r/EPA
United Stales
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/AP-93/004a
December 1993
External Review Draft
Air Quality
Criteria for
Ozone and
Related
Photochemical
Oxidants
Review
Draft
(Do Not
Cite or
Quote)
Volume I of
Notice
This document is a preliminary draft. It has not been formally
released by EPA and should not at this stage be construed to
represent Agency policy. It is being circulated for comment on its
technical accuracy and policy implications.
-------
TECHNICAL REPORT DATA
(fieae read Instructions on iht reverie btfort comple
1. REPORT NO.
EPA/60Q/AP-93/Q04a
2.
4, TITLE AND SUBTITLE
Air Quality Criteria for Ozone and Related
Photochemical Oxidants - Volume I of III
7. AUTMOR(S)
B. PERFORMING ORGANIZATION NAME AND ADDRESS
Environmental Criteria and Assessment Office (MD-52)
Office of Health and Environmental Assessment, ORD
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
12. SPONSORING AQENCY NAME AND ADC
Office of Health and Enviror
Office of Research and Devel
U.S. Environmental Protectic
Washington, D.C. 20460
IRESS
mental Assessment (RD-689)
opment
m Agency
3.
S. REPORT DATE
December 1993
«. PERFORMING ORGANIZATION CODE
*. PERFORMINO ORGANIZATION REPORT NO,
BCftO-R-0746
10.PROORAM CLEMENT NO,
11. CONTHACT/QRANT NO,
t*.TVre OF REPORT AND PERIOD COVERED
External Review Draft
14. SPONSORING AGENCY CODE
600/21
IS. SUPPLEMENTARY NOTES
i«. ABSTRACT jj^ y «, Environmental Protection Agency (EPA) promulgates the National Ambient Air
Quality Standards (NAAQS) on the basis of scientific information contained in air quality criteria
documents. The previous ozone (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 used as the basis for a March 1993 decision by EPA that revision
of the existing 1-h NAAQS for Oj 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 63
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
the end of 1993.
17.
1. DESCRIPTORS
• KEY WORDS AND DOCUMENT ANALYSIS
b. IDENTIFIERS/OPEN ENDED TERMS
>
18. DISTRIBUTION STATEMENT
Release to Public
1i, SECURITY CLASS (This Krporr/
Unclassified
20. SECURITY CLASS {Thiipagej
Unclassified
c. COS ATI FieWGioup
21, NO. Of PAGES
457
22. PRICE
f PA F«r» 2220-1 (R«v. 4-77) pntviou* COITION i» OBSOLETE
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RAFT-DO NOT QUOTE OR CITE EPA/600/AP-93/004a
December 1993
External Review Draft
Air Quality Criteria for Ozone
and Related Photochemical Oxidants
Volume I of
NOTICE
This document is a preliminary draft. It has not been formally
released by EPA and should not at this stage be construed to
represent Agency policy. It is being circulated for comment on
Its technical accuracy and policy implications.
Environmental Criteria and Assessment Office
Office of Health and Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
Printed on Recycled Paper
-------
DISCLAIMER
This document is an external draft for review purposes only and does not constitute
U.S. Environmental Protection Agency policy. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
December 1993 I-ii DRAFT-DO NOT QUOTE OR CITE
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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 (Oj). This document,
therefore, focuses primarily on the scientific air quality criteria for O3 and, to a lesser extent,
for other photochemical oxidants like hydrogen peroxide and the peroxyacyl nitrates.
The EPA promulgates the NAAQS on the basis of scientific information contained in
air quality criteria documents. 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 hi January 1992. These documents were used as 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 63 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 the end of 1993.
This document was prepared and peer reviewed by experts from various state and
Federal governmental offices, academia, and private industry for use by EPA to support
decision making regarding potential risks to public health and welfare. The Environmental
Criteria and Assessment Office of EPA's Office of Health and Environmental Assessment
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.
December 1993 T_«; DBAFT-nn NOT nTTrrrn r\o
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Previous Page Blank
Air Quality Criteria for Ozone
and Other 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
Volume n
5. ENVIRONMENTAL EFFECTS OF OZONE AND
RELATED PHOTOCHEMICAL OXIDANTS 5-1
APPENDED 5A: COLLOQUIAL AND LATIN NAMES 5A-1
Volume HI
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; GLOSSARY OF TERMS AND SYMBOLS A-l
December 1993 I_v DRAFT-DO NOT OTTOTR m? rrra
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TABLE OF CONTENTS
Page
LIST OF TABLES I-xiii
LIST OF FIGURES I-xvii
LIST OF ABBREVIATIONS AND ACRONYMS . I-xxiii
AUTHORS, CONTRIBUTORS, AND REVIEWERS I-xxvii
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
2. INTRODUCTION 2-1
2.1 LEGISLATIVE BACKGROUND 2-2
2.2 REGULATORY BACKGROUND 2-2
2.3 SUMMARY OF MAJOR SCIENTIFIC TOPICS
PRESENTED 2-6
2.3.1 Air Chemistry 2-6
2.3.2 Air Quality 2-6
2.3.3 Environmental Effects 2-7
2.3.4 Health Effects 2-7
2.4 ORGANIZATION AND CONTENT OF THE DOCUMENT ... 2-8
REFERENCES . 2-11
3. TROPOSPHERIC OZONE AND ITS PRECURSORS 3-1
3.1 INTRODUCTION 3-1
3.2 TROPOSPHERIC OZONE CHEMISTRY 3-2
3.2.1 Background Information 3-2
3.2.2 Structure of the Atmosphere 3-4
3.2.2.1 Vertical and Horizontal Mixing in
the Atmosphere 3-5
3.2.2.2 Formation of Stratospheric Ozone 3-5
3.2.3 Tropospheric Ozone in the Unpolluted Atmosphere . . . 3-8
3.2.3,1 Tropospheric Hydroxyl Radicals 3-9
3,2.3.2 Tropospheric Nitrogen Oxides Chemistry .... 3-10
3.2.3.3 The Methane Oxidation Cycle 3-13
3.2.3.4 Cloud Processes in the
Methane-Dominated Troposphere 3-20
3.2.4 Photochemistry of the Polluted Atmosphere 3-20
3,2.4,1 Tropospheric Loss Processes of
Volatile Organic Compounds 3-22
3.2.4.2 Chemical Formation of Ozone in Polluted
Air 3-39
3.2.4.3 Hydrocarbon Reactivity with Respect to
Ozone Formation 3-44
December 1993
T .^:
rvp A TJT_nr» Krrvr nTTnrn rvn
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TABLE OF CONTENTS (coot'd)
Page
3.2.5 Photochemical Production of Aerosols 3-47
3.2.5.1 Phase Distributions of Organic Compounds . . . 3-48
3.2.5.2 Acid Deposition . . 3-50
3.3 METEOROLOGICAL PROCESSES INFLUENCING OZONE
FORMATION AND TRANSPORT 3-53
3.3.1 Meteorological Processes 3-53
3.3.1.1 Surface Energy Budgets 3-53
3.3.1.2 Planetary Boundary Layer 3-55
3.3.1.3 Cloud Venting 3-59
3.3.1.4 Stratospheric-Tropospheric Ozone
Exchange 3-60
3.3.2 Meteorological Parameters 3-61
3.3.2.1 Sunlight 3-62
3.3.2.2 Temperature 3-63
3.3.2.3 Wind Speed 3-70
3.3.2.4 Air Mass Characteristics 3-74
3.3.3 Normalization of Trends 3-75
3.4 PRECURSORS OF OZONE AND OTHER OXTOANTS 3-77
3.4.1 Sources and Emissions of Precursors 3-77
3.4.1,1 Introduction 3-77
3.4.1.2 Nitrogen Oxides 3-78
3.4.1.3 Volatile Organic Compounds 3-89
3.4.1.4 Relationship of Summertime Precursor
Emissions and Ozone Production 3-99
3.4.2 Concentrations of Precursor Substances in Ambient
Air 3-101
3.4.2.1 Nonmethane Organic Compounds 3-101
3.4.2.2 Nitrogen Oxides 3-106
3.4.2.3 Ratios of Concentrations of Nonmethane
Organic Compounds and Nitrogen Oxides .... 3-107
3.4.3 Source Apportionment and Reconciliation 3-108
3.4.3.1 Source Apportionment 3-108
3.4.3.2 Source Reconciliation 3-113
3.5 ANALYTICAL METHODS FOR OXIDANTS AND
THEIR PRECURSORS 3-115
3.5.1 Sampling and Analysis of Ozone and Other
Oxidants . 3-115
3.5.1.1 Ozone .... . . . .... . . 3-115
3.5.1.2 Peroxyacetyl Nitrate and Its Homologues .... 3-129
3.5.1.3 Gaseous Hydrogen Peroxide 3-134
3.5.2 Sampling and Analysis of Volatile Organic
Compounds 3-138
3.5.2.1 Introduction 3-138
3.5.2.2 Nonmethane Hydrocarbons 3-139
December 1993 I-viii DRAFT-DO NOT QUOTE OR CITE
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TABLE OF CONTENTS (cont'd)
Page
3.5.2.3 Carbonyl Species ................... 3-148
3.5.2.4 Polar Volatile Organic Compounds ........ 3-151
3.5.3 Sampling and Analysis of Oxides of Nitrogen ....... 3-152
3.5.3.1 Introduction ...... . ............... 3-152
3.5.3.2 Measurement of Nitric Oxide ..... ...... 3-153
3.5.3.3 Measurements for Nitrogen Dioxide ....... 3-157
3.5.3.4 Calibration Methods ...... .......... 3-166
3.6 OZONE AIR QUALITY MODELS ................... 3-167
3.6.1 Definitions, Description, and Uses .... ......... . 3-168
3.6.1.1 Grid-Based Models .......... ........ 3-169
3.6.1.2 Trajectory Models ... ............... 3-170
3.6.2 Model Components .... ................... 3-173
3.6.2.1 Emissions Inventory . . ............... 3-173
3.6.2.2 Meteorological Input to Air Quality
Models ......................... 3-175
3.6.2.3 Chemical Mechanisms ................ 3-182
3.6.2.4 Deposition Processes ................. 3-184
3.6.2.5 Boundary and Initial Conditions .......... 3-187
3.6.3 Urban and Regional Ozone Air Quality Models ...... 3-188
3.6.3.1 The Urban Airshed Model ............. 3-195
3.6.3.2 The Regional Oxidant Model ............ 3-196
3.6.3.3 The Regional Acid Deposition Model ...... 3-201
3.6.4 Evaluation of Model Performance ............. . 3-202
3.6.4.1 Model Performance Evaluation
Procedures ....................... 3-203
3.6.4.2 Performance Evaluation of Ozone Air
Quality Models .................... 3-205
3.6.4.3 Data Base Limitations ................ 3-207
3.6.5 Use of Ozone Air Quality Models for Evaluating
Control Strategies ........................ 3-209
3.6.6 Conclusions . . .......................... 3-211
3.7 SUMMARY AND CONCLUSIONS .................... 3-212
3.7.1 Tropospheric Ozone Chemistry ................ 3-212
3.7.1.1 Ozone in the Unpolluted Atmosphere ....... 3-212
3.7.1.2 Ozone Formation in the Polluted
Troposphere ...................... 3-212
3.7.2 Meteorological Processess Influencing Ozone
Formation and Transport .................... 3-215
3.7.2.1 Meteorological Processes .............. 3-215
3.7.2.2 Meteorological Parameters ............. 3-216
3.7.2.3 Normalization of Trends ......... ..... 3-217
3.7.3 Precursors ..... . ...................... 3-217
3.7.3.1 Nitrogen Oxides Emissions ............. 3-217
3.7.3.2 Volatile Organic Compound Emissions ...... 3-218
December 1993 T ;~ npA*5T_rw-v -vrrvr
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TABLE OF CONTENTS (cont'd)
3.7.3.3 Concentrations of Volatile Organic
Compounds in Ambient Air 3-219
3.7.3.4 Concentrations of Nitrogen Oxides in
Ambient Air 3-219
3.7.3.5 Source Apportionment and Reconciliation .... 3-220
3.7.4 Analytical Methods for Oxidants and Their
Precursors 3-221
3.7.4.1 Oxidants 3-221
3.7.4.2 Volatile Organic Compounds 3-223
3.7.4.3 Oxides of Nitrogen 3-224
3.7.5 Ozone Air Quality Models 3-225
3.7.5.1 Definitions, Descriptions, and Uses 3-225
3.7.5.2 Model Components 3-226
3.7.5.3 Evaluation of Model Performance 3-227
3.7.5.4 Use of Ozone Air Quality Model for
Evaluating Control Strategies . 3-227
3.7.5.5 Conclusions .... * 3-228
REFERENCES 3-229
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 4-6
4.2 TRENDS IN OZONE CONCENTRATIONS 4-9
4.2.1 Trends in Ambient Ozone Concentrations 4-9
4.3 SURFACE OZONE CONCENTRATIONS 4-19
4.3.1 Introduction 4-19
4.3.2 Urban Area Concentrations 4-21
4.3.3 Nonurban Area Concentrations 4-34
4.3.3.1 Pristine Areas 4-34
4.3.3.2 Urban-Influenced Nonurban Areas . 4-39
4.4 DIURNAL VARIATIONS IN OZONE CONCENTRATIONS . . 4-54
4.4.1 Introduction 4-54
4.4.2 Urban Area Diurnal Patterns 4-55
4.4.3 Nonurban Area Diurnal Patterns 4-60
4.5 SEASONAL PATTERNS IN OZONE CONCENTRATIONS . . . 4-67
4.5.1 Urban Area Seasonal Patterns 4-67
4.5.2 Nonurban Area Seasonal Patterns 4-67
4.5.3 Seasonal Pattern Comparisons with "Pristine" Sites . . . 4-69
4.6 SPATIAL VARIATIONS IN OZONE CONCENTRATIONS . . . 4-70
4.6.1 Urban-Nonurban Area Concentration Differences ..... 4-70
4.6.2 Concentrations Experienced at High-Elevation Sites ... 4-71
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Previous Page Blank
TABLE OF CONTENTS (cont'd)
4.6.3 Other Spatial Variations in Ozone Concentrations .... 4-74
4.7 INDOOR OZONE CONCENTRATIONS 4-82
4.8 ESTIMATING EXPOSURE TO OZONE 4-83
4.8.1 Introduction . 4-83
4.8.2 Fixed-Site Monitoring Information Used To Estimate
Population and Vegetation Exposure 4-87
4.8.3 Personal Monitors 4-90
4.8.4 Population Exposure Models , 4-90
4.8.5 Concentration and Exposures Used in Research
Experiments 4-92
4.9 CONCENTRATIONS OF PEROXYACETYL NITRATES IN
AMBIENT ATMOSPHERES 4-95
4.9.1 Introduction 4-95
4.9.2 Urban Area Peroxyacetyl Nitrate Concentrations 4-96
4.9.3 Concentration of Peroxyacetyl Nitrate and
Peroxypropionyl Nitrate in Rural Areas 4-100
4.10 CONCENTRATION AND PATTERNS OF HYDROGEN
PEROXIDE IN THE AMBIENT ATMOSPHERE 4-102
4.11 CO-OCCURRENCE OF OZONE 4-103
4.11.1 Introduction 4-103
4.11.2 Nitrogen Oxides 4-104
4.11.3 Sulfur Dioxide 4-105
4.11.4 Acidic Sulfate Aerosols 4-106
4.11.5 Acid Precipitation 4-108
4.11.6 Acid Cloudwater 4-110
4.12 SUMMARY 4-111
REFERENCES . 4-124
December 1993 I-*,' DRAFT-DO NOT niTrvrn nt»
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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 Oxides
of Nitrogen into the Earth's Atmosphere from
Biogenic and Anthropogenic Sources ................... 3-21
3-2 Calculated Tropospheric Lifetimes of Selected
Volatile Nonmethane Organic Compounds Due to
Photolysis and Reaction with Hydroxyl and NO3
Radicals and with Ozone 3-24
3-3 Calculated Incremental Reactivities of Selected
VOCs as a Function of the VOC/NOX Ratio for an
