, increases relatively

1-28
more in asthmatics from an already higher baseline. Recovery from the effects of O3 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 O3 concentration for areas with

high O3 levels (e.g., Los Angeles) has been suggested, although the magnitude

of such an effect is unclear.

· Increased O3 levels are associated with increased hospital admissions and

emergency department visits for respiratory causes. Analyses from data in the

northeastern United States suggest that O3 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

O3 concentration. Meta-analysis indicates that a 0.5-mL decrease in FEV1 is

associated with a 1-ppb increase in O3 concentration. For preadolescent

children exposed to 120 ppb (0.12 ppm) ambient O3, this amounts to an

average decrement of 2.4 to 3.0% in FEV1. Similar responses are reported for

children and adolescents exposed to O3 in ambient air or O3 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 to 3 h of exposure as a function of the level of

exercise performed and the O3 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 FEV1)

occur

(1) at >0.50 ppm O3 when at rest,

(2) at >0.37 ppm O3 with light exercise (slow walking),

(3) at >0.30 ppm O3 with moderate exercise (brisk walking),

(4) at >0.18 ppm O3 with heavy exercise (easy jogging), and

(5) at >0.16 ppm O3 with very heavy exercise (running).

Smaller group mean changes (e.g., <5%) in FEV1 have been observed at lower

O3 concentrations than those listed above. For example, FEV1 decrements have

been shown to occur with very heavy exercise in healthy adults at 0.15 to 0.16

ppm O3, 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 O3 with heavy

exercise. Some individuals within a study may experience FEV1 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 FEV1 were

observed at

(1) 0.08 ppm O3 after 5.6 h,

(2) 0.10 ppm O3 after 4.6 h, and

(3) 0.12 ppm O3 after 3 h.

1-29
For these same subjects, 10% group mean FEV1 decrements were observed at

0.12 ppm O3 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 O3 concentrations

as low as 0.12 ppm in healthy adults during 1 to 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 O3 with heavy and very

heavy exercise. Respiratory symptoms also have been observed following

exposure to 0.08, 0.10, and 0.12 ppm O3 for 6.6 h with moderate levels of

exercise.

· Increases in nonspecific airway responsiveness in healthy adults have been

observed after 1 to 3 h of exposure to 0.40 but not 0.20 ppm O3 at rest and

have been observed at concentrations as low as 0.18 but not to 0.12 ppm

O3 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 O3.

Short-term O3 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 O3 during 6.6-h exposures with moderate

exercise; lower concentrations have not been tested.

Short-term O3 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 O3 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 O3-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 O3. Some inflammatory markers also are reversed during this

time period, but others still show attenuation even after 20 days without O3. 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 O3 within the lung, allowing more O3 to reach sensitive

1-30
regions, 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 O3 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 O3 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 O3 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 O3 exposure

provide only suggestive evidence that such a linkage exists.

Long-term exposure of one strain of female mice to high O3 levels (1 ppm) caused

a 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

O3 support the hypothesis that coexposure to pollutants, each at low-effect levels, may result

in effects of significance. The data from human studies of O3 in combination with NO2, SO2,

H2SO4, HNO3, or CO show no more than an additive response on lung spirometry or

respiratory symptoms. The larger number of laboratory animal studies with O3 in mixture

with NO2 and H2SO4 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 O3 is

based on their biological responses to O3, 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 O3 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 O3, with at least a 10-fold difference between the most and least responsive

individuals. Individual sensitivity to O3 also may vary throughout the year, related to

seasonal variations in ambient O3 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 O3 in the RT, available data show little difference on O3 deposition in

the lungs for inhalation through the nose or mouth.

1-31
Daily 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 O3 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 O3 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 O3.

Unfortunately, not enough is known about the responses of these individuals to make

definitive conclusions regarding their relative responsiveness to O3. 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 O3.

· Human studies have identified a decrease in pulmonary function responsiveness

to O3 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 O3. If gender differences exist

for lung function responsiveness to O3, 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 O3. In particular, the responses of

nonwhite asthmatics have not been investigated.

· Information derived from O3 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 O3-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

O3 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 O3 consist of exercising,

healthy and asthmatic individuals, including children, adolescents, and adults.

1-32
 The 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 O3 are

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 (O3) and nitrogen dioxide (NO2). Other oxidants, such as hydrogen peroxide (H2O2) and

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 O3 and non-O3 photochemical oxidants in the atmosphere resulting from the

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

O3-specific commercial analytical methods became available, as did additional information on

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 O3 and, to a lesser extent, on

those for H O and the peroxyacyl nitrates, particularly peroxyacetyl nitrate. The scientific air 2 2

quality criteria for NO2 are discussed in a separate document (U.S. Environmental Protection

Agency, 1993).

The previous O3 air quality criteria document (AQCD), Air Quality Criteria for Ozone

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 O3 was not appropriate at that time. That decision 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 document is to summarize the air quality criteria

for O3 available in the published literature through early 1995. This review was performed in

accordance with provisions of the Clean Air Act (CAA) to provide the scientific basis for periodic

reevaluation of the O NAAQS. 3
This 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 Background2

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 O3 and other photochemical oxidants was published on

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 photochemical
2-3

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 O3, and revised the

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 Ozonea

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 O3 be considered in a second draft of the

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 O3 at a public review meeting

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. 3
2-4

A draft O3 supplement, Summary of Selected New Information on Effects of Ozone on

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 O3 supplement reviewed and evaluated selected

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 O3 would be published in

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 O3. On May 4, 1992, the

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 O3 by

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 3
2-5

(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 O3 are appropriate. After reviews of the draft staff paper by the public and

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 O3

and its precursors?

• What is the order of magnitude of current estimates of natural emissions of

O3 precursors and emissions from anthropogenic sources and their relevance to

tropospheric O3 photochemistry?

• What new scientific information exists on the roles of meteorologic and

climatologic factors in O3 formation and transport?

• 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 O3 concentrations across the

United States?

• What are diurnal and seasonal patterns of 1-h average O3 concentrations for urban

and nonurban sites and for attainment versus nonattainment areas?

• What is known about patterns of co-occurrence of O3 with other pollutants in the

atmosphere?

• What O3 exposure assessment data are available for agricultural crops and forests?

• To what level and to what extent are humans typically exposed to O3 in the course

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 O3 exposures on natural ecosystems?

• Is there important new information on the effects of O3 on nonbiological materials?
2-6

2.3.4 Health Effects

• What O3 concentration and exposure duration relationships exist for effects on lung

structure, function, and host defense mechanisms, and what are the important

modifiers of these effects?

• What are the mechanisms of O3-induced lung injury?

• 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

O3?

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 O3 and related photochemical oxidants in

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 O3 are

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 O3. The reader should, however, consult the more extensive discussion of these

"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 "adverse
2-7

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 O3. The

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

O3 NAAQS. For example, certain issues related to the control strategies for attainment of the

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 O3 and oxidant air quality. This is followed by Chapter 5, which deals with

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 . 3
2-8

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 (O3) and other oxidants found in ambient air, such as peroxyacyl nitrates

(PANs) and hydrogen peroxide (H2O2), are formed as the result of atmospheric physical and

chemical processes involving two classes of precursor pollutants, volatile organic compounds

(VOCs) and nitrogen oxides (NOx). The formation of O3 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

NOx, 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 O3 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 O3. In addition, the identification and

quantification of O3 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 O3 and related oxidants of multiple air pollutants.

The ability to measure O3 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 O3 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 O3 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

3-1
concentrations 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 O3 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 O3 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 O3 in the natural or "clean" troposphere. The presence of O3 in

the clean troposphere, in the absence of perturbations caused by human activities, is highly

important because O3 is a precursor to the hydroxyl (OH) radical, the key intermediate species

in the tropospheric degradation of VOCs emitted into the atmosphere. Although O3 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

O3 and other photochemical air pollutants are complex. Tropospheric O3 is formed as a result

of (1) the emissions of NOx 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 O3 and other photochemical oxidants,

such as peroxyacetyl nitrate (PAN), nitric acid (HNO3), and sulfuric acid (H2SO4), 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 O3 in the troposphere

involves the photolysis of nitrogen dioxide (NO2) to yield nitric oxide (NO) and a groundstate oxygen atom, O(3

P),

NO (3-1) 2 hν → NO O(3

P),

which then reacts with molecular oxygen to form O3:

O( (3-2) 3

P) O2 M → O3 M, where M air.

The NO and O3 react to reform NO2:

3-2
NO O (3-3) 3 → NO2 O2 .

The presence of reactive VOCs leads to the conversion of NO to NO2 without the

intermediary of O3 (Reaction 3-3), and the photolysis of NO2 then leads to the formation of

elevated levels of O3:

The photochemical cycles leading to O3 production are best understood through a

knowledge of the chemistry of the atmospheric oxidation of methane (CH4), 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 CH4 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 NOx chemistry. The photochemical

formation of tropospheric O3 from the oxidation of CH4 then is discussed in some detail

because the CH4 oxidation cycle serves as a model for the photochemical formation of O3. In

Section 3.2.4, the chemistry of the major classes of nonmethane VOCs and the formation of

O3 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 O3 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.

3-3
3.2.2.1 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 O3 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 [N2], 21.0% molecular oxygen [O2], 0.9% argon [Ar], 0.03%

carbon dioxide [CO2], 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 O2 in the

stratosphere, leading to dissociation into two ground-state oxygen atoms, O(3P),

O (3-5) 2 hν → 2 O(3P),

followed by the reaction of O(3P) atoms with O2 in the presence of a third body, M, to form

O3:

O( (3-2) 3P) O2 M → O3 M, where M air.

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

O (3-6) 3 hν → O2 O,

where the oxygen atom produced can be in the ground state, O(3

P), or electronically excited

state, O(1D). The O(1D) atoms produced are deactivated to the ground-state O(3P) atom by

N2, O2, CO2, and Ar:

3-4
O( (3-7) 1

D) M → O(3P) M, where M N2, O2, Ar, and CO2.

The reaction of O(3P) atoms with O3 is the termination step of this reaction sequence,

O( (3-8) 3P) O3 → 2 O2 .

These reactions, called the Chapman reactions (Chapman, 1930), are responsible

for the layer of O3 found in the stratosphere. Because the stratospheric O3 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 O3 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 O3. Model calculations indicate that O3 in the troposphere could

increase with increasing UV-B in urban and rural areas impacted by anthropogenic NOx

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

NOx levels (Liu and Trainer, 1988; Thompson et al., 1989). Besides the implications of longterm trends in stratospheric O3 concentrations leading to corresponding changes in the

intensity of UV-B radiation impacting the troposphere, short-term changes (including daily

changes) in stratospheric O3 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 O3 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

O3 concentrations is needed as input to urban and regional airshed computer models of

photochemical air pollution formation.

In the clean atmosphere, stratospheric O3 also is influenced by the emission of

nitrous oxide (N2O) from soils and oceans (World Meteorological Organization, 1992).

Because N2O 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(1D) atoms (DeMore et al., 1992; Atkinson et al., 1992a). The reaction of N2O

with the O(1

D) atom is the major source of stratospheric NO, which then participates in a

series of reactions known as the NOx catalytic cycle (Crutzen, 1970; Johnston, 1971).

NO O (3-3) 3 → NO2 O2

The Chapman reactions and the NOx catalytic cycle reactions control the O3 concentrations in

the lower clean stratosphere.

Additional reaction sequences leading to the removal of stratospheric O3 arise from

the ClOx and BrOx catalytic cycles, which result when chlorine (Cl)- and bromine

3-5
(Br)-containing organic compounds are emitted into the atmosphere. These O3-depleting

(3-9)

Net:

NO2 O(3P) → NO O2

O(3P) O3 → 2 O2

compounds include the chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs),

carbon tetrachloride (CCl4), methyl chloroform, halons, and methyl bromide (Anderson et al.,

1991; Rowland, 1990, 1991; World Meteorological Organization, 1992). Analogous to N2O,

the CFCs, CCl4, and certain halons (CF3Br and CF2ClBr) 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, O3 is present in the troposphere even in the absence of

human activities. Although this "natural" O3 has received widely varying estimates in the

literature, there has not been an attempt to standardize the definition because natural

background O3 is a multidimensional and complex concept. Concentrations of background O3

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 O3 concentration in the clean troposphere may be taken

to be reasonably independent of altitude at ≈7 × 1011 molecules cm−3.

For purposes of this document, the primary focus is on the background

O3 concentration near the surface over the United States during the O3 season. However, the

length of the O3 season varies from state-to-state, depending predominantly on latitude (see

Chapter 4). These variations in O3 season affect the determination of which seasonal

averaging period to apply when estimating O3 concentrations. Based on available assessments

of O3 monitoring measurements, a daytime, 7-h (0900 to 1559 hours), seasonal (April to

October) average O3 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 O3. This assumption is consistent with the relatively

long lifetime of O3 in the troposphere, which can be as long as 30 to 60 days.

The background of O3 can be attributed to the following sources: downward

transport of stratospheric O3 through the free troposphere to near ground level, in situ

O3 production from methane emitted from swamps and wetlands reacting with natural NOx

emitted from soils and lighting strikes and from the downward transport of NO from the

stratosphere into the troposphere, and in situ production of O3 from the reactions of biogenic

VOCs with natural NOx (National Research Council, 1991). Another source to be considered

is the long-range transport of O3 from distant pollutant sources.

It is important to appreciate that NOx has a limited lifetime, often estimated to be

as short as 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 NOx emitted from cultivated areas in

3-6
the 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 NOx from anthropogenic NOx 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

O3 and, hence, to O3 concentrations above those encountered in the clean troposphere. The

OH radical is produced from the ultraviolet (UV) photolysis of O3. Ozone photolyzes in the

UV radiation at wavelengths <320 nm to generate the electronically excited O(1D) atom

(DeMore et al., 1992; Atkinson et al., 1992a),

O (3-6a) 3 hν → O2 O(1D).

The O(1

D) atoms either are deactivated to the ground state O(3P) atom by Reaction 3-7 or

they react with water vapor to form the OH radical:

O( (3-10) 1D) H2O → 2 OH.

The O(3P) atoms formed directly in the photolysis of O3 or formed from deactivation of O(1D)

atoms (Reaction 3-7) reform O3 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 O3.

Hydroxyl radical production from Reactions 3-6a and 3-10 is balanced by reaction of the OH

radical with carbon monoxide (CO) and CH4. Because the water vapor mixing ratio decreases

with increasing altitude in the troposphere (Logan et al., 1981; World Meteorological

Organization, 1992) and the O3 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 (HO2) radicals are interrelated through a series of

reactions (Section 3.2.3.3), concurrent measurements of OH and HO2 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 106 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 (CH3CCl3)

and the rate constant for the reaction of the OH radical with CH3CCl3 (its major tropospheric

loss process). Using this method, Prinn et al. (1992) have derived a 24-h average OH radical

concentration of 8 × 105 molecules cm-3 (equivalent to a 12-h daytime average of 1.6 × 106

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

3-7
hydrocarbons 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 NOx is necessary for the formation of O3 from the oxidation of

CH4 and other VOCs. Sources of tropospheric NOx 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 NOx 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 NOx

concentrations (≈100 ppt) have been observed in the boundary layer over relatively unpolluted

continental areas (Carroll et al., 1990), with the NOx 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, NO2, and O3 are interrelated by the following reactions:

NO O (3-3) 3 → NO2 O2

NO (3-1) 2 hν → NO O(3P)

O( (3-2) 3

P) O2 M → O3 M.

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

≈10-5 s), the O3 concentration at photoequilibrium is given by

[O (3-11) 3

] J1[NO2]/k3[NO],

where J1 and k3 are the photolysis rate of NO2 (≈0.5 min-1 for an overhead sun) and the rate

constant for the reaction of NO with O3, respectively.

There are other important reactions involving NOx. The reaction of NO2 with

O3 leads to the formation of the nitrate (NO3) radical,

NO (3-12) 2 O3 → NO3 O2

,

which in the lower troposphere is nearly in equilibrium with dinitrogen pentoxide (N2O5):

NO (3-13, -3-13) 3 NO2

M N2

O5.

3-8
However, because the NO3 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 × 107 to 1 × 1010 molecules cm-3 (<2 to 430 ppt) over continental areas

influenced by anthropogenic emissions of NOx (Atkinson et al., 1986). Nitrate radical

concentrations over marine areas are low because NOx concentrations are low over lower

tropospheric marine areas (Noxon, 1983), and an NO3 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 NO3 radical concentration of 5 × 108 molecules cm-3 in the

lower troposphere over continental areas, with an uncertainty of a factor of ≈10.

The tropospheric chemical removal processes for NOx involve the daytime reaction

of NO2 with the OH radical and the nighttime wet and dry deposition of N2O5 to produce

HNO3.

OH NO (3-15) 2

M

→ HNO3

The gas-phase reaction of the OH radical with NO2 is the major and ultimate removal process

for NOx in the troposphere. This reaction removes radicals (OH and NO2) and competes with

the reaction of the OH radical with VOCs. Gaseous HNO3 formed from Reaction 3-15

undergoes wet and dry deposition, including combination with gaseous ammonia (NH3) to

form particulate phase ammonium nitrate (N2H4O3). The tropospheric lifetime of NOx due to

chemical reaction (mainly Reaction 3-15) is ≈1 to 2 days. The tropospheric NOx reactions are

shown schematically in Figure 3-1. It should be noted that OH radicals also can react with

NO to produce nitrous acid (HNO2):

In urban areas, HNO2 also can be formed during nighttime hours (Harris et al., 1982; Pitts

OH NO (3-17) M→ HNO2.

et al., 1984a; Rodgers and Davis, 1989), apparently from the heterogeneous hydrolysis of NO2

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

3-9
Figure 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

HNO2 during the early morning hours,

HNO (3-18) 2 hν → 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 NOx (NO + NO2), then

to reservoir and termination species (PAN and its homologues, organic nitrates, HNO3, and

particulate nitrate). These reservoir and termination species are referred to as NOz. The term

"NOy" refers to the total amount of nitrogen, with NOy = (NOx + NOz). 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 O3 levels and NOy. Trainer et al. (1993) further showed that O3 levels correlate

even better with NOz than with NOy, as may be expected because NOz quantifies the amount

of initially emitted NO that has been processed photochemically, forming O3 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 CH4 is by reaction with the OH radical, with the CH4

lifetime equal to

(k21 [OH])-1, (3-19)

3-10
where k21 is the rate constant for Reaction 3-21, and [OH] is the (variable) atmospheric

OH radical concentration. The calculated lifetime of CH4 in the troposphere is ≈10 to

12 years. As for other saturated organic compounds, the OH radical reaction with CH4

proceeds by hydrogen (H)-atom abstraction from the carbon (C)-H bonds to form the methyl

(CH3) radical:

OH CH (3-20) 4 → H2O CH ̇ 3 .

In the troposphere, the methyl radical reacts solely with O2 to yield the methyl peroxy

(CH3O2

.

) radical (Atkinson et al., 1992a):

In the troposphere, the methyl peroxy radical can react with NO, NO2, HO2

(3-21) CH ̇ 3 O2

M

→ CH3O2.

radicals, and other organic peroxy (RO2

.

) radicals, with the reactions with NO and HO2

radicals being the most important (see, for example, World Meteorological Organization,

1990b). The reaction with NO leads to the formation of the methoxy (CH3 ) radical, O ̇

(3-22) CH3

O2 NO → CH3O ̇ NO2 .

The reaction with the HO2 radical leads to the formation of methyl hydroperoxide

(CH3OOH),

CH (3-23) 3

O2 HO2 → CH3OOH O2 ,

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

CH (3-24) 3OOH hν → CH3

O ̇ OH,

Methyl hydroperoxide also undergoes wet and dry deposition or incorporation into cloud

water. The lifetime of CH3OOH in the troposphere due to photolysis and reaction with the

3-11
OH 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

O2 to form HCHO and the HO2 radical,

CH (3-26) 3

O ̇ O2 → HCHO HO2 .

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,

OH HCHO → H (3-28) 2O HCO.  ̇

In the troposphere, the H atom and H O (formyl) radical produced in these processes react C ̇

solely with O2 to form the HO2 radical:

H O (3-29) 2 M → HO2 M

HCO ̇ O (3-30) 2 → HO2 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 CH4 in the earth’s atmosphere involves the

oxidation of CO by reaction with the OH radical (the only tropospheric reaction of CO) to

form CO2:

OH CO → H CO (3-31) 2

H O (3-29) 2 M → HO2 M.

The lifetime of CO in the lower troposphere is ≈2 mo.

The overall reaction sequence leading to CO2 formation, through the HCHO and

CO intermediate products, is shown in Figure 3-2.

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

There is competition between NO and the HO2 radical for reaction with the CH3O2

.

radical, and the reaction route depends on the rate constants for these two reactions and the

tropospheric concentrations of HO2 radicals and NO. The rate constants for the reaction of

the CH3O2

. radicals with NO (Reaction 3-22) and HO2 radicals (Reaction 3-23) are of

comparable magnitude (Atkinson et al., 1992a). Based on the expected HO2 radical

concentration in the troposphere, Logan et al. (1981) calculated that the reaction of the CH3O2

.

radical with NO dominates for NO mixing ratios of >30 ppt (equivalent to an NO

concentration of >7 × 108 molecules cm-3 in the lower troposphere). For NO mixing ratios

<30 ppt, the reaction of the CH3O2

. radical with HO2 dominates.

Hydroperoxy radicals formed from, for example, Reactions 3-26, 3-29, and 3-30

can react with NO, O3, or themselves, depending mainly on the concentration of NO. The

reaction with NO leads to regeneration of the OH radical,

HO (3-32) 2 NO → OH NO2 ,

whereas the reactions with O3 and HO2 radicals lead to a net destruction of tropospheric O3:

HO (3-33) 2 HO2 → H2

O2 O2

HO (3-34) 2 O3 → OH 2 O2

.

3-13
This net loss of tropospheric O3 occurs because the photolytic production of the OH radical

from O3, via the intermediary of the O(1D) atom, represents a loss process for tropospheric

O3. Hence, the absence of any O3 formation from the CH4 oxidation cycle is equivalent to a

net O3 loss. Using the rate constants reported for Reactions 3-32 and 3-34 (Atkinson et al.,

1992a) and the tropospheric O3 mixing ratios given above, it is calculated that the HO2 radical

reaction with NO dominates over reaction with O3 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 HO2 radical reaction with NO dominates over the self-reaction of HO2 radicals.

There are therefore two regimes, depending on the fate of HO2 and CH3O2 radicals:

(1) a high-NO regime in which HO2 and CH3O2 radicals react with NO to convert NO to

NO2, regenerate the OH radical, and, through the photolysis of NO2, produce O3; and

(2) a low-NO regime in which HO2 and CH3O2 radicals combine (Reaction 3-23), and HO2

radicals undergo self-reaction and react with O3 (Reactions 3-33 and 3-34), leading to a net

destruction of O3 and inefficient OH radical regeneration (see also Ehhalt et al., 1991; Ayers

et al., 1992).

Under high-NO conditions, the oxidation of CH4 leading to the formation of

HCHO can be written as the net reaction,

OH CH (3-35) 4 2 NO 2 O2 H2O HCHO 2 NO2 OH,

indicating the conversion of two molecules of NO to NO2 and regeneration of the OH radical.

Because NO2 photolyzes to form O3 in the presence of O2,

the oxidation of CH4 to HCHO under high-NO conditions can be written as

OH CH (3-36) 4 4 O2 H2

O HCHO 2 O3 OH,

showing the formation of O3 from CH4 oxidation in the troposphere. The reaction cycles

oxidizing CH4 to HCHO, converting NO to NO2, and forming O3 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

O2

→ CO 0.44 H2 0.2 H2

O 0.92 NO2 0.92 OH.

3-14
Figure 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 HO2 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 CH4 will lead to a net destruction of O3 (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 NOx emissions from combustion sources,

NO mixing ratios are high enough (high NO-conditions) for CH4 oxidation to lead to net

O3 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 NOx-CH4-air

photooxidation (e.g., wet and dry deposition of HNO3 and CH3OOH [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, HO2 radicals, and N2O5 into clouds can decrease markedly

the production of O3. The incorporation of HCHO into cloudwater removes HCHO from the

gas phase and, hence, reduces the gas-phase formation of HO2. The uptake of HO2 radicals

into cloudwater has the same effect. Moreover, the aqueous-phase reactions of CH2(OH)2 (the

hydrated form of HCHO) lead to the formation of O2

-

, which reacts with dissolved O3 to act as

a sink for O3 during cloudy periods. During nighttime, N2O5 formed in the gas phase from

Reactions 3-3, 3-12, and 3-13 can be readily incorporated into cloudwater with hydrolysis to

HNO3, precluding the reformation of NOx during the following day from Reactions -3-13 and

3-14. Dentener and Crutzen (1993) have also concluded from a computer modeling study that

3-15
the heterogeneous reactions of NO3 radicals and N2O5 on aerosols can have significant effects

on global O3 mixing ratios and on OH concentrations by reducing NOx levels.

The net effect of these cloud processes is to reduce the gas-phase concentrations of

HCHO, NOx, HOx, and O3. 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 NOx (NO + NO2) and both CH4- 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, [C10H16]) 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 Sourcesa

Emissions (Tg/yearb)

Chemical Biogenic Sources Anthropogenic Sources

CH4

c ≈150 ≈350

NMOCd ≈1,000 ≈100

N2O (as N)e ≈7 ≈6

NOx (as N)f ≈10 ≈40

a

See Appendix A for abbreviations and acronyms.

b

Teragram = 1012 g, or ≈106 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 CH4, the interaction of NOx 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 NOx 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

3-16
of 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, CH4-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

NOx 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 NO3 radical during

nighttime hours, and reaction with O3, 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, σλ, in this wavelength region).

Having absorbed radiation, a chemical compound must then undergo chemical change (i.e.,

have a nonzero quantum yield, φλ, 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, kphotolysis, for the process,

C (3-38) hν → products

is given by

(3-39) kphotolysis ⌡

⌠ Jλ σλ φ λ dλ,

where Jλ is the radiation flux at wavelength λ, and σλ and φλ 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

τ (3-40) photolysis kphotolysis

1 .

3-17
For the reaction of a VOC with a reactive species, X (for tropospheric purposes,

X = OH, NO3, and O3), the lifetime for the reaction process,C+X → products, is given by

τ (3-41) X (kX[X]) 1

and depends on the concentration of the reactive species X and the rate constant (kX) for

reaction of the VOC with X. In general, the ambient atmospheric concentrations of OH

radicals, NO3 radicals, and O3 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 × 106 molecule cm-3 (equivalent to a 24-h average

concentration of 8 × 105 molecule cm-3) (Prinn et al., 1992); NO3 radicals, a 12-h nighttime

average concentration of 5 × 108 molecule cm-3 (Atkinson, 1991); and O3, a 24-h average of

7 × 1011 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, NO3 radicals, and O3. 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 C4 to ≥C15, 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 C3 to C10 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 NO3 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),

OH RH → H (3-42) 2O R,  ̇

3-18
Table 3-2. Calculated Tropospheric Lifetimes of Selected

Volatile Nonmethane Organic Compounds Due to Photolysis

and Reaction with Hydroxyl and Nitrate Radicals and with Ozonea

Lifetime Due to Reaction with

Organic OH NO3 O3 hv

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, NO3, and O3 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, NO3

radical, and O3 concentrations used (molecule cm-3 were: OH, 12-h average of 1.6 × 106; NO3, 12-h average of 5

× 108; O3, 24-h average of 7 × 1011.

which rapidly adds O2 to form an alkyl peroxy radical (RO2

. ),

R ̇ O (3-43) 2

M

→ RO2

,

3-19
with the simplest of the RO2

. radicals being the methylperoxy radical, described in

Section 3.2.3.3 dealing with methane oxidation. Alkyl peroxy radicals can react with NO,

NO2, and HO2 radicals, and other organic peroxy radicals (R′O2

. ):

(3-44a) RO2 NO → RO ̇ NO2

RO (3-45) 2 NO2 ROONO2

RO (3-46) 2 HO2 → ROOH O2

(3-47a) RO2 R′O2

→ RO ̇ R′O ̇ O2

(3-47b) RO2 R′O2

→ α carbonyl (1 α) ROH O2

products of R′O2 .

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 HO2 radical reactions with RO2 radicals and the subsequent

chemistry must be considered. However, because of space constraints and a general lack of

knowledge concerning the tropospheric chemistry of RO2 radicals under low-NO conditions,

only the reactions occurring under high-NO conditions are presented and discussed here. For

the ≥C3 alkyl peroxy radicals, in addition to the reaction pathway leading to NO-to-NO2

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

RO (3-44b) 2 NO M

→ RONO2 .

with decreasing temperature (Carter and Atkinson, 1989a).

Analogous to the case for the methoxy radical, those alkoxy radicals (RO2

.

) formed

from the higher alkanes that have an abstractable H atom can react with O2 to form the HO2

radical and a carbonyl; for example,

(3-48) (CH3

)2CHO ̇ O2 → CH3C(O)CH3 HO2.

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:

3-20
with the alkyl radicals C4H9

. and HOCH2CH2CH2 HCH3 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 C1-C3 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 O2,

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 HO2 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 ≥C5 alkenes. The major biogenic alkenes emitted

from vegetation are isoprene (2-methyl-1,3-butadiene) and C10H16 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 NO3 radicals and O3.

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

OH CH (3-49) 3

CH CH2

M

→ CH3CH(OH)CH ̇ 2 CH3CHCH  ̇ 2OH.

H-atom abstraction from the alkanes. Taking the CH3 HCH2OH radical as an example, C ̇

under high-NO conditions, the following chemistry occurs:

3-21
The underlined species represent products that, although stable, can undergo further reaction;

and, hence, they can lead to "second-generation" products. For the simple ≤C4 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 CH3CHO,

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 NOx show that the products expected from

reaction schemes analogous to that shown above for propene (i.e., HCHO + methyl vinyl

ketone [CH3C(O)CH=CH2] and HCHO + methacrolein [CH2=C(CH3)CHO], arising from

initial OH radical addition to the CH2=CH- and CH2=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 C7-C10 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

3-22
cleavage 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 NO3 radical reactions proceed by reaction schemes

generally similar to the OH radical reactions, except that, when NO3 radicals are present, NO

concentrations are low (see above) and RO2

. + RO2. and RO2. + HO2 radical reactions are

expected to dominate over RO2

. + NO reactions. For propene, the initial reaction is

NO (3-50) 3 CH3CH CH2 → CH3

CHCH  ̇ 2

ONO2 and CH3CH(ONO2)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, CH3CHO,

CH3CH(ONO2)CHO, and CH3C(O)CH2ONO2 from propene). Few data are presently available

concerning the products and detailed mechanisms of NO3-alkene reactions (Atkinson, 1991,

1994, and references therein). In particular, the reaction products and mechanisms for the

NO3 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 O3 reactions also proceed by addition of O3 to the alkene,

to form an energy-rich ozonide that rapidly decomposes to form carbonyls and energy-rich

biradicals ([ ]*):

The energy-rich biradicals, [ H2O ]* and [CH3 HO ]*, undergo collisional stabilization or C ̇ O ̇ C ̇ O ̇

decomposition:

3-23
There 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 O3 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 O3 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 H2O2 and organic peroxides from

O3 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 for

benzene 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).

3-24
The 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 NO2 and

O2. Knispel et al. (1990) reported rate constants for the reactions of NO2 and O2 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 O2. 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 O2 under atmospheric

conditions. However, care must be taken in using product yields obtained in the laboratory at

higher than ambient NO2 concentrations because data may be influenced by the NO2 reaction

and, hence, may not be applicable to the O2 reaction with the OH-aromatic adduct. Clearly,

the products formed and their yields from the O2 and NO2 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.

3-25
Carbonyl 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 NO3 radicals and O3 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 (CH3C(O)CH=CH2) and methacrolein (CH3C(CHO)=CH2). The OH radical-initiated

reactions of these two carbonyl compounds in the presence of NOx 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 (CH3C(O)CH3), 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 CH3CHO, the reaction proceeds by H-atom abstraction from the -CHO

group to form the acetyl (CH3C

.

O) radical,

OH CH (3-53) 3CHO → H2O CH3CO,  ̇

which rapidly adds O2 to form the acetyl peroxy radical:

This O2 addition pathway is in contrast to the reaction of O2 with the formyl (H O) radical

CH (3-54) 3

CO ̇ O2

M

→ CH3C(O)OO.  ̇

C ̇

formed from HCHO, which reacts by an H-atom abstraction pathway (Reaction 3-30). The

acetyl peroxy radical reacts with NO and NO2,

with the NO2 reaction forming the thermally unstable PAN. The higher aldehydes also lead

CH (3-56, -3-56) 3C(O)OO ̇ NO2

M

CH3C(O)OONO2 ,

to PANs (Roberts, 1990); for example, propionaldehyde reactions lead to the formation of

3-26
peroxypropionyl 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 NOx, allowing the long-range

transport of NOx to less polluted areas. Release of NO2 in these less polluted areas via

Reaction -3-56, with subsequent photolysis of NO2, then leads to O3 formation and the

pollution of remote areas.

Because the H3 C ̇ radical formed from the NO reaction with CH3C(O)O leads to O ̇

HCHO formation, the OH radical reaction with CH3CHO subsequently leads to the formation

of HCHO. The same process occurs for propionaldehyde, which reacts to form CH3CHO 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

(C6H5C(O)OONO2).

The formation of HCHO from CH3CHO and of CH3CHO 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 CO2 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 O3.

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,

OH CH (15%) (3-57a) 3

OH → H2O CH3O

OH CH (85%) (3-57b) 3OH → H2O CH2OH.

3-27
In the troposphere, both of the CH3O and CH2OH radicals react only with O2 to form HCHO.

CH (3-58) 3O O2 → HCHO HO2

CH (3-59) 2

OH O2 → HCHO HO2

The overall reaction is then

OH CH (3-60) 3OH O2 → H2O HCHO HO2.

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 NOx 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 [CH3C(O)OCH3] and, from ETBE, tert-butyl formate, tert-butyl acetate, HCHO,

CH3CHO, 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 the

atmospheric 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 (≤C4) alkanes.

· Alkyl nitrates are formed from the ≥C3 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 (ROONO2) 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 RONO2 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 NO2 conversion occurring and, hence, the amounts of O3 that are formed during the

NOx-air photooxidations of the alkanes.

Alkenes.

· Carbonyl compounds (aldehydes and ketones) are formed as major products of the OH

radical, NO3 radical, and O3 reactions.

· Organic acids are formed from the O3 reactions, but probably in low yields.

· Hydroxynitrates and nitratocarbonyls are formed from the OH radical reactions and NO3

radical reactions, respectively. The hydroxynitrates are formed in low yields from the OH

radical reactions, whereas the nitratocarbonyls may be major products of the NO3 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 O3 reactions; these include CO, CO2, esters, hydroperoxides, and, in the presence of

NOx, peroxyacyl nitrates (RC(O)OONO2 and PANs).

The major areas of uncertainty concern the products and mechanisms of the

O3 reactions (in particular, the radical yields from these reactions that affect the O3 formation

yields from the NOx-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 NOx 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

3-29
degradation 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 NOx,

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, CH3CHO, and CH3COCH3.

3.2.4.2 Chemical Formation of Ozone in Polluted Air

Major Steps in Ozone Formation

As discussed earlier, NOx and VOCs interact under the influence of sunlight to

form O3 and other photochemical air pollutants. The major steps in this process are the

conversion of NO to NO2 by peroxy radicals, with the photolysis of NO2 leading to

O3 production. In the absence of a VOC, Reactions 3-1 through 3-3,

do not lead to any net formation of O3. The reaction of a VOC with the OH radical, or its

NO O (3-3) 3 → NO2 O2

NO (3-1, 3-2) 2 hν

O2 → NO O3 ,

photolysis, leads to the formation of HO2 and organic peroxy (RO2) radicals, which react with

NO under high-NO conditions:

VOC( OH, hν) (3-61)

O2 → RO ̇

2

RO ̇ (3-44a) 2 NO → RO ̇ NO2

NO (3-1, 3-2) 2 hν

O2 → NO O3

Net: (3-62) VOC( OH, hν)

O2 → RO ̇

2 O3 .

with the alkoxy (RO) radical producing further HO2 or RO2 radicals or both, and, hence,

resulting in further production of O3. This process is shown schematically in Figure 3-4.

3-30
Figure 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

NOx-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-NOx-air irradiation

are described by the reactions described below.

The conversion of NO to NO2 occurs through the oxidation reactions:

organic( OH, hν (3-63) , O3) → RO ̇

2

RO ̇ (3-44a) 2 NO → RO ̇ NO2

R (3-64) O ̇ O2 → carbonyl HO2

HO (3-32) 2 NO → OH NO2 .

The maximum concentration of NO2 is less than the initial NO + NO2

concentration because NO2 is removed through the reaction

This reaction removes radicals (OH and NO2) and NOx from the system. In addition to the

OH NO (3-15) 2

M

→ HNO3 .

removal of NOx through Reaction 3-15, NOx also can be removed through the formation of

temporary reservoir species such as organic nitrates (Reaction 3-44b) and PAN

(Reaction 3-56).

3-31
Figure 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.

3-32
The O3 concentration increases with the NO2/NO concentration ratio, and

RO (3-44b) 2 NO → RONO2

CH (3-56) 3C(O)OO NO2 → CH3C(O)OONO2

O3 formation ceases when NO2 (and hence NOx) 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 NO2/NO concentration ratio, and PAN

formation also ceases when NOx has been depleted.

The removal processes for NOx are by reaction of NO2 with the OH radical to

form HNO3 (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 NOx

is converted to organic nitrates, HNO3, and thermally unstable PANs. At ambient

temperature, the PANs will gradually thermally decompose to yield NO2 and the acylperoxy

radicals; hence, the ultimate fate of NOx will be to form HNO3 and organic nitrates.

Effects of Varying Initial Nitrogen Oxide and Nonmethane Volatile Organic Compound

Concentrations

As discussed in Section 3.2.4.2, NOx and VOC interact in sunlight to form O3 and

other photochemical air pollutants. The formation of O3 from the NOx 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 NOx-VOC-O3 system. The

major reactions in irradiated VOC-NOx-air mixtures (see Section 3.2.4.2) include

which is in competition with Reaction 3-15,

OH VOC (3-61) O2

→

RO2 ,

OH NO (3-15) 2 → HNO3 ,

which removes both radicals and NOx.

RO (3-44a) 2 NO → RO NO2

NO (3-1, 3-2) 2 hν

O2

→

NO O3

NO O (3-3) 3 → NO2 O2

Based on these reactions, as the VOC/NOx 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 O3 (and vice-versa). The ultimate

amount of O3 formed depends on the NO2 available for photochemical processing through

3-33
Reactions 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, O3 formation from VOCNOx-air mixtures, especially as a function of the VOC/NOx 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/NOx ratio

by reducing VOC emissions slows down the formation rate of O3, leading to lower O3 levels

in urban areas and in areas downwind of urban complexes. Reduction of NOx emissions

leads to a more rapid formation of O3, although with less O3 formed, and, hence, inner-urban

areas may experience higher O3 levels with NOx control than with VOC control, whereas

suburban and rural areas may have lower O3 levels with NOx 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 O3 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 O3 levels. This is not

unexpected from the shape of the O3 isopleths at high VOC/NOx 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 NOx emissions are left

unaltered) suggest that anthropogenic NOx together with biogenic VOCs may form sufficient

O3 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 O3 levels. Therefore, as discussed for the Atlanta region (Chameides

et al., 1988), NOx 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

O3 (Section 3.2.4.3) and that the monoterpenes react rapidly with OH radicals, NO3 radicals,

and O3, the O3-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, NO3 radicals, and O3.

In the presence of sunlight, the degradation reactions of the VOCs lead to the conversion of

NO to NO2 and the formation of O3 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

O3 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 O3 (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 NOx-VOC-air irradiations, the rate of NO to NO2

conversion in NOx-VOC-air irradiations, O3 formation in NOx-single VOC-air irradiations, eye

3-34
irritation, 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 O3 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):

IR ∆[O (3-65) 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:

OH RH → H (3-42) 2O R ̇

R ̇ O (3-43) 2 → RO ̇

2

RO ̇ (3-44a) 2 NO → RO ̇ NO2

RO ̇ → (3-65) carbonyl HO2

HO (3-32) 2 NO → OH NO2 .

The net reaction,

can be viewed as involving the two separate reaction sequences:

OH RH 2 NO (3-66)

O2 → carbonyl 2 NO2 OH,

(1) the formation of organic peroxy (R 2) radicals from the reactions, O ̇

3-35
and (2) the conversion of NO to NO2 and the formation of O3 and other products,

The photolysis of NO2 then leads to O3 formation (Reactions 3-1 and 3-2). The first reaction

sequence determines how fast R 2 O ̇ radicals are generated from the VOC, which is called

"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 NO2

conversion, regeneration of OH radicals, and the formation of product species, determines the

efficiency of formation of O3 from the R 2 O ̇ radicals formed from the first reaction sequence

and is termed "mechanistic reactivity" (Carter and Atkinson, 1989b). The second reaction

sequence can be represented as

RO ̇ (3-67) 2 αNO → β NO2 γOH 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

O3-forming potential of the VOC. Thus, the existence of NOx sinks in the reaction

mechanism (low values of β or values of α-β > 0) leads to a decrease in the amount of

O3 formed. Examples of NOx 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/NOx ratio. Nitrogen

oxides sinks are most important at high VOC/NOx ratios (NOx-limited), affecting the

maximum O3 formed; although the formation or loss of OH radicals is most important at low

VOC/NOx ratios, affecting the initial rate at which O3 is formed (Carter and Atkinson, 1989b).

In addition to depending on the VOC/NOx 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).

3-36
Table 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 Mixturea and

Low-Dilution Conditionsb

VOC/NOx 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 O3-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 O3-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 O3-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 O3-forming potential of alternative fuels. Emissions from vehicles

using 85% methanol and 15% gasoline (M-85) were shown to have ≈40% of the O3-forming

3-37
potential of emissions from gasoline-fueled vehicles. Emissions from vehicles using liquefied

petroleum gas (LPG) and compressed natural gas (CNG) were shown to have O3-forming

potentials that are ≈50% and ≈18%, respectively, of the O3-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 O3 and other photochemical

pollutants from the interaction of NOx 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 RO2 radicals and

conversion of NO to NO2 (with subsequent photolysis of NO2 form O3) also react with NO2

and sulfur dioxide (SO2) to form HNO3 and H2SO4, respectively, which can become

incorporated into aerosols as particulate nitrate (NO3

-

) and sulfate (SO4

2-). The low-volatility

VOC reaction products can condense onto existing particles in the atmosphere to form

secondary organic aerosol matter. Hence, O3 formation, acid formation, and secondary

aerosol formation in the atmosphere are so related that controls aimed at reducing O3 levels

can impact (positively or negatively) acid and secondary aerosol formation in the atmosphere.

3.2.5.1 Phase Distributions of Organic Compounds

Chemical 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,

A TSP (3-68) 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:

K (3-69) F/(TSP)A.

3-38
Because 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 HNO3, SO2, and H2O2 as well

as for O3 and PAN, whereas wet deposition is important for water-soluble gases such as

HNO3, H2O2, phenols, and, under atmospheric conditions, SO2. 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 NO2 to HNO3, followed by neutralization by NH4 or

through combination with other cations to form aerosol nitrates:

NH (3-69a) 3

(gas) HNO3(gas) NH4NO3(aerosol);

· the conversion of SO2 (and other sulfur-containing species) to H2SO4, 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).

3-39
3.2.5.2 Acid Deposition

As noted above, the chemical processes involved in the formation of O3 and other

photochemical pollutants from the interaction of NMOC and NOx also lead to the formation

of acids in the atmosphere. The two major acidic species in ambient air are HNO3 and

H2SO4, arising from the atmospheric oxidation of NOx and SO2, respectively. Reduced sulfur

compounds emitted from biogenic sources and certain anthropogenic sources also may lead to

SO2 or sulfonic acids (Tyndall and Ravishankara, 1991).

The major sulfur-containing compound emitted into the atmosphere from

anthropogenic sources is SO2. In the troposphere, the important loss processes of SO2 are dry

deposition (Atkinson, 1988, and references therein), reactions within cloud water, and gasphase reaction with the OH radical. The rate constant for the reaction of SO2 with the OH

radical is such that the lifetime of SO2 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)

OH SO (3-70) 2

M

→ HOSO2

HOSO (3-71) 2 O2 → HO2 SO3

SO (3-72) 3 H2O → H2SO4 .

The reaction of SO3 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, H2SO4 exists

in the aerosol or particle phase in the atmosphere.

Dry deposition is an important atmospheric loss process for SO2, because SO2 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 H2O2 or O3, in the aqueous phase, displaces the equilibrium and

allows gas-phase SO2 to be incorporated into cloud, rain, and fog water, where it is oxidized

rapidly (Schwartz, 1989; Pandis and Seinfeld, 1989, and references therein):

SO (3-73) 2(gas) SO2(aqu)

SO (3-74) 2

(aqu) H2O HSO3 H SO 2

3 2 H

HSO (3-75) 3 H2O2 → SO 2

4 H H2

O

SO (3-76) 2

3 O3 → SO 24 O2 .

In addition, aqueous sulfur can be oxidized in a process catalyzed by transition metals such as

iron(III) (Fe3+) and manganese(II) (Mn2+) (Graedel et al., 1986b; Weschler et al., 1986; Pandis

and Seinfeld, 1989).

3-40
SO (3-77) 2

3 1/2 O2 → SO 24

The oxidation rate of aqueous sulfur by O3 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 SO2 by H2O2 appears to be the dominant aqueous-phase

oxidation process of SO2 (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

SO2 may also be important (Jacob et al., 1989). It should be noted that aqueous-phase H2O2

arises, in part, from the absorption of HO2 radicals and H2O2 into the aqueous phase, with

HO2 radicals being converted into H2O2 (Zuo and Hoigne, 1993).

The oxidation of SO2 to sulfate in clouds and fogs is often much faster than the

homogeneous gas-phase oxidation of SO2 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 NOx to HNO3 and nitrates was discussed in Section 3.2.3.

During daylight hours, oxidation occurs by the gas-phase reaction of NO2 with the OH

radical:

with the lifetime of NO2 due to Reaction 3-15 calculated to be ≈1.4 days. Nitric acid is

OH NO (3-15) 2

M

→ HNO3 ,

removed from the troposphere by wet and dry deposition, with wet deposition being efficient.

During nighttime hours, NO2 can be converted into NO3 radicals and N2O5:

with N2O5 undergoing wet or dry deposition, or both. The reader is referred to Schwartz

NO (3-12) 2 O3 → NO3 O2

NO (3-13, -3-13) 3 NO2

M

N2O5,

(1989) for further discussion of the conversion of NOx to NO3 and HNO3 and of acid

deposition.

3.3 Meteorological Processes Influencing Ozone

Formation and Transport

Day-to-day variability in O3 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 O3 and other oxidants in

urban and rural areas. Included in this list of processes are the vertical structure and

3-41
dynamics 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

Q (3-78) sfc K↓ K↑ L↓ L↑ QH QE .

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 QH and QE are the heat flux and latent heat flux to the soil, respectively. On a

global annual average, Qsfc 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, Qsfc 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 O3, 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.

3-42
Figure 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. The

reflected 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.

3-43
Also, 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

Po

R/cp

,

where θ is the virtual potential temperature, P is the pressure of the air parcel, Po is the

reference pressure to which the air parcel will be moved (usually 1,000 mb), R is the gas law

constant, and cp 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 O3 precursors

are mixed uniformly through the layer below the mixing height. As this layer grows, it both

entrains remnant O3 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 or 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

3-44
fumigation. During fumigation, surface O3 concentrations will increase if the morning

O3 concentration is higher aloft than at the ground and if insufficient scavenging by NO

occurs at ground-level. In fact, the rapid rise in O3 concentrations in the morning hours is

often the result of vertical (downward) transport from an elevated reservoir of O3. After this

initial increase, surface concentrations can continue to increase as a result of photochemistry

or transport of O3-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 O3 and its precursors have been mixed vertically, the reduction of

turbulence and mixing at the end of the daylight hours leaves O3 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 O3 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 O3 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 O3, NOx, and VOC forms as the Chicago plume

travels over Lake Michigan, a pattern consistent with observed O3 at surface monitoring sites.

Near-total suppression of dry deposition of O3 and NOx over the lake is needed to produce

high O3. Results for the East Coast suggest that the observed peak O3 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 O3 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 O3 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

3-45
cases, 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 O3 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 O3 field

experiments.

3.3.1.3 Cloud Venting

Vertical redistribution of O3 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 O3 and its precursors from the

3-46
PBL 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 O3 flux in the cloud layer was a linear

function of the difference in O3 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 O3 and its precursors. Additionally, the cumulonimbus clouds also are

associated with precipitation, a scavenger of pollutants, and with lightning, a potential source

for NOx. 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 O3 concentrations above lower concentration

surface layers (e.g., Westberg et al., 1976). This is a clear indication that O3 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 O3 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 O3 near the surface attributed to stratospherictropospheric O3 can be in the 5 to 15 ppb range for a seasonal average, this amount of O3 by

itself cannot account for peak urban O3 values or regional episodes of elevated O3 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 O3-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 O3 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 O3 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

O3 concentrations in the range 60 to 100 ppb.

The monthly stratospheric-tropospheric total O3 flux from tropospheric folding

events in the Northern Hemisphere has been estimated. These fluxes, in units of

1035 O3 molecules per month per tropospheric folding event, increase from 1.0 in January to

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2.1 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 O3 near ground-level often observed at more remote sites (e.g., Logan, 1985). Four of the

10 episodes in which ground-level O3 has been attributed to stratospheric O3 transport

occurred during March, but several such episodes during summer months have been reported

(see below).

Using the 7Be-to-O3 ratio as an indicator of O3 of stratospheric origin and SO4

2-

concentrations as a tracer for anthropogenic sources, Altshuller (1987) estimated stratospheric

contributions of O3 in the range 0 to 40 ppb (0 to 95% of observed O3) 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 7Be and O3 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 O3 data that hindered definition of the 7Be-to-O3 ratio. Altshuller also

concluded that removal and dilution processes result in the loss of most stratospheric O3

before it reaches ground level. In other work performed in England, O3 of stratospheric

origin was estimated to contribute 10 to 15 ppb to the daily maximum hourly mean

O3 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 O3 levels? and (2) are relationships between O3 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 O3 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 O3 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 O3 provides a direct link between O3 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

3-48
formation (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/NOx 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 O3 levels. Samson and Shi

(1988) illustrated that the number of O3 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 O3 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 O3 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 O3 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 O3 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 O3 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 O3 concentrations that increases with temperature. Likewise, Figure 3-11

shows that a similar qualitative relationship exists between O3 and temperature even at a

number of rural locations.

The notable trend in these plots is the apparent upper-bound to O3 concentrations

as a function of temperature. It is clear that, at a given temperature, there is a wide range of

possible O3 concentrations because other factors (e.g., cloudiness, precipitation, wind speed)

can reduce the O3 production. The upper bound presumably represents the maximum

O3 concentration achieved under the most favorable conditions. These plots, based on

ambient air measurements, show wide scatter in O3 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 O3-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 O3-temperature relationship is statistically significant at all sites. The rate

of increase for 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 =

3-49
Week 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 O3-temperature relationship is comparable to that at

rural eastern sites.

Relationships between peak O3 and temperature also have been recorded by

Wunderli and Gehrig (1991) for three locations in Switzerland. At two sites near Zurich,

peak O3 increased 3 to 5 ppb/°C for diurnal average temperatures between 10 and 25 °C, and

little change in peak O3 occurred for temperatures below 10 °C. At the third site, a

high-altitude location removed from anthropogenic influence, much less variation of O3 with

temperature was observed.

The hypotheses for this correlation of O3 with temperature include, but are not

necessarily limited to:

1. Reduction in photolysis rates under meterological conditions associated with

low temperatures;

2. Reduction in H2O concentrations at low temperatures;

3. Thermal decomposition of PAN and its homologues;

4. Increased anthropogenic emissions of reactive hydrocarbons or NOx, or both;

5. Increased natural emissions of reactive hydrocarbons; and

6. Relationships between high temperatures and stagnant circulation patterns.

3-50
Figure 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.

3-51
Figure 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, 1988a

T < 300 K T > 300 K

Location ∆O3/∆T T-Statistic ∆O3/∆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.

3-52
The 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

O3 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

O3 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 H2O concentrations are related in that both are

linked to the supply of OH radicals, which determines the rate of O3 production in clean

atmospheres and contributes to O3 production in polluted atmospheres. A reduction in either

photolysis rates or H2O 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 O3 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), O3 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 NOx.

Consequently, photolysis rates and H2O had less impact on O3 production in rural

environments.

Thermal Decomposition of Peroxyacetyl Nitrate

Temperature-dependent photochemical rate constants provide a link between

O3 and temperature (Sillman et al., 1990a; Cardelino and Chameides, 1990). The reason for

the decline in O3 in rural areas when the PAN decomposition rate decreases is that PAN

represents a sink for NOx in rural environments. When the rate of PAN decomposition is

decreased, NOx drops sharply, whereas OH and HO2 remain largely unaffected.

Consequently, the rate of the important HO2 + 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 O3 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 NOx (Cardelino and Chameides, 1990). Sillman et al. (1990a) have shown that the

well-known division of O3 photochemistry into NOx- 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 O3/°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 O3 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

3-53
anthropogenic VOC emissions might be expected to cause increased rates of O3 production in

urban areas where O3 is sensitive to VOC, but would be less likely to have impact on rural

areas where biogenic VOC emissions can predominate. However, O3 in rural areas is NOx

dependent. The NOx-sensitive rural areas also would show increased O3 production with a

rise in temperature as biogenic NOx 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 O3 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 O3-temperature correlation.

Correlation with Stagnation

Recently, Jacob et al. (1993b) found that model-simulated O3 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 O3-temperature correlation was less than observed but large enough to

represent 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 O3 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 O3 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 O3 ≥ 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 O3 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

3-54
Figure 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).

3-55
Figure 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

3-56
Figure 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 O3 levels on a regional scale. Meteorological processes play an important

role in determining the amount of "accumulation" of O3 and its precursors that occurs under

such stagnant conditions.

Episodes of high O3 concentrations in urban areas often are associated with high

concentrations of O3 in the surroundings. This accumulated O3 forms under the same

atmospheric conditions that lead to high O3 levels in urban areas and exacerbates the urban

problem by supplying relatively high O3 and precursor concentrations to the urban area from

upwind. The transport of O3 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 O3 criteria document (U.S.

Environmental Protection Agency, 1986a) and will not be reiterated here. The phenomena of

high nonurban O3 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 O3 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, O3 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 O3 accumulated across a

3-57
wide expanse of the eastern United States at levels ≥120 ppb. Nonetheless, even when the

regional accumulation is at a level below the current O3 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 O3 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 O3 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

O3 concentration and "trends" data depends in large part on the strength of the relationships

between O3 and meteorological components. As part of the SOS Atlanta intensive field

campaign, an attempt was made to model statistically the O3 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 (O3) levels using regression techniques. The use of wind

speed, temperature, and previous-day ozone provided a means to forecast O3

levels.

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Figure 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 O3 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 O3 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, O3 is formed in the atmosphere through a

series of chemical reactions that involve VOCs and NOx. Control of O3 depends on reducing

emissions of VOCs or NOx 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 NOx 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

3-59
Table 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 NOx 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, NOx is formed from

nitrogen contained in the combustion fuel. Major NOx source categories include

transportation, stationary source fuel combustion, industrial processes, solid waste disposal,

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and some miscellaneous combustion-related activities. Table 3-6 provides a more detailed

summary 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 NOx 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, NOx emission inventories are quantified relative to NO2

(mol wt = 46). Nitrogen dioxide is a secondary pollutant produced via oxidation of NO in

the atmosphere.

Quantifying NOx 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 NOx 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 NOx emissions from the various categories

mentioned previously (U.S. Environmental Protection Agency, 1993b). The 1991 total is

21.39 Tg of NOx 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 NOx emission sources are

electric power generation and highway vehicles. Local NOx source apportionment may differ

substantially from these national figures.

Because of the dominance of the electric utility and transportation sources, the

geographical distribution of NOx 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 NOx 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. NOx emissions

occur within U.S. Environmental Protection Agency Regions III, IV, V, and VI

(Figure 3-19). It is interesting to compare the annual NOx 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 NOx annually,

which compares to approximately 0.12 Tg of NOx emitted by transportation sources in the

Atlanta urban area (U.S. Environmental Protection Agency, 1993b).

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Table 3-6. Source Categories Used to Inventory Nitrogen Oxides Emission

Transportation

Stationary Source

Fuel Combustion Industrial Processes Solid Waste Disposal 

Highway vehicles

Gasoline-poweredPassenger carsLight trucks - 1Light trucks - 2Heavy-duty vehiclesMotorcycles

Coal

Electric utilitiesIndustrialCommercial/InstitutionalResidential

Pulp millsOrganic chemicalsAmmoniaNitric acidPetroleum refiningGlassCementLimeIron and steel

IncinerationOpen burning

Diesel-powered

Passenger carsLight trucksHeavy-duty vehicles

Fuel oil

Electric utilitiesIndustrialCommercial/InstitutionalResidential

AircraftRailcarsVesselsFarm machineryConstruction machineryIndustrial machineryOther off-highway vehicles

Natural gas

Electric utilitiesIndustrialCommercial/InstitutionalResidential

Wood

IndustrialResidential

Other Fuels

IndustrialResidential

Source: U.S. Environmental Protection Agency (1992a).

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Table 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).

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Figure 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 NOx 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 NOx emissions, including the categories of highway vehicles, electric utilities, and

industrial combustion sources. For off-highway sources (comprising 20% of the total annual

NOx emissions), 29% of the off-highway NOx emissions occur in the summer and 21% in the

winter. In contrast, the category of other combustion sources (comprising 5% of the total

annual NOx emissions) emit 47% of the NOx 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 NOx emissions, indicated that very little

variation occurred in NOx emissions among the winter, spring, summer, and fall seasons. The

contributions of these NOx categories also were shown to vary seasonally by region of the

United States.

Trends in Nitrogen Oxides Emissions

Estimates of NOx 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 NOx 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.

3-64
Source: U.S. Environmental Protection Agency (1992a).

20 Tg/year. Currently, more than 90% of the national NOx 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 NOx emissions grew

steadily until the 1980s and then exhibited a moderate decrease. However, the recent trends

in transportation-related NOx emissions shown in Table 3-8 indicate no trend between 1987

and 1991. Emissions of NOx from fuel combustion sources have increased continually from

1940 to the present time.

Recent trends in the major NOx emission categories are shown in Table 3-8.

Between 1987 and 1991, transportation-related NOx emissions have remained essentially

constant, whereas the stationary source NOx emissions have increased about 10%.

Transportation and stationary source fuel combustion will likely show downward

trends in their NOx 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 O3 nonattainment areas. In addition, the average grams of NOx 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, NOx

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 NOx emissions by 10%

between 1990 and 2000. Projections based on VMT and emission factors from the MOBILE

model suggest nearly a 50% decrease in NOx emissions from highway vehicles manufactured

from 1990 to 2000 (U.S. Environmental Protection Agency, 1992a).

3-65
Figure 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

1990a 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).

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Uncertainty of Anthropogenic Nitrogen Oxides Emission Estimates

Because a large proportion of the U.S NOx 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) NOx 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

NOx 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 NOx emission factors or inventories have involved

comparing ambient NOx concentrations with values predicted using emissions-based models.

These generally have taken one of two forms: (1) comparisons between NOx 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 NOx

concentrations are compared to ambient NOx 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 NOx

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 NOx concentrations. It is important to keep in

mind that ambient NOx 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

NOx 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, NOx inventory validation has involved comparing annual emission

estimates reported by different groups. Table 3-9 shows several annual U.S. NOx 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 NOx 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

3-67
Table 3-9. Comparison of Estimates of Nitrogen Oxides Emissions

from Manmade Sources in the United Statesa

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).

NOx 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 NOx 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 NOx (1 to 3 days) and small

NOx emissions from sea water, transport of NOx from oceans is thought to be a negligible

source in the United States.

Lightning. Lightning produces high enough temperatures to allow N2 and O2 to be

converted to NO. Two methods have been employed to estimate the NOx 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 NOx production to NO3

- deposition in remote areas where lightningproduced NOx is thought to be the dominant NO3

- precursor.

Method 1 yields an annual NOx 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

NOx 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 NOx for this region (continental United States) is about 1.0 Tg. Roughly

60% (0.6 Tg) of this NOx 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 NOx.

The relative importance of these two pathways is probably highly variable from biome to

biome. Nitric oxide is the principal NOx species emitted from soils, with emission rates

depending mainly on fertilization levels and soil temperature. Several reports have noted a

large increase in NOx emissions from agricultural soils treated with NO3

-

-containing fertilizers

(Johansson and Granat, 1984; Kaplan et al., 1988; Johansson, 1984). Measurements of soil

NOx emissions have established that the relationship with temperature is exponential,

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consisting of approximately a twofold increase for each 10 °C rise in temperature (Williams

et al., 1992; Valente and Thornton, 1993).

Inventorying soil NOx 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 NOx emissions from the 10 U.S.

Environmental Protection Agency regions. Approximately 60% of this NOx 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 Regiona

U.S. Environmental Protection Agency Region NOx 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 1.20b

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 NOx 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 NOx is provided, however, by comparing emission estimates generated

independently. Two estimates of the amount of lightning-generated, summertime NOx in the

southeastern United States (2.4 and 8.5 × 10-2 Tg) varied by approximately a factor

of 4 (Placet et al., 1991).

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Sources of uncertainty when inventorying NOx 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 NOx emission rates versus temperature for various land-use

categories. Measurement accuracy is approximately ±30%. However, because of the natural

variability of NOx 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 NOx to the troposphere over the United States. This compares to the 1990

anthropogenic emission estimate of 19.4 Tg. Annual NOx 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 NOx emissions from soils account for about 19% of

summertime NOx 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 O3 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 organic

compounds 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

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Table 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

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1970 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

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Figure 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

1990a 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).

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assigned 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

3-74
measurements 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 NOx 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 NOx

concentrations, it was shown that ambient NOx levels were consistent with the urban NOx

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 NOx 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 O3 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

3-75
exponentially 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

3-76
Table 3-13. Annual Biogenic Hydrocarbon Emission Inventory for the United Stat

Land Use

Compound

Oak

Forests

Other

Deciduous

Forests

Coniferous

Forests

Scrublands Grass- lands Crop- lands Inland Waters 

Isoprene 2.31 1.01 0.61 1.17 0.49 0.2 0.02 α-pinene 0.19 0.23 2.07 0.78 0.13 0.85 0.06 Other terpenes 0.41 0.44 3.08 1.41 0.24 0.81 0.06 Other VOCs 1.12 0.88 2.72 2.49 0.45 4.51 0.07 Total 4.03 2.56 8.48 5.85 1.31 6.37 0.21 

Percent of Total 13.9 8.8 29.2 20.1 4.5 21.9 0.7 

aSee Appendix A for abbreviations and acronyms.Source: Lamb et al. (1993).

3-77
Table 3-14. Annual Biogenic Hydrocarbon Emission Inventory by Month an

by U.S. Environmental Protection Agency Region for United States Emissions 

U.S. Environmental Protection Agency Region

Month III IV

V VI VII VIII IX

X 

1 0.018 0.092 0.004 0.084 0.007 0.022 0.060 0.043 2 0.017 0.139 0.004 0.123 0.007 0.020 0.054 0.039 3 0.071 0.428 0.067 0.519 0.078 0.108 0.113 0.102 4 0.169 0.460 0.189 0.567 0.211 0.303 0.320 0.202 5 0.206 0.475 0.240 0.586 0.226 0.362 0.331 0.208 6 0.427 0.874 0.550 1.146 0.508 0.809 0.710 0.424 7 0.441 0.903 0.568 1.184 0.524 0.836 0.734 0.438 8 0.439 0.903 0.568 1.184 0.524 0.820 0.734 0.438 9 0.123 0.461 0.137 0.561 0.136 0.280 0.357 0.212 

10 0.069 0.286 0.066 0.394 0.026 0.290 0.369 0.219 11 0.066 0.162 0.063 0.174 0.025 0.110 0.130 0.109 12 0.018 0.080 0.004 0.073 0.007 0.022 0.060 0.043 

Total 2.1 5.3 2.5 6.6 2.3 4.0 4.0 2.5 

Source: Lamb et al. (1993).

3-78
not 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 O3 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

NOx and VOCs peak during this same time frame. Biogenic emissions are very dependent on

temperature; and, as ambient temperatures rise during the summer months, NOx and VOC

emissions reach a maximum. Figure 3-24 clearly demonstrates this for biogenic VOC

emissions, and a plot of monthly biogenic NOx emissions would show a similar pattern. Well

over 50% of biogenic NOx and VOC emissions occur during the period of maximum

photochemical activity.

Seasonal changes in anthropogenic emissions of NOx are believed to be relatively

small. The transportation sector produces slightly less NOx 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 NOx and changes

in seasonal emissions tend to offset each other, there is no reason to expect that NOx

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

3-79
Total NMHC Emissions (Tg)

Jan.

Feb.

March

April

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

0 1 2 3 4 5 6



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KPVGTRTGVCVKQP$CWIWGU6JGVQVCN0/1%OGCUWTGOGPVUVJTQWIJQWVVJG,WPGVJTQWIJ

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QHVJGCOVQCORGTKQFUECPKUVGTUCORNGUYGTGEQNNGEVGFHQTUWDUGSWGPV)%CPCN[UKU


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+P#VNCPVCFWTKPIVJGUWOOGTQHJ[FTQECTDQPEQPEGPVTCVKQPUYGTGOGCUWTGF

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CPFMGVQPGUCTGTCRKFN[RTQFWEGFHTQOTGCEVKQPUQH1*TCFKECNUYKVJCNKRJCVKECPFCTQOCVKE

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UVWFKGUD[FKPKVTQRJGP[NJ[FTC\KPGJKIJRGTHQTOCPEGNKSWKFEJTQOCVQITCRJ[


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HTQOOGCUWTGOGPVUKP9GUV1TCPIG6:YJGTGCUVJGJKIJGUVOGFKCP01 EQPEGPVTCVKQPQH Z

RROYCUQDVCKPGFHTQOOGCUWTGOGPVUKP/GORJKU+PVJGNQYGUVOGFKCP01Z

EQPEGPVTCVKQPQHRROYCUQDVCKPGFHTQOOGCUWTGOGPVUKP9GUV1TCPIGYJGTGCUVJG

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%NGXGNCPF1*6JGOGFKCP01 EQPEGPVTCVKQPXCNWGUHQTUKVGUKPOQUVQHVJGUGEKVKGUKP Z

CPFTCPIGFDGVYGGPCPFRRO$GECWUGQHJKIJXGJKEWNCTGOKUUKQPTCVGUCPF

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DGVYGGPCPF75'PXKTQPOGPVCN2TQVGEVKQP#IGPE[C+PVJG#VNCPVC

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VQRRO2WTFWGGVCN

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CUHQNNQYU2QKPV#TGPC%#URTKPIRROVQRRO0KYQV4KFIG

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4KFIG%1YKVJWRUNQRGHNQYHTQOVJG&GPXGT$QWNFGT%1WTDCPCTGCJKIJGT01Z


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TCVKQQHYCUQDVCKPGFKP6GZCU%KV[6:+PVJGNQYGUVOGFKCP0/1%01 TCVKQ Z

QHYCUQDVCKPGFKP2JKNCFGNRJKC2#YJGTGCUVJGJKIJGUVOGFKCP0/1%01 TCVKQQH Z

YCUQDVCKPGFKP$GCWOQPV6:6JGTCPIGKPFCKN[COVQCO0/1%01 TCVKQU Z

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WUWCNN[D[HCEVQTUQHVQCPFCVUGXGTCNUKVGUD[HCEVQTUQHVQ$CWIWGU6JGTG

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CPFUQWVJYGUVGTP7PKVGF5VCVGUVJCPKPVJGPQTVJGCUVGTPCPFOKFYGUVGTP7PKVGF

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TCPIKPIDGVYGGPCPF#NVUJWNNGTD

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VJGUWOOGTCPFKPVJGHCNNQH(WLKVCGVCN*QYGXGTVJGKPVGPUKXGFC[UKP

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TCVKQU9QNHHCPF-QTUQI*QYGXGTVJGEQTTGNCVKQPQH0/1%01 TCVKQUYKVJ Z

OCZKOWOJ1 EQPEGPVTCVKQPUYCUXGT[YGCM+VYCUEQPENWFGFVJCVVJGWUGQHVJGCOVQ 

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COQPI0/1%01 QTEQODKPGF81%01 UVTCVGIKGUCUQRVKOWOUVTCVGIKGUHQTWTDCPCTGCUZ Z

#EQORKNCVKQPQH81%01 TCVKQUDGVYGGPCPFKPEKVKGUKPVJG Z

PQTVJGCUVGTPCPFOKFYGUVGTP7PKVGF5VCVGURTGUGPVUTCVKQUTCPIKPIHTQOVQDWV

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QHVJGUGEKVKGU0GY;QTM0GYCTM0,2JKNCFGNRJKCCPF9CUJKPIVQP&%UJQYFQYPYCTF

VTGPFUVQYCTFU81%01 TCVKQUDGVYGGPCPF<CNGYUM[GVCN9QNHH(QT Z

2JKNCFGNRJKCCPFVJGQVJGTUKVGUVJGFQYPYCTFVTGPFKP81%01 KUCUUQEKCVGFYKVJ Z

FGETGCUKPI81%EQPEGPVTCVKQPUYKVJNKVVNGEJCPIGKP01 <CNGYUM[GVCN9QNHH Z


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TCVKQUQHQTOQTG$CUGFQPVJGUGTGUWNVUKVOC[DGEQPENWFGFVJCVKPOCP[EKVKGUKPVJG

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YKNNDGOQTGGHHGEVKXGKPTGFWEKPI1 9QNHH+VCNUQJCUDGGPEQPENWFGFD[9QNHH 


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VQCODKGPVCKTRQNNWVCPVEQPEGPVTCVKQPU+PRTKPEKRNGKVKPENWFGUVYQHWPFCOGPVCNN[FKHHGTGPV

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RTQFWEGCPGUVKOCVGQHCODKGPVRQNNWVCPVEQPEGPVTCVKQPUVJCVECPDGGZRGEVGFCVCURGEKHKGF

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CODKGPVEQPEGPVTCVKQPOGCUWTGOGPVUQHCXCTKGV[QHRQNNWVCPVURGEKGUCPFCMPQYNGFIGQHVJG

TGNCVKXGCOQWPVUQHVJGURGEKGUUQWTEGRTQHKNGUVJCVCTGRTGUGPVKPVJGGOKUUKQPUQHVJGUQWTEGU

VJCVCTGRQVGPVKCNEQPVTKDWVQTU#OCVJGOCVKECNTGEGRVQTOQFGNQRGTCVGUQPVJGUQWTEGRTQHKNG

CPFCODKGPVURGEKGUEQPEGPVTCVKQPKPHQTOCVKQPVQFGEQPXQNWVGVJGCODKGPVEQPEGPVTCVKQPUKPVQ

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VYQOQTGURGEKHKEKPUVTWOGPVCNOGVJQFUDCUGFQPVJGRTKPEKRNGUQHEJGOKNWOKPGUEGPEGCPF78

CDUQTRVKQPURGEVTQOGVT[6JGUGVYQCRRTQCEJGUCTGFGUETKDGFDGNQY+PCFFKVKQPTGEGPV

FGXGNQROGPVUKPURGEVTQUEQRKEOGCUWTGOGPVUKPQVJGTEJGOKECNCRRTQCEJGUCPFKPRCUUKXG

UCORNKPIFGXKEGUHQT1 CTGFGUETKDGF 

%JGOKNWOKPGUEGPEG/GVJQFU


)CU2JCUG%JGOKNWOKPGUEGPEG6JGOQUVEQOOQPEJGOKNWOKPGUEGPEGOGVJQF

HQT1 KUFKTGEVICURJCUGTGCEVKQPQH1 YKVJCPQNGHKPVQRTQFWEGGNGEVTQPKECNN[GZEKVGF  

RTQFWEVUYJKEJFGEC[YKVJVJGGOKUUKQPQHNKIJV6JKUCRRTQCEJYCUHKTUVWUGFPGCTN[[GCTU

CIQHQTEJGOKECNCPCN[UKUD[0GFGTDTCIV0GFGTDTCIVGVCNCPFFGXGNQROGPVQHC

RQTVCDNGOQPKVQT9CTTGPCPF$CDEQEMCPFCRRNKECVKQPVQCVOQURJGTKEOGCUWTGOGPVU


5VGXGPUCPF*QFIGUQPHQNNQYGFUQQPCHVGT6[RKECNN[CP1 OQPKVQTDCUGFQPVJKU 

CRRTQCEJHWPEVKQPUD[OKZKPICEQPUVCPVHNQYQHCDQWV.OKPQHUCORNGCKTYKVJCUOCNN

EQPUVCPVHNQYEO OKPQHGVJ[NGPG/KZKPIQEEWTUKPCUOCNNKPGTVTGCEVKQPEJCODGT 

HKVVGFYKVJCUGCNGFYKPFQYVJTQWIJYJKEJNKIJVECPRCUUVQVJGRJQVQECVJQFGQHC

RJQVQOWNVKRNKGTVWDG'NGEVTQPKECNN[GZEKVGFHQTOCNFGJ[FGOQNGEWNGUIGPGTCVGFD[CUOCNN

HTCEVKQPQHVJG1 GVJ[NGPGTGCEVKQPURTQFWEGCDTQCFDCPFQHGOKUUKQPEGPVGTGFCVPO

6JGGOKUUKQPKPVGPUKV[KUNKPGCTN[RTQRQTVKQPCNVQVJG1 EQPEGPVTCVKQPQXGTVJGTCPIGQH 

RROVQCVNGCUVRRO%CNKDTCVKQPQHVJGOQPKVQTYKVJCMPQYP1 UQWTEGRTQXKFGUVJG 

TGNCVKQPUJKRDGVYGGPOQPKVQTTGURQPUGCPF1 EQPEGPVTCVKQP&GVGEVKQPNKOKVUQHRRO 

CPFCTGURQPUGVKOGQHNGUUVJCPUCTGGCUKN[CVVCKPGFCPFCTGV[RKECNQHEWTTGPVN[CXCKNCDNG

EQOOGTEKCNKPUVTWOGPVU

#NVJQWIJPQKPVGTHGTGPEGJCUDGGPHQWPFHTQOEQOOQPCVOQURJGTKERQNNWVCPVUC

RQUKVKXGKPVGTHGTGPEGHTQOCVOQURJGTKEYCVGTXCRQTJCUDGGPTGRQTVGF%CNKHQTPKC#KT

4GUQWTEGU$QCTF-NGKPFKGPUVGVCNCPFTGHGTGPEGUVJGTGKPCPFJCUTGEGPVN[DGGP

EQPHKTOGF-NGKPFKGPUVGVCN*WFIGPUGVCN6JGTGEGPVTGUWNVUKPFKECVGC

RQUKVKXGKPVGTHGTGPEGQHCDQWVRGTRGTEGPV* 1D[XQNWOGCV  %DCUGFQPVGUVUCV1

EQPEGPVTCVKQPUQHVQRROCPFCV* 1EQPEGPVTCVKQPUQHVQKGFGYRQKPV 

VGORGTCVWTGUQHVQ%+VJCUDGGPGUVKOCVGFVJCVVJGKPVGTHGTGPEGQHYCVGTKPGVJ[NGPG

EJGOKNWOKPGUEGPVOGCUWTGOGPVUCV%CPFTGNCVKXGJWOKFKV[EQWNFDGCUJKIJCU

RRDXQH1 QTQHVJG1 TGCFKPICVRRDX-NGKPFKGPUVGVCN%CNKDTCVKQP  

YKVJMPQYP1 EQPEGPVTCVKQPUKPCKTQHVGORGTCVWTGCPFJWOKFKV[UKOKNCTVQVJCVQHVJGUCORNG 

CKTECPOKPKOK\GVJKUUQWTEGQHGTTQT

#UGRCTCVGRQVGPVKCNRTQDNGOYKVJVJGGVJ[NGPGEJGOKNWOKPGUEGPVOGVJQFKUNGCMCIG

QHVJGRWTGGVJ[NGPGTGCIGPVICU$GECWUG1 CPFJ[FTQECTDQPOGCUWTGOGPVUCTGQHVGP 

EQNQECVGFHQTOQPKVQTKPIRWTRQUGUNGCMCIGQHGVJ[NGPGEQWNFECWUGFKHHKEWNV[KPQDVCKPKPI

XCNKFOGCUWTGOGPVUQHVQVCNPQPOGVJCPGJ[FTQECTDQPU60/*%KPCODKGPVCKT

6JGOGCUWTGOGPVRTKPEKRNGUGVHQTVJD['2#HQTEQORNKCPEGOQPKVQTKPIHQT1 KU 

VJGEJGOKNWOKPGUEGPEGOGVJQFWUKPI% * (GFGTCN4GIKUVGT/GVJQFUQHVGUVKPICPF  

VJGTGSWKTGFRGTHQTOCPEGURGEKHKECVKQPUVJCVEQOOGTEKCN1 OQPKVQTUOWUVOGGVVQDG 

FGUKIPCVGFCTGHGTGPEGQTGSWKXCNGPVOGVJQFCTGFQEWOGPVGF(GFGTCN4GIKUVGT

#OQPKVQTOC[DGFGUKIPCVGFCTGHGTGPEGOGVJQFKHKVGORNQ[UICURJCUGEJGOKNWOKPGUEGPEG

YKVJ% * CUVJGOGCUWTKPIRTKPEKRNGCPFCEJKGXGUVJGTGSWKTGFRGTHQTOCPEGURGEKHKECVKQPU 

#PGSWKXCNGPVOGVJQFOWUVUJQYCEQPUKUVGPVTGNCVKQPUJKRYKVJVJGTGHGTGPEGOGVJQFCPFOWUV

OGGVVJGTGSWKTGFRGTHQTOCPEGURGEKHKECVKQPU6CDNGUJQYUVJQUGURGEKHKECVKQPUHQT

1 OQPKVQTU0QVGVJCVGVJ[NGPGEJGOKNWOKPGUEGPEGOQPKVQTUV[RKECNN[JCXGTGURQPUGVKOGUHCT 

UWRGTKQTVQVJQUGTGSWKTGFKP6CDNG

6JGNKUVQHEQOOGTEKCN1 OQPKVQTUFGUKIPCVGFCUTGHGTGPEGQTGSWKXCNGPVOGVJQFU 

D['2#KUUJQYPKP6CDNGWRFCVGFCUQH#WIWUV&GVCKNUQPVJTGGOQPKVQTUPQV

FGUETKDGFKPVJG'2#ETKVGTKCFQEWOGPVHQT1 CPFQVJGTQZKFCPVUCTGRTGUGPVGFKP6CDNG 

#NNQHVJGTGHGTGPEGOGVJQFUCTG% * EJGOKNWOKPGUEGPEGKPUVTWOGPVUCUTGSWKTGFD[  

VJGFGHKPKVKQPQHCTGHGTGPEGOGVJQF6JGGSWKXCNGPVOGVJQFUCTGDCUGFQPGKVJGTICUUQNKF


EJGOKNWOKPGUEGPEGQT78CDUQTRVKQPOGCUWTGOGPVU6JQUGOGVJQFUCTGFGUETKDGFDGNQY#

ICUNKSWKFEJGOKNWOKPGUEGPEGCPCN[\GTHQT1 YJKEJOC[DGUWDOKVVGFHQT'2#GSWKXCNGPE[ 

KPVJGPGCTHWVWTGCNUQKUFGUETKDGFDGNQY

)CU5QNKF%JGOKNWOKPGUEGPEG6JGTGCEVKQPQH1 YKVJ4JQFCOKPG$CFUQTDGF 

QPCEVKXCVGFUKNKECIGNRTQFWEGUEJGOKNWOKPGUEGPEGKPVJGTGFTGIKQPQHVJGXKUKDNGURGEVTWO

6JKUYCUVJGHKTUVEJGOKNWOKPGUEGPEGOGVJQFGXGTFGXGNQRGFHQTCODKGPV1 OGCUWTGOGPV


6CDNG2GTHQTOCPEG5RGEKHKECVKQPUHQT#WVQOCVGF/GVJQFUQH1\QPG#PCN[UKU

2GTHQTOCPEG2CTCOGVGT 7PKVU 5RGEKHKECVKQP

4CPIG RRO VQ

0QKUG RRO 

.QYGTFGVGEVCDNGNKOKV RRO 

+PVGTHGTGPEGGSWKXCNGPV

'CEJKPVGTHGTGPEG RRO v

6QVCNKPVGTHGTGPEG RRO 

<GTQFTKHVCPFJ RRO v

5RCPFTKHVJ

QHWRRGTTCPIGNKOKV  v

QHWRRGTTCPIGNKOKV  v

.CIVKOG OKP 

4KUGVKOG OKP 

(CNNVKOG OKP 

2TGEKUKQP

QHWRRGTTCPIGNKOKV RRO 

QHWRRGTTCPIGNKOKV RRO 

5QWTEG (GFGTCN 4GIKUVGT 
 %QFG QH (GFGTCN 4GIWNCVKQPU 



4GIGPGT6JGGOKVVGFNKIJVKPVGPUKV[KUNKPGCTN[TGNCVGFVQVJG1 EQPEGPVTCVKQP 

CPFVJGFGVGEVKQPNKOKVECPDGCUNQYCURRO0QFKTGEVKPVGTHGTGPEGUHTQOQVJGTICU

RJCUGRQNNWVCPVUCTGMPQYPJQYGXGTFGEC[QHVJGUGPUKVKXKV[DGECWUGQHUWTHCEGCIKPIECP

QEEWT*QFIGUQPGVCN#FFKVKQPQHICNNKECEKFVQVJGUWTHCEGUVCDKNK\GUVJGTGURQPUG

EJCTCEVGTKUVKEUCRRCTGPVN[D[CNNQYKPIFKTGEVTGCEVKQPQH1 YKVJVJGICNNKECEKFTCVJGTVJCP 

YKVJVJG4JQFCOKPG$$GTUKUCPF8CUUKNKQW#EQOOGTEKCNCPCN[\GT2JKNNKRU/QFGN

29DCUGFQPVJKUCRRTQCEJJCUDGGPFGUKIPCVGFCPGSWKXCNGPVOGVJQFHQTCODKGPV1 UGG 

6CDNGDWVICUUQNKFEJGOKNWOKPGUEGPEGEWTTGPVN[KUWUGFTCTGN[HQTCODKGPV

OGCUWTGOGPVU

)CU.KSWKF%JGOKNWOKPGUEGPEG#TGEGPVN[FGXGNQRGFEQOOGTEKCNOQPKVQTWUGU

VJGEJGOKNWOKPGUEGPVTGCEVKQPQH1 YKVJVJGF[GGQUKP;KPUQNWVKQP6QRJCOGVCN

6JGOQPKVQTHWPEVKQPUD[GZRQUKPICHCDTKEYKEMYGVVGFYKVJVJGGQUKP;UQNWVKQPVQCHNQY

QHUCORNGCKTYKVJKPXKGYQHCTGFUGPUKVKXGRJQVQOWNVKRNKGTVWDG6JGOQPKVQTFGUKIPCVGFVJG

.1<KUEQORCEVRQTVCDNGCPFTGSWKTGUPQTGCIGPVICUGU6JG.1<RTQXKFGUXGT[HCUV

TGURQPUGCNCIVKOGQHUCTKUGVKOGQHUCPFCHCNNVKOGQHUCNNTGNCVKXGVQCUVGR

EJCPIGQHRRDX1 CTGTGRQTVGF6QRJCOGVCN+PUVTWOGPVPQKUGCV\GTQCPFCV 

RRDXQ\QPGKURRDXQTNGUUECNEWNCVGFCUVJGUVCPFCTFFGXKCVKQPQHUWEEGUUKXG

OKPCXGTCIGU6JGRTGEKUKQPQHVJG.1<KUTGRQTVGFVQDGRRDXCVRRDX1 CPF 

RRDXCVRRDX1 DQVJECNEWNCVGFCUQPGUVCPFCTFFGXKCVKQPQHUKZTGRGCVGF 

OGCUWTGOGPVUCVVJGUGNGXGNU6QRJCOGVCN6JGKPUVTWOGPVRTQXKFGUNKPGCTTGURQPUG

WRVQRRDXYKVJCITCFWCNN[FGETGCUKPIUNQRGQHVJGTGURQPUGEWTXGCDQXGVJCV

6CDNG4GHGTGPEGCPF'SWKXCNGPV/GVJQFUHQT1\QPG

&GUKIPCVGFD[VJG75'PXKTQPOGPVCN2TQVGEVKQP#IGPE[C

2TKPEKRCN/GVJQF0WODGT %QFG

&GUKIPCVKQP /GVJQF

4GHGTGPEG/GVJQFU


'VJ[NGPG%JGOKNWOKPGUEGPEG

$GEMOCP# 4(1# 

$GPFKZ 4(1# 

%5+ 4(1# 

/E/KNNCP 4(1# 

/E/KNNCP 4(1# 

/E/KNNCP 4(1# 

/GNQ[1#4 4(1# 

/GNQ[1#4 4(1# 

/QPKVQT.CDU' 4(1# 

'SWKXCNGPV/GVJQFU


78#DUQTRVKQP

#FXCPEGF2QNNWVKQP+PUVTWOGPV '31# 

&CUKDK#*2%45 '31# 

&CUKDK#*2%45 '#1# 

'PXKTQPKEU '31# 

.GCT5KGINGT/. '31# 

/QPKVQT.CDU '31# 

2%+1\QPG%QTRQTCVKQP.% '31# 

6JGTOQ'NGEVTQP '31# 

'SWKXCNGPV/GVJQFU)CU5QNKF%.