Eight-Component VOC Mixture and Low-Dilution
Conditions 3-47
3-4 Rates of Increase of Peak Ozone with Diurnal Maximum
Temperature for T < 300 K and T > 300 K, Based on
Measurements for April 1 to September 30, 1988 3-67
3-5 Recent Studies Examining Trends in Ozone Data After Removal of
Variability Associated with Meteorological Factors 3-77
3-6 Source Categories Used To Inventory Nitrogen Oxides
Emissions 3-79
3-7 1991 Emission Estimates for Manmade Sources of Nitrogen
Oxides in the United States 3-81
3-8 Recent Trends in Nitrogen Oxides Emissions for Major
Manmade Source Categories 3-84
3-9 Comparison of Estimates of Nitrogen Oxides Emissions
from Manmade Sources in the United States 3-86
3-10 Annual Nitrogen Oxides Emissions from Soils by
U.S. Environmental Protection Agency Region 3-88
3-11 Estimated 1991 Emissions of Volatile Organic Compounds
from Manmade Sources in the United States 3-90
3-12 Recent Trends in Emissions of Volatile Organic Compounds
from Major Categories of Manmade Sources 3-93
December 1993 o-—J:- LI—i- T.^ii DRAFT-DO NTOT OTTOTR nu PTTTT
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LIST OF TABLES (cont'd)
Number Page
3-13 Annual Biogenic Hydrocarbon Emission Inventory for the
United States 3-96
3-14 Annual Biogenic Hydrocarbon Emission Inventory by Month
and U.S. Environmental Protection Agency Region for the
United States 3-98
3-15 Performance Specifications for Automated Methods of Ozone
Analysis 3-117
3-16 Reference and Equivalent Methods for Ozone Designated
by the U.S. Environmental Protection Agency 3-118
3-17 List of Designated Reference and Equivalent Methods for
Ozone 3-119
3-18 Performance Specifications for Nitrogen Dioxide Automated
Methods 3-158
3-19 Comparability Test Specifications for Nitrogen Dioxide 3-158
3-20 Reference and Equivalent Methods for Nitrogen Dioxide
Designated by the U.S. Environmental Protection Agency 3-159
3-21 Grid-Based Urban and Regional Air Pollution Models: Overview
of Three-Dimensional Air Quality Models 3-190
3-22 Grid-Based Urban and Regional Air Pollution Models: Treatment
of Emissions and Spatial Resolution 3-191
3-23 Grid-Based Urban and Regional Air Pollution Models: Treatment
of Meteorological Fields, Transport and Dispersion 3-192
3-24 Grid-Based Urban and Regional Air Pollution Models: Treatment
of Chemical Processes 3-193
3-25 Grid-Based Urban and Regional Air Pollution Models: Treatment
of Cloud and Deposition Processes 3-194
3-26 Regional Oxidant Model Geographical Domains 3-197
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LIST OF TABLES (cont'd)
Number Page
3-27 Applications of Photochemical Air Quality Models to Evaluating
Ozone 3-210
4-1 Ozone Monitoring Season by State 4-8
4-2 Summary by Forestry and Agricultural Regions for Ozone Trends
Using the W126 Exposure Parameter Accumulated on a Seasonal
Basis . . . 4-18
4-3 The Highest Second Daily Maximum One-Hour Ozone
Concentration by Metropolitan Statistical Area for the Years
1989 to 1991 4-22
4-4 Summary of Percentiles of Hourly Average Concentrations for
the April-to-October Period 4-25
4-5 The Highest Second Daily Maximum Eight-Hour Average Ozone
Concentration by Metropolitan Statistical Area for the Years
1989 to 1991 4-27
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, and W126 Values for Sites in
Selected Class I Areas with Data Capture Greater
Than or Equal to 75% 4-36
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% 4-37
4-8 The Value of the W126 Sigmoidal Exposure Parameter
Calculated Over the Annual Period 4-38
4-9 The Value of the Ozone Season (Seven-Month) Average
of the Daily Seven-Hour (0900 to 1559 Hours)
Concentration 4-40
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IJST OF TABLES (cont'd)
Number Page
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) 4-42
4-11 Summary of Percentiles of Hourly Average Concentrations
for Electric Power Research Institute Sulfate Regional
Experiment Sites/ERAQS Ozone Monitoring
Sites 4-43
4-12 Seven-Hour Growing Season Mean, W126 Values, and
Number Greater Than or Equal to 80 ppb for Selected
Eastern National Dry Deposition Network Sites 4-45
4-13 Summary of Percentiles for National Dry Deposition
Network Monitoring Sites 4-48
4-14 Description of Mountain Cloud Chemistry Program
Sites 4-72
4-15 Seasonal (April to October) Percentiles, SUM06, SUM08,
and W126 Values for the Mountain Cloud Chemistry
Program Sites 4-73
4-16 Summary Statistics for 11 Integrated Forest
Study Sites 4-76
4-17 Quarterly Maximum One-Hour Ozone Values at
Sites in and Around New Haven, Connecticut, 1976 4-78
4-18 Summary of Reported Indoor-Outdoor Ozone
Ratios 4-84
4-19 Summary of Measurements of Peroxyacetyl Nitrate and
Peroxypropionyl Nitrate in Urban Areas 4-98
4-20 Summary of Measurements of Peroxyacetyl Nitrate and
Peroxypropionyl Nitrate in Rural Areas 4-101
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1JST OF FIGURES
Number Page
3-1 The cyclic reactions of tropospheric nitrogen oxides 3-13
3-2 Atmospheric reactions in the complete oxidation
of methane 3-17
3-3 Cyclic reactions of methane oxidation to formaldehyde,
conversion of nitric oxide to nitrogen dioxide, and concomitant
formation of ozone in the atmosphere 3-19
3-4 Major steps in production of ozone in ambient air 3-40
3-5 Time-concentration profiles for selected species during
irradiations of an NOx-propene-air mixture in an indoor
chamber with constant light intensity , 3-41
3-6 Time-concentration profiles for selected species during
irradiations of an NOx-propene-air mixture in an outdoor
chamber with diurnally varying light intensity 3-42
3-7 Surface radiation budget for short-wave and long-wave radiation . . 3-54
3-8 The number of reports of ozone concentrations ^120 ppb at
the 17 cities studied in Samson et al. (1988) 3-63
3-9 A scatter plot of maximum daily ozone concentration in Atlanta,
Georgia, and New York, New York, versus maximum daily
temperature 3-64
3-10 A scatter plot of maximum daily ozone concentration in Detroit,
Michigan, and Phoenix, Arizona, versus maximum daily
temperature 3-65
3-11 A scatter plot of maximum ozone concentration versus maximum
daily temperature for four nonurban sites 3-65
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
of a nationwide study, 1983 to 1985 3-71
December 1993 I-xvii DRAFT-DO NOT nTTrvrn nt»
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LIST OF FIGURES (cont'd)
Number
3-13 The frequency of 24-h trajectory transport distance en route to
city when ozone was S120 ppb in four New England cities
compared with the percent frequency distribution for all 17 cities
of a nationwide study, 1983 to 1985 . , 3-71
3-14 The root-mean-square-difference between CLASS
observations and profiler observations as a function of height
above ground level ..'.., 3-72
3-15 The root-mean-square-difference between CLASS
observations and lidar observations as a function of height
above ground level 3-73
3-16 Model of ozone levels using regression techniques . 3-76
3-17 Simulated versus observed ozone levels using regression
techniques on an independent data set obtained in summer 1992
in Atlanta, Georgia 3-76
3-18 The 50 largest sources of nitrogen oxides (power plants) in the
United States 3-82
3-19 Nitrogen oxides emissions from manmade sources in the
10 U.S. Environmental Protection Agency regions of the
United States, 1991 3-82
3-20 Changes in nitrogen oxides emissions from manmade sources in
the United States, 10-year intervals, 1940 through 1990 3-83
3-21 Growth in nitrogen oxides emissions from stationary source
fuel combustion and transportation from 1940 through
1990 . 3-83
3-22 Changes in emissions of volatile organic compounds from major
manmade sources in the United States, 10-year intervals, 1940
through 1990 3-92
3-23 Changes in emissions of volatile organic compounds from major
manmade sources, 1940 through 1990 . .-... 3-92
3-24 Estimated biogenic emissions of volatile organic compounds in
the United States as a function of season 3-100
December 1993 I-xviii DRAFT-DO NOT QUOTE OR CITE
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LIST OF FIGURES (cont'd)
Number Page
3-25 Example of EKMA diagram for high-oxidant urban area 3-172
3-26 Regional oxidant model superdomain with modeling domains .... 3-198
4-1 National trend in the composite average of the second highest
maximum one-hour ozone concentration at both NAMS and all
sites with 95% confidence intervals, 1982 to 1991 4-10
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 4-13
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 4-14
4-4 United States map of the highest second daily maximum one-hour
average ozone concentration by Metropolitan Statistical Area,
1991 4-21
4-5 The relationship between the second highest daily maximum
hourly average ozone concentration and the maximum three-
month SUM06 value and the second highest daily maximum
eight-hour average concentration and the maximum three-
month SUM06 value for specific site years at rural
agricultural sites for the 1980-to-1991 period 4-31
4-6 The relationship between the second highest daily maximum
hourly average ozone concentration and the maximum three-
month SUM06 value and the second highest daily maximum
eight-hour average concentration and the maximum three-
month SUM06 value for specific site years at rural forested
sites for the 1980-to-1991 period 4-32
4-7 The location of National Dry Deposition Network monitoring sites
as of December 1990 , , 4-44
4-8 The kriged 1985 to 1986 maximum seven-hour and twelve-hour
average concentrations of ozone across the United States 4-52
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LIST OF FIGURES (cont'd)
Number Page
4-9 The kriged estimates of the W126 integrated ozone exposure index
for the eastern United States for 1988 and 1989 4-53
4-10 The comparison of the seasonal diurnal patterns using 1988 data
for Jefferson County, Kentucky, and Oliver County, North
Dakota 4-56
4-11 Diurnal behavior of ozone at rural sites in the United States
in July 4-57
4-12 Diurnal pattern of one-hour ozone concentrations on July 13, 1979,
Philadelphia, Pennsylvania 4-58
4-13 Diurnal and one-month composite diurnal variations in ozone
concentrations, Washington, District of Columbia, July 1981 .... 4-59
4-14 Diurnal and one-month composite diurnal variations in ozone
concentrations, St. Louis County, Missouri, September 1981 .... 4-60
4-15 Diurnal and one-month composite diurnal variations in ozone
concentrations, Alton, Illinois, October 1981 (fourth quarter) .... 4-61
4-16 Composite diurnal patterns of ozone concentrations by quarter,
Alton, Illinois, 1981 4-62
4-17 Quarterly composite diurnal patterns of ozone concentrations
at selected sites representing potential for exposure of major
crops, 1981 4-63
4-18 Composite diurnal ozone pattern at a rural National Crop Loss
Assessment Network site in Argonne, Illinois, August 6 through
September 30, 1980 4-64
4-19 Composite diurnal ozone pattern at selected National Dry
Deposition Network sites 4-65
4-20 Composite diurnal pattern at Whiteface Mountain, New York, and
Mountain Cloud Chemistry Program's Shenandoah National
Park site for May to September 1987 4-66
4-21 Seasonal variations in ozone concentrations as indicated by
monthly averages and the one-hour maximum in each month at
selected sites, 1981 4-68
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LIST OF FIGURES (cont'd)
Number
4-22 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 4-74
4-23 Integrated exposures for three non-Mountain Cloud Chemistry
Program's Shenandoah National Park sites, 1983 to 1987 4-75
4-24 Maximum one-hour ozone concentrations (in parts per billion) and
average 8:00 a.m. through 8:00 p.m. strong acid concentrations
(expressed as micrograms per cubic meter of sulfuric acid) for
each day that pulmonary function data were collected at
Fairview Lake camp in 1988 4-89
4-25 Maximal one-hour ozone concentrations at Fairview Lake during
the study period 4-89
4-26 The number of occurrences for each of the seven categories
described in text 4-95
4-27 The co-occurrence pattern of ozone and sulfuric acid for
July 25, 1986 4-107
4-28 Sulfate, hydrogen ion, and ozone measured at Breadalbane Street
(Site 3) during July and August, 1986, 1987, and 1988 ....... 4-108
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ADOM
AGL
AQCD
AUSPEX
CAA
CAL-RAMS
CASAC
CBM
CCM
CFC(s)
CFR
CI
CIT
CL
CTWM
3-D
DIAL
DOAS
DWM
EKMA
EMS
EPA
EPRI
EPS
FDDA
GMEP
GRIDS
GTP
HCFC(s)
IR
K
LMOS
LT
LIST OF ABBREVIATIONS AND ACRONYMS
Acid Deposition and Oxidant Model
Above ground level
Air Quality Criteria Document
Atmospheric Utility Signatures, Predictions, and Experiments
Clean Air Act
Coast and Late Regional Atmospheric Modeling System
Clean Air Scientific Advisory Committee
Carbon Bond Mechanism (has several versions)
Community Climate Model
Chlorofluorocarbon(s)
Code of Federal Regulations
Chemical ionization
California Institute of Technology/Carnegie Institute
of Technology Model
Chemiluminescence
Complex Terrain Wind Model
Three-dimensional
Differential absorption lidar
Differential optical absorption spectrometry
Diagnostic Wind Model
Empirical Kinetic Modeling Approach (has several versions)
Emissions Modeling System
U.S. Environmental Protection Agency
Electric Power Research Institute
Emissions Preprocessor System
Four-dimensional data assimilation
Geoeoded Model of Emissions and Projections
Topography database operated by U.S. EPA
Gas-phase titration