2JKNKRU29 '31# 

#U QH #WIWUV   UGG #RRGPFKZ # HQT CDDTGXKCVKQPU CPF CETQP[OU C

NGXGN6GORGTCVWTGCPFRTGUUWTGUGPUKVKXKV[CTGEQTTGEVGFD[KPVGTPCNEKTEWKVT[6QRJCOGVCN

#PKPKVKCNNCTIGRQUKVKXGKPVGTHGTGPEGHTQO51 VJCVDGEQOGUUOCNNGTCPFPGICVKXGCUVJG 

GQUKPUQNWVKQPCIGUKUTGRQTVGFCPFCRQUKVKXGKPVGTHGTGPEGHTQO%1 KUCNUQRTGUGPV6QRJCO 

GVCNTGRQTVVJCVCRTGVTGCVOGPVVGEJPKSWGCRRNKGFVQVJGGQUKPTGCIGPVUQNWVKQP

OKPKOK\GUDQVJQHVJGUGKPVGTHGTGPEGU5GXGTCNQHVJGRGTHQTOCPEGEJCTCEVGTKUVKEUQHVJG.1<

CTGKORTGUUKXGDWVXGTKHKECVKQPQHVJGTGRQTVGFKPVGTHGTGPEGNGXGNUCPFVJGGHHGEVKXGPGUUQH

VGORGTCVWTGCPFRTGUUWTGEQTTGEVKQPUCRRGCTUVQDGPGGFGF

6CDNG.KUVQH&GUKIPCVGF4GHGTGPEGCPF'SWKXCNGPV/GVJQFUHQT1\QPG

(

&GUKIPCVKQP0WODGT +FGPVKHKECVKQP 5QWTEG

/CPWCN

QT #WVQ

4GH QT

'SWKX 8QN 2C

'31# 'PXKTQPKEU 5GTKGU  %QORWVGTK\GF 1\QPG

#PCN[\GT QRGTCVGF QP VJG  RRO TCPIGYKVJ VJG HQNNQYKPI RCTCOGVGTU GPVGTGF KPVQVJG CPCN[\GT	U EQORWVGT U[UVGO#DUQTRVKQP %QGHHKEKGPV   v (NWG 6KOG  +PVGITCVKQP (CEVQT  1HHUGV #FLWUVOGPV   RRO1\QPG #XGTCIG 6KOG  5KIPCN #XGTCIG  6GORGTCVWTG2TGUUWTG %QTTGEVKQP  1PCPF YKVJ QT YKVJQWV VJG 45 5GTKCN &CVC+PVGTHCEG

'PXKTQPKEU +PE 4KXGT 4QCF9GUV 9KNNKPIVQP %6

#WVQ 'SWKX  

'31# #FXCPEGF 2QNNWVKQP +PUVTWOGPVCVKQP +PE

/QFGN  1\QPG #PCN[\GT QRGTCVGF QPCP[ HWNNUECNG TCPIG DGVYGGP  RRO CPF  D

 RRO CV CP[ VGORGTCVWTG KP VJG TCPIG QH  VQ % YKVJ VJG F[PCOKE \GTQ CPF URCPCFLWUVOGPV HGCVWTGU UGV 1(( YKVJ C O6GHNQPs HKNVGT GNGOGPV KPUVCNNGF KP VJGTGCTRCPGN HKNVGT CUUGODN[ CPF YKVJ QT YKVJQWVCP[ QH VJG HQNNQYKPI QRVKQPU+PVGTPCN <GTQ5RCP 
+<54CEM /QWPV YKVJ 5NKFGU45 YKVJ 5VCVWU 1WVRWVU<GTQ5RCP 8CNXGU

#FXCPEGF 2QNNWVKQP+PUVTWOGPVCVKQP +PE 2TQFWEVKQP #XGPWG5CP &KGIQ %#

#WVQ 'SWKX  


6CDNGEQPV	F.KUVQH&GUKIPCVGF4GHGTGPEGCPF'SWKXCNGPV/GVJQFUHQT1\Q

(

&GUKIPCVKQP0WODGT +FGPVKHKECVKQP 5QWTEG

/CPWCN

QT #WVQ

4GH QT

'SWKX 8QN 2C

'31# .GCT 5KGINGT /GCUWTGOGPV %QPVTQNU

%QTRQTCVKQP /QFGN /. 1\QPG#PCN[\GT QRGTCVGF QP CP[ HWNNUECNG TCPIGDGVYGGP  RRO CPF  RRO YKVJ D

CWVQTCPIKPI GPCDNGF QT FKUCDNGF CV CP[

VGORGTCVWTG KP VJG TCPIG QH  VQ  %

YKVJ C O 6GHNQPs HKNVGT GNGOGPV KPUVCNNGFKP VJG HKNVGT CUUGODN[ DGJKPF VJG UGEQPFCT[RCPGN VJG UGTXKEG UYKVEJ QP VJG UGEQPF RCPGNUGV VQ VJG +P RQUKVKQP YKVJ VJG HQNNQYKPI OGPWEJQKEGU UGNGEVGF %CNKDTCVKQP /CPWCN QT

6KOGF &KCIPQUVKE /QFG 1RGTCVGF (KNVGT

6[RG -CNOCP 2TGU6GOR(NQY %QOR 1P

5RCP %QOR &KUCDNGF YKVJ VJG RKP +1

DQCTF KPUVCNNGF QP VJG TGCT RCPGN EQPHKIWTGF CVCP[ QH VJG HQNNQYKPI QWVRWV TCPIG UGVVKPIU8QNVCIG    CPF 8 %WTTGPV   CPF  O# CPF YKVJ QT YKVJQWV CP[ QHVJG HQNNQYKPI QRVKQPU8CNXG #UUGODN[ HQT 'ZVGTPCN <GTQ5RCP


'<5 4CEM /QWPV #UUGODN[

+PVGTPCN (NQRR[ &KUM &TKXG

.GCT 5KGINGT/GCUWTGOGPV %QPVTQNU%QTR +PXGTPGUU &TKXG 'CUV'PINGYQQF %1

#WVQ 'SWKX  

&GUKIPCVGF UKPEG RWDNKECVKQP QH VJG  '2# ETKVGTKC FQEWOGPV HQT Q\QPG CPF QVJGT RJQVQEJGOKECN QZKFCPVU 
75 'PXKTQPOGPVCN 2

C

7UGTU UJQWNF DG CYCTG VJCV FGUKIPCVKQP QH VJKU CPCN[\GT HQT QRGTCVKQP QP CP[ HWNNUECNG TCPIG NGUU VJCP  RRO KU DCUGF QP OGGVKPI V

D

RGTHQTOCPEG URGEKHKECVKQPU TGSWKTGF HQT VJG  VQ RRO TCPIG 6JWU FGUKIPCVKQP QH CP[ HWNNUECNG TCPIG NQYGT VJCP VJG  VQ REQOOGPUWTCDN[ DGVVGT RGTHQTOCPEG VJCP VJCV QDVCKPGF QP VJG  VQ RRO TCPIG


6JKUOGVJQFKUWPFGTIQKPIVGUVKPICPFKUNKMGN[VQDGUWDOKVVGFHQT'2#EGTVKHKECVKQPCUCP

GSWKXCNGPVOGVJQF

7NVTCXKQNGV2JQVQOGVT[

6JKUOGVJQFKUDCUGFQPVJGHCEVVJCV1 JCUCTGCUQPCDN[UVTQPICDUQTRVKQPDCPF 

YKVJCOCZKOWOPGCTPOEQKPEKFKPIYKVJVJGUVTQPIGOKUUKQPNKPGQHCNQYRTGUUWTG

OGTEWT[NCOR6JGOQNCTCDUQTRVKQPEQGHHKEKGPVCVVJGOGTEWT[NKPGKUYGNNMPQYPVJG

CEEGRVGFXCNWGDGKPIv/ EO KPDCUGWPKVUCV%CPFCVOQURJGTGRTGUUWTG  


*CORUQPGVCN7NVTCXKQNGVCDUQTRVKQPJCUHTGSWGPVN[DGGPWUGFVQOGCUWTG1 KP 

NCDQTCVQT[EJGOKECNCPFMKPGVKEUUVWFKGU7NVTCXKQNGVRJQVQOGVT[CNUQYCUWUGFHQTUQOGQHVJG

HKTUVCVOQURJGTKE1 OGCUWTGOGPVUDWVVJGGCTN[KPUVTWOGPVUUWHHGTGFHTQORQQTRTGEKUKQP 

DGECWUGQHVJGUOCNNCDUQTDCPEGUDGKPIOGCUWTGF75&GRCTVOGPVQH*GCNVJ'FWECVKQPCPF

9GNHCTG

/QFGTPFKIKVCNGNGEVTQPKEUJCXGPQYUQNXGFVJGRTGEKUKQPRTQDNGOUTGUWNVKPIHTQO

OGCUWTGOGPVQHUOCNNCDUQTDCPEGUCPFUGXGTCNEQOOGTEKCN1 OQPKVQTUPQYGORNQ[ 

78RJQVQOGVT[5GXGTCNKPUVTWOGPVUDCUGFQPVJKURTKPEKRNGJCXGDGGPFGUKIPCVGFD['2#CU

GSWKXCNGPVOGVJQFUHQTCODKGPV1 6CDNGUCPF7NVTCXKQNGVRJQVQOGVT[KUPQYVJG 

RTGFQOKPCPVOGVJQFHQTCUUGUUKPIEQORNKCPEGYKVJVJG0##35HQT1 6JGEQOOGTEKCN 

OQPKVQTUWUGRCVJNGPIVJUQHOQTNGUUCPFQRGTCVGKPCUGSWGPVKCNUKPINGDGCOOQFG

6TCPUOKUUKQPQHPONKIJVVJTQWIJVJGUCORNGCKTKUCXGTCIGFQXGTCUJQTVRGTKQFQHVKOG


CUUJQTVCUCHGYUGEQPFUCPFKUEQORCTGFVQCUWDUGSWGPVVTCPUOKUUKQPOGCUWTGOGPVQPVJG

UCOGCKTUVTGCOHTQOYJKEJ1 JCUDGGPUGNGEVKXGN[TGOQXGFD[COCPICPGUGFKQZKFG/P1   

UETWDDGT6JGGNGEVTQPKEEQORCTKUQPQHVJGVYQUKIPCNUECPDGEQPXGTVGFFKTGEVN[KPVQCFKIKVCN

TGCFQWVQHVJG1 EQPEGPVTCVKQP6JGOGVJQFKUKPRTKPEKRNGCDUQNWVGDGECWUGVJGCDUQTRVKQP 

EQGHHKEKGPVCPFRCVJNGPIVJCTGCEEWTCVGN[MPQYPCPFVJGOGCUWTGFCDUQTDCPEGECPDG

EQPXGTVGFFKTGEVN[VQCEQPEGPVTCVKQP

%QOOGTEKCN78RJQVQOGVGTUHQTCODKGPV1 OGCUWTGOGPVUJCXGFGVGEVKQPNKOKVUQH 

CRRTQZKOCVGN[RRO6KOGTGURQPUGFGRGPFUQPVJGCXGTCIKPIVKOGWUGFDWVKUV[RKECNN[

OKP.QPIVGTORTGEKUKQPECPDGYKVJKPv6JGOGVJQFJCUVJGCFXCPVCIGQH

TGSWKTKPIPQICUUWRRNKGUCPFEQOOGTEKCNKPUVTWOGPVUCTGEQORCEVCPFTGCUQPCDN[RQTVCDNG

5CORNGCKTHNQYEQPVTQNKUPQVETKVKECNYKVJKPVJGNKOKVCVKQPUQHVJG/P1 UETWDDGT$GECWUG 

VJGOGCUWTGOGPVKUCDUQNWVG78RJQVQOGVT[CNUQKUWUGFVQCUUC[1 ECNKDTCVKQPUVCPFCTFUCU 

KP5GEVKQP#ODKGPVCKTOQPKVQTUWUKPI78RJQVQOGVT[CTGIGPGTCNN[ECNKDTCVGFYKVJ

UVCPFCTF1 OKZVWTGUVQCEEQWPVHQTNQUUGUQH1 KPUCORNKPINKPGU  

#RQVGPVKCNFKUCFXCPVCIGQH78RJQVQOGVT[KUVJCVCP[CVOQURJGTKEEQPUVKVWGPVVJCV

CDUQTDUPONKIJVCPFKUTGOQXGFHWNN[QTRCTVKCNN[D[VJG/P1 UETWDDGTYKNNDGCRQUKVKXG 

KPVGTHGTGPEGKP1 OGCUWTGOGPVU2QVGPVKCNKPVGTHGTGPVUKPENWFGCTQOCVKEJ[FTQECTDQPU 

OGTEWT[XCRQTCPF51 #TGEGPVUVWF[-NGKPFKGPUVGVCNFGOQPUVTCVGFVJCVVQNWGPG 

CPFRQUUKDN[CTQOCVKETGCEVKQPRTQFWEVUUWEJCUDGP\CNFGJ[FGRTQFWEGRQUKVKXGKPVGTHGTGPEGU

KP78RJQVQOGVTKE1 OGCUWTGOGPVU6JKUTGUWNVYCUHQWPFWUKPIRJQVQEJGOKECNN[TGCEVKXG 

OKZVWTGUQHVQNWGPGCPF01 CVEQPEGPVTCVKQPUCVCHCEVQTQHVQJKIJGTVJCPVJQUGGZRGEVGF Z

KPRQNNWVGFWTDCPCKT%QPUKFGTCVKQPQHVJGTGNCVKXGCDUQTRVKQPEQGHHKEKGPVUQH1 CPFVJG 

CTQOCVKEUKPFKECVGFVJCVCVJKIJGTJWOKFKVKGUVQNWGPGECPECWUGCPKPVGTHGTGPEGQHRRDX1

RGTRRDXQHVQNWGPGYJGTGCUDGP\CNFGJ[FGOC[ECWUGCPKPVGTHGTGPEGCUJKIJCURRDX1 RGT 


RRDXDGP\CNFGJ[FG-NGKPFKGPUVGVCN6JKUKPVGTHGTGPEGOC[DGJWOKFKV[FGRGPFGPV

+PGCTNKGTYQTMCVXGT[NQYJWOKFKVKGUPQKPVGTHGTGPEGYCUQDUGTXGFYKVJVQNWGPGCPFQPN[C

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FGHKEKGPEKGUYKVJRQVCUUKWOKQFKFG-+OGVJQFUVJGOQUVPQVCDNGQHYJKEJYGTGRQQT

RTGEKUKQPQTKPVGTNCDQTCVQT[EQORCTCDKNKV[CPFCRQUKVKXGDKCUQH0$-+OGCUWTGOGPVUTGNCVKXG

VQUKOWNVCPGQWUCDUQNWVG78CDUQTRVKQPOGCUWTGOGPVU

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RQVGPVKCNTGHGTGPEGECNKDTCVKQPRTQEGFWTGUCPFUGNGEVGF78RJQVQOGVT[QPVJGDCUKUQHUWRGTKQT

CEEWTCE[CPFRTGEKUKQPCPFUKORNKEKV[QHWUG4GJOGGVCN+P78RJQVQOGVT[

YCUFGUKIPCVGFVJGTGHGTGPEGECNKDTCVKQPRTQEGFWTGD['2#(GFGTCN4GIKUVGTC

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6GEJPQNQI[0+56HQTOGTN[0CVKQPCN$WTGCWQH5VCPFCTFU=0$5?JCXGGUVCDNKUJGFC

PCVKQPYKFGPGVYQTMQHUVCPFCTFTGHGTGPEGRJQVQOGVGTU542UVJCVCTGWUGFVQXGTKH[NQECN

RTKOCT[UVCPFCTFUCPFVTCPUHGTUVCPFCTFU#UGEQPFCT[QTVTCPUHGTUVCPFCTFKUCFGXKEGQT

OGVJQFVJCVECPDGECNKDTCVGFCICKPUVVJGRTKOCT[UVCPFCTFCPFVJGPOQXGFVQCPQVJGTNQECVKQP

HQTECNKDTCVKQPQH1 OQPKVQTU%QOOGTEKCN78RJQVQOGVGTUHQT1 QHVGPCTGWUGFCU  

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6JGNCVVGTOGVJQFICURJCUGVKVTCVKQP)26QH1 YKVJ0101
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01 
1 KUCFKTGEVCPFCDUQNWVGOGCPUQHFGVGTOKPKPI1 RTQXKFGF01KUKPGZEGUUUQVJCV   

PQUKFGTGCEVKQPUQEEWT7PFGTUWEJEQPFKVKQPU)26JCUVJGCFXCPVCIGVJCVOGCUWTGOGPVQH

VJG01QT1 EQPUWOGFQTVJG01 RTQFWEGFIKXGUCUKOWNVCPGQWUOGCUWTGOGPVQHVJGQVJGT  

VYQURGEKGU#NNVJTGGOQFGUJCXGDGGPWUGFCPFVJKUOGVJQFKUQHVGPWUGFHQTECNKDTCVKQPQH

0101 CPCN[\GTU)CURJCUGVKVTCVKQPJCUDGGPEQORCTGFVQ78RJQVQOGVT[KPUGXGTCN Z

UVWFKGU6JGOQUVFGVCKNGFUVWF[KUVJCVQH(TKGFCPF*QFIGUQPYJQWUGFCP0$5

RTKOCT[UVCPFCTF78RJQVQOGVGTJKIJN[CEEWTCVGHNQYOGCUWTGOGPVURJQVQCEQWUVKEFGVGEVKQP

QH01 CPF0$5PQY0+565VCPFCTF4GHGTGPEG/CVGTKCNUCUUQWTEGUQH01CPF01 6JCV  

UVWF[UJQYGFVJCVFGETGCUGUKP1 CUOGCUWTGFD[VJG78OGVJQFCXGTCIGFNQYGTVJCP 

VJGEQTTGURQPFKPIFGETGCUGKP01CPFKPETGCUGKP01 OGCUWTGFKPFGRGPFGPVN[$GECWUGQH 

WPEGTVCKPV[CDQWVVJGQTKIKPQHVJGUOCNNDKCUTGNCVKXGVQ78RJQVQOGVT[)26KUWUGFCUC

VTCPUHGTUVCPFCTFDWVPQVCUCRTKOCT[TGHGTGPEGUVCPFCTF

2GTQZ[CEGV[N0KVTCVGCPF+VU*QOQNQIWGU

&WTKPINCDQTCVQT[QTICPKERJQVQQZKFCVKQPUVWFKGU5VGRJGPUGVCNCD

FGVGTOKPGFVJGRTGUGPEGQHCPWODGTQHCNM[NPKVTCVGUCPFCPWPKFGPVKHKGFURGEKGUECNNGF

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EQPHKTOGFD[5EQVVGVCN+PNCVGTYQTM5VGRJGPUGVCNKVUUVTWEVWTGYCU

FGVGTOKPGFCPF%QORQWPF:YCUPCOGFRGTQZ[CEGV[NPKVTCVGQT2#05KPEGVJGFKUEQXGT[QH

2#0OWEJGHHQTVJCUDGGPFKTGEVGFVQYCTFKVUCVOQURJGTKEOGCUWTGOGPV+PVJGHQNNQYKPI

UWDUGEVKQPU2#0OGCUWTGOGPVCPFECNKDTCVKQPVGEJPKSWGUCTGFGUETKDGF6JGFKUEWUUKQPQP


OGCUWTGOGPVVGEJPKSWGUKPENWFGUCUWOOCT[FGUETKRVKQPCPFKFGPVKHKGUNKOKVUQHFGVGEVKQP

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QHGCEJOGVJQFCNUQCTGRTGUGPVGF6JGUWDUGEVKQPQPECNKDTCVKQPVGEJPKSWGUKPENWFGUVJQUG

OGVJQFUOQUVQHVGPGORNQ[GFFWTKPICODKGPVCKTOGCUWTGOGPVUVWFKGU

/GCUWTGOGPV/GVJQFU

6YQOGVJQFUIGPGTCNN[JCXGDGGPGORNQ[GFVQOCMGCVOQURJGTKEOGCUWTGOGPVUQH

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VJGUCORNKPICPFCPCN[UKUVQDGEQPFWEVGFKPTGCNVKOG$GECWUG2#0KUXGT[TGCEVKXGKPVJG

ICURJCUGCPFGZJKDKVUUWTHCEGCFUQTRVKXGGHHGEVUVJGOKPKOCNEQPVCEVVKOGQHHGTGFD[+4OCMGU

VJKUOGVJQFXGT[CVVTCEVKXG*QYGXGT+4KPUVTWOGPVCVKQPKUGZRGPUKXGCPFEQORNGZCPF

TGSWKTGUCIQQFFGCNQHURCEG1PVJGQVJGTJCPF)%KUKPGZRGPUKXGCPFTGSWKTGUOKPKOCN

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CPFQVJGTQTICPKEPKVTCVGUTGEGPVN[JCUDGGPTGXKGYGFD[4QDGTVU

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KPVJGNKVGTCVWTG5VGRJGPU$TWEMOCPPCPF9KNNPGT*QNFTGPCPF5RKEGT

0KMKGVCN6UCNMCPKCPF6QWRCPEGRGTOKVVJGSWCPVKVCVKXGCPCN[UKUQH2#0

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EQWNFDGFGVGEVGFD[VJGDCPFUCVCPFEO 6JGEO DCPFKUEJCTCEVGTKUVKEQH  

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KPVGITCVGFUCORNGUQHCODKGPVCKT6JGOCVTKZKUQNCVKQPVGEJPKSWGJCUCVJGQTGVKECNNGXGNQH

FGVGEVKQPQHRRV)TKHHKVJCPF5EJWUVGT

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OGVJQFYCUQTKIKPCNN[FGUETKDGFD[&CTNG[GVCNCPFUWDUGSWGPVN[JCUDGGPTGHKPGFCPF

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WUGFVQHVKPNGPIVJ(KPCNN[EQNWOPRCEMKPIPQTOCNN[KPENWFGUC%CTDQYCZUVCVKQPCT[

RJCUGEQCVGFQPVQCFGCEVKXCVGFUQNKFUWRRQTV7UKPIRCEMGFEQNWOPUFGVGEVKQPNKOKVUQH


RRVJCXGDGGPTGRQTVGFWUKPIFKTGEVUCORNKPIYKVJCO.UCORNGNQQR8KGTMQTP4WFQNRJ

GVCN&GVGEVKQPNKOKVUYGTGHWTVJGTGZVGPFGFVQVQRRVWUKPIET[QIGPKEGPTKEJOGPV

QHUCORNGU8KGTMQTP4WFQNRJGVCN5KPIJCPF5CNCU6JGUGUVWFKGUJCXGHQWPF

QPN[UNKIJVQXGTCNNNQUUGUQH2#0VQCUUQEKCVGFYKVJET[QUCORNKPIRTQXKFGFUCORNGU

CTGYCTOGFQPN[VQTQQOVGORGTCVWTGFWTKPIFGUQTRVKQP

4GEGPVN[KORTQXGFRTGEKUKQPCPFUGPUKVKXKV[JCXGDGGPTGRQTVGFWUKPIHWUGFUKNKEC

ECRKNNCT[EQNWOPUKPUVGCFQHRCEMGFEQNWOPU*GNOKIGVCN4QDGTVUGVCN

5KIPCNVQPQKUGGPJCPEGOGPVQHJCUDGGPENCKOGF4QDGTVUGVCN

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WUGF)%CPFCEQOOGTEKCNN[CXCKNCDNGNWOKPQNDCUGFKPUVTWOGPVKG5EKPVTGZ./#

.WOKPQZVQFGVGEVDQVJ01 CPF2#07UKPICUCORNKPIKPVGTXCNQHUNKPGCTTGURQPUGYCU 

ENCKOGFHTQOVQRRD01 CPFHTQOVQRRD2#0#NVJQWIJVJG2#0ECNKDTCVKQP 

YCUPQPNKPGCTDGNQYRRDCFGVGEVKQPNGXGNQHRRDYCUTGRQTVGF&TWOOQPFGVCN


UNKIJVN[OQFKHKGFVJGCDQXGCRRTQCEJD[EQPXGTVKPIVJG2#0HTQOVJG)%EQNWOPVQ

01 CPFOGCUWTKPIVJGTGUWNVKPI01 YKVJCNWOKPQNDCUGFKPUVTWOGPV  

2GTQZ[CEGV[N0KVTCVG5VCDKNKV[

2GTQZ[CEGV[NPKVTCVGKUCPWPUVCDNGICUCPFKUUWDLGEVVQUWTHCEGTGNCVGF

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01 UJQWNFHQTEGVJGGSWKNKDTKWOVQYCTF2#0CPFGPJCPEGKVUUVCDKNKV[+PVJGRTGUGPEGQH 

01RGTQZ[CEGV[NTCFKECNUTGCEVTCRKFN[VQHQTO01 CPFCEGVQZ[TCFKECNUYJKEJFGEQORQUGKP 

1 VQTCFKECNUVJCVCNUQEQPXGTV01VQ01 #UCTGUWNVVJGRTGUGPEGQH01CEVUVQTGFWEG  

2#0UVCDKNKV[CPFGPJCPEGKVUFGEC[TCVG.QPPGOCPGVCN5VGRJGPUTGRQTVGF

VJCVCRRTGEKCDNG2#0NQUUKPCOGVCNUCORNKPIXCNXGYCUVTCEGFVQFGEQORQUKVKQPQPCUKNXGT

UQNFGTGFLQKPV/G[TCJPGVCNTGRQTVGFVJCV2#0FGEC[GFCEEQTFKPIVQHKTUVQTFGT

MKPGVKEUCVCTCVGQHVQJKPINCUUXGUUGNUCPFVJG[UWIIGUVGFHKTUVQTFGTFGEC[CUVJGDCUKU

HQTCRTQRQUGFOGVJQFQHKPHKGNF2#0ECNKDTCVKQP+PEQPVTCUV*QNFTGPCPF5RKEGT

HQWPFVJCVYKVJQWV01 CFFGFRRD2#0FGEC[GFKP6GFNCTDCIUCEEQTFKPIVQHKTUVQTFGT 

MKPGVKEUCVCTCVGQHJ6JGCFFKVKQPQHRRD01 CEVGFVQUVCDKNK\GVJG2#0RRD 

KPVJG6GFNCTDCIU

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4CUOWUUGP.QYTGURQPUGUQDUGTXGFCVJWOKFKVKGUDGNQYCPF2#0EQPEGPVTCVKQPU

QHCPFRRDDWVPQVRRDYGTGCVVTKDWVGFVQUCORNGEQNWOPKPVGTCEVKQPU#

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GZRGTKOGPVUCVRRDCPFFKFPQVEQPENWFGVJCVVJGTGFWEGFTGURQPUGYCUHTQOHCWNVUKPVJG

FGVGEVQTQTVJGKPUVTWOGPV6JG[EQPENWFGFVJCVVJGTGYCUPQEQNWOPTGNCVGFGHHGEVCPFVJG[

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#PQVJGTUWTHCEGTGNCVGFGHHGEVJCUDGGPTGRQTVGFHQT2#0CPCN[UGUQHTGOQVGOCTKPG

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VQRRVCPCXGTCIGHCEVQTQHYJGPVJGUCORNGYCUUVQTGFKPCINCUUXGUUGNHQTVQ

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2TGRCTCVKQPCPF%CNKDTCVKQP

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CPCN[\GTUJCXGPGCTSWCPVKVCVKXGTGURQPUGVQ2#06JWUWPFGTUQOGEKTEWOUVCPEGU

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FKHHGTGPEGKP01 UKIPCNOGCUWTGFWRUVTGCOCPFFQYPUVTGCOQHCPCNMCNKPGDWDDNGT)TQULGCP Z

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01 CPCN[\GTVJCVJCUDGGPECNKDTCVGFD[VTCFKVKQPCNOGVJQFU Z

#UPQVGFRTGXKQWUN[01KPVJGRTGUGPEGQH2#0KUEQPXGTVGFVQ01 

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ECNKDTCVKQPOKZVWTGUQHECTDQP[NURGEKGUKPRTGUUWTK\GFICUE[NKPFGTU#NVJQWIJICURJCUGstandards are available commercially, the vendors do not guarantee long-term stability and

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

3-116
GC/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

(K2CO3) trap ahead of the cryogenic trap. Frequent replacement of the K2CO3 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 NOx in ambient air is of interest because of the role that

certain of those compounds play as precursors to O3 and because NO2 has been shown to

impact health effects. Most of the NOx emitted from combustion sources are NO and NO2.

Collectively these two compounds are called NOx. They contribute to O3 formation by means

of reactions discussed in Section 3.2. As a result, measurement of NOx is important in efforts

to understand and control O3 and NO2 in ambient air.

The atmospheric photochemistry that produces O3 also results in conversion of NO

and NO2 to products such as HNO3, nitrous acid (HNO2), organic nitrates such as PAN

(CH3C(O)O2NO2), and other species. The total of all of these labile nitrogen species in air,

NOx included, is termed NOy. Such compounds may be labile via photolysis (e.g., HNO2) or

thermal decomposition (e.g., PAN), and may be toxic, irritating, or acidic. The organic

nitrates can occur in the atmosphere as reservoirs for NO2. However, in general, they do not

play the same critical role that NO2 and NO play as O3 precursors. For that reason, this

section focuses on measurement methods for NO and NO2, as the primary O3 precursors of

NOx. Nitrogen oxides other than NOx may be important, however, as interferents in efforts to

measure NO and NO2. These non-NOx species are considered in this section in that regard.

Measurements of NOx may involve measurements of NO, of NO2, or of the sum of

NOx. Nitrogen dioxide, but not NO, is a criteria air pollutant, and, thus, reference and

equivalent methods are specified for NO2 measurements. In this section, the current state of

measurement methods for NO and NO2 will be summarized separately. Such methods in

some cases rely on measurements of total NOx, or at least an approximation of NOx. This

3-117
discussion 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 O3. In

this method, excess O3 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

O3 produce electronically excited NO2 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 O3 reactions

with other species (e.g., hydrocarbons). The excited NO2 is readily quenched in air, so that,

in typical instruments, air and O3 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 NO2; it requires a means of converting NO2 to

NO for detection. The complexities of this conversion are discussed in Section 3.5.3.3, on

NO2 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 NO2 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 O3 supply; for example, by use of

oxygen in the O3 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

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Howlett, 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, H2S 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 NO2, HNO3, PAN, N2O5, NH3, HCN, N2O, and HO2NO2; as well as no

interference from CH4, propylene, and H2O2.

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

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research 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 NO2 have been adapted for NO measurement, using an oxidizing

material that converts NO to NO2. Palmes tubes (Palmes and Tomczyk, 1979) have been

adapted for NO measurement by using two tubes in parallel. One tube collects NO2 on a

triethanolamine (TEA)-coated grid, whereas the other collects NO2 on a TEA grid, plus NO

oxidized by a chromic acid-coated surface. The grids are then extracted and analyzed for

NO2

- ion. Nitric oxide is determined by difference between the two results, after accounting

for the different diffusivities of NO and NO2. 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 CrO3 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 NO2

must 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 NO2 detection method must use the CL approach and be calibrated by the

specified methods (gas-phase titration of NO with O3, or use of an NO2 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 O3-CL method as the reference measurement principle for

ambient NO2 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 NO2 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 NO2. Three wet chemical methods are shown as equivalent

methods, but these rarely are used for ambient air measurements.

The O3 CL method does not measure NO2 directly, because the CL is produced by

reaction of NO with O3. As a result, NO2 must first be reduced to NO for detection.

In principle, such a reduction should readily result in measurement of NO + NO2 (i.e.,

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Table 3-18. Performance Specifications for Nitrogen Dioxide Automated

Methodsa

Performance Parameter Units NO2

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 (SO2,NO,NH3,H2O)

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

NOx), and allow indirect measurement of NO2 by difference between NO and NOx responses,

measured either sequentially, or simultaneously by separate detectors. In practice, however,

selective measurement of NOx by this approach has proven very difficult.

Several methods have been employed to convert NO2 to NO, including catalytic

reduction with heated molybdenum or stainless steel, reaction with CO over a gold catalyst

surface, reaction with ferrous sulfate (FeSO4) at room temperature, reaction with carbon at

200 °C, and photolysis of NO2 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 NO2 to

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Table 3-20. Reference and Equivalent Methods for Nitrogen Dioxide

Designated by U.S. Environmental Protection Agencya

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-ANSAb 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 NOy 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 NO2 and for NOy

species such as HNO3, HNO2, 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 NOx, but more nearly NO and total NOy. Although

NOx is the predominant NOy species during early morning hours, other NOy species constitute

a substantial percent of the NOy later in the day, especially in rural areas. The NO2 value

inferred from such measurements may be significantly in error (see below), and may in turn

affect the results of models of ambient O3. The completeness of the measured NOy value is

also questionable because, for example, HNO3 is readily lost to surfaces, and, in ambient

sampling, may be removed within the sampling system before reaching the heated converter.

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Other conversion methods for NO2 have been tried in an effort to achieve higher

selectivity. Ferrous sulfate has been used for ambient NO2 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 NO2, but also has been found

to convert a portion of PAN, and possibly a portion of HNO2 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 FeSO4 has given

high readings in comparison with spectroscopic instruments and the photolytic NO2 converter,

and its use likely results in overestimating ambient NOx by a significant amount (Fehsenfeld

et al., 1987; Ridley et al., 1988a; Gregory et al., 1990b). Ferrous sulfate has never been used

in commercial NOx instruments and is no longer used in research measurements.

The most specific method for converting NO2 to NO is photolysis (Kley and

McFarland, 1980). In the most common approach, ambient NO2 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., HNO3, PAN, and organic nitrates), but less abundant

NOy species such as HNO2 or HO2NO2 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 NO2. However, it does not provide

complete conversion of NO2. 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 NOx and NOy 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 NO2

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

NOx measurements actually measure NO and NOy. The magnitude of the resulting

overestimation of NO2, determined by difference, obviously depends on the portion of NOy

that is NOx. The smaller the portion of NOy that is NOx, the greater will be the error in the

NO2 determined by difference. In rural/remote areas, where NOx has undergone extensive

conversion to other products during transport from a source region, NOx may contribute a

small fraction of NOy. In urban areas, close to sources, NOx may comprise nearly all of NOy.

For example, in measurements at Point Arena, Parrish et al. (1992) report NOx/NOy 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 NOx/NOy 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

NO2, the great majority of existing ambient air data for NO2 or NOx are biased high, due to

the inclusion of some portion of other NOy species. The magnitude of this bias may not be

large in urban areas, but, in any case, it is essentially unknown at this time.

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Luminol Chemiluminescence Method

This approach is based on the CL reaction of gaseous NO2 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 NO2 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

NO2 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 O3 and SO2 and a negative one from CO2. 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, HNO2, and O3 (Wendel et al., 1983; Schiff et al., 1986; Rickman

et al., 1989; Kelly et al., 1990; Spicer et al., 1991). Response to NO2 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 NO2 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 NO2.

An O3 scrubber is available to eliminate the O3 interference noted above, but it

also was found to remove a portion of the NO2 (Kelly et al., 1990). The luminol approach

also has been modified to measure NO, by using a CrO3 converter that oxidizes NO to NO2

for detection. Thus NO is detected by difference. This method has the potential for

measurement of total NOx; however, evaluations of the CrO3 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 NO2 detector has been reported to

provide measurements of total NOy, NO2, and NOx (Drummond et al., 1992). This adaptation,

called the LNC-3M, uses a commercial luminol instrument for NO2 detection, with a

CrO3 converter for NOx detection. The NOx measurement must be corrected for the few

percent of the ambient NO2 that is lost in the CrO3 converter (Drummond et al., 1992). The

NOy 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 NOy, than is provided by either the heated

molybdenum converters used in commercial O3 CL NOx detectors or the gold converters with

CO addition used in research instruments (Drummond et al., 1992). The LNC-3M adds a

small amount of NO2 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

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Several spectroscopic approaches to NO2 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 NO2

method. It can provide high selectivity for NO2, 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 NO2 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 NO2

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 NO2 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 NO2 detection limit of 0.3 ppt, but is not fully proven for ambient

measurements.

Fluorescence methods also have been used for NO2, 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 NO2 to NO for

detection. Thus NO2 is ultimately measured, by difference, as NO, but the NO is formed

directly by photolysis of NO2. With a 2-min integration time, an NO2 detection limit of

12 ppt is reported. The method is highly selective for NO2, 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

NO2 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 O3 CL and luminol

instruments to provide specific NO2 measurements (Walega et al., 1984; Sickles et al., 1990;

Fehsenfeld et al., 1990) and in an airborne comparison with PF/TPLIF and O3 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 NO2 levels (i.e., <0.4 ppbv) (Fehsenfeld et

al., 1990; Gregory et al., 1990b).

The spectroscopic NO2 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 NO2 using

wavelengths between 400 and 460 nm and achieved a detection limit of 0.6 ppbv, as noted

above. Simultaneous measurements of O2, SO2, HCHO, and HNO2 also were provided by the

DOAS instrument. Comparison of the DOAS NO2 results to those from a commercial CL

detector showed (DOAS NO2) = 1.14 × (CL NO2) + 2.7 ppbv, with an r2 of 0.93, at NO2

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

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Passive samplers are attractive, inexpensive, and simple means to obtain long-term

or personal exposure data for NO2 or NOx. The simplest passive sampler for NO2 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 NO2

- 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 NO2 or NOx.

Addition of a diffusion barrier to the nitration plate concept has led to badge-type

passive samplers for NO2 (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 NO2 collected,

typically as NO2

- ion. Such a device using TEA as the active material gave very good

agreement relative to a CL analyzer in laboratory tests with NO2 at 10 to 250 ppbv (Mulik

and Williams, 1987). However, interferences from PAN and HNO2 (the latter in both outdoor

and indoor air) are expected (Sickles and Michie, 1987). Comparison of ambient NO2 results

in the 5 to 25 μg/m3 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 NO2 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 NO2

-

. 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 HNO2 are expected (Sickles and Michie, 1987); likewise, the

devices are sensitive to the speed of ambient air movement.

Palmes tubes have been developed for NO2 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 NO2

- (Mulik and Williams, 1986). Adsorption of NO2

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 HNO2 are likely interferences (Sickles and Michie, 1987). In a

comparison with two commercially produced NO2 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

3-126
as 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 NO2 in nitrogen or air are sometimes used for NO2

calibration. 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

NO2 levels in such standards has been reported, and caution must be used in relying on NO2

standards as the primary means of calibration.

Two calibration methods for NO2 are specified in the Code of Federal Regulations

(1987) for calibration of ambient NO2 measurements: (1) permeation tubes and (2) gas-phase

titration.

An NO2 permeation tube is an inert enclosure, generally of Teflon®, glass and

Teflon®, or stainless steel and Teflon®, that contains liquid NO2. As long as liquid NO2 is

present, NO2 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 NO2 at a constant rate. Dilution of the emitted NO2 with a flow of dry air or

N2 results in known low NO2 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 (HNO3, HNO2, NO) may occur if they are not kept dry. As with

NO2 cylinder standards, the NO2 permeation tube requires care as a calibration method for

NO2.

Gas-phase titration uses the rapid reaction of NO with O3 to produce NO2 with

1:1 stoichiometry. In practice, excess NO generated from a standard cylinder containing

50 to 100 ppmv NO is reacted with O3 from a stable source. The resultant decrease in NO

concentration, usually measured on the NO channel of a CL NOx analyzer, equals the

concentration of NO2 generated. Varying amounts of NO2 can be produced by varying the

amount of O3.

3.6 Ozone Air Quality Models

To plan control strategies to achieve compliance with the NAAQS for O3 at some

future date, it is necessary to predict how O3 concentrations change in response to prescribed

changes in source emissions of precursor species (NOx and VOCs). This assessment requires

an air quality model, which in the case of O3 prediction is often called a photochemical air

quality model. The model, in effect, is used to determine the emission reductions needed to

achieve the O3 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 O3 and other air quality issues. Ozone,

PM10, visibility, and acid deposition are all connected as a result of similar sources and

complex chemical mechanisms. Consequently, strategies for O3 abatement that involve

reductions of VOC and NOx emissions also will impact particulate matter, visibility, and acid

deposition.

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Ozone air quality models provide the ability to address "what if" questions, such

as, what if emissions of VOCs or NOx 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 O3 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 O3 concentrations during simulated episodes,

although it now is recognized that comparisons of observations and predictions for other

compounds, such as organics and NOy components, are also important in assessing model

performance.

The purpose of Section 3.6 is to review briefly the main elements of O3 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

NOx 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 O3 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 NOx 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.

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· Pollutant transport. Once the O3 precursors are emitted into the atmosphere,

they are transported by the wind. When O3 is formed, it also is subject to

transport 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 NOx (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 O3 over urban areas, and (2) the trajectory model EKMA is identified as an

acceptable 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

O3 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 O3 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

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be 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 O3 by reaction with NO near strong sources of

NOx 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 NOx precursor

concentrations, affecting the computed chemical production of O3. 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 O3 formation processes in regions of intense source emissions

(Odman and Russell, 1991).

Jang (1992) has examined the sensitivity of O3 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 O3 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 NOx and VOCs. Because

of the smearing effect of the large grid sizes, the coarser grid model tended to underpredict

the O3 highs in the areas downwind of cities and overpredict the O3 lows in the intense NOx

emissions areas. It was found that the impact of model grid resolution on the chemistry of

NOx 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 (Ox = O3 + NO2) produced in the

models but has great impact on the interactions of chemistry and transport processes that

control the balance of Ox. As a result, the coarser grid model tends to predict higher O3 and

lower NO2 than does the finer grid model, and the coarser grid tends to transport Ox more in

the form of O3, whereas the finer grid model tends to transport the Ox more in the form of

NO2.

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

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during 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 O3 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 NOx 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 O3 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 O3 observed downwind of an urban area to the levels

of VOCs and NOx 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

NOx 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 O3 lines (or isopleths) as depicted in

Figure 3-25. The isopleths show the downwind, peak 1-h O3 levels as a function of the

concentrations of VOCs and NOx 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 NOx 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 O3 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

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Figure 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 O3 isopleths can be used to estimate the reduction in NMHC or NOx

levels needed to achieve the NAAQS for O3 in a specific urban area. The first step is to

determine the early-morning NMHC/NOx ratio for the urban area in question and the

maximum 1-h downwind O3 concentration. Both the NMHC/NOx ratio and the peak

O3 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 NOx, or both,

needed to achieve the O3 NAAQS can be determined.

As examination of Figure 3-25 reveals, for an NMHC concentration of 0.6 ppmC,

for example, increasing NOx leads to increased O3 until NMHC/NOx ratios of about 5:1 to

6:1 are reached; further NOx increases, leading to lower NMHC/NOx ratios, inhibit

O3 formation. Thus, in this example, there is a "critical" ratio (in the range of 5:1 to 6:1) at

which the NOx effect on O3 changes direction. Besides this "critical" ratio, an "equal control"

NMHC/NOx ratio also exists, above which the reduction of NOx is more beneficial in terms of

O3 reduction than an equal percentage reduction in NMHC. This ratio, for the isopleths

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shown 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 O3 to 0.12 ppm. Thus, for this particular case (Figure

3-25), the chemical mechanism modeling evidence suggests that NOx control will increase the

peak downwind O3 concentration at NMHC/NOx ratios of between 5:1 and 6:1 or lower; both

NOx control and NMHC control will be beneficial at somewhat higher ratios, with control of

NMHC being more effective; and, for ratios above 20:1, NOx control is relatively more

effective in reducing O3 to attain the O3 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/NOx 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/NOx 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/NOx ratio) near

city center can move increasingly into the NOx-controlling regime (high NMHC/NOx ratio) as

the air parcels age and move downwind. This progression occurs because NOx is

photochemically removed from an aging plume more rapidly than VOCs, causing the

VOC/NOx 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

O3 sensitivities to VOC and NOx changes. Because of this and other drawbacks, the 1990

CAAA (U.S. Congress, 1990) require that grid-based models be used in most

O3 nonattainment areas.

3.6.2 Model Components

3.6.2.1 Emissions Inventory

The spatial and temporal characteristics of VOC and NOx 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.

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· 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, NOx, 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/NOx ratios (Fujita et al., 1992).

1

The EMS has been renamed the GMEP (Geocoded Model of Emissions and Projections).

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3.6.2.2 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

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estimated 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 onto

the 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

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recent 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 O3 modeling programs in the United States are using

prognostic models to drive regional O3 models. These include LMOS; SARMAP; and a

regional O3 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 O3 distributions in four multiple-day O3 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 O3 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 O3 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

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assimilation (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

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support 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 O3 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 O3 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.

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Most 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 C4 or below, the chemistry is well

understood and the reaction rates are slow. For C5 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, O3, and the NO3 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 NO3 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 NOx sinks under low NOx 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 O3 generation in smog chamber experiments. Specific VOCs

may, however, be simulated poorly, and products other than O3 may not be simulated

accurately. Existing mechanisms are mostly applicable to single-day, high NOx conditions

because those are the conditions of almost all smog chamber experiments. Low NOx

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 NOx in O3 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 O3 episodes do not occur during

periods of significant clouds or rain.

Dry Deposition

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It 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 (zr), typically 10 m], resulting in the expression F = −vdC, 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, vd,

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 (ra), (2) the quasi-laminar layer

resistance (rb), and (3) the surface or canopy resistance (rs) (Wu et al., 1992). The

aerodynamic resistance characterizes the turbulent transport through the atmosphere from

reference height zr 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 rb. The chemical interaction between the surface and the pollutant of interest

once the gas molecules have reached the surface is characterized by rc. The total resistance

(rt) is the sum of the three individual resistances, and is, by definition, the inverse of the

deposition velocity, 1/vd = rt = ra + rb + rs. 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 HNO3 and SO2 are typically ≈2 cm s-1, and those of

O3 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). With 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, HNO3, SO2, and H2O2, as well as O3 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):

HNO3 > SO2 > NO2 ≈ O3 > PAN > NO;

Derwent and Hov (1988):

HNO3 > SO2 = O3 > NO2 > PAN;

McRae et al. (1982b):

O3 > NO2 > PAN > NO > CO; and

Chang et al. (1987):

HNO3 > H2O2 > NH3 > HCHO > O3 = SO2 = NO2 = NO > RCHO.

There is general agreement that HNO3 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 > SO2 > NO2 ≈

O3 > PAN > NO > CO. This suggests surface resistance values should be ordered

approximately as CO > NO > PAN > O3 ≈ NO2 > SO2 > HNO3 = 0. However, there is still a

substantial range of variability in reported deposition velocities. For example, McKeen et al.

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(1991) calculated dry deposition velocities for HNO3, O3, and PAN of 10, 0.5, and 0.3 cm s-1,

respectively. Note that a large deposition velocity for HNO3 will limit the lifetime of HNO3

relative to O3 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 O3,

surface resistance values by land-use type and season recommended by Sheih et al. (1986)

and Wesely (1988) are appropriate. For NO, NO2, NH3, H2O2, HCHO, and CH3CHO, the

surface resistance values for each land use can be estimated from that for SO2 (Wesely,

1988), except different proportionality factors should be used for NO and NO2.

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, Kaw,

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 104 to 106 (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 ≥ 104. Examples of such gaseous chemicals are HNO3, H2O2, phenol,

and cresols, all of which are highly soluble in water. Formaldehyde is present in the aqueous

phase as the glycol, H2C(OH)2, and has an effective washout ratio of 7 × 103 at 298 K

(Betterton and Hoffmann, 1988; Zhou and Mopper, 1990). Note that the importance of wet

deposition 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

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When 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 O3 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 O3 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 O3 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

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dispersion 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 O3 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 O3 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.

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Table 3-21. Grid-Based Urban and Regional Air Pollution Models:

Overview of Three-Dimensional Air Quality Modelsa

Model Major Applications

Major References for

Model Formulation

Selected References 

Evaluation an

UAM Urban and nonurban areas in the

United States and Europe

Reynolds 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 (1

Russell et al. (1988a,b)Harley et al. (1993)

ROM Eastern United States

(E of 99° W longitude)

Lamb (1983) Schere and Wayland (1

Meyer et al. (1991b)

RADM Eastern North America Chang et al. (1987) Middleton et al. (1988, 

Middleton and Chang (Dennis et al. (1993a)Cohn and Dennis (1994

ADOM Eastern North America and

Northern Europe

Venkatram et al. (1988) Venkatram et al. (1988

Macdonald et al. (1993Karamchandani and Ve

STEM-II Philadelphia area, Kentucky, and

northeastern United States, centralJapan

Carmichael et al. (1986) Carmichael et al. (1991

Saylor et al. (1991)

aSee Appendix A for abbreviations and acronyms.

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Table 3-22. Grid-based Urban and Regional Air Pollution Models: Treatment of Emissio

Resolutiona

Model Emitted Species Point-Source Emissions Area-Source Emissions Ver

UAM SO2, sulfate, NO, NO2, CO,

NH3, and 8 classes of ROG and

PM (4 size classes)

Released into grid cell in layercorresponding to plume rise inUAM; treated with a reactiveplume model in PARIS

Grid-average with resolutionranging from 4 km × 4 km to10 km × 10 km in pastapplications

Typically, 5-6 l

CIT SO2, sulfate, NO, NO2, CO,

NH3, and 6 classes of ROG and

PM (4 size classes)

Treated with a plume modelwith simple NOx and O3chemistry

Grid-average with 5 km × 5 kmresolution in past applications

Five layers up t

ROM CO, NO, NO2, and 8 classes

of ROG

Released into grid cell in layercorresponding to plume rise

Grid-average with 18.5 km ×18.5 km resolution in presentapplications

Three layers up 

RADM SO2, sulfate, NO, NO2, CO,

NH3, and 12 classes of ROG

Released into grid cell in layercorresponding to plume rise

Grid-average with 80 km ×80 km resolution in pastapplications

Fifteen layers u

ADOM SO2, sulfate, NO, NO2, NH3,

and 8 classes of ROG and PM

Released into grid cell in layercorresponding to plume rise

Grid-average with resolutionranging from 60 km × 60 km toabout 120 km × 120 km in pastapplications

Twelve layers u

STEM-II SO2, sulfate, NO, NO2, NH3,

and 8 classes of ROG

Released into grid cell in layercorresponding to plume rise

Grid-average with resolutionranging from 10 km × 10 km to56 km × 56 km in pastapplications

Ten to 14 layer

aSee Appendix A for abbreviations and acronyms.

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Table 3-23. Grid-based Urban and Regional Air Pollution Models:

Treatment of Meteorological Fields, Transport, and Dispersiona

Model Meteorology Transport Turbulent 

UAM Constructed through data interpolation

or calculated with land-sea breeze orcomplex terrain wind model.

3-D wind field. Finite differencenumerical technique.

Vertical turbulent diffusion stability and friction velocitturbulent diffusion coefficie

CIT Constructed through data interpolation

with diagnostic wind model.

3-D wind field. Finite elementnumerical technique.

Vertical turbulent diffusion stability and friction velocitdiffusion function of mixing velocity scale.

ROM Constructed through data interpolation. 3-D wind field with vertical transport

through cumulus clouds. Finitedifference numerical technique.

Vertical turbulent diffusion stability. Horizontal turbuleatmospheric stability, convevelocity scale, and the deptand clouds.

RADM Calculated with Community Climate

Model (CCM) and MM4.

3-D wind field with vertical transportthrough cumulus clouds. Finitedifference numerical technique.

Vertical turbulent diffusion stability and wind shear. Ndiffusion.

ADOM Constructed through data interpolation

in combination with prognostic planetaryboundary-layer model.

3-D wind field with vertical transportthrough cumulus clouds. Cubic splinenumerical technique.

Vertical turbulent diffusion boundary layer model. No diffusion.

STEM-II Calculated with dynamic wind model

(MASS) or constructed through datainterpolation.

3-D wind field with vertical transportthrough clouds. Finite elementnumerical technique.

Vertical turbulent diffusion stability and surface roughndiffusion proportional to ve

aSee Appendix A for abbreviations and acronyms.

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Table 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 O3 control strategy determination. The regional-scale O3 model, ROM, is being used by

EPA to evaluate O3 control measures for the eastern United States and to provide boundary

conditions for urban area simulations using UAM. Representative of a comprehensive stateof-the-science O3/acid deposition model, RADM has been used to evaluate combined O3 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) NO2, (5)

O3, 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

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Table 3-24. Grid-based Urban and Regional Air Pollution Models: Treatment of Chemic

Model Gas-Phase Chemistry Aqueous- Phase Chem

UAM Eighty-seven reactions among 36 species including NOx, O3,

ROG, and SO2 (CBM-IV) (Gery et al., 1988, 1989)

No treatment of aqueous-phase chemistry

CIT One hundred and twelve reactions among 53 species

including NOx, O3, ROG, and SO2 (Lurmann et al., 1986)

No treatment of aqueous-phase chemistry

ROM Eighty-seven reactions among 36 species including NOx, O3,

ROG, and SO2 (CBM-IV)

No treatment of aqueous-phase chemistry

RADM One hundred and fifty-seven reactions among

59 species including NOx , O3, ROG, and SO2

(Stockwell et al., 1990)

Forty-two equilibria and five reactions for S

ADOM One hundred and twelve reactions among 53 species

including NOx, O3, ROG, and SO2 (Lurmann et al., 1986)

Fourteen equilibria and five reactions for SO

STEM-II One hundred and twelve reactions among 53 species

including NOx, O3, ROG, and SO2 (Lurmann et al., 1986)

Twenty-six equilibria and about 30 reactions oxidation, radical chemistry, and transition m

aSee Appendix A for abbreviations and acronyms.

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Table 3-25. Grid-based Urban and Regional Air Pollution Models: Treatment of Cloud and De

Model Cloud Processes Wet Deposition Dry Dep

UAM No treatment of cloud processes. No treatment of wet deposition. Dry deposition velocity approa

friction velocity, land type, an

CIT No treatment of cloud processes. No treatment of wet deposition. Dry deposition velocity approa

stability, wind speed, land type

ROM No treatment of cloud processes, except

vertical transport treatment.

No treatment of wet deposition. Resistance transfer approach; f

speed, atmospheric stability, an

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; fstability, wind speed, season, lsurface wetness, and species.

ADOM Treatment of cumulus clouds and stratus

clouds, 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; fstability, wind speed, land typespecies.

STEM-II Treatment of clouds with the Advanced

Scavenging 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; fstability, land type, wind speed

aSee Appendix A for abbreviations and acronyms.

3-150
36 reacting species. Reactive organic compounds include alkanes, alkenes, aromatics, and

aldehydes, and nitrogen-bearing species include HNO2, HNO3, 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 O3 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 O3 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 the

bottom of Layer 1. During the day, the top model layer, Layer 3, represents the synopticscale subsidence inversion characteristic of high O3-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 O3 is assumed to be negligible. If

cumulus clouds are present, an upward flux of O3 and precursor species is injected into the

layer by penetrative convection. At night, O3 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

3-151
Table 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, primarily

originating 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

3-152
Figure 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 O3, 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 O3 are updated every 12 h based on

measurements, except for the large superdomain, where tropospheric background values are

3-153


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OKPKOCNKVECPDGCRRNKGFVQVJGYQTUVECUGU+PEQPVTCUVVJGPWODGTQHGRKUQFGUCXCKNCDNGHQT