Hydrochlorofluorocarbon (s)
Infrared radiation; in Section 3.2., "incremental reactivity"
Degrees Kelvin
Lake Michigan Oxidant Study
Local time
December 1993
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LIST OF ABBREVIATIONS AND ACRONYMS (cont'd)
M
MASS
MIDROXA
MM4/MM5
MOBILES
MODELS 3
NAAQS
NAPAP
NBS
NCAR
NECRMP
NEROS1
NEROXA
NIST
NMHC(s)
NMOC(s)
NO2
03
OAQPS
PAMS
PAN
PANs
PEL
PLANR
PF/TPLIF
PSD(s)
PVOC(s)
RADM
RAPS
RMSD
ROG
ROM
Third body in atmospheric chemical reactions; absorbs energy
Dynamic wind model used in STEM-n
Midwest domain of the ROM
Mesoscale Model, versions 4 and 5
U.S. EPA emissions model for mobile sources (version 5)
Modeling framework that consolidates all of U.S. EPA's
3-D photochemical air quality models
National Ambient Air Quality Standard(s)
National Acid Precipitation Assessment Program
National Bureau of Standards; has been renamed NIST
National Center for Atmospheric Research
Northeast Corridor Regional Modeling Project
Northeast Regional Oxidant Study; a northeast domain of the ROM
A northeast domain of the ROM
National Institute of Standards and Technology
Nonmethane hydrocarbon(s)
Nonmethane organic compound(s)
Nitrogen dioxide
Ozone
Office of Air Quality Planning and Standards
Photochemical Aerometric Monitoring System
Peroxyacetyl nitrate
Peroxyacyl nitrates
Planetary boundary layer
Practice for Low-cost Application in Nonattainment Regions
Photofragmentation TPLIF
Passive sampling device(s)
Polar volatile organic compound(s)
Regional Acid Deposition Model (has several versions)
Regional Air Pollution Study (Illinois and Missouri)
Root-mean-square difference
Reactive organic gas(es)
Regional Oxidant Model (has several versions)
December 1993
I-xxiv DRAFT-DO NOT QUOTE OR CITE
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LIST OF ABBREVIATIONS AND ACRONYMS (cont'd)
ROMNET
SAB
SAPRC
SARMAP
SCAQS
SCCCAMP
SEROS1
SEROXA
SIP(s)
SJVAQS
SOS
SRM(s)
STEM-H
SUPROXA
TDLAS
TEA
TPL1F
TTFMS
UAM
UV
UV-B
VMT
VOC(s)
A northeast domain of the ROM; also, Regional Ozone Modeling for
Northeast Transport program
Science Advisory Board
Statewide Air Pollution Research Center, University
of California, Riverside
SJVAQS/AUSPEX Regional Model Adaptation Project
South Coast Air Quality Study (California)
South Central Coast Cooperative Aerometric Monitoring Program
(California)
A southeast domain of the ROM
A southeast domain of the ROM
State Implementation Plan(s)
San Joaquin Valley Air Quality Study
Southern Oxidant Study
Standard Reference Material(s)
Sulfur Transport Eulerian Model (version n)
Super domain of the ROM
Tunable-diode laser absorption spectroscopy
Triethanolamine
Southern domain of the ROM
Two-photon laser-induced fluorescence
Two-tone frequency-modulated spectroscopy
Urban Airshed Model (has several versions)
Ultraviolet radiation
Ultraviolet radiation of wavelengths 280 to 320 nanometers
Vehicle miles traveled
Volatile organic compound(s)
December 1993
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
CHAPTER 1. EXECUTIVE SUMMARY
Principal Authors
Mr. James A. Raub—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. William G. Ewald—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. J.H.B. Garner—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Judith A. Graham—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Beverly E. Tilton—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
CHAPTER 2. INTRODUCTION
Principal Author
Mr. James A. Raub—Environmental Criteria and Assessment Office (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—Environmental Criteria and Assessment Office (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—BatteUe, 505 King Avenue, Columbus, OH 43201
Dr. Thomas J. Kelly—Battelle, 505 King Avenue, Columbus, OH 43201-2693
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)
Dr. Charles W. Lewis—Atmospheric Research and Exposure Assessment 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 JJ—Atmospheric Research and Exposure Assessment Laboratory (MD-75),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Beverly E. Tilton—Environmental Criteria and Assessment Office (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—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Robert R. Arnts—Atmospheric Research and Exposure Assessment Laboratory (MD-84)
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Frank M. Black—Atmospheric Research and Exposure Assessment Laboratory (MD-46),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Joseph J. Bufalini—Atmospheric Research and Exposure Assessment Laboratory
(MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Daewon Byun—Atmospheric Research and Exposure Assessment Laboratory (MD-80),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Jason K, S. Ching—Atmospheric Research and Exposure Assessment Laboratory
(MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Kenneth L. Demerjian—Atmospheric Sciences Research Center (SUMY-Albany),
100 Fuller Road, Albany NY 12205
Dr. Robin L. Dennis—Atmospheric Research and Exposure Assessment Laboratory (MD-80),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)
Dr. Basil Dimitriades—Atmospheric Research and Exposure Assessment Laboratory (MD-75),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Marcia C. Dodge—Atmospheric Research and Exposure Assessment Laboratory
(MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Chris Geron—Atmospheric Environmental Engineering 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—Atmospheric Research and Exposure Assessment Laboratory
(MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Jimmie W. Hodgeson—Atmospheric Research and Exposure Assessment 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—Atmospheric Research and Exposure Assessment Laboratory
(MD-47), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. William A. Lonneman—Atmospheric Research and Exposure Assessment Laboratory
(MD-84), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. William A. McClenny—Atmospheric Research and Exposure Assessment Laboratory
(MD-44), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Frank F. McElroy—Atmospheric Research and Exposure Assessment 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
Dr. Edwin L. Meyer—Office of Air Quality Planning and Standards (MD-14),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)
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—Atmospheric Research and Exposure Assessment Laboratory
(MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Larry J. Purdue—Atmospheric Research and Exposure Assessment Laboratory (MD-56),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Kenneth A. Rehme—Atmospheric Research and Exposure Assessment 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. RoseUe—Atmospheric Research and Exposure Assessment Laboratory
(MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Kenneth L. Schere—Atmospheric Research and Exposure Assessment Laboratory
(MD-80), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Jack H. Shreffler—Atmospheric Research and Exposure Assessment Laboratory (MD-75),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Joseph Sickles n—Atmospheric Research and Exposure Assessment Laboratory (MD-75),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Robert L. Seila—Atmospheric Research and Exposure Assessment Laboratory (MD-84),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Beverly E. TUton—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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
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CHAPTER 4. ENVIRONMENTAL CONCENTRATIONS, PATTERNS,
AND EXPOSURE SiTIMATlS
Principal Authors
Dr. Allen S. Lefohn—A.S.L, & Associates, 111 Last Chanee Gulch, Suite 4A,
Helena, MT 59601
Dr. A. Paul Altshuller—Environmental Criteria and Assessment Office (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—Atmospheric Research and Exposure Assessment Laboratory (MD-56),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. William G. Ewald—Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Paric, 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 Research and Development Center, Warren, MT 48090
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—Office of Air Quality Planning and Standards (MD-12),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Cornelius J. Nelson—Atmospheric Research and Exposure Assessment 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
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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, Environmental Criteria and Assessment Office
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. A. Paul Altshuller—Physical Scientist, Environmental Criteria and Assessment Office
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. William G. Ewald—Health Scientist, Environmental Criteria and Assessment Office
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. J.H.B. Garner—Ecologist, Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Judith A. Graham—Associate Director, Environmental Criteria and Assessment Office
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Ellie R. Speh—Secretary, Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Beverly E. Tilton—Physical Scientist, Environmental Criteria and Assessment Office
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Technical Support Staff
Mr. Douglas B. Fennell—Technical Information Specialist, Environmental Criteria and
Assessment Office (MD-52), U.S. Environmental Protection Agency, Research Triangle Park,
NC 27711
Mr. Allen G. Hoyt—Technical Editor and Graphic Artist, Environmental Criteria and
Assessment Office (MD-52), U.S. Environmental Protection Agency, Research Triangle Park,
NC 27711
Ms, Diane H. Ray—Technical Information Manager (Public Comments), Environmental
Criteria and Assessment Office (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC 27711
Mr. Richard N. Wilson—Clerk, Environmental Criteria and Assessment Office (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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U.S. ENVIRONMENTAL PROTECTION AGENCY
PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA
FOR OZONE AND RELATED PHOTOCHEMICAL OXIDANTS
(cont'd)
Document Production Staff
Ms. Marianne Barrier—Graphic Artist, ManTech Environmental, 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—Lead 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. Wendy B. Lloyd—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
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
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i 2. INTRODUCTION
2
3
4 The photochemical oxidants found in ambient air in the highest concentrations are
5 ozone (O3) and nitrogen dioxide (NC^). Other oxidants, such as hydrogen peroxide and the
6 peroxyacyl nitrates, have also been observed, but in lower and less certain concentrations.
7 In 1971, the U.S. Environmental Protection Agency (EPA) promulgated National Ambient
8 Air Quality Standards (NAAQS) to protect the public health and welfare from adverse effects
9 of photochemical oxidants. In 1979, the chemical designation of the standards was changed
10 from photochemical oxidants to O3. This document, therefore, focuses primarily on the
11 scientific air quality criteria for O3 and, to a lesser extent, for hydrogen peroxide and the
12 peroxyacyl nitrates, particularly peroxyacetyl nitrate. The scientific air quality criteria for
13 N02 are discussed in a separate document (U.S. Environmental Protection Agency, 1993),
14 The previous O3 criteria document, Air Quality Criteria for Ozone and Other
15 Photochemical Oxidants (U.S. Environmental Protection Agency, 1986) was released by
16 EPA in August 1986 and a supplement, Summary of Selected New Information on Effects of
17 Ozone on Health and Vegetation (U.S. Environmental Protection Agency, 1992), was
18 released in January 1992. These documents were used as the basis for a March 1993
19 decision by EPA that revision of the existing 1-h NAAQS for O3 was not appropriate at that
20 time. That decision did not take into account newer scientific data that became available
21 after completion of the 1986 criteria document. The purpose of this document is to
22 summarize the air quality criteria for O3 available in the published literature through the end
23 of 1993. This review was performed in accordance with provisions of the Clean Air Act
24 (CAA) to provide the scientific basis for periodic reevaluation of the O3 NAAQS.
25 This chapter provides a general introduction to the legislative and regulatory
26 background for decisions on the O3 NAAQS, as well as a general summary of the
27 organization, content, and major scientific topics presented in this document.
28
29
30
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1 2.1 LEGISLATIVE BACKGROUND
2 Two sections of the CAA govern the establishment, review, and revision of the
3 NAAQS. Section 108 (U.S. Code, 1991) directs the Administrator of EPA to identify
4 ubiquitous pollutants that may reasonably be anticipated to endanger public health or welfare
5 and to issue air quality criteria for them. These air quality criteria are to reflect the latest
6 scientific information useful in indicating the kind and extent of all identifiable effects on
7 public health or welfare that may be expected from the presence of the pollutant in ambient
8 air.