ITKFDCUGFOQFGNKPIKUNGUUVJCPFGUKTCDNGKPCNNCTGCU+PCFFKVKQPCP[CXCKNCDNGKPVGPUKXG

FCVCDCUGUQHVGPFQPQVKPENWFGVJGYQTUVECUGOGVGQTQNQI[KPVGPUKXGFCVCDCUGUV[RKECNN[TGUVTKEV

OQFGNKPIVQVYQQTVJTGG1 GRKUQFGUJCXKPICFWTCVKQPQHVQFC[UGCEJ/QTGQXGTVJG 

KPVGPUKXGFCVCDCUGUPGXGTGPEQORCUUVJGHWNNTCPIGQHOGVGQTQNQIKECNEQPFKVKQPUQHKPVGTGUVKH

1 GZEGGFCPEGUQEEWTKPCPCTGCWPFGTFKHHGTGPVOGVGQTQNQIKECNEQPFKVKQPUVJGTGNCVKXG 

GHHGEVKXGPGUUQHFKHHGTGPVEQPVTQNUVTCVGIKGUOKIJVXCT[YKVJVJGFKHHGTGPVOGVGQTQNQIKECN

EQPFKVKQPU6JG'2#URGEKHKGURTQEGFWTGUHQTGRKUQFGUGNGEVKQPHQTWUGYKVJITKFDCUGF

OQFGNU75'PXKTQPOGPVCN2TQVGEVKQP#IGPE[D

$GECWUGVJGPWODGTQHKPVGPUKXGFCVCDCUGUKUNKOKVGFDQVJKPVGTOUQHGRKUQFGUCPF

TGIKQPU'2#JCUKPXGUVKICVGFVJGHGCUKDKNKV[QHCRRN[KPI7#/YKVJQWVEQPFWEVKPIKPVGPUKXG

HKGNFUVWFKGU5EJGHHGCPF/QTTKU6JGUGUVWFKGUMPQYPCUVJG2TCEVKEGHQT.QY

EQUV#RRNKECVKQPKP0QPCVVCKPOGPV4GIKQPU2.#04YGTGEQPFWEVGFHQT0GY;QTM

2JKNCFGNRJKC#VNCPVC&CNNCU(QTV9QTVJ6:CPF5V.QWKU1HVJGHKXGEKVKGUUVWFKGF

5V.QWKU0GY;QTMCPF2JKNCFGNRJKCJCFKPVGPUKXGFCVCDCUGUCXCKNCDNG5KOWNCVKQPUYGTG

ECTTKGFQWVWUKPIDQVJTQWVKPGCPFKPVGPUKXGFCVCDCUGUHQT5V.QWKUCPF2JKNCFGNRJKC/QFGN

RGTHQTOCPEGWUKPITQWVKPGFCVCYCUOWEJDGVVGTHQT5V.QWKUVJCPHQT2JKNCFGNRJKC5EJGHHG

CPF/QTTKU5EJGHHGCPF/QTTKUECWVKQPGFVJCVVJGFKHHGTKPITGUWNVU

OC[DGEQORNKECVGFD[VJGSWCNKV[QHVJGFCVCDCUGUDWVVJG[URGEWNCVGVJCVOQFGNRGTHQTOCPEG

WUKPITQWVKPGFCVCDCUGUHQT2JKNCFGNRJKCOKIJVJCXGDGGPRQQTGTDGECWUGQHTGIKQPCNVTCPURQTV

2GTHQTOCPEGUVCVKUVKEUHQTCNNHQWTCRRNKECVKQPUWUKPITQWVKPGFCVCYGTGEQPUKUVGPVYKVJQVJGT

7#/CRRNKECVKQPU5EJGHHGCPF/QTTKUJQYGXGTVJGRCWEKV[QHFCVCKPVJG

TQWVKPGFCVCDCUGURTGENWFGFCP[KPXGUVKICVKQPQHVJGRQUUKDKNKV[VJCVEQORGPUCVKPIGTTQTU

QEEWTTGF

5EJGHHGCPF/QTTKUPQVGVJCVVJG2.#04NCEMQHCKTSWCNKV[FCVCYCU

CFFTGUUGFD[GZVGPFKPIVJGNGPIVJQHVJGUKOWNCVKQPUCPFGZRCPFKPIVJGWRYKPFDQWPFCT[

YJKEJKPGHHGEVKPETGCUGFVJGPGGFHQTCEEWTCVGGOKUUKQPUKPXGPVQTKGUDQWPFCT[EQPFKVKQPU

EQWNFCNUQDGQDVCKPGFVJTQWIJWUGQH41/(QT2.#04CRRNKECVKQPUITKFFGFGOKUUKQPU

YGTGETGCVGFHTQOTQWVKPGEQWPV[NGXGNGOKUUKQPKPXGPVQTKGUD[WVKNK\KPICPGOKUUKQPURTQITCO

VJCVOCFGWUGQHUWTTQICVGKPHQTOCVKQPUWEJCURQRWNCVKQPFKUVTKDWVKQP6JG2.#04UVWF[

TGRTGUGPVUCPKPVGTGUVKPIUVCTVQPVJGRTQDNGOQHOQFGNCRRNKECVKQPVQCTGCUYKVJQWVKPVGPUKXG

FCVCDCUGUJQYGXGTVJGTGUWNVUYGTGPQVUWHHKEKGPVN[FGHKPKVKXGHQTFTCYKPIEQPENWUKQPUQHC

DTQCFIGPGTCNPCVWTG


3.6.5 Use of Ozone Air Quality Models for Evaluating Control Strategies

2JQVQEJGOKECNCKTSWCNKV[OQFGNUCTGWUGFHQTEQPVTQNUVTCVGI[GXCNWCVKQPD[HKTUV

FGOQPUVTCVKPIVJCVCRCUVGRKUQFGQTGRKUQFGUECPDGCFGSWCVGN[UKOWNCVGFCPFVJGPTGFWEKPI

J[FTQECTDQPQT01 GOKUUKQPUKPVJGOQFGNKPRWVUCPFKPCUUGUUKPIVJGGHHGEVUQHVJGUG Z

TGFWEVKQPUQP1 KPVJGTGIKQP1\QPGEQPEGPVTCVKQPUECPDGFGETGCUGFD[TGFWEKPIGKVJGT 

81%QT01 EQPEGPVTCVKQPUVQUWHHKEKGPVN[NQYNGXGNU6JGGHHGEVUQH01 GOKUUKQPU Z Z

TGFWEVKQPUQP1 EQPEGPVTCVKQPUXCT[DGECWUG01 KUCPCV[RKECNRTGEWTUQTKGCNVJQWIJKVKU  Z

PGEGUUCT[HQT1 HQTOCVKQPHTGUJ01GOKUUKQPUTGOQXG1 CPFJKIJEQPEGPVTCVKQPUQH01  Z

TGVCTFVJGTCVGQH1 HQTOCVKQPD[TGOQXKPITCFKECNU%QPVTQNQH01 VGPFUVQCEEGNGTCVGVJG  Z

TCVGQH1 HQTOCVKQPJQYGXGTKVUGHHGEVUQPRGCM1 EQPEGPVTCVKQPFGRGPFQPVJGNQECVKQPCPF  

VKOKPIQHVJGEQPVTQNCPFQPCODKGPVEQPEGPVTCVKQPUQH81%UCPF01 YJKEJXCT[YKFGN[KP Z

VKOGCPFURCEGGXGPYKVJKPCUKPINGWTDCPCTGCFWTKPIFC[

#VCIKXGP81%NGXGNCUVJGKPKVKCN01 KUKPETGCUGF1 HKTUVKPETGCUGUVJGPRGCMU Z 

CPFVJGPFGETGCUGU6JGTGFWEVKQPKPRGCM1 YKVJKPETGCUKPI01 KUCYGNNGUVCDNKUJGF  Z

EJGOKECNRJGPQOGPQP6JGRGCMKP1 HQTOCVKQPQEEWTUCVCPKPKVKCN81%01 TCVKQQHCDQWV  Z

KGRRD%RRD#VHKZGF01 NGXGNCU81%KUKPETGCUGF1 HQTOCVKQPKPETGCUGU Z 

DWVVJGPNGXGNUQHH#UCTGUWNVQHVJKUDGJCXKQTCV81%01 TCVKQUDGNQYCDQWV81% Z

TGFWEVKQPJCUDGGPVJGRTGHGTTGFUVTCVGI[HQT1 TGFWEVKQP+PVJKUTGIKQP01 TGFWEVKQPU  Z

URGGFWR1 HQTOCVKQPCPFNGCFVQJKIJGTRGCM1 XCNWGU#V81%01 TCVKQUGZEGGFKPICDQWV  Z

DQVJ81%UCPF01 YKNNTGFWEG1 DWVNGUUVJCPRTQRQTVKQPCNN[6JGTGCUQPVJG Z 

TGFWEVKQPKP1 KUNGUUVJCPRTQRQTVKQPCNKUDGECWUGGSWCNTGFWEVKQPUQH81%UCPF01 CV  Z

KPVGTOGFKCVGTCVKQUVGPFVQMGGR1 RTQFWEVKQPCVKVUOCZKOWO6JGPQPNKPGCTEJGOKECN 

DGJCXKQTQHVJG81%01 U[UVGOFKUEWUUGFGCTNKGTKPVJKUEJCRVGTKUCVVJGJGCTVQHVJG Z

EQPVTQXGTU[QXGTVJGTQNGQH01 KP1 EQPVTQN*GWUUCPF9QNHH Z 

#UPQVGFKP5GEVKQPVJGEQPEGRVVJCVCTGIKQPKUEJCTCEVGTK\GFD[CUKPING

81%01 TCVKQKUQXGTUKORNKHKGFCPFOC[CEVWCNN[NGCFVQKPEQTTGEVEQPENWUKQPUEQPEGTPKPIVJG Z

QRVKOCNCRRTQCEJVQ1 TGFWEVKQP/KNHQTFGVCN6JG81%01 TCVKQKPCTGIKQPKUC  Z

HWPEVKQPQHNQECVKQPCPFVKOGQHFC[VJGUQWTEGTKEJEGPVGTEKV[CTGCOC[DGEJCTCEVGTK\GFD[C

NQYGTTCVKQVJCPVJCVKPFQYPYKPFUWDWTDCPCTGCUCVCP[IKXGPVKOGQHFC[$GECWUGQHVJG

EQORNGZURCVKCNCPFVGORQTCNFGRGPFGPEGQH1 HQTOCVKQPITKFDCUGFRJQVQEJGOKECNCKT 

SWCNKV[OQFGNUCTGPGEGUUCT[VQGXCNWCVGVJGGHHGEVQHGOKUUKQPTGFWEVKQPUVTCVGIKGUHQTC

TGIKQP

/QTGQXGTNQECVKQPURGEKHKEUVWFKGUPGGFVQDGRGTHQTOGFVQCUEGTVCKPYJGVJGTC

IKXGPCTGCKUKPVJG81%QT01 EQPVTQNNGFTGIKOG4GUGCTEJKUDGKPIEQPFWEVGFKPVQVJG Z

TGNCVKQPUJKRDGVYGGP1 CPF01 VQFGVGTOKPGYJGVJGT01 KUCDGVVGTKPFKECVQTQHVJG [ [

1 HQTOKPIRQVGPVKCNVJCPVJG81%01 TCVKQ5JGRUQPGVCND6TCKPGTGVCN  Z

-NGKPOCPGVCN/KNHQTFGVCN

+POQUVOQFGNKPICRRNKECVKQPUKPRWVUCTGCFLWUVGFYKVJKPVJGKTTCPIGQHWPEGTVCKPV[

VQKORTQXGRGTHQTOCPEG#MG[VGUVQHSWCNKV[QHRGTHQTOCPEGKUVQGXCNWCVGVJGOQFGN

RTGFKEVKQPUHQTQVJGTGRKUQFGUYKVJQWVCFLWUVOGPVUWUKPIVJGUCOGRTQEGFWTGUHQTGUVCDNKUJKPI

KPRWVUCUHQTVJGQTKIKPCNGRKUQFG

)TKFOQFGNKPICRRNKECVKQPUCTGEWTTGPVN[WPFGTYC[D[QTHQTUVCVGCIGPEKGUHQT

CRRTQZKOCVGN[CTGCUYKVJKPVJG7PKVGF5VCVGUVQUWRRQTVTGIKQPCN1 5+2TGXKUKQPU 

#PKOOGFKCVGRTQDNGOHCEGFHQTCNOQUVCNNWTDCPCTGCUKUVJCVGXGPKHCPCFGSWCVG

PWODGTQHGRKUQFGUGZKUVVJGGRKUQFGUOC[PQVKPENWFGVJGOQUVCFXGTUG1 NGXGNU

#PKPJGTGPVSWGUVKQPKPWUKPICNGUUCFXGTUGGRKUQFGVQFGXGNQREQPVTQNUVTCVGIKGUKUJQYFQ


VJGUGUVTCVGIKGUGZVTCRQNCVGVQCOQTGUGXGTGUGVQHEQPFKVKQPU!6JGTGKUPQENGCTCPUYGTVQVJKU

SWGUVKQP#VRTGUGPVEQPVTQNUVTCVGIKGUGXCNWCVGFD[WUKPIITKFDCUGFOQFGNUCTGFGVGTOKPGF

DCUGFQPCXCKNCDNGGRKUQFGUVJCVJCXGVJGNCTIGUVCOQWPVQHFCVCYJGVJGTQTPQVVJGUGGRKUQFGU

EQPVCKPVJGJKIJGUV1 EQPEGPVTCVKQPCEJKGXGF#PQVJGTKUUWGKUVJCVVJGHQTOQHVJG0##35 

HQT1 FQGUPQVEQTTGURQPFYKVJVJGQWVRWVHTQOCITKFDCUGFOQFGN6JGOQFGNQWVRWVFQGU 

PQVRTQXKFGCFKTGEVCPUYGTVQYJGVJGTCPCTGCYKNNOGGVVJGUVCPFCTFKPKVUEWTTGPVUVCVKUVKECNN[

DCUGFHQTO

6CDNGUWOOCTK\GUCPWODGTQHTGEGPV1 EQPVTQNUVTCVGI[GXCNWCVKQPUHQT 

FKHHGTGPVCTGCUQHVJG7PKVGF5VCVGU5QOGIGPGTCNQDUGTXCVKQPUECPDGOCFGEQPEGTPKPIKUUWGU

VJCVJCXGCTKUGPKPEQPVTQNUVTCVGI[GZGTEKUGURCTVKEWNCTN[CUVJG[TGNCVGVQRTQDNGOUCUUQEKCVGF

YKVJFKHHGTGPVCTGCUQHVJGEQWPVT[4QVJ+P%CNKHQTPKCOQFGNTGUWNVUKPFKECVGVJCV1

JCUDGGPWPFGTGUVKOCVGFOQUVNKMGN[DGECWUG81%GOKUUKQPUHTQOOQVQTXGJKENGUJCXGDGGP

UGTKQWUN[WPFGTGUVKOCVGF6JGWPFGTGUVKOCVKQPYCUJKFFGPD[CFLWUVKPIQVJGTOQFGNKPRWVU

YKVJKPVJGKTTCPIGQHWPEGTVCKPV[+P#VNCPVCKVJCUDGGPGUVKOCVGFVJCVCRRTQZKOCVGN[QH

VJG81%KPXGPVQT[KUQHDKQIGPKEQTKIKPCPFVJGXCTKCVKQPQHCPVJTQRQIGPKEGOKUUKQPU

TGFWEVKQPUTGSWKTGFVQCEJKGXG1 CVVCKPOGPVYKVJKPVJGWPEGTVCKPV[TCPIGQHVJGDKQIGPKE 

GOKUUKQPUKUQPVJGQTFGTQH6JGWPEGTVCKPV[TCPIGQHVJGDKQIGPKE81%GOKUUKQPUPGGFU

VQDGTGFWEGFVQQDVCKPVKIJVGTEQPVTQNUVTCVGI[GUVKOCVGU

6JGGCUVGTP7PKVGF5VCVGURQUGUURGEKCNRTQDNGOUKPTGIKQPCNUECNGRJQVQEJGOKECN

OQFGNKPI$QWPFCT[EQPFKVKQPUV[RKECNN[EQPVTKDWVGVQQHRQNNWVCPVNQCFKPIKPOCP[

WTDCPCTGCUGCUVQHVJG/KUUKUUKRRK4KXGT4GIKQPCNUECNGOQFGNUCTGQHVGPGKVJGTPQVCXCKNCDNG

QTPQVUWHHKEKGPVN[TGNKCDNGVQWUGKPGUVKOCVKPIWRYKPFDQWPFCT[EQPFKVKQPU(WTVJGTOQTGFCVC

CTGTCTGN[CXCKNCDNG+HFCVCCTGCXCKNCDNGVJGKTWUGKUNKOKVGFVQGUVKOCVKQPQHRTGUGPV

EQPFKVKQPU+HOQFGNUCTGWUGFKPEQPVTQNUVTCVGI[CUUGUUOGPVCPFVQQHRQNNWVCPV

NQCFKPIQTKIKPCVGUQWVUKFGQHVJGOQFGNKPITGIKQPOCLQTSWGUVKQPUCTKUGCUVQLWUVJQYEQPVTQN

UVTCVGIKGUCTGVQDGFGVGTOKPGF+HWPEGTVCKPVKGUCVVJGTGIKQPCNUECNGCTGUKIPKHKECPVCPFKH

TGIKQPCNUECNGOQFGNKPIKUKPCEEWTCVGVJGNKOKVUQHCEEWTCE[HQTWTDCPUECNGEQPVTQNUVTCVGI[

FGVGTOKPCVKQPPGGFVQDGECTGHWNN[CUUGUUGF

#PGUUGPVKCNSWGUVKQPKUIKXGPVJGKPGXKVCDNGWPEGTVCKPVKGUCUUQEKCVGFYKVJ1 CKT 

SWCNKV[OQFGNRTGFKEVKQPUECPVJGGHHGEVQH81%CPF01 GOKUUKQPUEJCPIGUQP1 NGXGNUDG Z 

WPCODKIWQWUN[FGVGTOKPGF!6JGDGUVCRRTQCEJVQCPUYGTKPIVJKUSWGUVKQPKUCEQODKPCVKQPQH

UGPUKVKXKV[WPEGTVCKPV[UVWFKGU)KXGPVJGGUVKOCVGFWPEGTVCKPVKGUKPOQFGNKPRWVUCPF

RCTCOGVGTUHQTCRCTVKEWNCTCRRNKECVKQPVJGRTQRQUGF81%CPF01 GOKUUKQPUEJCPIGUEGPCTKQU Z

UJQWNFDGGZCOKPGFHQTVJGHWNNTCPIGQHOQFGNKPRWVUCPFRCTCOGVGTUVQFGVGTOKPGJQY

UGPUKVKXGEQPENWUKQPUCDQWVVJGGHHGEVQP1 NGXGNUCTGVQVJGKPJGTGPVWPEGTVCKPVKGU 

3.6.6 Conclusions

6JG%###75%QPITGUUJCXGOCPFCVGFVJGWUGQHRJQVQEJGOKECN

ITKFOQFGNUHQTFGOQPUVTCVKPIJQYOQUV1 PQPCVVCKPOGPVCTGCUECPCVVCKPVJG0##35

2TGFKEVKPI1 KUCEQORNGZRTQDNGO6JGTGCTGUVKNNOCP[WPEGTVCKPVKGUKPVJGOQFGNU 

PQPGVJGNGUUOQFGNUCTGWUGHWNHQTTGIWNCVQT[CPCN[UKUCPFEQPUVKVWVGQPGQHVJGOCLQTVQQNUHQT

CVVCEMKPIVJG1 RTQDNGO6JGUGOQFGNUJCXGFGXGNQRGFEQPUKFGTCDN[KPVJGRCUV[GCTU

*QYGXGTVJGKTWUGHWNPGUUKUEQPUVTCKPGFD[JCXKPINKOKVGFFCVCDCUGUHQTWUGKPOQFGN

GXCNWCVKQPCPFHTQOJCXKPIVQTGN[QPJ[FTQECTDQPGOKUUKQPUFCVCVJCVOC[DGKPCEEWTCVG


6CDNG#RRNKECVKQPUQH2JQVQEJGOKECN#KT3WCNKV[/QFGNUVQ'XCNWCVKPI1\QPGC

+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[DKCUTCPIGUHTQOVQ6JGUG

WPEGTVCKPVKGUCTGQHVGPQHVJGUCOGQTFGTCUVJGRGTEGPVCIGQHTGFWEVKQPKPVJG

RGCM1 EQPEGPVTCVKQPHQTCPCTGCHTQOVQRRD4GCUQPUHQTVJGUG 

KPCEEWTCEKGUKPENWFGWPEGTVCKPVKGUKPGOKUUKQPUKPXGPVQTKGU

i +PCFGSWCEKGUQHUWRRQTVKPIFCVCDCUGUKPOQUVIGQITCRJKECNCTGCU/QUVCTGCU

CTGNCEMKPIQTCTGFGHKEKGPVKPFCVCPGGFGFVQGUVKOCVGDQWPFCT[EQPFKVKQPUCPF


OGVGQTQNQIKECNCPFCKTSWCNKV[EQPFKVKQPUCNQHV6JGTGCTGHGYCTGCUYJGTG

URGEKCVGF81%EQPEGPVTCVKQPUCTGOGCUWTGFUWTHCEG01 FCVCOC[DG Z

KPCEEWTCVG9JGTGKORQTVCPVFCVCICRUGZKUVOQFGNKPICEEWTCE[UWHHGTUCPF

VJGRTQURGEVUHQTTGFWEKPIQTGNKOKPCVKPIVJGRTGUGPEGQHEQORGPUCVKPIGTTQTU

CTGFKOKPKUJGF

i %QPVKPWKPIPGGFHQTKORTQXGOGPVU'ZCORNGUKPENWFGVJGKPVTQFWEVKQPQH

RTQIPQUVKEOGVGQTQNQIKECNOQFGNKPIKPVJGOKFUVJGFKUEQXGT[QH

WPFGTGUVKOCVKQPQH81%GOKUUKQPUKPVJGNCVGUVJGKPENWUKQPQH01Z

GOKUUKQPUHTQOUQKNUKPCPFOCLQTCFLWUVOGPVQHVJGGOKUUKQPUTCVGUQH

KUQRTGPGKP

i 2TGUGPEGQHEQORGPUCVKPIGTTQTU+VCRRGCTUVJCVEQORGPUCVKPIGTTQTUJCXGDGGP

RTGUGPVKPOCP[RCUVCRRNKECVKQPUKPVTQFWEKPIVJGRQVGPVKCNHQTDKCUKPVQVJG

GUVKOCVKQPQHVJGKORCEVUQHGOKUUKQPUEQPVTQNUVTCVGIKGU

%QORCTKUQPQHOQFGNRTGFKEVKQPUCICKPUVQ\QPGOGCUWTGOGPVUCNVJQWIJPGEGUUCT[

KUPQVCTQDWUVVGUVQHCOQFGN	UCEEWTCE[+FGCNN[QPGUJQWNFGXCNWCVGRGTHQTOCPEGCICKPUV

OQTGGZVGPUKXGUGVUQHURGEKGUUWEJCUKPFKXKFWCN81%U01 CPF01 %QORGPUCVKPIGTTQTU Z [

KPKPRWVKPHQTOCVKQPVQCOQFGNCPFYKVJKPVJGOQFGNHQTOWNCVKQPECPECWUGCP1 OQFGNVQ 

IGPGTCVGEQTTGEV1 RTGFKEVKQPUHQTVJGYTQPITGCUQPU6JGTGHQTGOQFGNGXCNWCVKQPKPFKECVQTU 

CTGPGGFGFVQFGOQPUVTCVGVJGTGNKCDKNKV[QHCRTGFKEVKQPDGHQTGVJGOQFGNECPDGWUGF

GHHGEVKXGN[KPOCMKPIEQPVTQNUVTCVGI[FGEKUKQPU

+VKUKORQTVCPVVQUVTGUUVJCVKP1 OQFGNKPICOQFGNKPIU[UVGOCNUQKUCVKUUWGPQV 

LWUVVJGCKTSWCNKV[OQFGNKVUGNH6JGOQFGNKPIU[UVGOKPENWFGUCOGVGQTQNQIKECNOQFGNCP

GOKUUKQPUTGRTGUGPVCVKQPYJGTGCPGOKUUKQPUOQFGNKURTGHGTTGFVQVJGVTCFKVKQPCNKPXGPVQT[

CRRTQCEJVJGCKTSWCNKV[OQFGNCPFCEQORTGJGPUKXGUWRRQTVKPIFCVCDCUG9JGTGCRTQDNGO

GZKUVUVJGGPVKTGOQFGNKPIU[UVGOOWUVDGGXCNWCVGF

/QFGNUECPDGWUGFGHHGEVKXGN[KPCTGNCVKXGUGPUGVQTCPMFKHHGTGPVEQPVTQN

CNVGTPCVKXGUKPVGTOUQHVJGKTGHHGEVKXGPGUUKPTGFWEKPI1 CPFVQKPFKECVGVJGCRRTQZKOCVG 

OCIPKVWFGQHKORTQXGOGPVKPRGCM1 NGXGNUGZRGEVGFWPFGTXCTKQWUEQPVTQNUVTCVGIKGU6QFQ 

UQVJGTGOWUVDGCUQWPFGOKUUKQPUOQFGNCPFFCVCCPFCPCFGSWCVGFCVCDCUGQPYJKEJVQ

EQPUVTWEVVJGOQFGNKPI)TKFDCUGF1 CKTSWCNKV[OQFGNKPIKUUWRGTKQTVQVJGCXCKNCDNG 

CNVGTPCVKXGUHQT1 EQPVTQNRNCPPKPIDWVTGUWNVUECPDGOKUNGCFKPIKHVJGOQFGNKUPQVGXCNWCVGF 

UWHHKEKGPVN[6JGIQCNKUVQOKPKOK\GVJGEJCPEGUQHKPEQTTGEVWUGQHVJGOQFGN

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3.7.1 Tropospheric Ozone Chemistry

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EQORQWPFUKPCODKGPVCKTCPFYKVJKPTGCUQPCDNGEQPEGPVTCVKQPUTCPIGUVJGTCVKQDGVYGGPVJG

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6JGMG[TGCEVKXGURGEKGUKPVJGVTQRQURJGTGKUVJG1*TCFKECNYJKEJKUTGURQPUKDNG

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QPVJGTGRTGUGPVCVKQPQHOGEJCPKUOUKPOQFGNU

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RQNNWVCPVUNGCFVQVJGHQTOCVKQPQH1*TCFKECNUCPFQZKFK\GF81%TGCEVKQPRTQFWEVUVJCVCTGQH

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3.7.2 Meteorological Processess Influencing Ozone Formation and Transport

/GVGQTQNQIKECN2TQEGUUGU

6JGUWTHCEGGPGTI[TCFKCVKQPDWFIGVQHVJGGCTVJUVTQPIN[KPHNWGPEGUVJGF[PCOKEU

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UKIPKHKECPVN[QPVJGFGITGGQHOKZKPIVJCVQEEWTUDGVYGGPVJGVKOGCRQNNWVCPVQTKVU

RTGEWTUQTUKUGOKVVGFCPFVJGCTTKXCNQHVJGRQNNWVCPVCVVJGTGEGRVQT+PXGTUKQPNC[GTUNC[GTU

KPYJKEJVGORGTCVWTGKPETGCUGUYKVJJGKIJVCDQXGITQWPFNGXGNCTGRTQOKPGPVFGVGTOKPCPVUQH

VJGFGITGGQHCVOQURJGTKEXGTVKECNOKZKPICPFVJWUVJGFGITGGVQYJKEJ1 CPFQVJGT 

RQNNWVCPVUYKNNDGFKURGTUGFQTCEEWOWNCVG1\QPGNGHVKPCNC[GTCNQHVCUVJGTGUWNVQHTGFWEGF

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6I[GCT%QPKHGTQWUHQTGUVUCTGVJGNCTIGUVXGIGVCVKXGEQPVTKDWVQTQPCPCVKQPCNDCUKUDGECWUG

QHVJGKTGZVGPUKXGNCPFEQXGTCIG5WOOGTVKOGDKQIGPKEGOKUUKQPUEQORTKUGOQTGVJCPJCNHQH

VJGCPPWCNVQVCNUKPCNNTGIKQPUDGECWUGQHVJGKTFGRGPFGPEGQPVGORGTCVWTGCPFXGIGVCVKQPCN

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7PEGTVCKPVKGUKPDQVJDKQIGPKECPFCPVJTQRQIGPKE81%GOKUUKQPKPXGPVQTKGURTGXGPV

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0KVTQIGP1ZKFGU'OKUUKQPU

#PVJTQRQIGPKE01 KUCUUQEKCVGFYKVJEQODWUVKQPRTQEGUUGU6JGRTKOCT[RQNNWVCPV Z

GOKVVGFKU01HQTOGFCVJKIJEQODWUVKQPVGORGTCVWTGUHTQOVJGPKVTQIGPCPFQZ[IGPKPCKT

CPFHTQOPKVTQIGPKPEQODWUVKQPHWGN'OKUUKQPUQH01 KPKPVJG7PKVGF5VCVGUVQVCNGF Z

6I6JGVYQNCTIGUV01 GOKUUKQPUQWTEGUCTGGNGEVTKERQYGTIGPGTCVKQPRNCPVUCPF Z

JKIJYC[XGJKENGU'OKUUKQPUQH01 VJGTGHQTGCTGJKIJGUVKPCTGCUJCXKPICJKIJFGPUKV[QH Z

GNGEVTKERQYGTIGPGTCVKPIUVCVKQPUCPFKPWTDCPTGIKQPUJCXKPIJKIJVTCHHKEFGPUKVKGU$GVYGGP

CPFVTCPURQTVCVKQPTGNCVGFGOKUUKQPUTGOCKPGFGUUGPVKCNN[EQPUVCPVYJGTGCU

UVCVKQPCT[UQWTEG01 GOKUUKQPUKPETGCUGFCDQWVZ

0CVWTCN01 UQWTEGUKPENWFGUVTCVQURJGTKEKPVTWUKQPQEGCPUNKIJVPKPIUQKNUCPF Z

YKNFHKTGU.KIJVPKPICPFUQKNGOKUUKQPCTGVJGQPN[VYQUKIPKHKECPVPCVWTCNUQWTEGUQH01 KP Z

VJG7PKVGF5VCVGU6JGGUVKOCVGFCPPWCNNKIJVPKPIRTQFWEGF01 HQTVJGEQPVKPGPVCN7PKVGF Z

5VCVGUKU6ICDQWVQHYJKEJKUIGPGTCVGFQXGTVJGUQWVJGTPUVCVGU$QVJPKVTKH[KPI


CPFFGPKVTKH[KPIQTICPKUOUKPVJGUQKNECPRTQFWEG01 RTKPEKRCNN[01'OKUUKQPTCVGUFGRGPF Z

OCKPN[QPHGTVKNK\CVKQPNGXGNUCPFUQKNVGORGTCVWTG+PXGPVQT[KPIUQKN01 GOKUUKQPUKUFKHHKEWNV Z

DGECWUGQHNCTIGVGORQTCNCPFURCVKCNXCTKCDKNKV[DWVVJGPCVKQPYKFGVQVCNJCUDGGPGUVKOCVGFCV

6I[GCTQHYJKEJCDQWVKUGOKVVGFKPURTKPICPFUWOOGT#DQWVQHVJGVQVCN

UQKN01 KUGOKVVGFKPVJGCTGCQHVJGEQWPVT[EQPVCKPKPIVJGEGPVTCNEQTPDGNV Z

%QODKPGFPCVWTCNUQWTEGUEQPVTKDWVGCDQWV6IQH01 VQVJGVTQRQURJGTGQXGT Z

VJGEQPVKPGPVCN7PKVGF5VCVGU7PEGTVCKPVKGUKPPCVWTCN01 KPXGPVQTKGUCTGOWEJNCTIGTVJCP Z

VJCVHQTCPVJTQRQIGPKE01 GOKUUKQPU$GECWUGCNCTIGRTQRQTVKQPQHCPVJTQRQIGPKE01 Z Z

GOKUUKQPUEQOGHTQOFKUVKPEVRQKPVUQWTEGURWDNKUJGFCPPWCNGUVKOCVGUCTGVJQWIJVVQDGXGT[

TGNKCDNG

%QPEGPVTCVKQPUQH8QNCVKNG1TICPKE%QORQWPFUKP#ODKGPV#KT

6JG81%UOQUVHTGSWGPVN[CPCN[\GFKPCODKGPVCKTCTG0/*%U/QTPKPI

EQPEGPVTCVKQPUCOVQCOJCXGDGGPOGCUWTGFOQUVQHVGPDGECWUGQHVJGWUGQH

OQTPKPIFCVCKP'-/#CPFKPCKTSWCNKV[UKOWNCVKQPOQFGNU/CLQTHKGNFUVWFKGUKPEKVKGU

KPCPFKPEKVKGUKPRTQFWEGF0/*%OGCUWTGOGPVUVJCVUJQYGFOGFKCPXCNWGU

TCPIKPIHTQOVQRRO%HQTCPFVQRRO%KP1XGTCNNOGFKCP

XCNWGUHTQOCNNWTDCPUKVGUYGTGCDQWVRRO%KPCPFRRO%KP

%QORCTCVKXGFCVCQXGTVYQFGECFGUVJGUVJTQWIJVJGUKPVJG.QU

#PIGNGUCPF0GY;QTM%KV[CTGCUUJQYGFFGETGCUGUKP0/*%EQPEGPVTCVKQPUKPVJQUGCTGCU

%QPEQOKVCPVEQORQUKVKQPCNEJCPIGUYGTGQDUGTXGFQXGTVJGVYQFGECFGUYKVJKPETGCUGU

QDUGTXGFKPVJGRGTEGPVCIGQHCNMCPGUCPFFGETGCUGUKPVJGRGTEGPVCIGQHCTQOCVKE

J[FTQECTDQPUCPFCEGV[NGPG

%QPEWTTGPVOGCUWTGOGPVUQHCPVJTQRQIGPKECPFDKQIGPKE0/*%UJCXGUJQYPVJCV

DKQIGPKE0/*%UWUWCNN[EQPUVKVWVGFOWEJNGUUVJCPQHVJGVQVCN0/*%U(QTGZCORNG

CXGTCIGKUQRTGPGEQPEGPVTCVKQPUTCPIGFHTQOVQRRO%CPFVGTRGPGUHTQOVQ

RRO%

%QPEGPVTCVKQPUQH0KVTQIGP1ZKFGUKP#ODKGPV#KT

/GCUWTGOGPVUQH01 CVUKVGUKPCPF75EKVKGUKPCPF Z

TGURGEVKXGN[UJQYGFVJCVOGFKCP01 EQPEGPVTCVKQPUTCPIGFHTQOVQRROKPOQUVQH Z

VJGUGEKVKGU6JGCOVQCOOGFKCPEQPEGPVTCVKQPUKPOCP[QHVJGUGEKVKGUGZEGGFGFVJG

CPPWCNCXGTCIG01 XCNWGUQHVQRROHQWPFKP75OGVTQRQNKVCPCTGCUDGVYGGP Z

CPF0QPWTDCP01 EQPEGPVTCVKQPUTGRQTVGFCUCXGTCIGUGCUQPCNQTCPPWCN01  Z Z

TCPIGHTQOVQRRO

4CVKQUQHCOVQCO0/1%VQ01 CTGJKIJGTKPUQWVJGCUVGTPCPF Z

UQWVJYGUVGTP75EKVKGUVJCPKPPQTVJGCUVGTPCPFOKFYGUVGTP75EKVKGUCEEQTFKPIVQFCVC

HTQO'2#	UOWNVKEKV[UVWFKGUEQPFWEVGFKPCPF/GFKCPTCVKQUTCPIGFHTQOVQ

KPKPOGFKCPTCVKQUTCPIGFHTQOVQKPVJGEKVKGUUVWFKGF4WTCN

0/1%01 TCVKQUVGPFVQDGJKIJGTVJCPWTDCPTCVKQU/QTPKPICOVQCO Z

0/1%01 TCVKQUCTGWUGFKPVJG'-/#V[RGQHVTCLGEVQT[OQFGN6TGPFUHTQOVQ Z

UJQYFGETGCUGUKPVJGUGTCVKQUKPVJG5QWVJ%QCUV#KT$CUKPQH%CNKHQTPKC6JGEQTTGNCVKQPQH

0/1%01 TCVKQUYKVJOCZKOWOJ1 EQPEGPVTCVKQPUJQYGXGTYCUYGCMKPCTGEGPV Z 

CPCN[UKU


 4CVKQUQH%QPEGPVTCVKQPUQH0QPOGVJCPG1TICPKE%QORQWPFUVQ0KVTQIGP

1ZKFGU

6JGTCVKQUQH0/1%01 XCT[UWDUVCPVKCNN[DGVYGGPEKVKGUCPFYKVJKPCIKXGPEKV[Z

9KVJEGTVCKPGZEGRVKQPUWTDCP0/1%01 TCVKQUJCXGDGGPKPVJGTCPIGQHCPFDGNQYZ

+PEQPVTCUVTCVKQUQH0/1%01 KPTWTCNCTGCUVGPFVQGSWCNQTGZEGGF&KUETGRCPEKGU Z

JCXGDGGPHQWPFDGVYGGPCODKGPV0/1%01 TCVKQUCPFGOKUUKQPKPXGPVQT[0/1%01 Z Z

TCVKQUYKVJCODKGPVTCVKQUQH0/1%01 UKIPKHKECPVN[GZEGGFKPIGOKUUKQPTCVKQUQH Z

0/1%01 Z

6TGPFUKPTCVKQUQH0/1%01 JCXGUJQYPFQYPYCTFVTGPFUVQYGNNDGNQY Z

FWTKPIVJGUDQVJHQTVJG5QWVJ%QCUV#KT$CUKPCPFHQTEKVKGUKPVJGGCUVGTP7PKVGF

5VCVGU$CUGFQPVJGUGNQYTCVKQUJ[FTQECTDQPEQPVTQNUJQWNFDGOQTGGHHGEVKXGVJCP01Z

EQPVTQNYKVJKPCPWODGTQHEKVKGU

5QWTEG#RRQTVKQPOGPVCPF4GEQPEKNKCVKQP

5QWTEGCRRQTVKQPOGPVPQYTGICTFGFCUU[PQP[OQWUYKVJTGEGRVQTOQFGNKPITGHGTU

VQFGVGTOKPKPIVJGSWCPVKVCVKXGEQPVTKDWVKQPUQHXCTKQWUUQWTEGUQH81%UVQCODKGPVCKT

RQNNWVCPVEQPEGPVTCVKQPU5QWTEGTGEQPEKNKCVKQPTGHGTUVQVJGEQORCTKUQPQHOGCUWTGFCODKGPV

81%EQPEGPVTCVKQPUYKVJGOKUUKQPUKPXGPVQT[GUVKOCVGUQH81%UQWTEGGOKUUKQPTCVGUHQTVJG

RWTRQUGQHXCNKFCVKPIVJGKPXGPVQTKGU

'CTN[UVWFKGUKP.QU#PIGNGUGORNQ[KPICOCUUDCNCPEGCRRTQCEJVQTGEGRVQT

OQFGNKPIUJQYGFVJGHQNNQYKPIGUVKOCVGFEQPVTKDWVKQPUQHTGURGEVKXGUQWTEGUVQCODKGPVCKT

EQPEGPVTCVKQPUQH0/1%UVJTQWIJ% CWVQOQVKXGGZJCWUVYJQNGICUQNKPG 

GXCRQTCVKQPICUQNKPGJGCFURCEGXCRQTEQOOGTEKCNPCVWTCNICUIGQIGPKE

PCVWTCNICUCPFNKSWGHKGFPCVWTCNICU4GEGPVUVWFKGUKPGKIJV75EKVKGUUJQYGF

VJCVXGJKENGGZJCWUVYCUVJGFQOKPCPVEQPVTKDWVQTVQCODKGPV81%UGZEGRVKP$GCWOQPV

YJGTGYCUTGRQTVGF'UVKOCVGUQHVJGEQPVTKDWVKQPUQHICUQNKPGGXCRQTCVKQPFKHHGTKP

OGVJQFQNQI[VJGOQTGCRRTQRTKCVGOGVJQFUWUGFTGUWNVKPGUVKOCVGUQHNCTIGYJQNGICUQNKPG

EQPVTKDWVKQPUKGGSWCNVQXGJKENGGZJCWUVKPQPGUVWF[CPFQHXGJKENGGZJCWUVKPC

UGEQPFUVWF[

6JGEJGOKECNOCUUDCNCPEGCRRTQCEJWUGFHQTGUVKOCVKPICPVJTQRQIGPKE81%

EQPVTKDWVKQPUVQCODKGPVCKTECPPQVDGWUGFHQTTGEGRVQTOQFGNKPIQHDKQIGPKEUQWTEGU

#OQFKHKGFCRRTQCEJCRRNKGFVQFCVCHTQOCFQYPVQYPUKVGKP#VNCPVCKPFKECVGFCNQYGT

NKOKVQHJCXGTCIGHQTVJGDKQIGPKERGTEGPVCIGQHVQVCNCODKGPV81%UCVVJCVNQECVKQP


KUQRTGPGYCUWUGFCUVJGDKQIGPKEKPFKECVQTURGEKGU6JGRGTEGPVCIGXCTKGUFWTKPIVJGJ

RGTKQFDGECWUGQHVJGFKWTPCNGIVGORGTCVWTGNKIJVKPVGPUKV[FGRGPFGPEGQHKUQRTGPG

EQPEGPVTCVKQPU

5QWTEGTGEQPEKNKCVKQPFCVCJCXGUJQYPFKURCTKVKGUDGVYGGPGOKUUKQPKPXGPVQT[

GUVKOCVGUCPFTGEGRVQTGUVKOCVGFEQPVTKDWVKQPU(QTDKQIGPKEUGOKUUKQPGUVKOCVGUCTGITGCVGT

VJCPTGEGRVQTGUVKOCVGFEQPVTKDWVKQPU6JGTGXGTUGJCUDGGPVTWGHQTPCVWTCNICUEQPVTKDWVKQPU

GUVKOCVGFHQT.QU#PIGNGU%QNWODWUCPF#VNCPVCCPFHQTTGHKPGT[GOKUUKQPUKP%JKECIQ

3.7.4 Analytical Methods for Oxidants and Their Precursors

1ZKFCPVU

%WTTGPVOGVJQFUWUGFVQOGCUWTG1 CTG%.78CDUQTRVKQPURGEVTQOGVT[CPF 

PGYN[FGXGNQRGFURGEVTQUEQRKECPFEJGOKECNCRRTQCEJGUKPENWFKPIEJGOKECNCRRTQCEJGU

CRRNKGFVQRCUUKXGUCORNKPIFGXKEGUHQT1 


6JG%.OGVJQFFGUKIPCVGFCUVJGTGHGTGPEGOGVJQFD['2#KPXQNXGUVJGFKTGEV

ICURJCUGTGCEVKQPQH1 YKVJCPCNMGPG% * VQRTQFWEGGNGEVTQPKECNN[GZEKVGFRTQFWEVU  

YJKEJFGEC[YKVJVJGGOKUUKQPQHNKIJV&GVGEVKQPNKOKVUQHRROCPFCTGURQPUGVKOGQH

NGUUVJCPUCTGV[RKECNQHEWTTGPVN[CXCKNCDNGEQOOGTEKCNKPUVTWOGPVU#RQUKVKXG

KPVGTHGTGPEGHTQOCVOQURJGTKEYCVGTXCRQTYCUTGRQTVGFKPVJGUCPFJCUTGEGPVN[DGGP

EQPHKTOGF2TQRGTECNKDTCVKQPECPOKPKOK\GVJKUUQWTEGQHGTTQT

%QOOGTEKCN78RJQVQOGVGTUHQTOGCUWTKPI1 JCXGFGVGEVKQPNKOKVUQHCDQWV 

RRONQPIVGTORTGEKUKQPYKVJKPCDQWVvCPFCTGURQPUGVKOGQHOKP1\QPG

JCUCHCKTN[UVTQPICDUQTRVKQPDCPFYKVJCOCZKOWOPGCTPOKVUOQNCTCDUQTRVKQP

EQGHHKEKGPVCVVJCVYCXGNGPIVJKUYGNNMPQYP$GECWUGVJGOGCUWTGOGPVKUCDUQNWVG78

RJQVQOGVT[CNUQKUWUGFVQECNKDTCVGQVJGT1 OGVJQFU 

#RQVGPVKCNFKUCFXCPVCIGQH78RJQVQOGVT[KUVJCVCVOQURJGTKEEQPUVKVWGPVUVJCV

CDUQTDPOTCFKCVKQPCPFVJCVCTGTGOQXGFHWNN[QTRCTVKCNN[D[VJG/P1 UETWDDGTWUGFKP 

781 RJQVQOGVGTUYKNNDGRQUKVKXGKPVGTHGTGPEGUKP1 OGCUWTGOGPVU+PVGTHGTGPEGUJCXG  

DGGPTGRQTVGFKPVYQTGEGPVUVWFKGUDWVCUUGUUOGPVQHVJGRQVGPVKCNKORQTVCPEGQHUWEJ

KPVGTHGTGPEGUGIVQNWGPGUV[TGPGETGUQNUPKVTQETGUQNUKUJKPFGTGFD[NCEMQHCDUQTRVKQP

URGEVTCFCVCKPVJGPOTCPIGCPFD[NCEMQHCODKGPVOGCUWTGOGPVUQHOQUVQHVJGCTQOCVKE

RJQVQEJGOKECNTGCEVKQPRTQFWEVU#PKPVGTHGTGPEGHTQOYCVGTCNUQCRRGCTUVQQEEWTHTQO

EQPFGPUCVKQPQHOQKUVWTGKPUCORNKPINGXGN4GUWNVUHTQOEQNNQECVGF78CPF%.KPUVTWOGPVU

KPFKECVGFRQUKVKXGDKCUGUKPVJG78FCVCQHVQRRDQPJQVJWOKFFC[U

&KHHGTGPVKCNQRVKECNCDUQTRVKQPURGEVTQOGVT[JCUDGGPWUGFVQOGCUWTGCODKGPV1 

DWVHWTVJGTKPVGTEQORCTKUQPUYKVJQVJGTOGVJQFUCPFKPVGTHGTGPEGVGUVUCTGTGEQOOGPFGF

2CUUKXGUCORNKPIFGXKEGURGTOKVCESWKUKVKQPQHRGTUQPCNJWOCPGZRQUWTGFCVCCPFQH

1 OQPKVQTKPIFCVCKPCTGCUYJGTGVJGWUGQHKPUVTWOGPVCNOGVJQFUKUPQVHGCUKDNG6JTGG25&U 

CTGEQOOGTEKCNN[CXCKNCDNGCNNGORNQ[UQNKFCDUQTDGPVUVJCVTGCEVYKVJ1 

%CNKDTCVKQPQH1 OGCUWTGOGPVOGVJQFUQVJGTVJCP25&UKUFQPGD[78 

URGEVTQOGVT[QTD[)26QH1 YKVJ017NVTCXKQNGVRJQVQOGVT[KUVJGTGHGTGPEGECNKDTCVKQP 

OGVJQFCRRTQXGFD['2#1\QPGKUWPUVCDNGCPFOWUVDGIGPGTCVGFKPUKVWCVVKOGQHWUGVQ

RTQFWEGECNKDTCVKQPOKZVWTGU

6YQOGVJQFUIGPGTCNN[JCXGDGGPGORNQ[GFVQOGCUWTGCVOQURJGTKE2#0CPFKVU

JKIJGTJQOQNQIWGU+4CPF)%WUKPICP'%&#VJKTFOGVJQFNGUUQHVGPWUGFEQWRNGU)%

YKVJCOQN[DFGPWOEQPXGTVGTVJCVTGFWEGU2#0VQ01KPVJGICURJCUGCPFUWDUGSWGPVN[

OGCUWTGUVJG01YKVJC%.CPCN[\GT2GTQZ[CEGV[NPKVTCVGCPFVJGJKIJGT2#0UCTGPQTOCNN[

OGCUWTGFD[)%'%&&GVGEVKQPNKOKVUJCXGDGGPGZVGPFGFVQVQRRVWUKPIET[QIGPKE

GPTKEJOGPVQHUCORNGUCPFURGEKHKGFFGUQTRVKQPRTQEGFWTGUVJCVNKOKVNQUUGUCUUQEKCVGFYKVJ

ET[QUCORNKPI$GECWUG2#0KUWPUVCDNGGZRNQUKXGCPFUWDLGEVVQUWTHCEGTGNCVGF

FGEQORQUKVKQPVJGRTGRCTCVKQPQHTGNKCDNGECNKDTCVKQPUVCPFCTFUKUFKHHKEWNV/GVJQFUFGXKUGF

VQIGPGTCVGECNKDTCVKQPUVCPFCTFUKPENWFGRJQVQN[UKUQHUVCVKEEQPEGPVTCVKQPUQHICUGUPKVTCVKQP

QHRGTCEGVKECEKFKPUKPINGJ[FTQECTDQPUCPFCPCN[UKUQH2#0CU01WPFGTURGEKHKGF

EQPFKVKQPUQHVJGFKUUQEKCVKQPQH2#0KPVQKVURTGEWTUQTU

'CTN[OGCUWTGOGPVUQHVQRRD* 1 TGRQTVGFKPVJGUJCXGDGGPHQWPFVQ  

DGKPGTTQTDGECWUGQHCTVKHCEVHQTOCVKQPQH* 1 HTQOTGCEVKQPUQHCDUQTDGFICUGQWU1  

/QFGNKPITGUWNVUCNUQKPFKECVGVJCVNQYGTNGXGNUQH* 1 QPVJGQTFGTQHRRDQEEWTKPVJG  

CVOQURJGTG

+PUKVWOGCUWTGOGPVOGVJQFUHQT* 1 KPENWFG(6+4CPF6&.#56JG(6+4  

OGVJQFKUURGEKHKEHQT* 1 DWVJCUCJKIJFGVGEVKQPNGXGNQH  RRDWUKPICMORCVJ


NGPIVJ6JG6&.#5OGVJQFCNUQKUURGEKHKECPFJCUCFGVGEVKQPNGXGNQHRRDQXGT

CXGTCIKPIVKOGUQHUGXGTCNOKPWVGU(QWTHTGSWGPVN[WUGFYGVEJGOKECNOGVJQFUHQT

OGCUWTGOGPVQH* 1 CTGCXCKNCDNG#NNKPXQNXGVJGQZKFCVKQPQHCUWDUVTCVGHQNNQYGFD[  

KPUVTWOGPVCNFGVGEVKQPCPFSWCPVKHKECVKQPQHVJGTGUWNVKPI%.QTHNWQTGUEGPEG&GVGEVKQPNKOKVU

CTGEQORCTCDNGVQVJQUGQH(6+4CPF6&.#5DWVKPVGTHGTGPEGUCTGEQOOQPCPFOWUVDG

QDXKCVGFQTOKPKOK\GFYKVJURGEKHKGFRTQEGFWTGU

%CNKDTCVKQPQHOGVJQFUHQTICUGQWU* 1 OGCUWTGOGPVTGSWKTGUVJGKOOGFKCVGWUG  

QHUVCPFCTFOKZVWTGURTGRCTGFD[QPGQHUGXGTCNYGVEJGOKECNOGVJQFU

8QNCVKNG1TICPKE%QORQWPFU

+PETGCUGFOQPKVQTKPIQH81%UKUTGSWKTGFWPFGT6KVNG+5GEVKQPQHVJG%###

QHDGECWUGQHVJGTQNGQH81%UCURTGEWTUQTUVQVJGHQTOCVKQPQH1 CPFQVJGT 

RJQVQEJGOKECNQZKFCPVU8QNCVKNGQTICPKEEQORQWPFUCTGVJQUGICUGQWUQTICPKEEQORQWPFU

VJCVJCXGCXCRQTRTGUUWTGITGCVGTVJCPOOCPFIGPGTCNN[JCXGCECTDQPEQPVGPVTCPIKPI

HTQO% VJTQWIJ%  

6TCFKVKQPCNN[0/*%UJCXGDGGPOGCUWTGFD[OGVJQFUVJCVGORNQ[C(+&CUVJG

UGPUKPIGNGOGPVVJCVOGCUWTGUCEJCPIGKPKQPKPVGPUKV[TGUWNVKPIHTQOVJGEQODWUVKQPQHCKT

EQPVCKPKPIQTICPKEEQORQWPFU6JGOGVJQFTGEQOOGPFGFD['2#HQTVQVCN0/1%

OGCUWTGOGPVKPXQNXGUVJGET[QIGPKERTGEQPEGPVTCVKQPQHPQPOGVJCPGQTICPKEEQORQWPFUCPF

VJGOGCUWTGOGPVQHVJGTGXQNCVKNK\GF0/1%UWUKPI(+&6JGOCKPVGEJPKSWGHQTURGEKCVGF

0/1%0/*%OGCUWTGOGPVUKUET[QIGPKERTGEQPEGPVTCVKQPHQNNQYGFD[)%(+&5[UVGOU

HQTUCORNKPICPFCPCN[UKUQH81%UJCXGDGGPFGXGNQRGFVJCVTGSWKTGPQNKSWKFET[QIGPHQT

QRGTCVKQP[GVRTQXKFGUWHHKEKGPVTGUQNWVKQPQHURGEKGU

5VCKPNGUUUVGGNECPKUVGTUJCXGDGEQOGVJGEQPVCKPGTUQHEJQKEGHQTEQNNGEVKQPQH

YJQNGCKTUCORNGUHQT0/*%0/1%FCVC%CNKDTCVKQPRTQEGFWTGUHQT0/1%

KPUVTWOGPVCVKQPTGSWKTGVJGIGPGTCVKQPD[UVCVKEQTF[PCOKEU[UVGOUQHFKNWVGOKZVWTGUCV

EQPEGPVTCVKQPUGZRGEVGFVQQEEWTKPCODKGPVCKT

2TGHGTTGFOGVJQFUHQTOGCUWTKPIECTDQP[NURGEKGUCNFGJ[FGUCPFMGVQPGUKP

CODKGPVCKTCTGURGEVTQUEQRKEOGVJQFUQPNKPGEQNQTKOGVTKEOGVJQFUCPF*2.%OGVJQF

GORNQ[KPI&02*FGTKXCVK\CVKQPKPCUKNKECIGNECTVTKFIG6JGOQUVEQOOQPOGVJQFKPEWTTGPV

WUGHQTOGCUWTKPICNFGJ[FGUKPCODKGPVCKTKUVJG*2.%&02*OGVJQF7UGQHCP1 UETWDDGT 

JCUDGGPTGEQOOGPFGFVQRTGXGPVKPVGTHGTGPEGKPVJKUOGVJQFD[1 KPCODKGPVCKT%CTDQP[N 

URGEKGUCTGTGCEVKXGOCMKPIRTGRCTCVKQPQHUVCDNGECNKDTCVKQPOKZVWTGUFKHHKEWNVDWVUGXGTCN

OGVJQFUCTGCXCKNCDNG

+ORGVWUHQTVJGFGXGNQROGPVQHOGVJQFUHQTOGCUWTKPIVJGOQTGTGCEVKXGQZ[IGP

CPFPKVTQIGPEQPVCKPKPIQTICPKEEQORQWPFUJCUEQOGHTQOVJGKTTQNGUCURTGEWTUQTUQT

RTQFWEVUQHRJQVQEJGOKECNQZKFCVKQPCPFCNUQHTQOVJGKPENWUKQPQHOCP[QHVJGUGEQORQWPFU

QPVJGNKUVQHJC\CTFQWUCKTRQNNWVCPVUKPVJG%###/GCUWTGOGPVQHVJGUG281%UKU

FKHHKEWNVDGECWUGQHVJGKTTGCEVKXKV[CPFYCVGTUQNWDKNKV[/GVJQFUCTGUVKNNKPFGXGNQROGPV

1ZKFGUQH0KVTQIGP

0KVTKEQZKFGCPF01 EQORTKUGVJG01 KPXQNXGFCURTGEWTUQTUVQ1 CPFQVJGT Z 

RJQVQEJGOKECNQZKFCPVU

6JGOQUVEQOOQPOGVJQFQH01OGCUWTGOGPVKUVJGICURJCUG%.TGCEVKQPYKVJ

1 6JG%.OGVJQFKUGUUGPVKCNN[URGEKHKEHQT01%QOOGTEKCN01OQPKVQTUJCXGFGVGEVKQP 

NKOKVUQHCHGYRCTVURGTDKNNKQPD[XQNWOGKPCODKGPVCKT%QOOGTEKCN01CPCN[\GTUOC[PQV


JCXGUGPUKVKXKV[UWHHKEKGPVHQTUWTHCEGOGCUWTGOGPVUKPTWTCNQTTGOQVGCTGCUQTHQTCKTDQTPG

OGCUWTGOGPVU&KTGEVURGEVTQUEQRKEOGVJQFUHQT01GZKUVVJCVJCXGXGT[JKIJUGPUKVKXKV[CPF

UGNGEVKXKV[HQT01/CLQTFTCYDCEMUQHVJGUGOGVJQFUCTGVJGKTEQORNGZKV[UK\GCPFEQUV

YJKEJTGUVTKEVVJGUGOGVJQFUVQTGUGCTEJCRRNKECVKQPU0Q25&UGZKUVHQTOGCUWTGOGPVQH01

%JGOKNWOKPGUEGPEGCPCN[\GTUCTGVJGOGVJQFQHEJQKEGHQT01 OGCUWTGOGPVGXGP 

VJQWIJVJG[FQPQVOGCUWTG01 FKTGEVN[/KPKOWOFGVGEVKQPNGXGNUHQT01 JCXGDGGP  

TGRQTVGFVQDGVQRRDDWVOQTGTGEGPVGXCNWCVKQPUJCXGKPFKECVGFFGVGEVKQPNKOKVUQHVQ

RRDX4GFWEVKQPQH01 VQ01KUTGSWKTGFHQTOGCUWTGOGPV+PRTCEVKEGUGNGEVKXG 

OGCUWTGOGPVQH01 D[VJKUCRRTQCEJJCURTQXGFFKHHKEWNV%QOOGTEKCNKPUVTWOGPVUVJCVWUG Z

JGCVGFECVCN[VKEEQPXGTVGTUVQTGFWEG01 VQ01OGCUWTGPQV01CPF01 DWVOQTGPGCTN[  Z

01CPFVQVCN01 6JWUVJG01 XCNWGKPHGTTGFHTQOUWEJOGCUWTGOGPVUOC[DG [ 

UKIPKHKECPVN[KPGTTQTYJKEJOC[KPVWTPCHHGEVVJGTGUWNVUQHOQFGNKPIQHCODKGPV1 

5GXGTCNURGEVTQUEQRKECRRTQCEJGUVQ01 FGVGEVKQPJCXGDGGPFGXGNQRGF#UPQVGF 

CDQXGHQT01JQYGXGTVJGUGOGVJQFUJCXGOCLQTFTCYDCEMUVJCVKPENWFGVJGKTEQORNGZKV[

UK\GCPFEQUVYJKEJCVRTGUGPVQWVYGKIJVJGCFXCPVCIGUQHVJGKTUGPUKVKXKV[CPFUGNGEVKXKV[

2CUUKXGUCORNGTUHQT01 GZKUVDWVCTGUVKNNKPVJGFGXGNQROGPVCNUVCIGHQTCODKGPVCKT 

OQPKVQTKPI

%CNKDTCVKQPQHOGVJQFUHQT01OGCUWTGOGPVKUFQPGWUKPIUVCPFCTFE[NKPFGTUQH01

KPPKVTQIGP%CNKDTCVKQPQHOGVJQFUHQT01 OGCUWTGOGPVKPENWFGVJGWUGQHE[NKPFGTUQH01  

KPPKVTQIGPQTCKTVJGWUGQHRGTOGCVKQPVWDGUCPFICURJCUGVKVTCVKQP

3.7.5 Ozone Air Quality Models

&GHKPKVKQPU&GUETKRVKQPUCPF7UGU

2JQVQEJGOKECNCKTSWCNKV[OQFGNUCTGWUGFVQRTGFKEVJQY1 EQPEGPVTCVKQPUEJCPIG 

KPTGURQPUGVQRTGUETKDGFEJCPIGUKPUQWTEGGOKUUKQPUQH01 CPF81%U6JG[CTG Z

OCVJGOCVKECNFGUETKRVKQPUQHVJGCVOQURJGTKEVTCPURQTVFKHHWUKQPTGOQXCNCPFEJGOKECN

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VQRQITCRJ[CPFOGVGQTQNQI[QHCTGIKQPCPFRTQFWEGQWVRWVUVJCVFGUETKDGCKTSWCNKV[KPVJCV

TGIKQP

6YQMKPFUQHRJQVQEJGOKECNOQFGNUCTGTGEQOOGPFGFKPIWKFGNKPGUKUUWGFD['2#


VJGITKFDCUGF7#/KUTGEQOOGPFGFHQTOQFGNKPI1 QXGTWTDCPCTGCUCPF'-/#KU 

KFGPVKHKGFCUCPCEEGRVCDNGCRRTQCEJWPFGTEGTVCKPEKTEWOUVCPEGU6JG%###OCPFCVG

VJGWUGQHVJTGGFKOGPUKQPCNITKFDCUGFCKTSWCNKV[OQFGNUUWEJCU7#/KPFGXGNQRKPI5+2U

HQTCTGCUFGUKIPCVGFCUGZVTGOGUGXGTGUGTKQWUQTOWNVKUVCVGOQFGTCVG

+PITKFDCUGFCKTSWCNKV[OQFGNUVJGTGIKQPVQDGOQFGNGFVJGOQFGNKPIFQOCKPKU

UWDFKXKFGFKPVQCVJTGGFKOGPUKQPCNCTTC[QHITKFEGNNU2GTVKPGPVCVOQURJGTKERTQEGUUGUCPF

EJGOKECNTGCEVKQPUCTGTGRTGUGPVGFHQTGCEJEGNN

+PVTCLGEVQT[OQFGNUUWEJCU'-/#CJ[RQVJGVKECNCKTRCTEGNOQXGUVJTQWIJVJG

CTGCQHKPVGTGUVCNQPICRCVJECNEWNCVGFHTQOYKPFVTCLGEVQTKGU'OKUUKQPUCTGKPLGEVGFKPVQVJG

CKTRCTEGNCPFWPFGTIQXGTVKECNOKZKPICPFEJGOKECNVTCPUHQTOCVKQPU6TCLGEVQT[OQFGNU

RTQXKFGCF[PCOKEFGUETKRVKQPQHCVOQURJGTKEUQWTEGTGEGRVQTTGNCVKQPUJKRUVJCVKUUKORNGTCPF

NGUUGZRGPUKXGVQFGTKXGVJCPVJCVQDVCKPGFHTQOITKFOQFGNUDWVOGVGTQNQIKECNRTQEGUUGUCTG

JKIJN[UKORNKHKGFKPVTCLGEVQT[OQFGNU

6JG'-/#DCUGFOGVJQFHQTFGVGTOKPKPI1 EQPVTQNUVTCVGIKGUJCUUQOG 

NKOKVCVKQPUVJGOQUVUGTKQWUQHYJKEJKUVJCVRTGFKEVGFGOKUUKQPUTGFWEVKQPUCTGETKVKECNN[

FGRGPFGPVQPVJGKPKVKCN0/*%01 TCVKQWUGFKPVJGECNEWNCVKQPU6JKUTCVKQECPPQVDG Z


FGVGTOKPGFYKVJCP[EGTVCKPV[DGECWUGKVKUGZRGEVGFVQDGSWKVGXCTKCDNGKPVKOGCPFURCEGKP

CPWTDCPCTGC)TKFDCUGFOQFGNUJCXGVJGKTNKOKVCVKQPUCUYGNN6JGUGCTGRQKPVGFQWV

UWDUGSWGPVN[

/QFGN%QORQPGPVU

5RCVKCNCPFVGORQTCNEJCTCEVGTKUVKEUQH81%CPF01 GOKUUKQPUCTGOCLQTKPRWVUVQ Z

CRJQVQEJGOKECNCKTSWCNKV[OQFGN)TGCVGTCEEWTCE[KPGOKUUKQPUKPXGPVQTKGUKUPGGFGFHQT

DKQIGPKEUCPFHQTDQVJOQDKNGCPFUVCVKQPCT[UQWTEGEQORQPGPVU)TKFDCUGFCKTSWCNKV[

OQFGNUCNUQTGSWKTGCUKPRWVVJGVJTGGFKOGPUKQPCNYKPFHKGNFHQTVJGRJQVQEJGOKECNGRKUQFG

DGKPIUKOWNCVGF6JKUKPRWVKUUWRRNKGFD[OGVGQTQNQIKECNOQFWNGUYJKEJHCNNKPVQQPGQH

HQWTECVGIQTKGUQDLGEVKXGCPCN[UKURTQEGFWTGUFKCIPQUVKEOGVJQFUF[PCOKEQT

RTQIPQUVKEOGVJQFUCPFJ[DTKFOGVJQFUVJCVGODQF[GNGOGPVUHTQODQVJFKCIPQUVKECPF

RTQIPQUVKECRRTQCEJGU2TQIPQUVKEOQFGNUCTGDGNKGXGFVQRTQXKFGCF[PCOKECNN[EQPUKUVGPV

RJ[UKECNN[TGCNKUVKEVJTGGFKOGPUKQPCNTGRTGUGPVCVKQPQHVJGYKPFCPFQVJGTOGVGQTQNQIKECN

XCTKCDNGUCVUECNGUQHOQVKQPPQVTGUQNXCDNGD[CXCKNCDNGQDUGTXCVKQPU1WVRWVUQHRTQIPQUVKE

OQFGNUFQPQVCNYC[UCITGGYKVJQDUGTXCVKQPCNFCVCDWVOGVJQFUJCXGDGGPFGXKUGFVQOKVKICVG

VJGUGRTQDNGOU

#EJGOKECNMKPGVKEOGEJCPKUOCUGVQHEJGOKECNTGCEVKQPUTGRTGUGPVKPIVJG

KORQTVCPVTGCEVKQPUVJCVQEEWTKPVJGCVOQURJGTGKUWUGFKPCPCKTSWCNKV[OQFGNVQGUVKOCVGVJG

PGVTCVGQHHQTOCVKQPQHGCEJRQNNWVCPVUKOWNCVGFCUCHWPEVKQPQHVKOG%JGOKECNOGEJCPKUOU

VJCVGZRNKEKVN[VTGCVGCEJKPFKXKFWCN81%EQORQPGPVQHCODKGPVCKTCTGVQQNGPIVJ[VQDG

KPEQTRQTCVGFKPVQVJTGGFKOGPUKQPCNCVOQURJGTKEOQFGNU.WORGFOGEJCPKUOUCTGVJGTGHQTG

WUGF6JGEJGOKECNOGEJCPKUOUWUGFKPGZKUVKPIRJQVQEJGOKECN1 OQFGNUEQPVCKP 

WPEGTVCKPVKGUVJCVOC[NKOKVVJGCEEWTCE[QHVJGKTRTGFKEVKQPU$GECWUGQHFKHHGTGPVCRRTQCEJGU

VQNWORKPIQHTGCEVKQPUOQFGNUECPRTQFWEGUQOGYJCVFKHHGTGPVTGUWNVUWPFGTUKOKNCT

EQPFKVKQPU$QVJVJG7#/7#/+8CPF'2#	U41/WUGVJG%/$+86JG%$/+8CPF

VJG5#24%CPF4#&/OGEJCPKUOUCTGEQPUKFGTGFVQTGRTGUGPVVJGUVCVGQHVJGUEKGPEG

&T[FGRQUKVKQPVJGTGOQXCNQHEJGOKECNURGEKGUHTQOVJGCVOQURJGTGD[KPVGTCEVKQP

YKVJITQWPFNGXGNUWTHCEGUKUCPKORQTVCPVTGOQXCNRTQEGUUHQT1 QPDQVJWTDCPCPFTGIKQPCN 

UECNGUCPFKUKPENWFGFKPCNNWTDCPCPFTGIKQPCNUECNGOQFGNU9GVFGRQUKVKQPVJGTGOQXCNQH

ICUGUCPFRCTVKENGUHTQOVJGCVOQURJGTGD[RTGEKRKVCVKQPGXGPVUIGPGTCNN[KUPQVKPENWFGFKP

WTDCPUECNGRJQVQEJGOKECNOQFGNUDGECWUG1 GRKUQFGUFQPQVQEEWTFWTKPIRGTKQFUQH 

UKIPKHKECPVENQWFUQTTCKP

%QPEGPVTCVKQPHKGNFUQHCNNURGEKGUEQORWVGFD[VJGOQFGNOWUVDGURGEKHKGFCVVJG

DGIKPPKPIQHVJGUKOWNCVKQPVJGUGEQPEGPVTCVKQPHKGNFUCTGECNNGFVJGKPKVKCNEQPFKVKQPU6JGUG

KPKVKCNEQPFKVKQPUCTGFGVGTOKPGFOCKPN[YKVJCODKGPVOGCUWTGOGPVUGKVJGTHTQOTQWVKPGN[

EQNNGEVGFFCVCQTHTQOURGEKCNUVWFKGUDWVKPVGTRQNCVKQPECPDGWUGFVQFKUVTKDWVGVJGUWTHCEG

CODKGPVOGCUWTGOGPVU

'XCNWCVKQPQH/QFGN2GTHQTOCPEG

#KTSWCNKV[OQFGNUCTGGXCNWCVGFD[EQORCTKPIVJGKTRTGFKEVKQPUYKVJCODKGPV

QDUGTXCVKQPU#PCFGSWCVGOQFGNUJQWNFIKXGCEEWTCVGRTGFKEVKQPUQHEWTTGPVRGCM

1 EQPEGPVTCVKQPUCPFVGORQTCNCPFURCVKCN1 RCVVGTPU+VCNUQUJQWNFTGURQPFCEEWTCVGN[VQ  

EJCPIGUKP81%CPF01 GOKUUKQPUVQFKHHGTGPEGUKP81%TGCEVKXKV[CPFVQURCVKCNCPF Z

VGORQTCNEJCPIGUKPGOKUUKQPURCVVGTPUHQTHWVWTG[GCTU.KMGYKUGOWNVKURGEKGUEQORCTKUQPU

EQWNFDGVJGMG[KPFKUETKOKPCVKPICOQPICNVGTPCVKXGOQFGNKPICRRTQCEJGUVJCVRTQXKFGUKOKNCT


RTGFKEVKQPUQH1 EQPEGPVTCVKQPU#FGSWCVGOQFGNRGTHQTOCPEGHQTUGXGTCNTGCEVKXGURGEKGU 

KPETGCUGUVJGCUUWTCPEGVJCVEQTTGEV1 RTGFKEVKQPUCTGPQVCTGUWNVQHEJCPEGQTHQTVWKVQWU 

ECPEGNNCVKQPQHGTTQTUKPVTQFWEGFD[XCTKQWUCUUWORVKQPU

+HQPN[CTQWVKPGFCVCDCUGKUCXCKNCDNGHQTOQFGNKPI1 KPCPWTDCPCTGCVJGPUGXGTCN 

EQPEGTPUTGSWKTGCVVGPVKQPTGNCVKXGVQOQFGNRGTHQTOCPEGGXCNWCVKQPCKTSWCNKV[CNQHV

DQWPFCT[EQPFKVKQPUCODKGPV81%FCVCCPFOGVGQTQNQIKECNFCVCCNQHV+HCP[QHVJGUGHQWT

CTGCUKUOKUUKPIHTQOVJGFCVCDCUGVJGRGTHQTOCPEGGXCNWCVKQPCPFUWDUGSWGPVOQFGN

CRRNKECVKQPOWUVDGCFGSWCVGN[RNCPPGFVQOKPKOK\GVJGRQUUKDKNKV[QHEQORGPUCVQT[GTTQTU

7UGQH1\QPG#KT3WCNKV[/QFGNHQT'XCNWCVKPI%QPVTQN5VTCVGIKGU

2JQVQEJGOKECNCKTSWCNKV[OQFGNUCTGWUGFHQTEQPVTQNUVTCVGI[GXCNWCVKQPD[HKTUV

FGOQPUVTCVKPIVJCVCRCUVGRKUQFGQTGRKUQFGUECPDGUKOWNCVGFCFGSWCVGN[CPFVJGPTGFWEKPI

J[FTQECTDQPQT01 GOKUUKQPUQTDQVJKPVJGOQFGNKPRWVUCPFCUUGUUKPIVJGGHHGEVUQHVJGUG Z

TGFWEVKQPUQP1 KPVJGTGIKQP6JGCFGSWCE[QHEQPVTQNUVTCVGIKGUDCUGFQPITKFDCUGFOQFGNU 

FGRGPFUKPRCTVQPVJGPCVWTGQHKPRWVFCVCHQTUKOWNCVKQPUCPFOQFGNXCNKFCVKQPQPKPRWV

GOKUUKQPUKPXGPVQT[FCVCCPFQPVJGTGNCVKQPUJKRDGVYGGPOQFGNQWVRWVCPFVJGEWTTGPVHQTO

QHVJG0##35HQT1 

)TKFDCUGFOQFGNUVJCVJCXGDGGPYKFGN[WUGFVQGXCNWCVGEQPVTQNUVTCVGIKGUHQT

1 QTCEKFFGRQUKVKQPQTDQVJCTGVJG7#/VJG%+6OQFGNVJG41/VJG#&1/CPFVJG 

4#&/

%QPENWUKQPU

7TDCPCKTSWCNKV[OQFGNUCTGDGEQOKPITGCFKN[CXCKNCDNGHQTCRRNKECVKQPCPFJCXG

DGGPCRRNKGFKPTGEGPV[GCTUKPUGXGTCNWTDCPCTGCU5KIPKHKECPVRTQITGUUCNUQJCUDGGPOCFGKP

VJGFGXGNQROGPVQHTGIKQPCNOQFGNUCPFKPVJGKPVGITCVKQPQHUVCVGQHVJGCTVRTQIPQUVKE

OGVGQTQNQIKECNOQFGNUCUFTKXGTU

6JGTGCTGUVKNNOCP[WPEGTVCKPVKGUKPRJQVQEJGOKECNCKTSWCNKV[OQFGNKPI2TKOG

COQPIVJGUGCTGGOKUUKQPKPXGPVQTKGU*QYGXGTOQFGNUCTGGUUGPVKCNHQTTGIWNCVQT[CPCN[UKU

CPFEQPUVKVWVGQPGQHVJGOCLQTVQQNUHQTCVVCEMKPIVJG1 RTQDNGO)TKFDCUGF1 CKTSWCNKV[  

OQFGNKPIKUUWRGTKQTVQVJGCXCKNCDNGCNVGTPCVKXGUHQT1 EQPVTQNRNCPPKPIDWVVJGEJCPEGUQHKVU 

KPEQTTGEVWUGOWUVDGOKPKOK\GF

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 
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'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 
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QH CNMGPGU CPF CNMCPGU FWTKPI VJG PKIJV CPF GCTN[ OQTPKPI JQWTU #VOQU 'PXKTQP 2CTV #  

#NVUJWNNGT # 2 $WHCNKPK , , 
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#NVUJWNNGT # 2 .GHQJP # 5 
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#NVUJWNNGT # 2 /E2JGTUQP 5 2 
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9CUJKPIVQP &%  

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#PFTGCG 9 # 
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#PVJGU 4 # 9CTPGT 6 6 
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#TG[ , #VMKPUQP 4 #UEJOCPP 5 / 
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#TG[ , 9KPGT # / #VMKPUQP 4 #UEJOCPP 5 / .QPI 9 & /QTTKUQP % . 
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#TPVU 4 4 6GLCFC 5 $ 
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#TQPKCP 2 ( 5EJGHH 2 # 9CFFGP 4 # 
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#VMKPUQP 4 
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#VMKPUQP 4 
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, 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 YKVJ 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 $ 
 /GCUWTGOGPVU QH 01  *101 01  *%*1 2#* PKVTQCTGPGU  

CPF RCTVKEWNCVG OWVCIGPKE CEVKXKVKGU FWTKPI VJG ECTDQPCEGQWU URGEKGU OGVJQFU EQORCTKUQP UVWF[ (KPCN

TGRQTV 5CETCOGPVQ %# %CNKHQTPKC #KT 4GUQWTEGU $QCTF EQPVTCEV PQ #

#VMKPUQP 4 $CWNEJ & . %QZ 4 # *CORUQP 4 ( ,T -GTT , # 6TQG , 
C 'XCNWCVGF MKPGVKE

CPF RJQVQEJGOKECN FCVC HQT CVOQURJGTKE EJGOKUVT[ UWRRNGOGPV +8 +72#% 5WDEQOOKVVGG QP )CU

-KPGVKE &CVC 'XCNWCVKQP HQT #VOQURJGTKE %JGOKUVT[ , 2J[U %JGO 4GH &CVC  

#VMKPUQP 4 #UEJOCPP 5 / #TG[ , 5JQTGGU $ 
D (QTOCVKQP QH 1* TCFKECNU KP VJG ICU RJCUG

TGCEVKQPU QH 1 YKVJ C UGTKGU QH VGTRGPGU , )GQRJ[U 4GU =#VOQU?   

#VYCVGT / # 
 +PHNWGPEG QH OGVGQTQNQI[ QP JKIJ Q\QPG EQPEGPVTCVKQPU +P 'XCNWCVKQP QH VJG UEKGPVKHKE

DCUKU HQT Q\QPGQZKFCPVU UVCPFCTFU RTQEGGFKPIU QH CP KPVGTPCVKQPCN URGEKCNV[ EQPHGTGPEG 0QXGODGT

*QWUVQP 6: 2KVVUDWTIJ 2# #KT 2QNNWVKQP %QPVTQN #UUQEKCVKQP

#[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 < 
 -KPGVKEU CPF RTQFWEVU QH VJG TGCEVKQPU QH 01 YKVJ 

OQPQCNMGPGU FKCNMGPGU CPF OQPQVGTRGPGU , 2J[U %JGO  

$CWIWGU - 
 # TGXKGY QH 0/1% 01 CPF 0/1%01 TCVKQU OGCUWTGF KP  CPF  4GUGCTEJ 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

$TWEMOCPP 2 9 9KNNPGT * 
 +PHTCTGF URGEVTQUEQRKE UVWF[ QH RGTQZ[CEGV[N PKVTCVG 
2#0 CPF KVU

FGEQORQUKVKQP RTQFWEVU 'PXKTQP 5EK 6GEJPQN  

$WJT / 2 2CTTKUJ & & 0QTVQP 4 $ (GJUGPHGNF ( % 5KGXGTU 4 ' 4QDGTVU , / 


%QPVTKDWVKQP QH QTICPKE PKVTCVGU VQ VJG VQVCN TGCEVKXG PKVTQIGP DWFIGV CV C TWTCN GCUVGTP 75 UKVG

, )GQRJ[U 4GU =#VOQU?  

$WTMJCTFV / 4 /CPKIC 0 + 5VGFOCP & * 2CWT 4 , 
 )CU EJTQOCVQITCRJKE OGVJQF HQT OGCUWTKPI

PKVTQIGP FKQZKFG CPF RGTQZ[CEGV[N PKVTCVG KP CKT YKVJQWV EQORTGUUGF ICU E[NKPFGTU #PCN %JGO 



$WTPU 9 ( 6KPIG[ & 6 'XCPU 4 % $CVGU ' * 
 2TQDNGOU YKVJ C 0CHKQPs OGODTCPG FT[GT HQT

FT[KPI EJTQOCVQITCRJKE UCORNGU , %JTQOCVQIT  

$WUPGUU - 
 =7PRWDNKUJGF FCVC? 4KEJNCPF 9# 2CEKHKE 0QTVJYGUV .CDQTCVQT[

%CFNG 5 * )QTUG 4 # .CYUQP & 4 
 4GCNYQTNF XGJKENG GOKUUKQPU C UWOOCT[ QH VJG VJKTF CPPWCN

%4%#24#% QPTQCF XGJKENG GOKUUKQPU YQTMUJQR #KT 9CUVG  

%CFQHH $ % *QFIGUQP , 
 2CUUKXG UCORNGT HQT CODKGPV NGXGNU QH PKVTQIGP FKQZKFG #PCN %JGO

 

%CNKHQTPKC #KT 4GUQWTEGU $QCTF 
 # UVWF[ QH VJG GHHGEV QH CVOQURJGTKE JWOKFKV[ QP CPCN[VKECN QZKFCPV

OGCUWTGOGPV OGVJQFU 2TGUGPVGF CV VJ EQPHGTGPEG =QP? OGVJQFU KP CKT RQNNWVKQP UVWFKGU ,CPWCT[

.QPI $GCEJ %# 5CETCOGPVQ %# #KT CPF +PFWUVTKCN *[IKGPG .CDQTCVQT[

%CNKHQTPKC #KT 4GUQWTEGU $QCTF 
 #PCN[UKU QH VJG CODKGPV 81% FCVC EQNNGEVGF KP VJG UQWVJGTP %CNKHQTPKC #KT

3WCNKV[ 5VWF[ =HKPCN TGRQTV? 5CETCOGPVQ %# %CNKHQTPKC #KT 4GUQWTEGU $QCTF EQPVTCEV

PQ #

%CTFGNKPQ % # %JCOGKFGU 9 . 
 0CVWTCN J[FTQECTDQPU WTDCPK\CVKQP CPF WTDCP Q\QPG , )GQRJ[U

4GU =#VOQU?  


%CTFKP & $ .KP % % 
 #PCN[UKU QH UGNGEVGF RQNCT CPF PQPRQNCT EQORQWPFU KP CKT WUKPI CWVQOCVGF

FKOGPUKQPCN 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

%CTOKEJCGN ) 4 2GVGTU . - -KVCFC 6 
 # UGEQPF IGPGTCVKQP OQFGN HQT TGIKQPCNUECNG

VTCPURQTVEJGOKUVT[FGRQUKVKQP #VOQU 'PXKTQP  

%CTOKEJCGN ) 4 2GVGTU . - 5C[NQT 4 & 
 6JG 56'/++ TGIKQPCN UECNG CEKF FGRQUKVKQP CPF

RJQVQEJGOKECN QZKFCPV OQFGN`+ 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 
 #KTETCHV OGCUWTGOGPV QH 01 QXGT Z

VJG GCUVGTP 2CEKHKE CPF EQPVKPGPVCN 7PKVGF 5VCVGU CPF KORNKECVKQPU HQT Q\QPG RTQFWEVKQP , )GQRJ[U

4GU =#VOQU?  

%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 01Z

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 , * 
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&CUIWRVC 2 - *YCPI * 
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CSWGQWU RGTQZKFG #PCN %JGO  

&CUIWRVC 2 - &QPI 5 *YCPI * ;CPI *% )GPHC < 
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&CUIWRVC 2 - &QPI 5 *YCPI * 
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&CXKU & & 
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JKIJ UGPUKVKXKV[ 4GX 5EK +PUVTWO  

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CPF KVU EQPVTQN`RTQEGGFKPIU XQNWOG ++ 5GRVGODGT  4CNGKIJ 0% 4GUGCTEJ 6TKCPING 2CTM

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 6JG )NQDCN 6JKPMKPI 2TQLGEV 5WRRQTVGF D[ VJG 'KUGPJQYGT *KIJGT 'FWECVKQP

2TQITCO CPF )GQTIKC 5VCVG 7PKXGTUKV[

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 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 & & 
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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 # 
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)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 , 
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OGVJQFU 'PXKTQP 5EK 6GEJPQN  

)TQULGCP & *CTTKUQP , 
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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 ' 
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CVOQURJGTKE QZKFCVKQP QH EKUJGZGPQN 'PXKTQP 5EK 6GEJPQN  

)TQULGCP ' 9KNNKCOU ' . ++ )TQULGCP & 
D #ODKGPV NGXGNU QH HQTOCNFGJ[FG CPF CEGVCNFGJ[FG KP

#VNCPVC )GQTIKC #KT 9CUVG  

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 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 ) , 
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*CNVKPGT ) , 9KNNKCOU 4 6 
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0; ,QJP 9KNG[  5QPU

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.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 , 
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*CTNG[ 4 # *CPPKICP / 2 %CUU ) 4 
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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  

*GWUU , / 9QNHH ) 6 
 /GCUWTGOGPV PGGFU HQT FGXGNQRKPI CPF CUUGUUKPI Q\QPGEQPVTQN UVTCVGIKGU

9CVGT #KT 5QKN 2QNNWV  

*GYKVV % 0 -QM ) . 
 (QTOCVKQP CPF QEEWTGPEG QH QTICPKE J[FTQRGTQZKFGU KP VJG VTQRQURJGTG

NCDQTCVQT[ CPF HKGNF QDUGTXCVKQPU , #VOQU %JGO  

*KNFGOCPP . / /CTMQYUMK ) 4 %CUU ) 4 
C %JGOKECN EQORQUKVKQP QH GOKUUKQPU HTQO WTDCP

UQWTEGU QH HKPG QTICPKE CGTQUQN 'PXKTQP 5EK 6GEJPQN  

*KNFGOCPP . / /C\WTGM / # %CUU ) 4 5KOQPGKV $ 4 6 
D 3WCPVKVCVKXG EJCTCEVGTK\CVKQP QH

WTDCP UQWTEGU QH QTICPKE CGTQUQN D[ JKIJTGUQNWVKQP ICU EJTQOCVQITCRJ[ 'PXKTQP 5EK 6GEJPQN

 


*KNN # % %JCODGTNCKP ' / ,T 
 6JG TGOQXCN QH YCVGT UQNWDNG ICUGU HTQO VJG CVOQURJGTG D[

XGIGVCVKQP +P #VOQURJGTGUWTHCEG GZEJCPIG QH RCTVKEWNCVG CPF ICUGQWU RQNNWVCPVU 


RTQEGGFKPIU QH C U[ORQUKWO 5GRVGODGT  4KEJNCPF 9# 1CM 4KFIG 60 'PGTI[ 4GUGCTEJ CPF

&GXGNQROGPV #FOKPKUVTCVKQP RR  
'4&# U[ORQUKWO UGTKGU PQ  #XCKNCDNG HTQO

06+5 5RTKPIHKGNF 8# %10(

*LQTVJ , .QJUG % 0KGNUGP % , 5MQX * 4GUVGNNK ) 
 2TQFWEVU CPF OGEJCPKUOU QH ICURJCUG

TGCEVKQPU DGVYGGP 01 CPF C UGTKGU QH CNMGPGU , 2J[U %JGO   

*QEJCPCFGN % , 
 'HHGEVU QH EQDCNV TCFKCVKQP QP YCVGT CPF CSWGQWU UQNWVKQPU , 2J[U %JGO

 

*QFIGUQP , # -TQUV - , 1	-GGHHG # ' 5VGXGPU 4 - 
 %JGOKNWOKPGUEGPV OGCUWTGOGPV QH

CVOQURJGTKE Q\QPG TGURQPUG EJCTCEVGTKUVKEU CPF QRGTCVKPI XCTKCDNGU #PCN %JGO  

*QGNN , / ,T )TGIQT[ ) . /E&QWICN & 5 6QTTGU # . &CXKU & & $TCFUJCY ,

4QFIGTU / 1 4KFNG[ $ # %CTTQNN / # 
 #KTDQTPG KPVGTEQORCTKUQP QH PKVTKE QZKFG

OGCUWTGOGPV VGEJPKSWGU , )GQRJ[U 4GU =#VOQU?  

*QH\WOCJCWU # &QTP *2 %CNNKGU , 2NCVV 7 'JJCNV & * 
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OGCUWTGOGPVU D[ NCUGT NQPIRCVJ CDUQTRVKQP URGEVTQUEQR[ #VOQU 'PXKTQP 2CTV #  

*QMG , ' #PVJGU 4 # 
 6JG KPKVKCNK\CVKQP QH PWOGTKECN OQFGNU D[ C F[PCOKE KPKVKCNK\CVKQP VGEJPKSWG

/QP 9GCVJGT 4GX  

*QNFTGP / 9 4CUOWUUGP 4 # 
 /QKUVWTG CPQOCN[ KP CPCN[UKU QH RGTQZ[CEGV[N PKVTCVG 
2#0 'PXKTQP

5EK 6GEJPQN  

*QNFTGP / 9 5OKVJ & . 
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UVCKPNGUU UVGGN ECPKUVGTU =HKPCN TGRQTV? %QNWODWU 1* $CVVGNNG %QNWODWU .CDQTCVQT[ '2# EQPVTCEV

PQ  9#

*QNFTGP / 9 5RKEGT % 9 
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5EK 6GEJPQN  

*QNFTGP / 9 5OKVJ & . 2QNNCEM # , 2CVG # & 
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FGUKIPU QH CWVQOCVGF ICU EJTQOCVQITCRJU KP %QPPGEVKEWV CPF QVJGT NQECVKQPU 4GUGCTEJ 6TKCPING

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FGVGEVKQP NKOKV 'PXKTQP 5EK 6GEJPQN  

*QN\YQTVJ ) % 
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9GCVJGT 4GX  

*QN\YQTVJ ) % 
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2$

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QH UKORNG CNMGPGU #VOQU 'PXKTQP 2CTV #  


*QTQYKV\ # %CNXGTV , ) 
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, 2J[U %JGO  

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OGCUWTGOGPVU YKVJ CRRNKECVKQP VQ VJG FGVGTOKPCVKQP QH J[FTQIGP RGTQZKFG WUKPI .WOKPQN #PCN

%JGO  

*QUNGT % 4 
 .QYNGXGN KPXGTUKQP HTGSWGPE[ KP VJG EQPVKIWQWU 7PKVGF 5VCVGU /QP 9GCVJGT 4GX  



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CPF EJGOKNWOKPGUEGPEG OQPKVQTU +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/# URGEKCNV[ EQPHGTGPEG 8+2 

*WGDGTV $ , 4QDGTV % * 
 6JG FT[ FGRQUKVKQP QH PKVTKE CEKF VQ ITCUU , )GQRJ[U 4GU =#VOQU?

 

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EJGOKNWOKPGUEGPV VGEJPKSWG #VOQU 'PXKTQP  

+UKFQTQX 8 # <GPMGXKEJ + ) +QHHG $ 8 
 8QNCVKNG QTICPKE EQORQWPFU KP VJG CVOQURJGTG QH HQTGUVU

#VOQU 'PXKTQP  

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 %JGOKUVT[ QH C RQNNWVGF ENQWF[ DQWPFCT[ NC[GT , )GQRJ[U

4GU =#VOQU?  

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2TCVJGT / , 
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CVOQURJGTG , )GQRJ[U 4GU =#VOQU?  

,CEQD & , .QICP , # ;GXKEJ 4 / )CTFPGT ) / 5RKXCMQXUM[ % / 9QHU[ 5 % /WPIGT , 9

5KNNOCP 5 2TCVJGT / , 4QFIGTU / 1 9GUVDGTI * <KOOGTOCP 2 4 
D 5KOWNCVKQP

QH UWOOGTVKOG Q\QPG QXGT 0QTVJ #OGTKEC , )GQRJ[U 4GU =#VOQU?  

,CPI ,% % 
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0QTVJ %CTQNKPC%JCRGN *KNN &GRCTVOGPV QH 'PXKTQPOGPVCN 5EKGPEGU CPF 'PIKPGGTKPI #XCKNCDNG

HTQO 7PKXGTUKV[ /KETQHKNOU +PVGTPCVKQPCN RWDNKECVKQP PQ ##&

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 6JG CVOQURJGTKE EJGOKUVT[ QH QZ[IGPCVGF

HWGN CFFKVKXGU VDWV[N CNEQJQN FKOGVJ[N GVJGT CPF OGVJ[N VDWV[N GVJGT +PV , %JGO -KPGV

 

,C[CPV[ 4 - / $NCEMCTF # /E'NTQ[ ( ( /E%NGPP[ 9 # 
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06+5 5RTKPIHKGNF 8# 2$

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,GHHTKGU * (QZ & -COGPU 4 
 1WVFQQT UOQI EJCODGT UVWFKGU NKIJV GHHGEVU TGNCVKXG VQ KPFQQT

EJCODGTU 'PXKTQP 5EK 6GEJPQN  


,GHHTKGU * ' )GT[ / 9 %CTVGT 9 2 . 
 2TQVQEQNU HQT GXCNWCVKPI QZKFCPV OGEJCPKUOU HQT WTDCP CPF

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'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$

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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 
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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  ##

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 #P (6+4 URGEVTQUEQRKE UVWF[ QH VJG TGCEVKQPU

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 %* %*1  *$T 
 %* %1 CPF %* %
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VJG OGEJCPKUO HQT VJG ICURJCUG TGCEVKQP DGVYGGP Q\QPG CPF VGVTCOGVJ[NGVJ[NGPG , 2J[U %JGO

 

0QPFGM . 4QFNGT & 4 $KTMU , 9 
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CVOQURJGTG WUKPI C PGY *2.% VGEJPKSWG 'PXKTQP 5EK 6GEJPQN  

0QVJQNV , *LQTVJ , 4CGU ( 
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RTGUGPEG QH 01 CPF 01  #VOQU 'PXKTQP 2CTV #   

0QVJQNV , *LQTVJ , 4CGU ( 5EJTGOU 1 
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%* 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 # 
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1FOCP / 6 4WUUGNN # ) 
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=#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 % 
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2CPFKU 5 0 5GKPHGNF , * 
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EJGOKUVT[ , )GQRJ[U 4GU =#VOQU?  

2CPFKU 5 0 2CWNUQP 5 ' 5GKPHGNF , * (NCICP 4 % 
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KUQRTGPG CPF RKPGPG #VOQU 'PXKTQP 2CTV #  

2CPFKU 5 0 *CTNG[ 4 # %CUU ) 4 5GKPHGNF , * 
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VTCPURQTV #VOQU 'PXKTQP 2CTV #  

2CPMQY , ( $KFNGOCP 6 ( 
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FGVGTOKPKPI VJG ICURCTVKENG RCTVKVKQPKPI QH QTICPKE EQORQWPFU #VOQU 'PXKTQP 2CTV #

 

2CPMQY , ( $KFNGOCP 6 ( 
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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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DUGTXGF EQPXGEVKXG GXGPVU , )GQRJ[U 4GU =#VOQU?  

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 
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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 ) 
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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 # 
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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 VGTVDWV[N GVJGT YKVJ VJG 1* TCFKECN KP VJG RTGUGPEG QH 01  +PV , %JGO -KPGV Z

 

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8GPMCVTCO # -CTCOEJCPFCPK 2 - /KUTC 2 - 
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8KG\GG 9 ,QJPUQP 9 $ 5KPIJ * $ 
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8KG\GG 9 ,QJPUQP 9 $ 5KPIJ * $ 
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9CEMVGT & , $C[N[ 2 8 
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KPVGTPCVKQPCN URGEKCNV[ EQPHGTGPEG 0QXGODGT  *CTVHQTF %6 2KVVUDWTIJ 2# #KT 2QNNWVKQP

%QPVTQN #UUQEKCVKQP RR 

9CFFGP 4 # 7PQ + 9CMCOCVUW 5 
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OGCUWTGF CNQHV 'PXKTQP 5EK 6GEJPQN  

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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 * + 
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QH C EJGOKNWOKPGUEGPV CPF C VWPCDNG FKQFG NCUGT CDUQTRVKQP VGEJPKSWG HQT VJG OGCUWTGOGPV QH PKVTQIGP

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9CNNCEG . # 1VV 9 4 
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9CPI 9 9CTPGT 6 6 
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#VOQU 'PXKTQP 2CTV #  

9CTPGEM 2 
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9CTPGEM 2 
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9CTPGEM 2 <GTDCEJ 6 
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6GEJPQN  

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2CEG 6 ) 
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CPF 5VCPFCTFU TGRQTV PQ '2# #XCKNCDNG HTQO 06+5 5RTKPIHKGNF 8# 2$



9GK . 9GKJCP 5 
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9GPFGN ) , 5VGFOCP & * %CPVTGNN % # &COTCWGT . 
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9GUEJNGT % , /CPFKEJ / . )TCGFGN 6 ' 
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9GUGN[ / . 
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TGIKQPCNUECNG PWOGTKECN OQFGNU 4GUGCTEJ 6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP

#IGPE[ #VOQURJGTKE 5EKGPEGU 4GUGCTEJ .CDQTCVQT[ TGRQTV PQ '2# #XCKNCDNG HTQO

06+5 5RTKPIHKGNF 8# 2$#5

9GUVDGTI * .COD $ 
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6TKCPING 2CTM 0% 75 'PXKTQPOGPVCN 2TQVGEVKQP #IGPE[ #VOQURJGTKE 5EKGPEGU 4GUGCTEJ

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9GUVDGTI * * 4CUOWUUGP 4 # *QNFTGP / 
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9GUVDGTI * #NNYKPG - , 'NKCU & 
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CTGCU CETQUU VJG 7PKVGF 5VCVGU +P 5RGEKCNV[ EQPHGTGPEG QP Q\QPGQZKFCPVU`KPVGTCEVKQPU YKVJ VJG

VQVCN GPXKTQPOGPV /CTEJ &CNNCU 6: 2KVVUDWTIJ 2# #KT 2QNNWVKQP %QPVTQN #UUQEKCVKQP RR 

9GUVDGTI * * *QNFTGP / 9 *KNN * * ,T 
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9GUVDGTI * .QPPGOCP 9 *QNFTGP / 
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9JKVD[ - 6 *WUCT 4 $ .KW $ ; * 
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9KPGT # / 
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PQ #

9KPGT # / 2GVGTU , 9 5OKVJ , 2 2KVVU , 0 ,T 
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9KPGT # / #VMKPUQP 4 #TG[ , $KGTOCPP * 9 *CTIGT 9 2 6WC\QP ' % <KGNKPUMC $ 


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9QNHH ) 6 .KQ[ 2 , 
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9QNHH ) 6 .KQ[ 2 , 9KIJV ) & 2CUEGTK 4 ' 
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4

Environmental Concentrations,

Patterns, and Exposure Estimates

4.1 Introduction

The effects of ozone (O3) on humans, animals, and vegetation have received

extensive examination and are discussed elsewhere in this document. As indicated in the

previous O3 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 O3 that simulate ambient O3 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 O3 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 O3-monitoring data collected under ambient

conditions. Although most of the O3 air quality data summarized were gathered for

compliance and enforcement purposes, the hourly averaged O3 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 O3 data have been summarized in different ways to reflect the

interests of those who wish to know more about the potential for O3 to affect humans and the

environment. This chapter is not an exposure assessment for ambient O3; rather, this chapter

elucidates the features of O3 concentration patterns and exposure possibilities.

Trend patterns for O3 over several periods of time are described in Section 4.2.

The trends for O3 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 O3 monitoring sites that

experience low maximum hourly average concentrations because these locations form the

"basis for comparison" for O3 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

4-1
comparing indoor to outdoor O3 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 O3. Examples are provided on how both

fixed-site monitoring information and human exposure models are used to estimate risks

associated with O3 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 O3 criteria document (U.S. Environmental Protection

Agency, 1986), O3 is the only photochemical oxidant other than nitrogen dioxide (NO2) that is

routinely monitored and for which a comprehensive aerometric database exists. Data for

peroxyacetyl nitrate (PAN) and hydrogen peroxide (H2O2) have been obtained only as part of

special research investigations. Consequently, no data on nationwide patterns of occurrence

are available for these non-O3 oxidants; nor are extensive data available on the correlations of

levels and patterns of these oxidants with those of O3. Sections 4.9 and 4.10 summarize the

available data for these other oxidants. Section 4.11 describes the co-occurrence patterns of

O3 with NO2; sulfur dioxide (SO2); 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

O3 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

4-2
contrast 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 O3 as a function of time of day, period of growth, or edaphic

conditions can determine the severity of response. For example, high O3 concentrations may

cause minimum injury or damage to plants, whereas more moderate O3 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 O3 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

O3 concentrations (>0.1 ppm) have been identified by many researchers as being more

important than long-term, low O3 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 O3 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 O3 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 O3 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

4-3
the 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 O3 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 O3 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 O3. 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 O3 and other photochemical oxidants

(U.S. Environmental Protection Agency, 1986), there was some difficulty in interpreting the

O3 data obtained at most sites across the United States prior to 1979 because of calibration

problems.