9 Section 109(a) of the CAA (U.S. Code, 1991) directs the Administrator of EPA to
10 propose and promulgate primary and secondary NAAQS for pollutants identified under
11 Section 108, Section 109(b)(l) defines a primary standard as one the attainment and
12 maintenance of which, in the judgment of the Administrator and based on the criteria and
13 allowing for an adequate margin of safety, is requisite to protect the public health. The
14 secondary standard, as defined in Section 109(b)(2), must specify a level of air quality the
15 attainment and maintenance of which, in the judgment of the Administrator and based on the
16 criteria, is requisite to protect the public welfare from any known or anticipated adverse
17 effects associated with the presence of the pollutant in ambient air.
18 Section 109(d) of the CAA (U.S. Code, 1991) requires periodic review and, if
19 appropriate, revision of existing criteria and standards. If, in the Administrator's judgment,
20 the EPA's review and revision of criteria make appropriate the proposal of new or revised
21 standards, such standards are to be revised and promulgated in accordance with
22 Section 109(b). Alternatively, the Administrator may find that revision of the standards is
23 inappropriate and conclude the review by leaving the existing standards unchanged.
24
25
26 2.2 REGULATORY BACKGROUND*
27 On April 30, 1971, the EPA promulgated primary and secondary NAAQS for
28 photochemical oxidants under Section 109 of the CAA (Federal Register, 1971). These were
29 This text is excerpted and adapted from the Praposed Decision on the National Ambient Air Quality
30 Standards for Ozone (Federal Register, 1992).
31
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] set at an hourly average of 0.08 ppm total photochemical oxidants not to be exceeded more
2 than 1 h per year. On April 20, 1977, the EPA announced (Federal Register, 1977) the first
3 review and updating of the 1970 Air Quality Criteria Document (AQCD) for Photochemical
4 Oxidants in accordance with Section 109(d) of the CAA. In preparing the AQCD, the EPA
5 made two external review drafts of the document available for public comment, and these
6 drafts were peer reviewed by the Subcommittee on Scientific Criteria for Photochemical
7 Oxidants of EPA's Science Advisory Board (SAB). A final revised AQCD for 03 and other
8 photochemical oxidants was published on June 22, 1978.
9 Based on the 1978 revised AQCD and taking into account the advice and
10 recommendations of the Subcommittee, and the comments received from the public, the EPA
11 announced (Federal Register, 1979) a final decision to revise the NAAQS for photochemical
12 oxidants on February 8, 1979. The final ruling revised the level of the primary standard
13 from 0,08 ppm to 0.12 ppm, set the secondary standard identical to the primary standard,
14 changed the chemical designation of the standards from photochemical oxidants to O3, and
15 revised the definition of the point at which the standard is attained to "when the expected
16 number of days per calendar year with maximum hourly average concentrations above
17 0.12 ppm is equal to or less than one" (see Table 2-1).
18
TABLE 2-1. NATIONAL AMBIENT AIR QUALITY STANDARDS FOR OZONE
Date of Promulgation Primary and Secondary NAAQS Averaging Time
February 8; 1979 0. 12 ppma (235 ^g/m3) 1 hb
al ppm = 1962 jtg/m3, 1 pglm = 5.097 X IO"4 ppm @ 25 °C, 760 mm Hg.
The standard is attained when the expected number of days per calendar year with a maximum hourly average
concentration above 235 ng/m (0, 12 ppm) is equal to or less than one.
1 On March 17, 1982, in response to requirements of Section 109(d) of the CAA, the
2 EPA announced (Federal Register, 1982) that it was undertaking plans to revise the existing
3 1978 AQCD for O3 and other photochemical oxidants, and on August 22, 1983, it announced
4 (Federal Register, 1983) that review of the primary and secondary NAAQS for O3 had been
5 initiated. Public peer-review workshops on draft chapters of the revised AQCD were held on
6 December 15-17, 1982, and on November 16-18, 1983. The EPA considered comments
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1 made at both workshops in preparing the first external review draft that was made available
2 (Federal Register, 1984) on July 24, 1984, for public review.
3 On February 13, 1985 (Federal Register, 1985), and on April 2, 1986 (Federal
4 Register, 1986), the EPA announced two public meetings of the Clean Air Scientific
5 Advisory Committee (CASAC) of EPA's SAB to be held on March 4-6, 1985, and on April
6 21-22, 1986, respectively. At these meetings, the CASAC reviewed external review drafts
7 of the revised AQCD for 03 and other photochemical oxidants. After completion of this
8 review, the CASAC sent the Administrator of EPA a closure letter, dated October 22, 1986,
9 indicating that the document "represents a scientifically balanced and defensible summary of
10 the extensive scientific literature." The EPA released the final draft document in August
11 1986.
12 The first draft of the Staff Paper "Review of the National Ambient Air Quality
13 Standards for Ozone: Assessment of Scientific and Technical Information" was reviewed by
14 CASAC at the public meeting on April 21-22, 1986. At that meeting, the CASAC
15 recommended that new information on prolonged exposure effects of 03 be considered in a
16 second draft of the Staff Paper prior to closure. The CASAC reviewed this second draft and
17 also a presentation of new and emerging information on the health and welfare effects of
18 O3 at a public review meeting held on December 14-15, 1987. The CASAC concluded that
19 sufficient new information existed to recommend incorporation of relevant new data into a
20 supplement to the 1986 AQCD (O3 Supplement) and in a third draft of the Staff Paper,
21 A draft O3 Supplement, "Summary of Selected New Information on Effects of Ozone
22 on Health and Vegetation: Draft Supplement to Air Quality Criteria for Ozone and Other
23 Photochemical Oxidants," and the revised Staff Paper were made available to CASAC and to
24 the public for review in November 1988. The O3 Supplement reviewed and evaluated
25 selected literature concerning exposure- and concentration-response relationships observed for
26 health effects in humans and experimental animals and for vegetation effects. This literature
27 appeared as peer-reviewed journal publications or as proceedings papers from 1986 through
28 late 1988.
29 On December 14-15, 1988, CASAC held a public meeting to review these documents.
30 The CASAC sent the Administrator a closure letter dated May 1, 1989, indicating that the
31 draft O3 Supplement, along with the 1986 AQCD, and the draft Staff Paper "provide an
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1 adequate scientific basis for the EPA to retain or revise the primary and secondary standards
2 of ozone." The CASAC concluded that it would be some time before enough new
3 information on the health effects of miillihour and chronic exposure to O3 would be
4 published in scientific journals to receive full peer review and, thus, be suitable for inclusion
5 in a criteria document. The CASAC further concluded that such information could better be
6 considered in the next review of the Qj NAAQS. A final version of the O3 Supplement has
7 been published (U.S. Environmental Protection Agency, 1992).
8 On October 22, 1991, the American Lung Association and other plaintiffs filed suit to
9 compel the EPA to complete the review of the criteria and standards for O3 in accordance
10 with the CAA. The U.S. District Court for the Eastern District of New York subsequently
11 issued an order requiring the EPA to announce its proposed decision on whether to revise the
12 standards for O3 by August 1, 1992, and to announce its final decision by March 1, 1993.
13 The proposed decision on O3 appearing in the Federal Register on August 10, 1992
14 (Federal Register, 1992), indicated that revision of the existing 1-h NAAQS was not
15 appropriate at that time. A public hearing on this proposal took place on September 1, 1992,
16 at the EPA Education Center in Washington, DC, and public comments were received
17 through October 9, 1992. The final decision was published in the Federal Register on
18 March 9, 1993 (Federal Register, 1993). This decision does not take into consideration a
19 number of recent studies on the health and welfare effects of O3 that have been published
20 since the last literature review in early 1989. The EPA estimates that approximately 3 years
21 will be necessary to (1) incorporate the new studies into a revised criteria document,
22 (2) complete mandated CASAC review, (3) evaluate the significance of the key information
23 for regulatory decision-making purposes, and (4) publish a proposed decision on the
24 O3 NAAQS in the Federal Register.
25 As stated in the 1993 final decision, the EPA's Environmental Criteria and Assessment
26 Office in Research Triangle Park, NC, is proceeding as rapidly as possible with the next
27 periodic review of the air quality criteria for O3. Under the processes established in
28 Sections 108 and 109 of the CAA and refined by the EPA and CASAC, the EPA began by
29 announcing the commencement of the review in the Federal Register. After assessing and
30 evaluating the pertinent new studies, the EPA has prepared a preliminary draft of a revised
31 criteria document and subjected it successively to review at expert peer-review workshops.
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1 Comments received at the workshops were used to revise the preliminary draft for external
2 review. Once the public and CASAC have reviewed the external review draft of die revised
3 criteria document, thus providing a preliminary basis for review of the existing standards,
4 EPA's Office of Air Quality Planning and Standards (OAQPS) will complete their
5 preparation of a draft Staff Paper assessing the most significant information contained in the
6 draft criteria document and will develop recommendations for revisions, if appropriate, to the
7 NAAQS for O3. Subsequent reviews by the public and by CASAC will occur, as warranted.
8
9
10 2.3 SUMMARY OF MAJOR SCIENTIFIC TOPICS PRESENTED
11 A number of separate topics and issues are addressed in this revised O3 criteria
12 document. Some of the key topics and issues addressed are highlighted below by document
13 section.
14
15 2.3.1 Air Chemistry
16 • What concerns still exist regarding precision and accuracy of measurements
17 of O3 and its precursors?
18
19 • What is the order of magnitude of current estimates of natural emissions of
20 O3 precursors and emissions from anthropogenic sources and their relevance
21 to tropospheric O3 photochemistry?
22
23 • What new scientific information exists on the roles of meteorologic and
24 climatologic factors in O3 formation and transport?
25
26 • Are the reaction pathways of all major precursors to O3 understood? Have
27 all major reaction products been identified? How are the reactions and
28 products represented in air quality models?
29
30 • What is the status of development, application, evaluation, and verification
31 of air quality models?
32
33
34 2.3.2 Air Quality
35 • What are the trends and geographic differences in O3 concentrations across
36 the United States?
37
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] • What are diurnal and seasonal patterns of 1-h average O3 concentrations for
2 urban and nonurban sites, for aUainment versus nonattainment areas?
3
4 • What is known about patterns of co-occurrence of O3 with other pollutants
5 in the atmosphere?
6
7 • What O3 exposure assessment data are available for agricultural crops and
8 for forests?
9
10 • To what level and to what extent are humans typically exposed to O3 in the
11 course of normal everyday activities?
12
13
14 2.3.3 Environmental Effects
15 • What are the effects of ambient O3 concentrations on vegetation (i.e.,
16 agricultural and horticultural crops; urban landscape trees, shrubs, and
17 flowers; forest tree species)?
18
19 • What characteristics of air quality (e.g., summary statistics) are relevant to
20 these effects on vegetation?
21
22 • What are the long-term effects of O3 exposures on natural ecosystems?
23
24 • Is there important new information on the effects of O3 on nonbiological
25 materials?
26
27
28 2,3.4 Health Effects
29 • What O3 concentration and exposure duration relationships exist for effects
30 on lung structure, function, and host defense mechanisms and what are the
31 important modifiers of these effects?
32
33 • What are the mechanisms of O3-induced lung injury?
34
35 • Can dosimetry models predict human population responses to O3 on the
36 basis of laboratory animal data?
37
38 • Does long-term exposure to O3 lead to the development of chronic lung
39 disease or to an increased frequency or exacerbation of other chronic
40 respiratory outcomes?
41
42 • What segment(s) of the population are most susceptible to effects from
43 exposure to O3?
44
45
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1 2.4 ORGANIZATION AND CONTENT OF THE DOCUMENT
2 This document critically evaluates and assesses scientific information on the health and
3 welfare effects associated with exposure to the concentrations of Oj and related
4 photochemical oxidants in ambient air. Although the document is not intended to be an
5 exhaustive literature review, it is intended to cover all pertinent literature through 1993. The
6 references cited in the document should be reflective of the state of knowledge on those
7 issues most relevant to review of the NAAQS for 63, now set at 0.12 ppm for 1 h.
8 Although emphasis is placed on the presentation of health and welfare effects data, other
9 scientific data will be presented and evaluated in order to provide a better understanding of
10 the nature, sources, distribution, measurement, and concentrations of O3 and related
11 photochemical oxidants in ambient air, as well as the characterization of population exposure
12 to these pollutants.
13 To aid in the development of this document, summary tables of the relevant published
14 literature have been provided to supplement a selective discussion of the literature. Most of
15 the scientific information selected for review and comment in the text comes from the more
16 recent literature published since completion of the previous O3 criteria document (U.S.
17 Environmental Protection Agency, 1986). Some of these newer studies were briefly
18 reviewed in the supplement to that document (U.S. Environmental Protection Agency, 1992)
19 but more intense evaluation of these studies has been included. Emphasis is placed on studies
20 conducted at or near O3 concentrations found in ambient air. Other studies, however, are
21 included if they contain unique data, such as the documentation of a previously unreported
22 effect or of a mechanism of an effect; or if they were multiple-concentration studies designed
23 to provide exposure-response relationships. Generally, O3 concentration is not an issue for
24 human clinical or epidemiology studies; however, for animal toxicology studies, typically
25 only those studies conducted at less than 1 ppm Oj are considered. Studies that are
26 presented in the previous criteria document and whose data were judged to be significant
27 because of their usefulness in deriving the current NAAQS are briefly discussed in the text.