In the United States, O3 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 O3 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 O3, 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 O3, the reporting interval is 1 h, with the instruments operating continuously

and producing an integrated hourly average measurement. In many cases, EPA summarizes

4-4
air quality data by an O3 "season". Table 4-1 summarizes the O3 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 Texasa January December

Louisiana January December Texasb 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).

4-5
In 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 O3 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

O3 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

O3 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

O3 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, O3 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 O3 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 O3 levels between 1983 and 1992, which, in part, may be attributed to

relatively high O3 levels in 1983, compared to the low O3 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

4-6
station 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 O3 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 O3 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

O3 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

O3 concentrations decreased 7% at the 672 sites and 6% at the subset of 222 NAMS sites.

4-7
Also, from 1991 to 1992, the composite average of the number of estimated instances of

O3 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

O3 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

O3 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 O3 and the similarity of meteorological

conditions within each defined group. Adjusted O3 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 O3. Adjusted O3 statistics were

calculated by adding the expected O3 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 O3 thresholds was modeled as a

nonhomogeneous Poisson process where the parameter is a function of time and

meteorological variables. Kolaz and Swinford (1990) categorized O3 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 O3 was calculated and used to establish long-term trends in the annual

exceedance rate.

Cox and Chu (1993) modeled the daily maximum O3 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 O3 are decreasing in most urban areas over the period

1981 through 1991. The median rate of change was −1.1% per year, indicating that O3 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 O3 primarily because of the uneven year-to-year distribution of

meteorological conditions favorable to O3 formation.

Lefohn et al. (1993a) focused on a potentially useful method for identifying

monitoring sites whose improvement in the level of O3 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

O3 concentrations that are contained in the tail of the 1-h distributions, as indicated by the

4-8
successful 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

O3 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 O3 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 O3 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 O3 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 O3 trends is highly sensitive to meteorological

fluctuations and is not a reliable measure of progress in reducing O3 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 O3 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

O3 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

O3 concentration exceeded 0.2 ppm (Zeldin et al., 1991). Several other O3 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 O3 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

4-9
Figure 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

4-10
Figure 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 O3 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

O3 showed a more consistent downward trend than did unadjusted O3. 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 O3 behavior. Two regression procedures were performed to remove

the variability of meteorological conditions conducive to high O3 (i.e., O3 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 O3 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 O3 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

4-11
2.27 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

O3 air quality downwind of major urban areas. The authors examined daily maximum 1-h

O3 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 O3 air quality downwind of New York City, there has been little change in

O3 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: wi = weighting factor for concentration i,

M and A are positive arbitrary constants, and

ci = 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 ci was multiplied by the hourly average concentration, ci, 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 O3 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 O3 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 O3 exposure period 1989 to 1992, the analysis did reflect the effect of the higher

O3 exposure years (1983 and 1988). The hot, dry summer of 1988 was associated with the

highest O3 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

4-12
Ozone Trends Using the W126 Exposure Parameter Accumulated

on a Seasonal Basisa

Forestry

5-Year Trends 10-Year Trends

Significant Significant

Region Not Significantb − + 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 Significantb − + 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).

4-13
the 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 O3 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 O3 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 O3 trend. However, when

significant trends were observed, they were almost always positive. This can be attributed to

eastern O3 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 O3 is complex. Concentrations of background O3 can vary with

temperature, wind speed and direction, vertical motion, geographic location including latitude

and altitude, and season of the year. This background O3 can be attributed the following

sources: (1) downward transport of stratospheric O3 through the free troposphere to near

ground level; (2) in situ O3 production from methane emitted from swamps and wetlands

reacting with natural NOx emitted from soils, lighting strikes, and from downward transport of

NO from the stratosphere into the troposphere; and (3) in situ production of O3 from the

reactions of biogenic VOCs with natural NOx (National Research Council, 1991). A fourth

source to be considered is the O3 production resulting from long range transport of O3 from

distant pollutant sources (see Chapter 3).

The occasional occurrence of stratospheric injection of O3, 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

O3 concentration from stratospheric intrusion has been estimated (Altshuller, 1989).

For purposes of comparing how O3 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 O3 concentrations are difficult to make. The

definition of background and the use of O3 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 O3 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 O3 episodes.

4-14
It is possible for urban emissions, as well as O3 produced from urban area

emissions, to be transported to more rural downwind locations. This can result in elevated

O3 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 O3 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 O3 concentration values often are depressed

because of titration by NOx (Stasiuk and Coffey, 1974). Reagan (1984) and Lefohn et al.

(1987a) have observed this phenomenon where O3 concentrations at center-city sites were

lower than some rural locations. Because of the absence of chemical scavenging, O3 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 O3 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

O3 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 O3 concentrations

in 1991 across the United States. The highest second daily maximum 1-h O3 concentrations

by MSA for the years 1989 to 1991 are summarized in Table 4-3. The highest

O3 concentrations are observed in Southern California, but high levels of O3 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

O3 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, O3 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

O3 exposure regimes for sites experiencing daily maximum 8-h concentrations above specific

4-15
Figure 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 O3 concentrations by MSA for the years 1989 to 1991. The data have been

reported for the O3 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 O3 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 O3 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 O3 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 O3 levels may

produce distributions of 1-h O3 that result in a broader diurnal profile than those seen in

4-16
Table 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

4-17
Table 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

4-18
Table 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 O3 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

O3 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 O3 exposure with an effect. For example, using O3 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 O3. In this case, the predictive strength of using one O3 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).

4-19
Table 4-4. Summary of Percentiles of Hourly Average Concentrations (ppm

for the April-to-October Perioda

AIRS Site Name Year Min. 10 30 50 70 90 95 99 Max 060370016 Glendora, CA 1989

19901991

0.0000.0000.000

0.0000.0000.000

0.0200.0100.010

0.0300.0300.020

0.0600.0500.050

0.1200.1100.100

0.1500.1400.140

0.2200.2000.200

0.340.290.32

060595001 La Habra, CA 1989

19901991

0.0000.0000.000

0.0000.0000.000

0.0100.0100.010

0.0300.0200.020

0.0400.0400.040

0.0700.0700.070

0.0900.0900.090

0.1400.1400.130

0.260.210.21

060710005 San Bernardino County, CA 1989

19901991

0.0000.0000.000

0.0200.0200.020

0.0500.0400.040

0.0600.0600.060

0.0900.0800.080

0.1400.1200.120

0.1600.1500.140

0.2000.1800.190

0.270.330.27

060731001 Del Mar, CA 1989

19901991

0.0000.0000.000

0.0200.0200.020

0.0400.0300.040

0.0400.0400.050

0.0500.0500.050

0.0700.0600.060

0.0800.0700.070

0.1200.1000.100

0.250.170.15

090013007 Stratford, CT 1989

19901991

0.0010.0010.000

0.0080.0100.007

0.0240.0230.019

0.0360.0330.030

0.0460.0440.042

0.0640.0590.060

0.0770.0680.074

0.1150.1000.110

0.200.170.15

090093002 Madison, CT 1989

19901991

0.0010.0000.000

0.0080.0080.007

0.0220.0230.023

0.0330.0330.034

0.0430.0430.045

0.0590.0630.065

0.0700.0750.082

0.1030.1070.123

0.140.190.19

220330003 Baton Rouge, LA 1989

19901991

0.0000.0000.000

0.0010.0000.002

0.0090.0110.010

0.0210.0230.020

0.0340.0380.031

0.0590.0630.054

0.0690.0790.067

0.0940.1090.092

0.160.180.13

340170006 Bayonne, NJ 1989

19901991

0.0010.0010.001

0.0010.0010.002

0.0080.0090.011

0.0210.0220.024

0.0360.0360.038

0.0590.0580.065

0.0740.0730.082

0.0990.1060.110

0.140.180.16

360610063 New York, NY 1989

19901991

0.0000.0000.002

0.0150.0140.015

0.0280.0290.029

0.0400.0390.041

0.0510.0510.056

0.0730.0740.082

0.0860.0900.096

0.1100.1160.123

0.130.170.17

361030002 Babylon, NY 1989

19901991

0.0010.0000.001

0.0040.0060.005

0.0150.0170.018

0.0270.0270.030

0.0390.0400.044

0.0600.0600.067

0.0730.0750.081

0.1010.1050.111

0.150.140.21

4-20
Table 4-4 (cont’d). Summary of Percentiles of Hourly Average Concentrations (

for the April-to-October Perioda

AIRS Site Name Year Min. 10 30 50 70 90 95 99 Max 482010024 Harris County, TX 1989

19901991

0.0000.0000.000

0.0000.0000.000

0.0100.0100.000

0.0200.0200.020

0.0300.0400.030

0.0600.0700.060

0.0700.0900.080

0.1100.1300.110

0.2300.2200.170

550790085 Bayside, WI 1989

19901991

0.0020.0020.002

0.0060.0090.008

0.0240.0250.025

0.0350.0340.035

0.0460.0440.047

0.0660.0610.070

0.0770.0710.081

0.1010.0940.113

0.1510.1300.189

aSee Appendix A for abbreviations and acronyms.

4-21
Table 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

4-22
Table 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

4-23
Table 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 (O3) concentration and the maximum 3-mo SUM06 value and

(b) the second highest daily maximum 8-h average O3 concentration and the

maximum 3-mo SUM06 value for specific site years at rural agricultural sites

for the 1980-to-1991 period.

4-24
Figure 4-6. The relationship between (a) the second highest daily maximum hourly

average ozone (O3) concentration and the maximum 3-mo SUM06 value and

(b) the second highest daily maximum 8-h average O3 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

4-25
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. Using data from both urban and rural

O3 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 O3 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 O3 on both people and plants: what is

the possible significance of high concentrations lasting 1 h or longer and then recurring on

2 or more successive days?

The recurrence of high O3 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 O3 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 O3 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 O3 concentrations in charcoal-filtration

chambers used by NCLAN did not simulate the levels in those areas of the United States that

experience low maximum O3 concentrations (see Chapter 5, Section 5.5). Heuss (1982)

expressed concern that the O3 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 O3 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 O3 precursors were

eliminated, it is unlikely that O3 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

O3 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 O3 precursors and, thus,

changed the concentrations of O3.

Although not representing natural O3 background, attempts have been made, using

historical data, to estimate O3 concentrations in the late 1800s and early 1900s. Model

simulations and limited observations suggest that tropospheric O3 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 O3 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 O3 have not been observed across the

4-27
northern hemisphere (World Meteorological Organization/United Nations Environment

Program, 1994). Using ozonesonde data, observations show that tropospheric O3 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 O3 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 O3 concentration levels experienced in the United States with

today’s levels. However, it can be concluded that O3 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 O3 concentration measured at surface levels have not been

observed annually at sites monitoring O3 in the United States.

An alternative approach (adopted by Lefohn et al., 1990a, to establish reference

points) is to examine O3 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 O3 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 O3 concentrations has occurred since the late 1800s, two key points

argue against this line of reasoning. First, as indicated above, although O3 levels have

increased since the last century, consistent trends in tropospheric O3 have not been observed

across the northern hemisphere. Thus, at some monitoring sites, O3 levels may not show

increasing trends. Second, all the increases in background levels of O3 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 O3 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

O3 concentrations may be attributed to sources other than anthropogenic emissions. Thus,

using historical data that contain large uncertainties in the estimation of O3 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 O3 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 O3 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

4-28
concentrations for an O3 monitoring site in LaMoure County, ND (400 m), for 1978 through

1981, ranged from 0.030 to 0.035 ppm, whereas an O3 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

O3 concentration of 0.031 ppm.

The U.S. Environmental Protection Agency (1989) has estimated that background

O3 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 O3 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 O3 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 O3 monitoring sites experiencing

low maximum hourly average O3 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 O3 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 O3 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 O3 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 O3 monitoring site in the

4-29
Table 4-6. Seasonal (April to October) Percentile Distribution of Hourly

Ozone Concentrations, Number of Hourly Mean Ozone Occurrences

≥0.08 and

≥

Seasonal Seven-Hour Average Concentrations, W126, and SUM06 Values for S

Experiencing Low Hourly Average Concentrations with Data Capture

≥75%

(Concentrations in ppm)a

Location Site/AIRS ID Year Min. 10 30

Percentiles

50 70 90 95 99 Max

No. of

Obs.

Hours

≥0.08 ≥0

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 

060150002 1989 0.000 0.010 0.017 0.022 0.027 0.034 0.038 0.042 0.047 4,624 0 

1990 0.000 0.011 0.018 0.023 0.028 0.035 0.038 0.043 0.053 4,742 0 1991 0.001 0.012 0.019 0.025 0.031 0.038 0.041 0.045 0.054 4,666 0 1992 0.000 0.010 0.017 0.021 0.026 0.035 0.039 0.045 0.055 4,679 0 1993 0.000 0.010 0.017 0.022 0.027 0.035 0.038 0.042 0.054 4,666 0 

Olympic, 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 

530090012 1984 0.000 0.000 0.010 0.010 0.020 0.020 0.020 0.030 0.050 4,872 0 

1986 0.000 0.000 0.010 0.020 0.020 0.040 0.040 0.040 0.060 4,776 0 1989 0.000 0.003 0.010 0.015 0.022 0.030 0.035 0.046 0.065 4,220 0 1990 0.000 0.005 0.012 0.018 0.023 0.030 0.034 0.043 0.064 4,584 0 1991 0.000 0.006 0.014 0.019 0.024 0.033 0.036 0.044 0.056 4,677 0 1993 0.000 0.004 0.010 0.016 0.021 0.029 0.034 0.041 0.064 4,595 0 

Glacier, 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 

300298001 1990 0.000 0.003 0.014 0.026 0.035 0.044 0.047 0.052 0.066 5,092 0 

1991 0.000 0.001 0.014 0.027 0.036 0.046 0.049 0.056 0.062 5,060 0 1992 0.000 0.001 0.013 0.025 0.033 0.043 0.048 0.055 0.077 4,909 0 

Yellowstone, 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 

560391010 1989 0.002 0.018 0.027 0.036 0.044 0.052 0.057 0.063 0.071 4,079 0 

1990 0.000 0.015 0.023 0.029 0.036 0.043 0.046 0.053 0.061 4,663 0 1991 0.004 0.020 0.030 0.037 0.042 0.048 0.051 0.057 0.064 4,453 0 1992 0.001 0.018 0.029 0.036 0.042 0.051 0.056 0.064 0.075 4,384 0 1993 0.000 0.018 0.028 0.036 0.042 0.047 0.050 0.054 0.060 4,399 0 

022900003 1990 0.003 0.017 0.024 0.029 0.034 0.040 0.043 0.048 0.050 3,978 0 

1991 0.005 0.018 0.024 0.028 0.034 0.041 0.043 0.047 0.057 4,809 0 1992 0.003 0.016 0.023 0.028 0.034 0.044 0.047 0.050 0.054 4,800 0 

1993 0.002 0.017 0.023 0.028 0.033 0.041 0.043 0.048 0.055 4,773 0 

4-30
Table 4-6 (con’t). Seasonal (April to October) Percentile Distribution of Hour

Ozone Concentrations, Number of Hourly Mean Ozone Occurrences

≥00.08 and

Seasonal Seven-Hour Average Concentrations, W126, and SUM06 Values for S

Experiencing Low Hourly Average Concentrations with Data Capture

≥75%

(Concentrations in ppm)a

Location Site/AIRS ID Year Min. 10 30

Percentiles

50 70 90 95 99 Max

No. of

Obs.

Hours

≥00.08 ≥0

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 

460711010 1989 0.007 0.020 0.028 0.034 0.041 0.049 0.053 0.060 0.071 4,840 0 

1990 0.006 0.019 0.027 0.032 0.037 0.044 0.048 0.054 0.063 4,783 0 1991 0.005 0.020 0.028 0.034 0.040 0.047 0.050 0.056 0.066 4,584 0 

Great 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 

080030002 1989 0.011 0.031 0.037 0.041 0.045 0.050 0.052 0.057 0.063 4,436 0 

1990 0.010 0.030 0.037 0.041 0.045 0.052 0.055 0.062 0.070 4,624 0 1991 0.008 0.029 0.037 0.043 0.048 0.055 0.058 0.065 0.077 4,130 0 

Theodore Roosevelt,ND, North Unit

TheodoreRoosevelt NP380530002

1984 0.000 0.017 0.025 0.032 0.039 0.047 0.050 0.059 0.068 4,923 0 1985 0.000 0.019 0.026 0.032 0.038 0.046 0.049 0.054 0.061 4,211 0 1986 0.004 0.017 0.027 0.033 0.039 0.047 0.050 0.056 0.062 4,332 0 1989 0.004 0.023 0.032 0.039 0.045 0.054 0.058 0.065 0.073 4,206 0 1992 0.005 0.019 0.027 0.033 0.039 0.047 0.050 0.056 0.063 4,332 0 1993 0.004 0.018 0.025 0.031 0.037 0.045 0.048 0.055 0.064 4,281 0 

Point Reyes, CA Point Reyes NP

060410002

1989 0.007 0.021 0.026 0.031 0.036 0.042 0.045 0.058 0.080 4,577 1 1990 0.006 0.017 0.022 0.025 0.029 0.036 0.040 0.046 0.063 4,856 0 1991 0.006 0.019 0.025 0.030 0.034 0.040 0.043 0.048 0.072 4,588 0 1992 0.007 0.018 0.024 0.028 0.033 0.041 0.045 0.050 0.066 4,794 0 

Arches, UT Arches NP

490190101

1989 0.000 0.031 0.040 0.045 0.050 0.057 0.059 0.065 0.084 4,260 2 1990 0.000 0.020 0.025 0.028 0.031 0.036 0.039 0.045 0.056 4,639 0 

Montgomery 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 

050970001 1992 0.000 0.005 0.015 0.024 0.030 0.041 0.045 0.053 0.067 4,902 0 

1993 0.000 0.008 0.019 0.027 0.035 0.047 0.052 0.061 0.070 4,844 0 

aSee Appendix A for abbreviations and acronyms.

4-31
Quachita National Forest in Arkansas experienced distributions of hourly average

concentrations similar to some of the western sites.

Evans et al. (1983) summarized O3 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 O3 exposures experienced in plant chamber experiments

(see Chapter 5). Because O3 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 O3 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.

4-32
Table 4-7. Seasonal (April to October) Percentile Distribution of Hourly Ozo

Concentrations, Number of Hourly Mean Ozone Occurrences

≥0.08 and

≥0.1

Seasonal Seven-Hour Average Concentrations, and W126 Values for Three "Cl

National Forest Sites with Data Capture

≥75%

(Concentrations in ppm)a

Site AIRS ID Year Min. 10 30

Percentiles

50 70 90 95 99 Max

No. of

Obs.

H

≥0.08 

Custer NF, MT 300870101 1978

197919801983

0.0000.0100.0100.010

0.0100.0250.0250.025

0.0200.0350.0350.035

0.0350.0400.0400.040

0.0400.0450.0500.045

0.0500.0500.0550.050

0.0550.0550.0600.055

0.0600.0600.0650.060

0.0700.0750.0700.065

4,7595,0144,5744,835

Ochoco NF, OR 410130111 1980

198119821983

0.0100.0100.0100.010

0.0300.0250.0250.025

0.0350.0300.0300.035

0.0400.0350.0350.035

0.0450.0400.0400.040

0.0550.0450.0450.045

0.0550.0450.0500.050

0.0650.0550.0550.055

0.0800.0750.0650.060

4,7594,4594,6974,423

Apache NF, AZ 040110110 1981

19821983

0.0100.0150.004

0.0250.0300.025

0.0300.0400.035

0.0350.0450.040

0.0400.0500.045

0.0450.0550.055

0.0500.0600.055

0.0550.0650.065

0.0650.0750.070

4,8064,7144,788

aSee Appendix A for abbreviations and acronyms.

4-33
Table 4-8. The Value of the W126 Sigmoidal Exposure Parameter

Calculated Over the Annual Period

(Units in ppm-h)a

Site Elevation (m) 1976 1977 1978 1979 1980 1981 1982 1983 198South Pole, Antarctica 2,835 2.65 3.72 3.01 2.41 3.5Bitumount, Alberta, Canada 350 2.99Barrow, AK 11 2.60 2.60 3.15 2.36 2.7Theodore Roosevelt NP, ND 727 8.0Custer 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 2,103 Apache NF, AZ 2,424 81.39 10.24 27.18 17.48Mauna Loa, HI 3,397 27.48 45.68 33.68 48.90 19.1Whiteface Mountain, NY 1,483 86.50 68.30 33.75 32.03 37.82 42.9Hohenpeissenberg, FRG 975 61.28 25.04 35.64 21.76 18.53 29.53 49.00 19.85 40.4American Samoa 82 0.28 0.24 0.25 0.28 0.3

aSee Appendix A for abbreviations and acronyms.bCollection did not occur during the months of October, November, and December.Source: Lefohn et al. (1990a).

4-34
Table 4-9. The Value of the Ozone Season (Seven-Month) Average of

the Daily Seven-Hour (0900 to 1559 Hours) Concentration (ppm)a

Site Elevation (m) 1976 1977 1978 1979 1980 1981 1982 1983 1984 South Pole, Antarctica 2,835 0.025 0.027 0.026 0.027 Bitumount, Alberta, Canada 350 0.028Barrow, AK 11 0.022 0.025 0.024 0.024 0.022 Theodore Roosevelt NP, ND 727 0.038 Custer 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 2,103 Apache 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 Whiteface Mountain, NY 1,483 0.049 0.046 0.040 0.034 0.041 0.044 Hohenpeissenberg, FRG 975 0.047 0.040 0.044 0.040 0.037 0.043 0.047 0.040 0.043American Samoa 82 0.010 0.010 0.011 0.009 0.012 

aSee Appendix A for abbreviations and acronyms.bCollection did not occur during the months of October, November, and December.Source: Lefohn et al. (1990a).

4-35
4.3.3.2 Urban-Influenced Nonurban Areas

It is difficult to identify a set of unique O3 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 O3 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., NOx); the presence of high hourly average concentrations can be attributable to

the influence of either local generation or the long-range transport of O3. 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 O3. 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

O3 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), O3 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 NOx 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 O3 data from these

nine stations are summarized in Table 4-11. The sites are influenced either by local sources

or by transport of O3 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 NOx 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 O3 concentrations. Figure 4-7 shows the locations of the

NDDN sites. Edgerton and Lavery (1992) have summarized the O3 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

O3 concentration year, when compared with 1989 and 1990. The number of hourly

O3 concentrations ≥0.08 ppm is presented in Table 4-12. Edgerton and Lavery (1992) have

summarized O3 hourly average concentration data for several sites using the cumulative

4-36
Table 4-10. Summary of Percentiles, Number of Hourly Occurrences

≥0.10 pp

and Three-Month SUM06 Values for Selected Rural Ozone Monitoring Sites

in 1989 (April to October)

(Concentrations in ppm)a

AIRS Site Name Min. 10 30 50

Percentiles (ppm)

70 90 95 99 Max

Number of

Hourly

Values

O

RURAL AGRICULTURAL170491001 Effingham County, IL180970042 Indianapolis, IN240030014 Anne Arundel, MD310550032 Omaha, NE420070003 New Brighton, PA510610002 Fauquier County, VA

0.0000.0010.0000.0020.0000.000

0.0090.0060.0060.0210.0080.009

0.0230.0210.0210.0300.0210.021

0.0360.0340.0320.0370.0320.033

0.0460.0460.0450.0470.0430.045

0.0630.0630.0640.0620.0620.061

0.0700.0720.0730.067.0

.070

0.069

0.0810.0850.0900.0750.0870.084

0.1040.1030.1200.0980.1020.122

4,6004,5924,3604,1605,0555,050

RURAL FOREST060430004360310002470090101511870002

Yosemite NP, CAEssex County, NYSmoky Mountain NP, TNShenandoah NP (Dickey Ridge), VA

0.0000.0160.0000.004

0.0080.0310.0250.027

0.0220.0400.0360.037

0.0350.0490.0440.045

0.0490.0560.0530.054

0.0650.0660.0650.065

0.0720.0720.0700.071

0.0830.0860.0810.082

0.1110.1060.0980.100

4,8534,7924,7644,454

RURAL OTHER

040132004350431001370810011371470099550270001551390007

Scottsdale, AZSandoval County, NMGuilford County, NCFarmville, NCHoricon, WIOshkosh, WI

0.0000.0000.0040.0000.0020.002

0.0060.0100.0100.0100.0190.016

0.0180.0200.0230.0230.0290.028

0.0310.0300.0340.0340.0370.038

0.0450.0400.0460.0440.0470.048

0.0620.0600.0630.0620.0620.063

0.0710.0600.0700.0700.0700.070

0.0840.0700.0830.0830.0880.084

0.1070.0900.1130.1000.1110.121

5,0705,0594,8534,8334,1424,206

aSee Appendix A for abbreviations and acronyms.

4-37
Table 4-11. Summary of Percentiles of Hourly Average Concentrations for Electri

Research Institute Sulfate Regional Experiment (SURE) Program Sites/Eastern Reg

Quality Study (ERAQS) Ozone Monitoring Sites

(Units in ppm)

SURE/ERAQS Name Year Min. 10 30

Percentiles

50 70 90 95 99 Max

Montague, MA 1978

1979

0.0000.000

0.0020.000

0.0180.013

0.0320.025

0.0430.035

0.0610.056

0.0750.070

0.1190.103

0.2020.149

Scranton, PA 1978

1979

0.0000.000

0.0150.011

0.0310.022

0.0400.030

0.0480.040

0.0620.061

0.0730.074

0.0940.097

0.1260.132

Indian River, DE 1978

1979

0.0000.000

0.0100.008

0.0240.020

0.0350.031

0.0490.042

0.0720.063

0.0850.073

0.1030.092

0.1340.138

Duncan Falls, OH 1978

1979

0.0000.000

0.0050.010

0.0220.021

0.0340.029

0.0490.042

0.0710.060

0.0810.069

0.1100.086

0.1440.110

Rockport, IN 1978

1979

0.0000.000

0.0080.008

0.0210.019

0.0320.028

0.0440.038

0.0660.055

0.0780.064

0.1010.083

0.1450.104

Giles County, TN 1978

1979

0.0000.000

0.0000.000

0.0180.014

0.0320.024

0.0460.036

0.0660.055

0.0750.065

0.0870.081

0.1100.130

Roanoke, IN 1978

1979

0.0000.000

0.0040.004

0.0190.017

0.0320.026

0.0440.038

0.0670.061

0.0790.074

0.1060.098

0.1600.133

Research Triangle Park, NC 1978

1979

0.0000.000

0.0010.001

0.0170.012

0.0320.024

0.0490.037

0.0760.058

0.0870.068

0.1080.084

0.1420.131

Lewisburg, WV 1978

1979

0.0020.000

0.0200.013

0.0340.022

0.0450.029

0.0560.039

0.0720.056

0.0790.065

0.0910.080

0.1150.099

4-38
Figure 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 O3 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 O3 or its precursors into the area.

Taylor et al. (1992) have summarized the O3 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,

4-39
Table 4-12. Seven-Hour Growing Season Mean, W126 Values, and Number of Hour

Concentrations

≥80 ppb for Selected Eastern National Dry Deposition Network 

7-h Mean (ppb) W126 (ppm-h) SUM06 (ppm-h

Subregion State Site Land Classb 1988 1989 1990 1988 1989 1990 1988 1989 19

NORTHEASTConnecticut HillWashington’s CrossingPennsylvania State UniversityLaurel Hill State ParkBeltsvilleCedar Creek State Park

NY

NJ

PAPA

MD

WV

110144106117116119

RF

RARA

RF

RARA

55.0

—

59.062.7

—

59.8

48.352.846.048.454.644.9

45.352.451.048.655.548.2

75.5

—

63.568.8

—

50.4

40.346.025.429.145.419.6

36.843.742.731.045.724.3

86.8

—

65.675.5

—

56.3

47.252.128.130.848.619.0

354845324923

UPPER NORTHEASTWhiteface MountainAshland

NY

ME

105135

RF

RA

43.5

—

45.737.9

42.335.3

37.8

—

25.3

9.1

31.2

8.7

40.9

—

25.2

5.4

29

5

MIDWESTArgonne National LabVincennesOxford

IL

IN

OH

146140122

RARARA

61.162.065.3

51.451.153.5

46.350.951.7

59.168.191.8

29.636.448.4

21.635.846.4

69.478.5

103.2

35.040.355.3

254151

UPPER MIDWESTUnionvillePerkinstown

MI

WI

124134

RARA

——

51.544.2

47.438.8

——

35.419.0

30.711.6

——

41.618.3

31

7

SOUTH CENTRALSand MountainGeorgia StationPerryvilleResearch Triangle ParkCoweetaEdgar Evins State ParkHorton Station

AL

GAKY

NCNC

TN

VA

152153129101137127120

RARARA

RC

RFRFRF

——

65.262.355.6

—

62.3

52.648.150.850.841.047.251.4

63.662.6

——

47.956.154.4

——

103.6

62.344.3

—

127.6

40.628.139.731.716.126.961.2

68.769.7

——

21.344.570.6

——

99.671.0

—

150.7

33.221.838.935.524.464.0

8377

——

4982

SOUTHERN PERIPHERY

Caddo ValleySumatra

AR

FL

150156

RFRF

——

46.239.8

49.546.3

——

18.517.8

21.020.0

——

15.616.5

2517

aSee Appendix A abbreviations for and acronyms.bRA = Rural agricultural; RF = Rural forest; RC = Rural commercial; — = No data or insufficient data.

Source: Edgerton and Lavery (1992).

4-40
Table 4-13. Summary of Percentiles for National Dry Deposition Network Monitori

(Units in ppm)

Percentiles

Site No. Name Year Min. 10 30 50 70 90 95 99 Max RURAL AGRICULTURAL SITES106 Pennsylvania State University, PA 1988

19891990

0.0000.0000.000

0.0130.0100.015

0.0260.0220.027

0.0360.0330.038

0.0490.0430.048

0.0730.0590.065

0.0860.0660.074

0.1140.0820.090

0.1430.1040.120

116 Beltsville, MD 1989

1990

0.0020.000

0.0030.001

0.0140.015

0.0290.027

0.0440.041

0.0680.067

0.0810.080

0.0960.103

0.1310.137

119 Cedar Creek, WV 1988

19891990

0.0000.0010.001

0.0080.0060.007

0.0170.0130.014

0.0290.0240.024

0.0440.0370.038

0.0690.0560.057

0.0820.0650.067

0.1080.0820.085

0.1340.1720.116

122 Oxford, OH 1988

19891990

0.0010.0010.000

0.0190.0170.015

0.0320.0290.028

0.0440.0390.037

0.0580.0500.048

0.0830.0690.067

0.0960.0770.077

0.1170.0920.092

0.2210.1090.116

124 Unionville, MI 1989

1990

0.0030.004

0.0210.020

0.0310.029

0.0380.036

0.0470.044

0.0630.061

0.0710.069

0.0860.084

0.1130.105

129 Perryville, KY 1988

1989

0.0020.001

0.0240.020

0.0380.033

0.0490.043

0.0620.052

0.0800.066

0.0940.072

0.1100.086

0.1430.102

134 Perkinstown, WI 1989

1990

0.0070.006

0.0230.020

0.0320.028

0.0380.035

0.0460.041

0.0570.050

0.0620.056

0.0710.065

0.0850.074

135 Loring AFB/Ashland, ME 1989

1990

0.0020.002

0.0170.014

0.0260.023

0.0320.029

0.0390.036

0.0490.046

0.0550.051

0.0630.068

0.1030.088

140 Vincennes, IN 1988

19891990

0.0000.0000.000

0.0070.0090.009

0.0240.0250.025

0.0360.0360.035

0.0520.0470.045

0.0760.0640.062

0.0890.0720.073

0.1040.0850.089

0.1200.1120.110

144 Washington Crossing, NJ 1989

1990

0.0000.001

0.0060.008

0.0210.021

0.0330.032

0.0460.043

0.0670.065

0.0780.079

0.1000.104

0.1590.148

146 Argonne National Laboratory, IL 1988

19891990

0.0000.0000.000

0.0040.0050.004

0.0190.0190.017

0.0320.0290.028

0.0460.0410.039

0.0730.0610.057

0.0850.0700.065

0.1030.0880.077

0.1460.1260.097

4-41
Table 4-13 (cont’d). Summary of Percentiles for National Dry Deposition Network Mon

(Units in ppm)

Percentiles

Site No. Name Year Min. 10 30 50 70 90 95 99 Max RURAL AGRICULTURAL SITES (cont’d)152 Sand Mountain, AL 1989

1990

0.0000.000

0.0200.021

0.0310.035

0.0410.045

0.0510.057

0.0650.074

0.0720.080

0.0820.093

0.090.11

153 Georgia Station, GA 1989

1990

0.0020.002

0.0140.021

0.0250.034

0.0340.044

0.0450.056

0.0620.073

0.0690.084

0.0820.102

0.110.14

RURAL FOREST SITES105 Whiteface Mountain, NY 1988

19891990

0.0000.0030.005

0.0160.0220.018

0.0260.0300.028

0.0340.0380.036

0.0440.0470.046

0.0620.0590.060

0.0740.0660.069

0.0980.0780.086

0.120.090.11

110 Ithaca, NY 1988

19891990

0.0050.0020.001

0.0250.0250.022

0.0340.0360.033

0.0430.0440.041

0.0550.0520.049

0.0800.0650.063

0.0900.0710.069

0.1030.0810.081

0.120.100.09

117 Laurel Hill, PA 1988

19891990

0.0010.0000.001

0.0120.0090.009

0.0250.0200.020

0.0360.0310.030

0.0500.0430.042

0.0760.0610.060

0.0920.0690.071

0.1190.0870.086

0.150.110.10

120 Horton Station, VA 1988

19891990

0.0100.0020.004

0.0310.0320.032

0.0450.0430.044

0.0570.0500.052

0.0670.0590.059

0.0840.0700.071

0.0960.0760.075

0.1140.0850.084

0.140.100.09

127 Edgar Evins State Park, TN 1989

1990

0.0000.001

0.0170.019

0.0280.032

0.0370.041

0.0470.052

0.0620.067

0.0670.073

0.0770.085

0.090.10

137 Coweeta, NC 1988

19891990

0.0010.0010.000

0.0100.0070.008

0.0220.0160.018

0.0340.0250.029

0.0470.0370.043

0.0650.0550.059

0.0720.0610.064

0.0940.0710.072

0.140.090.08

150 Caddo Valley, AR 1989

1990

0.0020.002

0.0050.004

0.0160.015

0.0280.029

0.0410.041

0.0570.057

0.0630.065

0.0750.077

0.100.09

156 Sumatra, FL 1989

1990

0.0010.000

0.0120.011

0.0220.023

0.0300.033

0.0400.043

0.0570.057

0.0650.063

0.0750.072

0.090.11

RURAL COMMERCIAL SITE

101 Research Triangle Park, NC 1988

1989

0.0000.000

0.0040.004

0.0200.019

0.0350.030

0.0500.042

0.0720.063

0.0840.071

0.1110.083

0.130.12

4-42
although the sites were located in remote forested areas, the sites experienced elevated

O3 concentrations that were more than likely due to long-range transport of O3 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 O3 episodes, which they defined as any day in which a 1-h mean

O3 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 O3 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 O3 exposures in nonurban areas. In

the absence of actual O3 data, interpolation techniques have been applied to the estimation of

O3 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 O3 values for the NCLAN for 1978 through 1982 (May to September of

each year) (Reagan, 1984). These values, along with updated values, coupled with exposureresponse models were used to predict agriculturally related economic benefits anticipated by

lower O3 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 O3 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 O3 exposures that

occurred during the period 1985 to 1989. Figure 4-9 illustrates the integrated O3 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 O3 gradient pattern analyses described by

Lefohn et al. (1992b) identified contiguous areas of persistent,

4-43
Figure 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).

4-44
Figure 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).

4-45
relatively high seasonal O3 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

O3 exposures, was centered over the Ohio River Valley in the region near the KentuckyIndiana-Ohio borders. Relatively low O3 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 O3 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 O3 may be expected to vary with location, depending on the balance among the

many factors affecting O3 formation, transport, and destruction. Although they vary with

locality, diurnal patterns for O3 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 O3 from layers aloft; (2) destruction

of O3 through contact with surfaces and through reaction with nitric oxide (NO) at ground

level; and (3) in situ photochemical production of O3 (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 O3 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 O3 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 O3 exposure differences among sites. Logan (1989)

noted that diurnal variation of O3 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 O3 monitoring sites. A seasonal diurnal diagram provides the

investigator with the opportunity to identify whether a specific O3 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 O3 concentration between the predawn

minimum and midafternoon-to-early evening maximum. The authors reported that the

4-46
interday 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 O3 scavengers generally is

lower, so O3 concentration remains fairly constant (Wolff et al., 1987). A flat diurnal pattern

is usually interpreted as indicating a lack of efficient scavenging of O3 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 O3. 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 O3 and NOx.

The Oliver County, ND, site is fairly isolated from urban-influenced sources and hourly

average O3 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

O3 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 O3 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 O3 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

4-47
Figure 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 O3 by local NO emissions. In this regard, this station is typical of most urban

locations.

Diurnal profiles of O3 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 O3 concentrations.

4-48
Figure 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).

4-49
Figure 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.

4-50
Figure 4-14. Diurnal pattern of 1-h ozone concentrations on July 13, 1979, Philadelphia,

PA.

When diurnal or short-term composite diurnal O3 concentrations are compared with

longer term composite diurnal O3 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 O3.

4.4.3 Nonurban Area Diurnal Patterns

Nonurban areas only marginally affected by transported O3 usually have a flatter

diurnal profile than sites located in urban areas. Nonurban O3 monitoring sites experience

differing types of diurnal patterns (Böhm et al., 1991; Lefohn, 1992b). As indicated earlier,

O3 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

O3 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.

4-51
Figure 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 O3 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 O3 concentrations (Evans et al.,

1983; Lefohn, 1984). These sites tend to experience a weak diurnal pattern, with

4-52
Figure 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 O3 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 O3 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 O3 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

4-53
Figure 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 O3 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.

4-54
Figure 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 O3 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 O3 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

4-55
Figure 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).

4-56
Figure 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 O3 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 O3 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

O3 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 O3. 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 O3 on quarterly or annual periods exhibited less definitive patterns. Based on the exposure

index selected, different patterns were reported. The different patterns may be

4-57
Figure 4-21. Composite diurnal ozone pattern at selected National Dry Deposition Network

sites.

Source: Edgerton and Lavery (1992).

4-58
Figure 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).

4-59
Figure 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).

4-60
associated with the observations by Logan (1989) that rural O3 in the eastern United States in

the spring and summer is severely impacted by anthropogenic and possibly natural emissions

of NOx and hydrocarbons, and that O3 episodes occur when the weather is particularly

conducive to photochemical formation of O3. Meagher et al. (1987) reported for rural

O3 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

O3 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 O3 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 O3 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 O3 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

O3 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 O3 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 O3 exposures during the

spring and summer months. The sites experiencing their highest O3 exposures in the

fall-to-spring period did not necessarily experience the lowest O3 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 O3 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, O3 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 O3 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 O3 concentrations measured near

population-oriented areas were depressed in comparison with data collected in more isolated

areas. As noted earlier, urban O3 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 O3 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 O3 in

nonurban than in urban areas, it is well documented that O3 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 O3 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 O3 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

O3 concentrations were experienced. A similar observation was made, using the 1987

4-62
Table 4-14. Description of Mountain Cloud Chemistry

Program Sitesa

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 O3 sites. Among the MCCP Shenandoah National Park sites, the SH2 site

experienced marginally higher O3 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 O3 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

O3 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 O3 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.

4-63
Table 4-15. Seasonal (April to October) Percentiles, SUM06, SUM08, and W126 V

for the Mountain Cloud Chemistry Program Sitesa

Site

Elev.

(m) Year Min. 10 30 50 70 90 95 99 Max No. O

Howland Forest, ME(HF1)

65 1987

1988

0.0000.000

0.0130.012

0.0210.021

0.0280.028

0.0350.036

0.0460.047

0.0520.054

0.0650.076

0.0760.106

4,74,7

Mt. Moosilauke, NH(MS1)

1,000 1987

1988

0.0060.010

0.0270.026

0.0360.033

0.0450.043

0.0530.055

0.0650.076

0.0740.087

0.0860.113

0.1020.127

4,02,8

Whiteface Mountain, NY(WF1) (36-031-0002)

1,483 1987

1988

0.0110.014

0.0290.025

0.0370.033

0.0460.043

0.0530.056

0.0670.078

0.0740.089

0.0870.110

0.1040.135

4,74,6

Whiteface Mountain, NY(WF3)

1,026 1987 0.010 0.025 0.033 0.039 0.047 0.064 0.075 0.091 0.117 4,7

Whiteface Mountain, NY(WF4)

604 1987 0.000 0.011 0.023 0.031 0.041 0.056 0.065 0.081 0.117 4,4

Mt. Mitchell, NC(MM1)

2,006 1987

198819891992

0.0080.0110.0100.005

0.0340.0380.0380.036

0.0440.0540.0470.043

0.0510.0650.0540.048

0.0580.0750.0590.053

0.0670.0950.0680.063

0.0740.1060.0720.069

0.0850.1260.0810.081

0.1050.1450.1470.096

3,52,92,73,9

Mt. Mitchell, NC(MM2)

1,760 1987

1988

0.0170.009

0.0320.029

0.0420.041

0.0490.050

0.0560.060

0.0670.080

0.0730.092

0.0830.110

0.0960.162

3,12,9

Shenandoah Park, VA(SH1)

1,015 1987

1988

0.0000.006

0.0230.024

0.0360.036

0.0440.047

0.0540.058

0.0690.077

0.0760.087

0.0850.103

0.1350.140

3,63,9

Shenandoah Park, VA(SH2)

716 1987b 0.003 0.027 0.040 0.049 0.059 0.071 0.077 0.086 0.145 2,9

Shenandoah Park, VA(SH3)

524 1987

1988

0.0000.006

0.0180.020

0.0290.031

0.0370.040

0.0470.051

0.0610.067

0.0680.076

0.0800.097

0.1080.135

3,04,2

Whitetop Mountain, VA(WT1)

1,689 1987

1988

0.0110.000

0.0380.030

0.0510.046

0.0590.058

0.0660.068

0.0780.084

0.0850.094

0.0960.119

0.1110.163

4,33,7

aSee Appendix A for abbreviations and acronyms.bCalculations based on a May to September season.

4-64
Figure 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.

4-65
Figure 4-25. Integrated exposures for three non-Mountain Cloud Chemistry Program

Shenandoah National Park sites, 1983 to 1987.

Source: Lefohn et al. (1990b).

4-66
Table 4-16. Summary Statistics for 11 Integrated Forest Study Sitesa

Site Year Quarter

24-h

(ppb)

12-h

(ppb)

7-h

(ppb)

1-h Max

(ppb)

SUM06

(ppm-h

HIGH ELEVATION SITESWhiteface Mountain, NY 1987

198719881988

2323

42454944

43445043

42434943

104114131119

13.230.133.522.6

Great Smoky Mountain NP 1987

198719881988

2323

54537159

52517057

49496855

9995

119120

57.134.3

126.3

74.7

Coweeta Hydrologic Lab, NC 1987

198719881988

2323

50476157

48445954

47425951

8595

104100

32.424.181.663.6

LOW ELEVATION SITESHuntington Forest, NY 1987

198719881988

2323

36244037

42324646

42334648

8876

106

91

9.85.4

19.218.6

Howland, MA 1987

198719881988

2323

34263624

39324130

39314130

69769071

1.93.88.11.7

Oak Ridge, TN 1987

198719881988

2323

42294032

53445747

50415851

112105104122

39.524.326.419.7

4-67
Table 4-16 (cont’d). Summary Statistics for 11 Integrated Forest Study Site

Site Year Quarter

24-h

(ppb)

12-h

(ppb)

7-h

(ppb)

1-h Max

(ppb)

SUM06

(ppm-h)

LOW ELEVATION SITES (cont’d)Thompson Forest, WA 1987

198719881988

2323

36303232

43363939

41343736

103

94

103140

10.710.3

8.1

13.5

B.F. Grant Forest, GA 1987

198719881988

2323

32334732

46526347

48546448

99

102127116

26.131.353.124.1

Gainesville, FL 1987

198719881988

2323

42293520

53444829

50415130

bb

8470

bb

23.4

1.9

Duke Forest, NC 1987

198719881988

2323

38525438

48596951

52507554

100124115141

29.2b

b

52.9

Nordmoen, Norway 1987

198719881988

2323

32142211

40182815

41202916

75325330

2.40.00.00.0

aSee Appendix A for abbreviations and acronyms.bData were insufficient to calculate statistic.Source: Adapted from Taylor et al. (1992).

4-68
Table 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 O3 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 O3 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

O3 concentrations were recorded at sites about 10 to 70 km north-northeast of the urban core.

At greater distances or in other directions, O3 maxima decreased.

Chicago is an example where O3 concentrations increase as the distance from the

inner city increases. Figure 4-26 shows, for a number of O3 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.

4-69
Figure 4-26. Number of days in 1991 for which the maximum hourly average

ozone concentration was greater than 0.1 ppm at Chicago, IL.

4-70
Concentrations of O3 vary with altitude and with latitude. Although a number of

reports contain data on O3 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 O3 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 O3 concentrations were

similar at different altitudes, but that the O3 exposures increased with elevation. Wolff et al.

(1987) conducted a study of the effects of altitude on O3 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 O3 maxima were similar at different elevations. At night, however,

O3 concentrations were nearly zero in the valley (i.e., the lowest-elevation site) and increased

with elevation. Comparison of the O3 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 O3 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 O3 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 O3 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 O3 sites. Among the MCCP Shenandoah National Park sites, the SH2 site

experienced higher O3 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

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higher O3 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 O3 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

O3 concentration. In most cases, mixing ratios or mole fractions are used to describe

O3 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 the

monitoring 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 O3 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 O3 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, when

one 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 O3 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 O3 concentrations experienced inside buildings. The ratio of the

indoor/outdoor (I/O) O3 concentrations is a parameter that has been widely used for studying

the indoor and outdoor relationships, sources, and exposure patterns of O3. However, the

database on this subject is not large, and a wide range of I/O O3 concentration relationships

can be found in the literature. The only significant source of O3 in indoor residential air is

infiltration of outdoor O3, with ventilation rates affecting the flow of air between indoor and

outdoor (Zhang and Lioy, 1994).

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Reported I/O values for O3 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 O3 or photochemical oxidant. The results were highly

variable. A relatively large number of factors can affect the difference in O3 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 O3

within buildings (U.S. Environmental Protection Agency, 1986). Although indoor

concentrations of O3 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 O3. Experiments show that the fading of artists’ pigments in the presence of

O3 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 O3 concentrations more than two-thirds those of outdoor

concentrations. The author reported that museums with conventional air-conditioning systems

showed indoor O3 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 O3 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 an

I/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 O3 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

Cx,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

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Table 4-18. Summary of Reported Indoor-Outdoor Ozone Ratios

Structure Indoor-Outdoor Ratio Reference

Hospital 0.67a Thompson (1971)

Residence (with evaporative cooler) 0.60a 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)

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Table 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

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population, 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 O3 may be significantly different from ambient

O3 concentrations, and ambient outdoor concentrations of O3 that people come in contact with

may vary significantly from O3 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 O3 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

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function 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 O3 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 O3 dose over a 4-day episodic

period. The schedule of a hypothetical camper was matched to the actual O3 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 O3 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 O3 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 O3 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 O3 exposure year and Figure 4-28 summarizes the maximal 1-h O3 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 O3 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 O3 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 O3 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 R2 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 O3 exposure accurately. From these results, it is clear that

additional data are needed to better quantify the O3 exposures to which populations are

exposed.

Figure 4-27. Maximum 1-h ozone (O3) concentrations (in parts per billion) and average

0800 to 2000 hours strong acid concentrations (expressed as micrograms per

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cubic meter of sulfuric acid [H2SO4]) for each day that pulmonary function

data were collected at Fairview Lake camp in 1988. The correlation

coefficient between O3 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 O3 exposure models: (1) those that focus narrowly

on predicting indoor O3 levels and (2) those that focus on predicting O3 exposures on a

community-wide basis. The models that predict indoor O3 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 O3 exposure on a

community-wide basis. These models and their distinguishing features are:

1. pNEM/O3 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.

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Figure 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/O3.

· 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,

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community 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 O3 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 O3 exposure, a series of studies has

explored prolonged 6.6-h O3 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

O3 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;

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IV. 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 O3 and O3 + 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 NO2 as NOx 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).

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

In the previous air quality criteria for O3 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 O3 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 O3 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

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PPN to PAN ratios among studies in Los Angeles ranged from 0.15 to 0.20, whereas the PAN

to O3 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 O3 ratios were in the 0.01 to 0.05 range.

Seasonally, PAN to O3 ratios tended to be somewhat higher in the winter. The

diurnal characteristics of O3 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, CH2=C(CH3) C(O)OONO2) (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 O3 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

O3 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

4-83
Table 4-19. Summary of Measurements of Peroxyacetyl Nitrate

and Peroxypropionyl Nitrate in Urban Areasa

Site

Month/

Year

Number

of Days

Sampled

PAN

Concentration

(ppb)

Average/Mean Max

PPN

Concentration

(ppb)

Average/Mean Max 

Long Beach, CA 6-12/1987 16 NA 16 NA NA WillAnaheim, CA 6-12/1987 14 NA 19 NA NA WillLos Angeles, CA 6-12/1987 16 NA 13 NA NA WillBurbank, CA 6-12/1987 16 NA 19 NA NA WillAzusa, CA 6-9/1987 11 NA 13 NA NA WillClaremont, CA 6-9/1987 10 NA 30 NA NA WillPerrin, CA 6/1989 to 6/1990 NA 1.6 9.1 NA 0.73 GrosPalm Springs, CA 6/1989 to 6/1990 NA 1.6 7.6 NA 0.42 GrosDowney, CA 2/1984 10 1.2 6.7 0.06 0.40 SingBoulder, CO 5, 6 and

8, 9/1987

1245

0.630.59

2.03.8

0.080.07

0.30.6

Ridl

Denver, CO 3/1984 9 0.64 2.0 0.02 0.09 SingHouston, TX 3/1984 9 0.75 7.9 0.045 0.54 SingPhiladelphia, PA 4/1983 19 1.1 3.7 0.14 0.50 SingStaten Island, NY 4/1983 7 1.6 5.5 0.21 0.90 SingAtlanta, GA 7, 8/1992 36 0.71 2.9 0.14 0.37 WillEdmonton, Alberta,

Canada

12/1983 to 4/1984 66 7.5 NA NA Peak

Calgary, Alberta,

Canada

7/1981 to 2/1982 213 0.14 6.6 NA NA Peak

University of

Calgary, Alberta,Canada

10/1980 to 8/1981 175 0.22 2.4 NA NA Peak

Simcoe, Ontario,

Canada

6/1980 to 3/1981 191 1.3 5.6 NA NA Cork

4-84
Table 4-19 (cont’d). Summary of Measurements of Peroxyacetyl Nitrate

and Peroxypropionyl Nitrate in Urban Areasa

Site

Month/

Year

Number

of Days

Sampled

PAN

Concentration

(ppb)

Average/Mean Max

PPN

Concentration

(ppb)

Average/Mean Max 

Rio de Janeiro

Vila IsabelPUC/RJ

7/19857/1985

84

NANA

5.43.3

NANA

1.00.6

TanTan

Athens, Greece 2-11/1985 113 NA 3.7 NA NA Tsa

Paris, France 11/1985 to 11/1986 NA 1.1 20.5 NA NA Tsa

aSee Appendix A for abbreviations and acronyms.

4-85
much lower than those in urban areas. Average PAN concentrations ranged between 0.1 and

1.0 ppb, whereas the PAN to O3 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 O3 and PAN diurnal concentration patterns were similar to

those in upwind urban areas. The PAN to O3 ratios at the O3 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 H2O2 began in the 1970s, but the early reports of H2O2

concentrations above 10 ppb and even 100 ppb appear to be in error because of the artifact

H2O2 generated within the presence of O3 (Chapter 3, Section 3.5.1.3). Subsequent

measurements of H2O2 in the 1980s resulted in maximum H2O2 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 H2O2, which were

conducted in North Carolina, indicated differences among measurement methods in synthetic

mixtures of H2O2, 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 H2O2 that varyied by a factor or two (Lawson et

al., 1988). In the measurements of H2O2 discussed below, the cryogenic fluorescence method

or the scrubber-coil fluorescence method generally was used.

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Table 4-20. Summary of Measurements of Peroxyacetyl Nitrate

and Peroxypropionyl Nitrate in Rural Areasa

Site

Month/

Year

Number

of Days

Sampled

PAN

Concentration

(ppb)

Average/Mean Max

PPN

Concentration

(ppb)

Average/Mean Max 

Tanbark Flat, CA 8-10/1989 69 2.9 >16.1 0.75 5.1 Wi

8,9/1990 34 4.8 22.0 0.76 4.3 Gro8/1991 22 2.8 12.8 0.43 2.66 Gro

Franklin Canyon, CA 9/1991 9 1.6 7.0 0.18 1.15 GroNiwot Ridge, CO 7/1984 16 0.28 2.3 0.016 0.17 Sin

6, 7/1984 23

≈0.25 NA NA NA Fah

8, 9/1984 21

≈0.25 NA NA NA Fah

6, 7/1987 46 0.81 (b

)

0.21 (c

)

3.2 0.08 (b

)

0.01 (c

)

0.45 Rid

Point Arena, CA 1/1984 14 0.12 1.1 0.005 0.07 Sin

Spring 1985 NA 0.05 NA NA NA Rid

Scotia, PA Summer 1986 NA

≈0.6 NA NA NA Rid

6-8/1988 47 1.0 NA NA NA Bu

Kananaskis Valley, Alberta,

Canada

9/1979,4/1982,6-8/1982

NA

≈0.5 2.3 NA NA Pea

Frijoles Mesa, NM 10/1987 to

1/1989

NA 0.26 1.9 NA NA Ga

aSee Appendix A for abbreviations and acronyms.bFlow from Boulder-Denver area.cFlow across the Rockies.

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Based on interpretation of a compilation of H2O2 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 H2O2 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 H2O2

concentrations increase from the surface to the top of the boundary layer (Daum et al., 1990).

Available values for mean H2O2 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 H2O2 concentrations being observed in the afternoon

during the late spring and early summer months (Sakugawa and Kaplan, 1989). In the same

study, the average H2O2 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 O3 to H2O2

concentrations at the these sites were ≥100. In subsequent measurements, the same

relationship in H2O2 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 H2O2 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 NO2/O3 and

SO2/O3 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).

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For exploring the co-occurrence of O3 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 O3 and NO2 were defined by the presence or

absence of NO2. 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 O3/NO2.

Lefohn and Tingey (1984) reported that for Rubidoux, because NO2 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 O3, the number of co-occurrences with NO2 was small.

4.11.3 Sulfur Dioxide

Because elevated SO2 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 SO2 and O3. 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 O3 episodes above 0.05 ppm, and there was a high probability

that when the SO2 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 O3 episodes at specific

rural locations appeared to be associated with elevated SO2 levels. The investigators defined

the co-occurrence of O3 and SO2 to be when hourly mean concentrations were equal to or

greater than 0.10 and 0.01 ppm, respectively. On reviewing the hourly mean O3 and SO2 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 O3 and SO2 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 O3, nitrogen, and sulfur in forested areas using

4-89
cumulative exposures of O3 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

O3 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 O3 and SO2 or O3 and NO2.

4.11.4 Acidic Sulfate Aerosols

Acid sulfates, which are usually composed of sulfuric acid (H2SO4), 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/m3 at a specific location or a number of locations simultaneously (Lioy,

1989). For many summertime studies, peaks of H2SO4 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/m3

for over an hour and can be found at high concentrations of 10 to 20 μg/m3 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/m3 for periods longer than 5 h (Lioy, 1989).

As has been discussed earlier in this chapter, the highest O3 exposures for sites affected by

anthropogenically derived photooxidant precursors are expected to occur during the late spring

and summer months. Thus, the potential for O3 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 O3 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 O3 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 O3 and the 12-h average H+ concentrations. On 7 days, the acid aerosol

concentrations (reported as H2SO4) were higher than 10 μg/m3, reaching a 12-h maximum of

18.6 μg/m3. Figure 4-27 shows the relationship between daily maximum O3 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/m3 (Buffalo, NY) and a maximum daily

hourly average concentration of 0.164 ppm. Although lower than Buffalo, high O3 or H+

values were reported by the investigators for Albany and White Plains, NY. It is unclear

whether the O3 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 O3 and H2SO4 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

4-90
reported that their results indicated that acidic aerosol episodes (i.e., H+ ≥ 100 nmol/m3

)

occurred routinely during the summer months and that H+ peaks were correlated with sulfate

(SO4) episodes. Figure 4-31 illustrates the relationship among SO4, H+, and O3.

Figure 4-30. The co-occurrence pattern of ozone and sulfuric acid (H2SO4) 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 O3 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

O3 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 O3 and acidic deposition were comonitored.

Figure 4-31. Sulfate (SO4

-

), hydrogen ion (H+), and ozone (O3) measured at Breadalbane

Street (Site 3) in Toronto during July and August 1986, 1987, and 1988.

Source: Thurston et al. (1994).

4-91
As a result, Lefohn and Benedict (1983) focused their attention on O3 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 O3 monitoring data and weekly and event acidic

deposition data from the NADP and EPRI databases, the authors identified specific locations

where the hourly mean O3 concentrations were ≥0.1 ppm and 20% of the wetfall daily or

weekly samples were below pH 4.0. Elevated levels of O3 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 O3 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 O3 and H+ concentrations. The

authors stated that they believed, based on the available data, the greatest potential for

interaction between acid rain and O3 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., O3 and acidic deposition)

could be used because O3 exposures are regional in nature. However, work by Lefohn et al.

(1988a) has shown that hourly mean O3 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

O3 concentrations from known locations to other areas within a region may provide only

qualitative indications of actual O3 exposure patterns.

4-92
In 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 O3 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 O3 and cloud

deposition will be different than those patterns associated with O3 and deposition in

precipitation.

Smith and Lefohn (1991) explored the relationship between O3 and H+ in

precipitation, using data from sites that monitored both O3 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 O3 exposure were at

least moderately high (i.e., annual H+ deposition ≥0.5 kg ha-1 and an annual O3 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 O3 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 O3 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 O3 level and pH

(or H+ deposition) were weak to nonexistent. Sites most subject to relatively high levels of

both hydrogen ion and O3 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 O3 and acid precipitation, results have been

reported on the co-occurrence of O3 and acidic cloudwater in high-elevation forests. Vong

and Guttorp (1991) characterized the frequent O3-only and pH-only, single-pollutant episodes,

as well as the simultaneous and sequential co-occurrences of O3 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

O3 average concentrations ≥0.07 ppm and cloudwater events with pH ≤ 3.2. The authors

reported that simultaneous occurrences of O3 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

O3-only episode. As would be expected, O3-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

O3 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 O3 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 O3 produced from urban area

emissions, to be transported to more rural downwind locations, elevated O3 concentrations can

occur at considerable distances from urban centers. Urban O3 concentration values often are

depressed because of titration by NO. Because of the absence of chemical scavenging, O3

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 O3, 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 O3 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 O3 season for 509 trend sites and a subset of 196 NAMS sites,

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shows 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 O3 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 O3 formation.

From 1991 to 1992, the composite mean of the second highest daily maximum 1-h

O3 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 O3 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 O3 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 O3 trends for meteorological influences. Historically, the long-term

O3 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 O3 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 O3 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 O3 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 O3 concentrations showing only moderate peaks but showing multihour levels above

0.10 ppm.

An important question is whether an improvement in O3 levels would produce

distributions of 1-h O3 concentrations that result in a broader diurnal profile than those seen

in high-oxidant urban areas where O3 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 O3

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

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in 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 O3 exposure with an effect. For example, using O3 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 O3. However, there was not much predictive strength in using one O3 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

O3 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 O3 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 O3 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 O3 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

O3 may be expected to vary with location, depending on the balance among the many factors

affecting O3 formation, transport, and destruction. Although they vary with locality, diurnal

patterns for O3 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 O3 from layers aloft,

4-96
(2) destruction of O3 through contact with surfaces and through reaction with NO at ground

level, (3) in situ photochemical production of O3, and (4) horizontal transport of O3 and its

precursors.

Although it might appear that composite diurnal pattern diagrams could be used to

quantify the differences in O3 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 O3 exposure differences among sites. Unique families of diurnal average profiles

exist, and it is possible to distinguish between two types of O3 monitoring sites. A seasonal

diurnal diagram provides the investigator with the opportunity to identify whether a specific

O3 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 O3 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 O3 concentrations in urban areas usually show the pattern of

high O3 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 O3 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 O3 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 O3 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 O3.

However, for several clean rural sites, the highest exposures have occurred in the third quarter

rather than in the second. For rural O3 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 NOx and hydrocarbons.

Concentrations of O3 vary with altitude and with latitude. There appears to be no

generalizable conclusion concerning the relationship between O3 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 O3 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

O3 concentration. In most cases, mixing ratios, or mole fractions, are used to describe

O3 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

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are 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 O3 concentrations experienced inside

buildings. Even to date, the database on this subject is not extensive, and a wide range of I/O

O3 concentration relationships can be found in the literature. Reported I/O values for O3 are

highly variable. A relatively large number of factors can affect the difference in

O3 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 O3 within buildings. The I/O O3 concentration ratios generally fall in the

range of 0.1 to 0.7, and indoor concentrations of O3 will almost invariably be less than those

outdoors.

It is important that accurate estimates of both human and vegetation exposure to O3

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 O3 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 O3 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 O3 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 O3 exposure models exist: (1) those that focus narrowly on predicting

4-98
indoor O3 levels and (2) those that focus on predicting O3 exposures on a community-wide

basis. The following four distinct models address the prediction of O3 exposures on a

community-wide basis: (1) pNEM/O3, (2) SAI/NEM, (3) REHEX, and (4) EPEM. All four

O3 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

O3 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 O3 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 O3 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-O3 oxidants in ambient air in the United States,

other than the inorganic nitrogenous oxidants such as NO2 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 NOx 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 NO2/O3 and SO2/O3

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

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ambient exposure considerations, the simultaneous co-occurrence of NO2/O3 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 O3, nitrogen, and sulfur in forested areas,

combining cumulative exposures of O3 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 O3 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 O3 and SO2 or O3 and NO2.

Knowledge of the potential exposure of the co-occurrence of acidic sulfate aerosols

and O3 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 H2SO4, 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/m3 at a specific location or a number of locations simultaneously. For many

summertime studies, peaks of H2SO4 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/m3 for over an hour and at high concentrations of 10 to 20 μg/m3 for 6

to 24 h at one or more sites. Acidic sulfate aerosol concentrations can occur at

concentrations in the summertime above 10 μg/m3 for periods longer than 5 h. The highest

O3 exposures for sites affected by anthropogenically derived photooxidant precursors are

expected to occur during the late spring and summer months. Thus, the potential for O3 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 O3 and acid precipitation. One study explored the relationship

between O3 and H+ in precipitation, using data from sites that monitored both O3 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 O3 exposure were at least moderately high (i.e., annual H+ deposition ≥0.5 kg ha-1 and an

annual O3 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 O3 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 O3 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 O3 level and pH

(or H+ deposition) were weak to nonexistent. Sites most subject to relatively high levels of

both H+ and O3 were located in the eastern portion of the United States, often in mountainous

areas.

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The co-occurrence of O3 and acidic cloudwater in high-elevation forests has been

characterized. The frequent O3-only and pH-only, single-pollutant episodes, as well as the

simultaneous and sequential co-occurrences of O3 and acidic cloudwater, have been reported.

Both simultaneous and sequential co-occurrences were observed a few times each month

above the cloud base.

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#

Appendix A

Abbreviations and Acronyms

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2#4 2TQZKOCNCNXGQNCTTGIKQP

2$. 2NCPGVCT[DQWPFCT[NC[GT

2$\0 2GTQZ[DGP\Q[NPKVTCVG

2&(+& %T[QIGPKERTGEQPEGPVTCVKQPFKTGEVHNCOGKQPK\CVKQPFGVGEVKQP

2(62.+( 2JQVQHTCIOGPVCVKQPVYQRJQVQPNCUGTKPFWEGFHNWQTGUEGPEG

R* *[FTQIGPKQPEQPEGPVTCVKQP

2. .KSWKFRJCUGXCRQTRTGUUWTG

2.#04 2TCEVKEGHQT.QYEQUV#RRNKECVKQPKP0QPCVVCKPOGPV4GIKQPU

2/0 2QN[OQTRJQPWENGCTNGWMQE[VGCNUQECNNGFPGWVTQRJKN

RRO% 2CTVURGTOKNNKQPECTDQP

220 2GTQZ[RTQRKQP[NPKVTCVG

25& 2CUUKXGUCORNKPIFGXKEG

281% 2QNCTXQNCVKNGQTICPKEEQORQWPF

3 .CVGPVJGCVHNWZ '

3 *GCVHNWZ *

T .KPGCTTGITGUUKQPEQTTGNCVKQPEQGHHKEKGPV
#

4 /WNVKRNGEQTTGNCVKQPEQGHHKEKGPV 

4#&/ 4GIKQPCN#EKF&GRQUKVKQP/QFGN

4#25 4GIKQPCN#KT2QNNWVKQP5VWF[

4'*': 4GIKQPCN*WOCP'ZRQUWTG/QFGN

4/5& 4QQVOGCPUSWCTGFKHHGTGPEG

41) 4GCEVKXGQTICPKEICU

41/ 4GIKQPCN1ZKFCPV/QFGN

41/0'6 4GIKQPCN1\QPG/QFGNKPIHQT0QTVJGCUV6TCPURQTVRTQITCO

46 4GURKTCVQT[VTCEV

5#$ 5EKGPEG#FXKUQT[$QCTF

5#+ 5[UVGOU#RRNKECVKQPU+PVGTPCVKQPCN

5#24% 5VCVGYKFG#KT2QNNWVKQP4GUGCTEJ%GPVGT7PKXGTUKV[

QH%CNKHQTPKC4KXGTUKFG

5#4/#2 5CP,QCSWKP8CNNG[#KT3WCNKV[5VWF[5,8#35#VOQURJGTKE7VKNKV[

5KIPCVWTGU2TGFKEVKQPUCPF'ZRGTKOGPVU#752':4GIKQPCN

/QFGN#FCRVCVKQP2TQLGEV

5#41#& 5VQTCIGCPF4GVTKGXCNQH#GTQOGVTKE&CVC75'PXKTQPOGPVCN

2TQVGEVKQP#IGPE[EGPVTCNK\GFFCVCDCUGUWRGTUGFGFD[

#GTQOGVTKE+PHQTOCVKQP4GVTKGXCN5[UVGO=#+45?

5%#35 5QWVJ%QCUV#KT3WCNKV[5VWF[%CNKHQTPKC

5+2 5VCVG+ORNGOGPVCVKQP2NCP

5.#/5 5VCVGCPF.QECN#KT/QPKVQTKPI5VCVKQP

5,8#35 5CP,QCSWKP8CNNG[#KT3WCNKV[5VWF[

51 5WNHWTFKQZKFG 

51 5WNHCVG 



515 5QWVJGTP1ZKFCPV5VWF[

54/ 5VCPFCTFTGHGTGPEGOCVGTKCN

542 5VCPFCTFTGHGTGPEGRJQVQOGVGT

56'/++ 5WNHWT6TCPURQTV'WNGTKCP/QFGNXGTUKQP++

57/ 5GCUQPCNUWOQHCNNJQWTN[CXGTCIGEQPEGPVTCVKQPURRO

57/ 5GCUQPCNUWOQHCNNJQWTN[CXGTCIGEQPEGPVTCVKQPURRO

57/ 5GCUQPCNUWOQHCNNJQWTN[CXGTCIGEQPEGPVTCVKQPURRO

574' 5WNHCVG4GIKQPCN'ZRGTKOGPV2TQITCO

6 6GORGTCVWTG

6#/5 6QZKE#KT/QPKVQTKPI5VWF[75'PXKTQPOGPVCN2TQVGEVKQP#IGPE[

6&.#5 6WPCDNGFKQFGNCUGTCDUQTRVKQPURGEVTQUEQR[
8

#

6'# 6TKGVJCPQNCOKPG

6I 6GTCITCO

6)62 6JG)NQDCN6JKPMKPI2TQLGEV

60/*% 6QVCNPQPOGVJCPGJ[FTQECTDQPU

62.+( 6YQRJQVQPNCUGTKPFWEGFHNWQTGUEGPEG

66(/5 6YQVQPGHTGSWGPE[OQFWNCVGFURGEVTQUEQR[

7#/ 7TDCP#KTUJGF/QFGN

78 7NVTCXKQNGV

78$ 7NVTCXKQNGVTCFKCVKQPQHYCXGNGPIVJUVQPO

8/6 8GJKENGOKNGUVTCXGNGF

81% 8QNCVKNGQTICPKEEQORQWPF

' /KPWVGXGPVKNCVKQPGZRKTGFXQNWOGRGTOKPWVG

9(/ 9JKVG(CEG/QWPVCKP

9/170'2 9QTNF/GVGQTQNQIKECN1TICPK\CVKQP7PKVGF0CVKQPU'PXKTQPOGPV

2TQITCO

9 %WOWNCVKXGKPVGITCVGFGZRQUWTGKPFGZYKVJCUKIOQKFCNYGKIJVKPI

HWPEVKQP
B-1

Appendix B

Colloquial and Latin Names

#NFGT #NPWUUGTTWNCVC#KVQP9KNNFGPQY

#NFGTTGF #NPWUTWDTC$QPI

#NFGTURGEMNGF #NPWUKPECPC./QGPEJ

#NHCNHC /GFKECIQUCVKXC.

#NOQPF 2TWPWUCO[IFCNWU$CVUEJEX0QPRCTKGN

#RRNG /CNWUURR

#RTKEQV 2TWPWUCTOGPKCEC.

#UJITGGP (TCZKPWURGPPU[NXCPKEC/CTUJ

#UJYJKVG (TCZKPWUCOGTKECPC.

#URGPVTGODNKPI 2QRWNWUVTGOWNQKFGU.

#XQECFQ 2GTUGCCOGTKECPC/KNN

#\CNGC 4JQFQFGPFTQPURR

$CTNG[URTKPI *QTFGWOXWNICTG.

$CUUYQQFNKPFGP 6KNKCCOGTKECPC.

$GCPDTQCF 8KEKCHCDC.

$GCPDWUJ 2JCUGQNWUXWNICTKU.XCTJWOWNKU#NGH

$GCPMKFPG[RKPVQUPCRYJKVG 2JCUGQNWUXWNICTKU.

$GGEJ'WTQRGCP (CIWUU[NXCVKEC.

$GGVUWICT $GVCXWNICTKU.

$GIQPKC $GIQPKCUR

$GIQPKCDGFFKPI $GIQPKCUGORGTHNQTGPU.KPM1VVQ

$GPVITCUU #ITQUVKUECRKNNCTKU.

$KTEJ'WTQRGCPYJKVG $GVWNCRGPFWNC4QVJ

$KTEJFQYP[ $GVWNCRWDGUEGPU'JTD

$KTEJRCRGT $GVWNCRCR[TKHGTC/CTUJ

$NCEMDGTT[EQOOQP 4WDWUCNNGIJGPKGPUKU2QTVGT

$NCEMITCO 8KIPCOWPIQ.

$NWGITCUU-GPVWEM[
B-2

2QCRTCGVGPUKU.

$WEMJQTP 2NCPVCIQNCPEGQNCVC.

%CDDCIG $TCUUKECQNGTCEGCECRKVCVC.

%CORKQPDNCFFGT 5KNGPGEWECDCNWU9KDGN

%CORKQPOQUU 5KNGPGCECWNKU.

%CTPCVKQP &KCPVJWUECT[QRJ[NNWU.

%GFCTKPEGPUG .KDQEGFTWUFGEWTTGPU6QTT

%CNQEGFTWUFGEWTTGPU=6QTT?(NQTKP

%GFCTYGUVGTPTGF 6JWLCRNKECVC&QPPGZ&&QP

%GNGT[ #RKWOITCXGQNGPU.XCTFWNEG2GTU

%JGUVPWV#OGTKECP%CUVGPGCFGPVCVC/CTUJ$QTMJ

%JGTT[DNCEM 2TWPWUUGTQVKPC'JTJ

%JKEMRGC %KEGTCTKGVKPWO.

%NQXGTNCFKPQYJKVG 6TKHQNKWOTGRGPU.

%NQXGTTGF 6TKHQNKWORTCVGPUG.

%QTP <GCOC[U.

%QVVQP )QUU[RKWOJKTUWVWO.

%QVVQPYQQFRQRNCT 2QRWNWUFGNVQKFGU/CTUJ

%TGUUICTFGP .GRKFKWOUCVKXWO.

%WEWODGT %WEWOKUUCVKXWU.

&QEM 4WOGZQDVWUKHQNKWU.

(GPWITGGM 6TKIQPGNNCHQGPWOITCGEWO.

(GUEWGVCNN (GUVWECGNCVKQT.(GUVWECRTCGVGPUKU*WFU

(KTDCNUCO #DKGUDCNUCOGC./KNN

(KT&QWINCU 2UGWFQVUWICOGP\KGUKK/KTD(TCPEQ

(KT&QWINCUDKIEQPG2UGWFQVUWICOCETQECTRC8CUG[/C[T

(KT(TCUGT #DKGUDCNUCOGC.HTCUGTK2WTUJ2QKT

(KTUKNXGT #DKGUCNDC/KNN

(KTYJKVG #DKGUEQPEQNQT.KPFN

)GTCPKWO 2GNCTIQPKWOZJQTVQTWO$CKNG[

)QNFGPTCKP -QGNTGWVGTKCRCPKEWNCVC.CZO

)TCRG8KVKUNCDTWUECPC$CKNG[

)TCRGYKNF 8KVKUURRB-3

)TCRGHTWKV4WD[4GF %KVTWURCTCFKUK.

)TCUUEQNQPKCNDGPV #ITQUVKUVGPWKU5KDVJQTR

)TCUUQTEJCTF &CEV[NKUINQOGTCVC.

)TCUUTGF (GUVWECTWDTC)CWF

)TCUUT[G .QNKWORGTGPPG.

)WOUYGGV .KSWKFCODCTUV[TCEKHNWC.

*GONQEMGCUVGTP

6UWICECPCFGPUKU.%CTT

*GONQEMYGUVGTP 6UWICJGVGTQRJ[NNC4CH5CTI

+X[ *GFGTCJGNKZ.

-GPCH *KDKUEWUECPPCDKPWU.

,WPKRGTUJQTG ,WPKRGTWUEQPHGTVC2CTN

.GOQP8QNMCOGT %KVTWUXQNMCOGTKCPC  6GP2CUS

.GVVWEG .CEVWECUCVKXC.

.KEJGP .QDCTKCURR

.KEJGPRCTOGNKC (NCXQRCTOGNKCECRGTCVC

.KEJGPWODKNKECN 7ODKNKECTKCOCOOWNCVC

.KNCE 5[TKPICXWNICTKU.

.QEWUVDNCEM 4QDKPKCRUGWFQCECEKC.

.QEWUVJQPG[ )NGFKVUKCVTKCECPVJQU.

.WRKPG .WRKPWUDKEQNQT.KPFN

/CPIGN $GVCXWNICTKU.

/CRNGTGF #EGTTWDTWO.

/CRNGUWICT #EGTUCEEJCTWO/CTUJ

/KNMYGGF #UENGRKCUU[TKCEC.

/KNMYGGF #UENGRKCUUR

/KPV /GPVJCRKRGTKVC.

1CM%CNKHQTPKCDNCEM 3WGTEWUMGNNQIIKK0GYD

1CM%CP[QPNKXG3WGTEWUEJT[UQNGRKU.KGDO

1CMTGF 3WGTEWUTWDTC.

1CMYJKVG 3WGTEWUCNDC.

1CVU #XGPCUCVKXC.
B-4

1PKQP #NNKWOEGRC.

1TCPIG %KVTWUUKPGPUKU.1UDGEM

2GC 2KUWOUCVKXWO.

2GCEJ 2TWPWURGTUKEC.$CVUEJEX*CNHQTF

2GRRGT %CRUKEWOCPPWWO.

2GCT 2[TWUR[TKHQNKC4JFEXVJ%GPVWT[

2GVWPKC 2GVWPKCJ[DTKFC8KNO

2KPGGCUVGTPYJKVG 2KPWUUVTQDWU.

2KPG%QWNVGT 2KPWUEQWNVGTK&&QP

2KPG,GHHTG[ 2KPWULGHHTG[K)TGX$CNH

2KPGNQDNQNN[ 2KPWUVCGFC.

2KPGRKVEJ 2KPWUTKIKFC/KNN

2KPGRQPFGTQUC 2KPWURQPFGTQUC.CYU

2KPG5EQVU 2KPWUU[NXGUVTKU.

2KPGUJQTVNGCH 2KPWUGEJKPCVC/KNN

2KPG5KGTTCNQFIGRQNG 2KPWUEQPVQTVCXCTOWTTC[CPC)TGX$CNH

'PIGNO

2KPGUNCUJ 2KPWUGNNKQVVK'PINGOGZ8CUG[

2KPGUWICT 2KPWUNCODGTVKCPC&QWIN

2KPG6CDNG/QWPVCKP 2KPWURWPIGPU.COD

2KPG8KTIKPKC 2KPWUXKTIKPKCPC/KNN

2NCPG.QPFQP 2NCVCPWUZCEGTKHQNKC#KV9KNNF

2NCPVCKPRNCPVCIQEQOOQP 2NCPVCIQOCLQT.

2NWO 2TWPWUFQOGUVKEC.

2QKPUGVVKC 'WRJQTDKCRWNEJGTTKOC9KNNF

2QRNCTJ[DTKF 2QRWNWUOCZKOQYKE\KKZ2VTKEJQECTRC

2QRNCT[GNNQYQTVWNKR .KTKQFGPFTQPVWNKRKHGTC.

2QVCVQ 5QNCPWOVWDGTQUWO.

4CFKUJ 4CRJCPWUUCVKXWU.

4CFKUJ 4CRJCPWUUCVKXWU.EX%JGTT[$GNN

4CRGURTKPI $TCUUKECPCRWU.XCTPCRWU

4JQFQFGPFTQPC\CNGC 4JQFQFGPFTQPQDVWUWO.KPFN2NCPEJ

4KEGFQOGUVKE 1T[\CUCVKXC.

5CUUCHTCU 5CUUCHTCUCNDKFWO=0WVV?0GGU

5GSWQKCIKCPV 5GSWQKCFGPFTQPIKICPVGWO$WEJJQN\
B-5

5QTIJWOJ[DTKF 5QTIJWODKEQNQT./QGPEJZ5QTIJWOZ

FTWOOQPFKK5VGWFGN/KNNUR%JCUG

5Q[DGCP )N[EKPGOCZ./GTT

5RKPCEJ 5RKPCEGCQNGTCEGC.

5RTWEG0QTYC[ 2KEGCCDKGU.-CTUV

5RTWEGTGF 2KEGCTWDGPU5CTI

5RTWEGUKVMC 2KEGCUKVEJGPUKU$QPI%CTT

5VTCYDGTT[EWNVKXCVGF (TCICTKCZCPCPCUUC&WEJ

5VTCYDGTT[YKNF (TCICTKCXKTIKPKCPC&WEJ

5WPHNQYGT *GNKCPVJWUCPPWWU.

5MWPMDWUJ 4JWUVTKNQDCVC0WVV

5[ECOQTG 2NCVCPWUQEEKFGPVCNKU.

6KOQVJ[ 2JNGWORTCVGPUG.

6QDCEEQ 0KEQVKCPCVCDCEWO.

6QOCVQ .[EQRGTKEQPGUEWNGPVWO/KNN

8KTIKP	U$QYGT %NGOCVKUXKTIKPKCPC.

9CVGTOGNQP %KVTWNNWUNCPCVWU6JWPD/CUVUWO0CMCK

9JGCV 6TKVKEWOCGUVKXWO.