28 The reader should, however, consult the more extensive discussion of these "key" studies in
29 the previous document. Other, older studies are also discussed in the text if they were
30 judged to be (1) open to reinterpretation because of newer data, or (2) potentially useful in
31 deriving revised standards for 03. Generally, only published information that has undergone
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1 scientific peer review is included in the criteria document. Some newer studies not published
2 in the open literature but meeting high standards of scientific reporting and review have been
3 included.
4 Certain issues of direct relevance to standard setting are not explicitly addressed in this
5 document, but are instead analyzed in documentation prepared by OAQPS as part of its
6 regulatory analyses. Such analyses include (1) a discussion of what constitutes an "adverse
7 effect" and delineation of particular adverse effects that the primary and secondary NAAQS
8 are intended to protect against, (2) exposure analyses and assessment of consequent risk, and
9 (3) a discussion of factors to be considered in determining an adequate margin of safety.
10 Key points and conclusions from such analyses are summarized in the Staff Paper prepared
11 by OAQPS and reviewed by CAS AC. Although scientific data contribute significantly to
12 decisions regarding the above issues, their resolution cannot be achieved solely on the basis
13 of experimentally acquired information. Final decisions on items (1) and (3) are made by the
14 Administrator, as mandated by the CAA.
15 A fourth issue directly pertinent to standard setting is identification of populations at
16 risk, which is basically a determination by EPA of the subpopulation(s) to be protected by
17 the promulgation of a given standard. This issue is addressed only partially in this
18 document. For example, information is presented on factors, such as preexisting disease,
19 that may biologically predispose individuals and subpopulations to adverse effects from
20 exposures to O3. The identification of a population at risk, however, requires information
21 above and beyond data on biological predisposition, such as information on levels of
22 exposure, activity patterns, and personal habits. Such information is included in the Staff
23 Paper developed by OAQPS.
24 The structure of this document includes, first, an Executive Summary and Conclusions
25 (Chapter 1) providing a concise presentation of key information and conclusions from all
26 subsequent chapters. This is followed by this brief Introduction (Chapter 2) containing
27 information on the legislative and regulatory background for review of the 03 NAAQS, as
28 well as an overview of the organization of this document. Chapter 3 provides information on
29 the chemistry, sources, emissions, measurement, and transport of O3 and related
30 photochemical oxidants and their precursors, whereas Chapter 4 covers environmental
31 concentrations, patterns, and exposure estimates of O3 and oxidant air quality. This is
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1 followed by Chapter 5, dealing with environmental effects of Oj and related photochemical
2 oxidants. Chapters 6, 7, and 8 discuss, respectively, animal lexicological studies, human
3 health effects, and extrapolation of animal lexicological data to humans. The last chapter,
4 Chapter 9, provides an integrative, interpretive evaluation of health risks associated with
5 exposure to O3.
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1 REFERENCES
2
3 Federal Register. (1971) National primary and secondary ambient air quality standards. F. R. (April 30)
4 36: 8186-8201.
5
6 Federal Register. (1977) Review of the photochemical oxidantand hydrocarbon air quality standards. F. R.
7 (April 20) 42: 20493-20494.
8
9 Federal Register. (1979) National primary and secondary ambient air quality standards: revisions to the national
10 ambient air quality standards for photochemical oxidants. F. R. (February 8) 44: 8202-8221.
11
12 Federal Register. (1982) Air quality criteria document for ozone and other photochemical oxidants. F. R,
13 (March 17)47: 11561.
14
15 Federal Register. (1983) Review of the national ambient air quality standards for ozone. F. R. (August 22)
16 48:38009.
17
18 Federal Register. (1984) Draft air quality criteria document for ozone and other photochemical oxidants. F. R.
19 (July 24) 49: 29845.
20
21 Federal Register, (1985) Science Advisory Board; Clean Air Scientific Advisory Committee; open meeting. F. R.
22 (February 13) 50: 6049.
23
24 Federal Register. (1986) Science Advisory Board; Clean Air Scientific Advisory Committee; open meeting. F. R.
25 (April 2)51: 11339.
26
27 Federal Register. (1992) National ambient air quality standards for ozone; proposed decision. F. R. (August 10)
28 57: 35542-35557.
29
30 Federal Register. (1993) National ambient air quality standards for ozone—final decision. F. R. (March 9)
31 58: 13008-13019,
32
33 U.S. Code. (1991) Clean Air Act, §108, air quality criteria and control techniques, §109, national ambient air
34 quality standards. U.S. C. 42: §§7408-7409.
35
36 U.S. Environmental Protection Agency. (1986) Air quality criteria for ozone and other photochemical oxidants,
37 Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria
38 and Assessment Office; EPA report nos. EPA-600/8-84-020aF-eF. 5v. Available from: NTIS,
39 Springfield, VA; PB87-142949.
40
41 U.S. Environmental Protection Agency. (1992) Summary of selected new information on effects of ozone on
42 health and vegetation: supplement to 1986 air quality criteria for ozone and other photochemical oxidants.
43 Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria
44 and Assessment Office; EPA report no. EPA/600/8-88/105F. Available from: NTIS, Springfield, VA;
45 PB92-235670.
46
47 U.S. Environmental Protection Agency, (1993) Air quality criteria for oxides of nitrogen. Research Triangle
48 Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment
49 Office; EPA review draft no. EPA/600/8-91/049F.
50
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i 3. TROPOSPHERIC OZONE AND ITS PRECURSORS
2
3
4 3.1 INTRODUCTION
5 Ozone and other oxidants found in ambient air, such as the peroxyacyl nitrates and
6 hydrogen peroxide, are formed as the result of atmospheric physical and chemical processes
7 involving two classes of precursor pollutants, volatile organic compounds (VOCs) and
8 nitrogen oxides (NOX). The formation of ozone and other oxidants from these precursors is
9 a complex, nonlinear function of many factors, including the intensity and spectral
10 distribution of sunlight; atmospheric mixing and related meteorological conditions; the
11 concentrations of the precursors hi ambient air and, within reasonable concentration ranges,
12 the ratio between VOC and NOX (VOC/NOj); and the reactivity of the organic precursors.
13 An understanding of the atmospheric chemistry and meteorological parameters and
14 processes responsible for the formation and occurrence of elevated concentrations of ozone in
15 ambient air is basic to the formulation of strategies and techniques for its abatement. Such
16 an understanding is required for representing those parameters and processes adequately in
17 predictive models used to determine the emission reductions needed for complying with the
18 NAAQS for ozone. In addition, the identification and quantification of ozone precursors in
19 ambient air is essential, along with emission inventories or emission models, or both, for the
20 development, verification, and refinement of photochemical air quality models and for
21 comparisons of ambient concentrations with emission inventories as a check on the accuracy
22 of measurements and of inventories.
23 Product identification and quantification of yields, in chambers and in ambient air, are
24 helpful in the verification of photochemical air quality models and in testing theoretical
25 chemical mechanisms. Likewise, product identification and quantification are useful in
26 determining the need for research on the potential effects of the simultaneous or sequential
27 co-occurrence with ozone and related oxidants of multiple air pollutants.
28 The ability to measure ozone and its precursors, its reaction products, and the products
29 of the atmospheric reactions of its respective precursors is essential for (1) understanding
30 atmospheric chemistry of ozone formation, (2) for verifying chemical mechanisms, (3) for
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1 verifying models, (4) for quantifying emission rates, and (5) for adequately characterizing
2 exposure-response factors for both biological and nonbiological receptors.
3 For these reasons, this chapter presents information on a broad range of topics. The
4 chapter describes the chemical processes by which ozone and other photochemical oxidants
5 are formed in ambient air (Section 3.2). It also characterizes the nature of the precursors in
6 terms of their sources and emissions into the atmosphere and their concentrations in ambient
7 air (Section 3.4), and methods by which their concentrations in ambient air are measured
8 (Section 3.5).
9 In addition to information on the chemistry of oxidants and their precursors, the chapter
10 includes a discussion of meteorological processes (Section 3.3) that contribute to the
11 formation of ozone and other oxidants and that govern their transport and dispersion once
12 formed. Finally, an overview is given (Section 3.6) of models of the relationships between
13 precursor emissions and ozone formation in the atmosphere.
14 Readers are referred to other sources (e.g., Finlayson-Pitts and Pitts, 1986; Seinfeld,
15 1986; U.S. Environmental Protection Agency, 1986; National Research Council, 1991) for
16 additional information on the chemical and physical aspects of photochemical air pollution.
17
18
19 3.2 TROPOSPHERIC OZONE CHEMISTRY
20 3.2.1 Background Information
21 Ozone is formed photochemically in the stratosphere and transported downward,
22 leading to the presence of Qj at low concentrations in the natural, or "clean", troposphere.
23 The presence of Og at low concentrations in the "clean" troposphere, in the absence of
24 perturbations caused by human activities, is highly important since O3 is a precursor to the
25 hydroxyl (OH) radical, the key intermediate species in the tropospheric degradation of VOCs
26 emitted into the atmosphere. Although Q$ at low concentrations is an integral part of the
27 "clean" troposphere, its presence at higher concentrations is detrimental.
28 The chemical processes occurring in the atmosphere that lead to the formation of ozone
29 and other photochemical air pollutants are complex. Tropospheric ozone is formed as a
30 result of (1) emissions of NOX and VOCs into the atmosphere from anthropogenic and
31 biogenic sources; (2) transport of these emissions and their reaction products; and
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1 (3) chemical reactions occurring in the atmosphere concurrent with transport and dispersion
2 of the emissions, leading to the formation of O3 and other photochemical oxidants such as
3 peroxyacetyl nitrate (PAN), nitric and sulfuric acids, and to other compounds such as
4 formaldehyde (HCHO) and other carbonyl compounds, and paniculate matter. Additionally,
5 deposition of gases and particles along the trajectory occurs to reduce the concentrations of
6 precursors and products in the atmosphere, but may lead to adverse impacts on the earth's
7 environment.
8 The chemical process leading to the chemical formation of O3 in the troposphere
9 involves the photolysis of NO2 to yield nitric oxide (NO) and a ground-state oxygen atom,
10
N02 + hv -» NO + O^P), (3-1)
11
12 which then reacts with molecular oxygen to form O3:
13
O(3P) + O2 * M -» O3 + M. (3-2)
14
15 The NO and O3 react to reform NO2:
16
NO + O3 -» NO2 + O2. (3-3)
17
18 The presence of reactive VOCs leads to the conversion of NO to NO2 without the
19 intermediary of 03 (Reaction 3-3), and the photolysis of NO2 then leads to the formation of
20 elevated levels of O3:
21
vex:
NO + NO2 (3-4)
22
23 The photochemical cycles leading to O3 production are best understood through a
24 knowledge of the chemistry of the atmospheric oxidation of methane, which can be viewed as
25 being the chemistry of the clean, or unpolluted, troposphere (although this is also a
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1 simplification, since vegetation releases large quantities of complex VOCs into the
2 atmosphere). Although the chemistry of the VOCs emitted from anteopogMiK and biogenic
3 sources in polluted urban and rural areas is more complex, a knowledge of the methane
4 oxidation reactions aids in underatauding toe ebej»cal processes oecwring in $a& poliaJed
5 atmosphere because the underlying chemical principles are the same,
6 This section first describes the structure of the atmosphere, and then discusses the
7 formation of the OH radical, the key intermediate species in the chemistry of the
8 troposphere; and tropospheric NOX chemistry. The photochemical formation of tropospheric
9 O3 from the oxidation of methane is then discussed in some detail since, as noted above, the
10 methane oxidation cycle serves as a model for the photochemical formation of O3 from the
11 more complex nonmethane VOCs emitted into the atmosphere from anthropogenic and
12 biogenic sources. In Section 3.2.4, the chemistry of the major classes of nonmethane VOCs
13 and the formation of O3 from these VOCs are discussed. Finally, in Section 3.2.5, the
14 photochemical formation of aerosols is briefly discussed, since the same processes that lead
15 to the formation of elevated levels of 03 {over those present in the "clean" troposphere)
16 result in the formation of paniculate matter, leading to visibility degradation, and in the
17 formation of atmospheric acidity.
18
19 3.2.2 Structure of the Atmosphere
20 The earth's atmosphere is composed of a number of layers (Mcflveen, 1992). For the
21 purposes of this chapter, those of importance are the troposphere and the stratosphere, and
22 the boundary between them, which is the tropopause.
23 The troposphere extends from the earth's surface to the tropopause, which is at «10 to
24 18 km altitude, depending on the latitude and season. The altitude of the tropopause is
25 greatest in the tropics and lowest in the wintertime polar regions, with an average altitude of
26 »14 km. The temperature in the troposphere decreases with increasing altitude from an
27 average of 290 K at the earth's surface to *210 to 220 K at the tropopause, and the pressure
28 decreases from »760 torr at the earth's surface to * 100 torr at the tropopause.
29 The stratosphere extends from the tropopause to an altitude of »50 km. In the
30 stratosphere, the temperature increases with increasing altitude from »210 to 220 K at the
31 tropopause to * 270 K at the top of the stratosphere. The pressure in the stratosphere
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1 decreases with increasing altitude from »100 torr at the tropopause to »1 torr at the top of
2 the stratosphere.
3
4 3.2.2.1 Vertical and Horizontal Mixing in the Atmosphere
5 In the troposphere, temperature generally decreases with increasing altitude.
6 As discussed in Section 3.3., the lowest 1 to 2 km of the troposphere is influenced by the
7 planetary boundary layer and, in certain locales, by inversion layers. These boundary and
8 inversion layers inhibit the vertical movement of pollutants into the free troposphere. Above
9 inversion and boundary layers, vertical mixing in the "free" troposphere has a time scale of
10 «10 to 30 days (Langner et al., 1990; World Meteorological Organization, 1990a),
11 Because temperature increases with increasing altitude in the stratosphere, vertical
12 mixing in the stratosphere is slow, with a time scale of the order of months to a few years.
13 Horizontal mixing in the troposphere occurs both within and between the hemispheres.
14 The time scale for mixing between the northern and southern hemispheres is »1 year
15 (Cicerone, 1989; Singh and Kanakidou, 1993). Transport within a hemisphere is more rapid
16 (Graedel et al., 1986a), and local, regional, and global transport distances of < 100 km,
17 100 to 1,000 km, and > 1,000 km, respectively, are observed. For a wind speed of
18 15 km h"1 («4 m s"1), transport times over these local, regional, and global distances are
19 a few hours, a few hours to a few days, and ^ 10 days, respectively.
20
21 3.2.2.2 Formation of Stratospheric Ozone
22 At altitudes between approximately 20 and 35 km, the stratosphere has a layer of air
23 containing O3 at mixing ratios up to approximately 10 ppm. The sun emits radiation
24 > 170 nm, and this radiation impacts the upper levels of the atmosphere. The bulk
25 composition of the atmosphere (78.1% N2, 21.0% O2, 0.9% Ar, 0.03% CO2, with variable
26 trace gas concentrations) is invariant up to >50 km (Mcllveen, 1992), and the shorter
27 wavelength radiation (175 to 240 nm) is absorbed by molecular oxygen (O^) in the
28 stratosphere, leading to dissociation into two ground-state oxygen atoms, OfP),
29
02 - hv -+2 0(3P), (3-5)
30
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1 followed by the reaction of O(3P) atoms with QJJ in the presence of a third-body, M, to form
2 ozone.
3
OfP) + O2 + M -» O3 + M (where M » air) (3-2)
4
5 Ozone also photolyzes, at wavelengths <360 nm (DeMore et al., 1992),
6
O3 + h? - O2 + O (3-6)
7
8 where the oxygen atom produced can be in the ground state, O(~P), or electronically excited
9 state, O^D). The O(1D) atoms produced are deactivated to the ground state O(3P) atom by
10 N2, O2, CO2, and Ar
11
OOD) + M -* O<3P) + M (where M = N2, O2, COj) (3-7)
12
13 The reaction of O(3P) atoms with O^ is the termination step of this reaction sequence,
14
O(3P) + 03 -» 2 O2. (3-8)
15
16 These reactions, called the Chapman reactions (Chapman, 1930), are responsible for the
17 layer of ozone found in the stratosphere. Because the stratospheric ozone layer absorbs the
18 sun's radiation below =290 nm, only radiation of wavelengths ^290 nm can penetrate into
19 the troposphere and impact the earth's surface. Any depletion of the stratospheric ozone
20 layer allows shorter wavelength ultraviolet radiation to be transmitted through the
21 stratosphere and into the troposphere.
22 In addition to the biological effects expected from increased UV-B radiation (280 to
23 320 nm), increased penetration of UV-B into the troposphere can lead to changes in
24 tropospheric ozone. Model calculations indicate that 63 in the troposphere could increase
25 with increasing UV-B in urban and rural areas impacted by anthropogenic NOX emissions
26 (Gery et al., 1988; Liu and Trainer, 1988; Thompson et al., 1989; Thompson, 1992), but
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1 could decrease with increasing UV-B in remote tropospheric areas characterized by low NOX
2 levels (Liu and Trainer, 1988; Thompson et al., 1989). Besides the implications of long-
3 term trends in stratospheric O3 concentrations leading to corresponding changes in the
4 intensity of UV-B radiation impacting the troposphere, short-term changes, including daily
5 changes, in stratospheric ozone levels lead to short-term changes in the rates of photolysis of
6 several important species, including the photolysis of formaldehyde to produce radicals and
7 of 03 to form the OH radical. These changes in photolysis rates affect the formation rates
8 and ambient concentrations of key radical intermediates, specifically of the OH radical, in the
9 troposphere. Information concerning such short-term changes in stratospheric
10 03 concentrations is needed as input to urban and regional airshed computer models of
11 photochemical air pollution formation.
12 In the clean atmosphere, stratospheric ozone is also influenced by the emission of N2O
13 from soils and oceans (World Meteorological Organization, 1992). Because N2O is
14 chemically inert in the troposphere and does not photolyze (Prinn et al., 1990), it is therefore
15 transported into the stratosphere, where it undergoes photolysis and also reacts with O(*D)
16 atoms (DeMore et al., 1992; Atkinson et al., 1992a). The reaction of N2O with the O(:D)
17 atom is the major source of stratospheric NO, which then participates in a series of reactions
18 known as the NOX catalytic cycle (Crutzen, 1970; Johnston, 1971).
19
NO + O3 -* NO2 + O2 (3-3)
20
NO2 + O(3P) -» NO + O2 (3-9)
Net; O(3P) + 03 -* 2 O,
21
22 The Chapman reactions and the NOX catalytic cycle reactions control the ozone
23 concentrations in the lower "clean" stratosphere.
24 Additional reaction sequences leading to the removal of stratospheric ozone arise from
25 the C1OX and BrOx catalytic cycles, which result when chlorine- and bromine-containing
26 organic compounds are emitted into the atmosphere. These O3-depleting compounds include
December 1993 3.7 DRAFT-DO NOT QUOTE OR CTTP
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1 the chlorofluorocarbons (CFCs), hydrocMorofluorocarbons (HCFCs), carbon tetrachloride
2 (CC^), methyl chloroform, halons, and methyl bromide (Anderson et al., 1991; Rowland,
3 1990, 1991; World Meteorological Organization, 1992). Analogous to N2O, the CFCs,
4 CC^, and certain halons (CF3Br and CF2ClBr) are inert in the troposphere and are
5 transported into the stratosphere, where they photolyze to generate Cl or Br atoms (World
6 Meteorological Organization, 1992). Methyl bromide and the HCFCs react to a large extent
7 in the troposphere, so that only a fraction of these Cl- and fir-containing species that are
8 emitted into the troposphere are transported into the stratosphere (World Meteorological
9 Organization, 1990b, 1992).
10
11 3.2.3 Tropospheric Ozone in the Unpolluted Atmosphere
12 As noted in Section 3.2,1, ozone is present in the troposphere, even in the absence of
13 human activities. The presence of ozone in the clean, unpolluted, troposphere is the result of
14 downward transport from the stratosphere and in situ production from the oxidation of
15 methane (National Research Council, 1991), emitted from swamps and wetlands, in the
16 presence of natural sources of NOX (emissions from soils, lightning, and downward transport
17 from the stratosphere). It is believed that on a global basis the photochemical formation of
18 ozone in the "clean" troposphere would be approximately balanced by its destruction (Logan,
19 1985; World Meteorological Organization, 1992; Ayers et al., 1992). In the clean,
20 unpolluted lower troposphere, the ozone mixing ratios are in the range 10 to 40 ppb
21 (Oltmans, 1981; Logan, 1985), with higher mixing ratios of »100 ppb in the upper
22 troposphere (Logan, 1985). A reasonable estimate for background O3 mixing ratios near
23 sea-level in the United States is 20 to 35 ppb (U.S. Environmental Protection Agency, 1989)
24 (see Chapter 4). Because of the decrease of total pressure with increasing altitude, the ozone
25 concentration in the "clean" troposphere may be taken to be reasonably independent of
26 altitude at *7 x 1011 molecule cm"3. Transport of 03 from polluted urban areas (see
27 Section 3.3) impacts the "clean" troposphere and leads to O3 concentrations in the
28 troposphere that have been increasing with time over the past few decades (Logan, 1985).
29
30
December 1993 3-8 DRAFT-DO NOT QUOTE OR CITE
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1 3.2.3.1 Tropospheric Hydroxyl Radicals
2 It is now recognized that the key reactive species in the troposphere is the hydroxyl
3 (OH) radical, which is responsible for initiating the degradation reactions of almost all
4 VOCs. In the presence of NO, these OH radical reactions with VOCs lead to the formation
5 of O3 and hence to O3 concentrations above those encountered in the "clean" troposphere.
6 The OH radical is produced from the ultraviolet photolysis of 03. Ozone photolyzes in the
7 ultraviolet at wavelengths < 320 nm to generate the electronically excited O( D) atom
8 (DeMore et al., 1992; Atkinson et al., 1992a),
9
03 + hy -* O2 + O^D) (3-6a)
10
11 The 0(LD) atoms are either deactivated to the ground state O( P) atom by Reaction 3-7 or
12 they react with water vapor to form the OH radical:
13
O(ID) + HjO -» 2 OH (3-10)
14
15 The O(3P) atoms formed directly in the photolysis of 03 or formed from deactivation of
16 O(*D) atoms (Reaction 3-7) reform 03 through Reaction 3-2. At room temperature and 50%
17 relative humidity, 0.2 OH radicals are formed per O(*D) atom generated from the photolysis
18 of ozone. Hydroxyl radical production from reactions (3-6a) and (3-10) is balanced by
19 reaction of the OH radical with CO and methane. Because the water vapor mixing ratio
20 decreases with increasing altitude in the troposphere (Logan et al., 1981; World
21 Meteorological Organization, 1992), whereas the ozone mixing ratio generally increases with
22 increasing altitude, the OH radical concentration is expected to be reasonably independent of
23 altitude (Dentener and Crutzen, 1993).
24 A knowledge of ambient tropospheric OH radical concentrations is needed to test our
25 understanding of tropospheric chemistry and allow the lifetimes of chemical compounds to be
26 reliably calculated. Since, as shown below in Section 3.2.3.3, OH and HO2 radicals are
27 interrelated through a series of reactions, concurrent measurements of OH and HO2 radical
28 concentrations are a further test of our knowledge of tropospheric chemistry. Only in the
29 past few years have measurements been made of lower tropospheric OH radical
December 1993 3.9 DRAFT-DO NOT QUOTE OR CITE
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1 concentrations (see, for example, Felton et si, 199Q; Hofj^mahaMS et al,, 1991; MseJe and
2 Tanner, 1991; Mount and Eisele, 1992; Comes fit«L, 1992; Maid et a!.s 1992). The limited
3 data available show that, as expected, the OH radical concentrations exhibit a diurnal profile,
4 with daytime maximum concentrations of several x 106 motaeajfe eM3. A global, annually,
5 seasonally, and diurnaUy averaged topospheric ;QH laslieaJ s^&®faj$m can tlso be derived
6 from the estimated emissions and measured atmesj&eric e^no^tratjoft
7 (CH3CCl3> and the rate constant for the reaction of the OH radical with
8 (its major tropospberic loss process). Using this method, Man et al. (1992) have d&riv^d a
9 24-h average OH radical concentration ©f 8 x 10 molecule cm" (equivalent to a 12'h
10 daytime average of 1.6 x 1Q6 molecule cm"3). Ambient air measurements of the decays of
11 nonmethane hydrocarbons in urban plumes (Hate et al,, 1993) give OH radical
12 concentrations of a similar magnitude as direct tropospheiic measurements and globally
13 averaged estimates.
14
15 3.2.3.2 Tropospherk Nitrogen Oxides Chemistry
16 The presence of oxides of nitrogen is nejoessary for the ft>rmatiQn of Oj from the
17 oxidation of methane and other VOCs. Sources of trop^spheric NOX include downward
18 transport from the stratosphere, in situ formation from lightning (National Research Council,
19 1991; World Meteorological Organfeatioji, 1992) (Section 3.4.1.2), and emission from soils
20 (National Research Council, 1991; World M«*KMaalo|Krt Organisation, 1992). Recent
21 measurements show that the NOX concentrations over maritime areas increase slightly with
22 increasing altitude, from »15 ppt in the marine boundary layer (Carroll et al., 1990) to
23 *30 to 40 ppt at 3 to 7 km altitude (Ridley et at, 1989; Carroll et al., 1990). Significantly
24 higher NOX concentrations (** 100 ppt) have been observed in the boundary layer over
25 relatively unpolluted continental areas (Carroll et al,, 1990), with the NOX concentrations
26 decreasing with increasing altitude to »50 ppt at 3 to 7 km (Ridley et al., 1989; Carroll
27 etal.,1990).
28 In the troposphere, NO, NO2, and 03 are interrelated by the following reactions:
29
NO + O3 -* NOj + O2 (3-3)
30
1993 3,10 DRAFT-DO NOT QUOTE OR CTTE
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2
NO2 + hi* -* NO * O(3P) (3-1)
O2 * M -> O3 + M (3-2)
3 Because Reaction 3-2 is fast (the lifetime of an O(3P) atom at 298 K and 760 torr of air is
4 *> 10"s s), the ozone concentration at photoequilibrium is given by,
5
[OJ = J.praj/kJNQ] (3-H)
6
7 where Jj and k3 are the photolysis rate of NO^ («0.5 min" for an overhead sun) and the
8 rate constant for the reaction of NO with O3, respectively.
9 There are other important reactions involving NOX. The reaction of NO2 with O3 leads
10 to the formation of the nitrate (NO,) radical,
11
NO2 + O3 -* NO3 + O2 (3-12)
12
13 which in the lower troposphere is nearly in equilibrium with dinitrogen pentoxide (N2O5):
14
M
NO3 + NO2 ^ N2OS (3-13, -3-13)
15 However, because the NOj radical photolyzes rapidly [with a lifetime of «»5 s for an
16 overhead sun (Atkinson et al., 1992a)],
17
• - •- NO + O2 (10%) (3-14a)
N03+hv -
! - * N02 + 0(V) (90%) (3-14b)
18
19
December 1993 3-11 DRAFT-DO NOT QUOTE OR CITE
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1 its concentration remains low during daylight hours, but can increase after sunset to
7 If) ^
2 nighttime concentrations of <5 x 10 to 1 x 10 molecule cm (<2 to 430 ppt) over
3 continental areas influenced by anthropogenic emissions of NOX (Atkinson et al., 1986),
4 Nitrate radical concentrations over marine areas are low because NOX concentrations are low
5 over lower tropospheric marine areas (Noxon, 1983), and an NO3 radical mixing ratio of
6 0,25 ppt has been measured at 3 km altitude in Hawaii {Noxon, 1983). Atkinson (1991) has
7 suggested the use of a 12-h nighttime average NO3 radical concentration of 5 X 10
-3
8 molecule cm in the lower troposphere over continental areas, with an uncertainty of a factor
9 of «10,
10 The tropospheric chemical removal processes for NOX involve the daytime reaction of
11 NO2 with the OH radical and the nighttime wet and dry deposition of N2O5 to produce nitric
12 acid (HNO3),
13
M
OH + NO2 ™ HNO3 (3-15)
ILO
N205 * HNO3 (3-16)
14
15 Gaseous nitric acid formed from Reaction 3-15 undergoes wet and dry deposition, including
16 combination with gaseous ammonia to form paniculate phase ammonium nitrate. The
17 tropospheric lifetime of NOX due to chemical reaction (mainly Reaction 3-15) is »1 to
18 2 days. The tropospheric NO^ reactions are shown schematically in Figure 3-1 below:
19 It should be noted that OH radicals can also react with NO to produce nitrous acid (HONO):
20
OH + NO ^ HONO (3-17)
21 In urban areas, HONO can also be formed during nighttime hours (Harris et al., 1982; Pitts
22 et al., 1984a; Rodgers and Davis, 1989), apparently from the heterogeneous hydrolysis of
23 NC>2 or NOX, or both (Sakamaki et al., 1983; Pitts et al., 1984b; Svensson et al., 1987;
December 1993 3-12 DRAFT-DO NOT QUOTE OR CITE
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HNO,
Emission
Figure 3-1. The cyclic reactions of tropospheric nitrogen oxides.
* N205
wet/dry
deposition
HNO3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Jenkin et al., 1988; Lammel and Perner, 1988; Notholt et al., 1992a,b). The photolysis of
HONO during the early morning hours,
HONO + h»> -» OH + NO
(3-18)
can thus become an important source of OH radicals, leading to die rapid initiation of
photochemical activity (Harris et al., 1982).
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 methane is by reaction with the OH radical, with methane
lifetime equal to
(k2t [OH])
-l
(3-19)
December 1993
343 DRAFT-DO NOT QUOTE OR CITE
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1 where k2i is the rate constant for Reaction 3-21 and JOH] is the (variable) atmospheric
2 OH radical concentration. The calculated lifetime of methane in the troposphere is *» 10 to
3 12 years. As for other saturated organic compounds, the OH radical reaction with methane
4 proceeds by H-atom abstraction from the C-H bonds to form the methyl radical:
5
OH + CH, - Hp + ^Hj. #-20)
6
7 In the troposphere, the methyl radical reacts solely with ©2 to yield the mefiiyl peroxy
8 (CH3O£) radical (Atkinson et al., 1992a):
9
CHs + 02 ^ CHjO' (3-21)
10 In the troposphere, the methyl peroxy radical can react with NO, NO^, HO2 radicals,
11 and other organic peroxy (RO^) radicals, with the reactions with NO and HO2 radicals being
12 the most important (see, for example, World Meteorological Organization, 1990b). The
13 reaction with NO leads to the formation of the methoxy (CH3Q) radical,
14
CH3O; + NO -» O^O f NO2. (3~22)
15
16 The reaction with the HO2 radical leads to the formation of methyl hydroperoxide,
17
CHjOj + HO2 - CHgOOH + O2, (3-23)
18
19 which can photolyze or react with the OH radical (Atkinson et al., 1992a):
20
CHjOOH + hv -» CHjO + OH (3-24)
21
22
December 1993 3-H DRAFT-DO NOT QUOTE OR CITE
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* H20 -i- CH3Oj (3-25a)
OH + CH3OOH -------- -j
- + H2O + CH2OOH (3-25b)
fast
HCHO + OH
1
2 Methyl hydroperoxide also undergoes wet and dry deposition or incorporation into cloud
3 water, or both. The lifetime of methyl hydroperoxide in the troposphere due to photolysis
4 and reaction with the OH radical is calculated to be =2 days. Methyl hydroperoxide is then
5 a temporary sink of radicals, with its wet or dry deposition being a tropospheric loss process
6 for radicals.
7 The only important reaction for the methoxy radical in the troposphere is with O2 to
8 form formaldehyde (HCHO) and the HO2 radical,
9
CH36 * O2 -» HCHO +• HO2. (3-26)
10
1 1 Formaldehyde is a "first-generation" product that reacts further, by photolysis:
12
. - P* H2 + CO (55%) (3-27a)
HCHO + hv - 1
i - *H + HCO (45%) <3-27b)
13
14 where the percentages are for overhead sun conditions (Rogers, 1990); and also by reaction
15 with the OH radical,
16
OH + HCHO -* HjO + HCO (3-28)
17
December 1993 3-15 DRAFT-DO NOT QUOTE OR CITE
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1 In the troposphere, the H atom and HCO (formyl) radical produced in these processes react
2 solely with O2 to form the HO2 radical:
3
H + 0^ - M -* H02 + M (3-29)
HCO + O2 -* HO2 + CO (3-30)
5
6 The lifetimes of HCHO due to photolysis and OH radical reaction are *4 h and 1.5 days,
7 respectively, leading to an overall lifetime of » 3 h for overhead sun conditions.
8 The final step in the oxidation of methane in the earth's atmosphere involves the
9 oxidation of carbon monoxide by reaction with the OH radical (the only tropospheric reaction
10 of CO) to form CO2:
11
12
OH + CO - H + CO2 (3-31)
H + O2 + M -* HO2 + M (3-29)
13
14 The lifetime of CO in the lower troposphere is »2 mo.
15 The overall reaction sequence leading to CO2 formation, through the HCHO and CO
16 intermediate products, is shown in Figure 3-2.
17 There is competition between NO and the HC^ radical for reaction with the CH3O2
18 radical, and the reaction route depends on the rate constants for these two reactions and the
19 tropospheric concentrations of HOs radicals and NO. The rate constants for the reaction of
20 the CH302 radicals with NO (Reaction 3-22) and HO2 radicals (Reaction 3-23) are of
21 comparable magnitude (Atkinson et al., 1992a). Based on the expected HO2 radical
22 concentration in the troposphere, Logan et al. (1981) calculated that the reaction of the
23 CH3O^ radical with NO dominates for NO mixing ratios of > 30 ppt (equivalent to an NO
o 3
24 concentration of > 7 x 10 molecule cm" in the lower troposphere). For NO mixing ratios
25 <30 ppt, the reaction of the CH3O2 radical with HO2 dominates.
December 1993 3-16 DRAFT-DO NOT QUOTE OR CITE
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OH + CH4 + H2O + CH3
02
wetfdry deposition -— CH3OOH
OH
HCHO
HO**10H,
HOf
I
CO
OH
CO,
Figure 3-2. Atmospheric reactions in the complete oxidation of methane.
1
2
3
4
5
6
7
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,
HO2 +• NO -*OH + NO2,
(3-32)
whereas the reactions with 03 and HO2 radicals lead to a net destruction of tropospheric 03:
HO
O
2
(3-33)
9
10
11
HO2 + O3 -* OH + 2 O2
(3-34)
This net loss of tropospheric O3 occurs because the photolytic production of the OH radical
from O3, via the intermediary of the O( D) atom, represents a loss process for tropospheric
December 1993
3-17 DRAFT-DO NOT QUOTE OR CITE
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1 ozone. Hence the absence of any Qj formation from the methane oxidation cycle is
2 equivalent to a net ozone loss. Using the rate constants reported for Reactions 3-32 and 3-34
3 (Atkinson et al., 1992a) and the tropospheric ozone mixing ratios given above, it is
4 calculated that the HO2 radical reaction with NO dominates over reaction with O3 for NO
5 mixing ratios > 10 ppt. The rate constant for Reaction 3-33 is such that an NO mixing ratio
6 of this magnitude also means that the HO2 radical reaction with NO dominates over the self-
7 reaction of HO2 radicals.
8 There are therefore two regimes, depending on the fate of HQ^ and CH3O2 radicals:
9 (1) a high-NO regime hi which HO2 and CH3O2 radicals react with NO to convert NO to
10 NO2, regenerate the OH radical and, through the photolysis of NO2, produce O3; and
11 (2) a low-NO regime in which HO2 and CH3O2 radicals combine (Reaction 3-23) and HO2
12 radicals undergo self-reaction and react with O3 (Reactions 3-33 and 3-34), leading to a net
13 destruction of O3 and inefficient OH radical regeneration (see also Ehhalt et al., 1991; Ayers
14 etal., 1992).
15 Under high-NO conditions, the oxidation of methane leading to the formation of HCHO
16 can be written as the net reaction,
17
OH + CH4 + 2 NO + 2 Oj = HjO •*• HCHO + 2 NO2 + OH, (3-35)
18
19 showing the conversion of two molecules of NO to NO2 and regeneration of the OH radical.
20 Because NO2 photolyzes to form O-j in the presence of O2,
21
N02 + hv -5?-» NO + 03, (3-1,3-2)
22
23 the oxidation of methane to HCHO under high-NO conditions can be written as,
24
OH + CH4 * 4 O2 = HjO + HCHO + 2 O3 + OH, (3-36)
25
December 1993 3-18 DRAFT-DO NOT QUOTE OR CITE
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1
2
3
4
showing the formation of ozone from methane oxidation in the troposphere. The reaction
cycles oxidizing methane to formaldehyde, converting NO to NO2, and forming ozone are
shown schematically in Figure 3-3.
HNO.
emission
emission
HNOj
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.
1 In a similar manner, under high-NO conditions, the photolysis of HCHO and its
2 reaction with the OH radical is given approximately by:
0,2 OH + HCHO + 0.92 NO -*? CO + 0.44 H^ + 0.2 H/) + 0.92 NO2 + 0.92 OH
(3-37)
3 Formaldehyde photooxidation is thus a source of HO2 radicals (and of OH radicals in high-
4 NO conditions) (Ehhalt et al., 1991), especially in urban areas where its concentration is
5 elevated because it is produced during the oxidation of anthropogenic nonmethane VOCs
6 (Finlayson-Pitts and Pitts, 1986).
7 Nitric oxide mixing ratios are sufficiently low in the lower troposphere over marine
8 areas that oxidation of methane will lead to a net destruction of 03 (low-NO conditions), as
9 discussed by Carroll et al, (1990) and Ayers et al. (1992). However, in the upper
10 troposphere and over continental areas impacted by NOX emissions from combustion sources,
December 1993
3-19
DRAFT-DO NOT QUOTE OR CITE
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1 NO inixing ratios are high enottgh (high NQ^coadMons} for methane oxidation to leal lo
2 ozone fomiation (Carroll et al., 1990; WviM Me*e»^|peal OrgarazaiiQit, l§92s),
3
4 3.2.3.4 Cloud Processes in the Methane-Dominated Troposphere
5 In addition to the dry and wet deposition of certain products of the
6 photooxidation (for example, wet and dry deposition of nitric acid aad
7 [Atkinson, 1988 and references therein; Hellpointner and Gab, 1989]), cloud processes cam
8 have significant effects on the gasiphase chemisGy of the "clean19 tepc«SftoeiB
-------
TABLE 3-1. ESTIMATED EMISSIONS OF METHANE, NONMETHANE
ORGANIC COMPOUNDS, NITROUS OXIDE, AND OXIDES OF NITROGEN
(NO + N02) INTO THE EARTH'S ATMOSPHERE FROM BIOGENIC
AND ANTHROPOGENIC SOURCES
Chemical
CH4b
NMOCC
N2O (as N)d
NOX (as N)e
Emissions
Biogenic Sources
«150
«1,000
~7
= 10
(Tg/year8)
Anthropogenic Sources
= 350
»100
= 6
«40
"Teragram = 10 g; or * 10 metric tons.
Fung et al (1991a); World Meteorological Organization (1992). Emissions from ruminants, rice paddies, and
biomass burning are considered as anthropogenic emissions.
cLogan et al. (1981); World Meteorological Organization (1992), with biogenic emissions being assumed to be
50% isoprene and 50% monoterpenes.
dPrinnetal. (1990).
National Research Council (1991); World Meteorological Organization (1992); biogenic sources =50% from
soils; *s5Q% from lightning.
1 of nonmethane VOCs from anthropogenic sources, large quantities of biogenic nonmethane
2 VOCs (mainly of isoprene and monoterpenes) are emitted, both in polluted and nonpolluted
3 areas, into the atmosphere from vegetation (see, for example, Isidorov et al., 1985; Lamb
4 et al., 1987; Winer et al., 1991a,b).
5 Analogous to the photooxidation of methane, the interaction of NOX with nonmethane
6 VOCs from anthropogenic and biogenic sources under the influence of sunlight leads to the
7 formation of photochemical air pollution (National Research Council, 1991). In urban areas,
8 emissions of NOX and VOCs from human activities (combustion sources, including
9 transportation; industrial sources; solvent usage; landfills; etc.) dominate over biogenic
10 sources (National Research Council, 1991; Chameides et al,, 1992). However, the emissions
11 of VOCs from vegetation have been implicated in the formation of photochemical air
12 pollution in urban (Chameides et al., 1988; 1992) as well as rural (Trainer et al., 1987;
13 Roselle et al., 1991; Chameides et al., 1992) areas.
14 In essence, the chemistry of the polluted urban and regional atmosphere is an extension
15 of that of the clean, methane-dominated troposphere, with a number of additional
December 1993 3-21 DRAFT-DO NOT QUOTE OR CITE
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1 complexities because of the number and types of VOCs emitted from anthropogenic and
2 biogenic sources. At least in certain urban areas, the NMOC content of ambient air is
3 similar to the composition of typical gasolines (Mayrsohn and Crabtree, 1976; Mayrsohn
4 et aL, 1977; Harley et al., 1992; see Section 3,4.3). For example, gasolines typically
5 consist of *55 to 65% alkanes, »5 to 10% alkenes, and »25 to 35% aromatic
6 hydrocarbons (Lonneman et al., 1986; Sigsby et al., 1987), whereas in Los Angeles the
7 ambient urban air composition is »50 to 55% alkanes, »5 to 15% alkenes, » 25 to 30%
8 aromatic hydrocarbons, and *5 to 15% earbonyls (Grosjean and Fung, 1984; California Air
9 Resources Board, 1992). Emissions of NOX and VOCs are dealt with in detail in
10 Section 3.4.1.
11
12 3.2.4.1 Tropospheric Loss Processes of Volatile Organic Compounds
13 The chemical loss processes of gas-phase VOCs include photolysis and chemical
14 reaction with the OH radical during daylight hours, reaction with the NO3 radical during
15 nighttime hours, and reaction with O3) which is often present throughout the 24-h period
16 (Atkinson, 1988).
17 As discussed earlier, photolysis of chemical compounds in the troposphere is restricted
18 to the wavelength region above »290 nm. Because of the strength of chemical bonds, the
19 tropospheric wavelength region in which photolysis can occur extends from **290 to
20 ^800 nm, and this wavelength region is often referred to as the "actinic" region. For
21 photolysis to occur, a chemical compound must be able to absorb radiation in the actinic
22 region (and hence have a non-zero absorption cross-section, ^, is
25 defined as (number of molecules of the chemical undergoing change)/(number of photons of
26 light absorbed). The photolysis rate, kphotoiysis> ft>r the process,
27
C + hv -» products (3-38)
28
29 is given by,
30
December 1993 3-22 DRAFT-DO NOT QUOTE OR CUE
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} 'x *x *. <*• (3-39)
1
2 where Jx is the radiation flux at wavelength X, and trx ^d «^x are tne absorption cross-section
3 and photolysis quantum yield, respectively, at wavelength X. Photolysis is therefore a
4 pseudo-first-order process (depending on the radiation flux and spectral distribution) and the
5 lifetime of a chemical with respect to photolysis is given by:
6
T = * *• '
phololy»i«
1
8 For the reaction of a VOC with a reactive species, X (for tropospheric purposes,
9 X = OH, NO3, and O3), the lifetime for the reaction process, C + X -» products, is given
10 by:
11
TX = (kJXD- (3-41)
12
13 and depends on the concentration of the reactive species X and the rate constant (kx) for
14 reaction of the VOC with X. In general, the ambient atmospheric concentrations of OH
15 radicals, NO3 radicals, and O3 are variable, depending on tune of day, season, latitude,
16 altitude, etc. For the purpose of comparing lifetime calculations for various classes of
17 VOCs, average ambient tropospheric concentrations of these three species are often used.
18 The concentrations used here have been presented in the sections above and are: OH
19 radicals, a 12-h average daytime concentration of 1.6 X 106 molecule cm"3 (equivalent to a
20 24-h average concentration of 8 x 10s molecule cm"3) (Prinn et al., 1992); isTCXj radicals, a
21 12-h nighttime average concentration of 5 x 10 molecule cm" (Atkinson, 1991); and 03, a
22 24-h average of 7 x 1011 molecule cm"3 (30 ppb) (Logan, 1985).
23 The major classes of VOCs are the alkanes, alkenes (including alkenes from biogenic
24 sources), aromatic hydrocarbons, carbonyl compounds, alcohols, and ethers (see California
25 Air Resources Board, 1992). The calculated lifetimes with respect to the individual
26 atmospheric loss processes of compounds representing a range of reactivities in each class
27 are given in Table 3-2, Note that the lifetimes given are dependent on the reaction rate
December 1993 3-23 DRAFT-DO NOT QUOTE OR CITE
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TABLE 3-2. CALCULATED TROPOSPHERIC LIFETIMES OF SELECTED
VOLATILE NONMETHANE ORGANIC COMPOUNDS DUE TO PHOTOLYSIS
AND REACTION WITH HYDROXYL AND NO3 RADICALS AND WITH OZONE
Organic
n-Butane
2-MethyIbutane
n-Octane
Ethane
Propene
Isoprene
Limonene
Benzene
Toluene
m-Xylene
Formaldehyde
Acetaldehyde
Acetone
2-Butanone
Methanol
Ethanol
Methyl f-butyl ether
Ethyl r-butyl ether
Methylglyoxal
OH
5.7 days
3.7 days
1.7 days
1.7 days
6.6 h
1.7 h
l.Oh
12 days
2.4 days
7.4 h
1 .5 days
11 h
66 days
13 days
15 days
4.4 days
4.9 days
1.6 days
10 h
Lifetime Due
NO3
2.8 years
290 days
250 days
230 days
4.9 days
0.8 h
3 min
>4 years
1.9 years
200 days
80 days
17 days
—
—
> 77 days
> 50 days
—
—
—
to Reaction with
03
>4,500 years
> 4,500 years
> 4,500 years
10 days
1.6 days
1.3 days
2.0 h
>4.5 years
>4.5 years
>4.5 years
>4.5 years
>4.5 years
>4.5 years
>4.5 years
—
—
—
—
>4.5 years
hv
4h
6 days
60 days
2h
Sources: Lifetimes resulting from reaction with OH, NO3, and 03 were calculated using rate constants given in
Atkinson and Carter (1984) and Atkinson (1989, 1991, 1993); data for photolysis lifetimes are from Horowitz
and Cdvert (1982), Meyrahn et al. (1982; 1986), Plum et al. (1983), and Rogers (1990).g The OH radical
radical, and O3 concentrations used (molecule cm" were: OH, 12-h average of 1.6 X 10 ; NQj, 12-h average
of 5 X 108; 03, 24-h average of 7 x 1011.
1 constants and the assumed ambient concentrations of OH radicals, NOj radicals, and 03.
2 Uncertainties in the ambient concentrations of the reactive species translate directly into
3 corresponding uncertainties in the lifetimes
December 1993
3-24
DRAFT-DO NOT QUOTE OR CITE
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1 The following brief discussions of the tropospheric chemistry of the important classes
2 of VOCs are based on the recent review and evaluation article of Atkinson (1993), and that
3 article should be consulted for further details of the tropospheric reactions of VOCs,
4
5 3.2.4.1.1 Alkanes
6 Since gasoline and diesel fuels contain alkanes of carbon number C4 to SC15, a large
7 number of alkanes are present in ambient air (see, for example, Grosjean and Fung, 1984;
8 California Air Resources Board, 1992; Section 3.4). Table 3-2 shows that the only
9 important tropospheric loss process for the alkanes is by reaction with the OH radical, with
10 calculated lifetimes of the 03 to C10 alkanes ranging from ~ 1 to 15 days. As for methane,
11 the OH radical reaction proceeds by H-atom abstraction from the various C-H bonds. The
12 nighttime reactions of the NO3 radical with alkanes (calculated to be generally of minor
13 importance, but see Penkett et al. [1993]) also proceed by initial H-atom abstraction. For an
14 alkane (RH) the initially formed radical is an alkyl radical (R):
15
OH + RH -» H,O + R, (3-42)
16
17 which rapidly adds O2 to form an alkyl peroxy (RO^) radical,
R + 02 ^ RO;, (3-43)
18 with the simplest of the RO£ radicals being the methylperoxy radical, described in
19 Section 3.2.3.3 dealing with methane oxidation. Alkyl peroxy radicals (RO^) can react with
20 NO, NO2, HO2 radicals, and other organic peroxy radicals (
21
ROj + NO -* RO + NO,;
22
RO* + NO2 ** ROONO2; (3-45)
23
December 1993 3-25 DRAFT-DO NOT QUOTE OR CITE
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ROj + HO2 - ROOH + O2; (3-46)
1
ROj + R'Oj -* RO + R'6 + 02; (3"47a)
2
ROj + R'Oj -» a carbonyl + (1 - ct) ROH * O2 (3-47b)
+ products of R'Oj.
3
4 The reactions with organic peroxy radicals are expected to be of less importance in the
5 troposphere than the other reactions listed. Since low NO conditions occur even in air
I 6 masses in urban areas, the HO2 radical reactions with RO2 radicals and the subsequent
7 chemistry must be considered. However, because of space constraints and a general lack of
8 knowledge concerning the tropospheric chemistry of R02 radicals under low-NO conditions,
9 only the reactions occurring under high-HO conditions are presented and discussed here. For
10 the SC3 alkyl peroxy radicals, in addition to the reaction pathway leading to NO-to-NQj
11 conversion (Reaction 3-44a), a second reaction pathway leading to formation of an alkyl
12 nitrate becomes important:
ROj + NO ^ RONO2 (3-44b)
13 For a given alkyl peroxy radical, the alkyl nitrate yield increases with increasing pressure
14 and with decreasing temperature (Carter and Atkinson, 1989a).
15 Analogous to the case for the methoxy radical, those alkoxy radicals (RO^) formed
16 from the higher alkanes that have an abstractable H atom can react with 02 to form the HO2
17 radical and a carbonyl; for example,
18
(CH^CHO + O2 -* CH3C(O)CH3 + HO2 (3-48)
19
20 In addition, unimolecular decomposition by C-C bond scission and unimolecular
21 isomerization via a six-member transition state (Atkinson and Carter, 1991; Atkinson, 1993)
22 can be important for the larger alkoxy radicals. For example, the following chemistry can
23 occur for the 1-pentoxy radical:
December 1993 3-26 DRAFT-DO NOT QUOTE OR CTTB
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decomposition O2 isomerization
CH3CHCH£H/M2OH
+ +
H02
1
2 with the alkyl radicals C4H^ and HOCH2CH2CH2CHCH3 undergoing further reaction,
3 The majority of the reaction rate constants and reaction pathways in the alkane
4 degradation schemes are assumed by analogy with the chemistry of CrC3 alkyl, alkyl
5 peroxy, and alkoxy radicals (Atkinson, 1990, 1993; Carter, 1990; Atkinson et al., 1992a).
6 A number of areas of uncertainty still exist for the tropospheric chemistry of the alkanes
7 (Atkinson, 1993). These include (a) the relative importance of alkoxy radical reaction with
8 O2, decomposition and isomerization, and the reactions occurring subsequent to the
9 isomerization reaction; (b) the formation of alkyl nitrates from the reactions of the peroxy
10 radicals with NO; and (c) reactions of the alkyl peroxy radicals with HO^ and other peroxy
11 radicals, reactions that can be important in the nonurban troposphere.
12
13 3.2.4.1.2 Alkenes (Anthropogenic and Biogenic)
14 The alkenes emitted from anthropogenic sources are mainly ethene, propene, and the
15 butenes, with lesser amounts of the ^C5 alkenes. The major biogenic alkenes emitted from
16 vegetation are isoprene (2-methyH,3-butadiene) and C^ft^ monoterpenes (Isidorov et al.,
17 1985; Winer et al., 1992), and their tropospheric chemistry is currently the focus of much
18 attention (see, for example, Hatakeyama et al., 1989, 1991; Arey et al., 1990; Tuazon and
19 Atkinson, 1990a; Pandis et al., 1991; Paulson et al., 1992a,b; Paulson and Seinfeld, 1992a;
20 Zhang et al., 1992; Hakola et al., 1993a,b).
21 As evident from Table 3-2, the alkenes react with OH and NO3 radicals and O3. All
22 three processes are important atmospheric transformation processes, and all three reactions
23 proceed by initial addition to the > C=C < bond(s). These reactions are briefly discussed
24 below.
December 1993 3-27 DRAFT-DO NOT OUOTE OR CITF
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1 Hydroxyl Radical Reactions
2 As noted above, the OH radical reactions with the aJkenes proceed mainly by OH
3 radical addition to the > C=C < bond(s). For example, the OH radical reaction with
4 propene leads to the formation of the two OH-containing radicals,
5
OH + CH3CH=CH2 CHjCHCO^CHj and CI^CHCHpH. (3-49)
6 The subsequent reactions of these radicals are similar to those of the alkyl radicals formed by
7 H-atom abstraction from the alkanes. Taking the CH3 CHCH2OH radical as an example,
8 under high-NO conditions, the following chemistry occurs:
9
CH3CHCHpH
NO
+ NO2
/ \
/ \
decomposition \
\
GHqqO)CH^OH +
HCHO + HO2
10
11 The underlined species represent products that, although stable, can undergo further reaction;
12 and hence they can lead to "second-generation" products. For the simple £C4 alkenes, the
13 intermediate OH-containing radicals appear to undergo mainly decomposition at room
14 temperature and atmospheric pressure of air. Hence for propene, the "first-generation"
15 products of the OH radical reaction in the presence of NO are HCHO and CH3CHO,
16 irrespective of which OH-containing radical is formed.
December 1993 3-28 DRAFT-DO NOT QUOTE OR CITE
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1 However, this is not the case for the more complex alkenes of biogenic origin. The
2 product studies of Tuazon and Atkinson (1990a) and Paulson et al. (1992a) for the OH
3 radical reaction with isoprene in the presence of N(\ show that the products expected from
4 reaction schemes analogous to that shown above for propene (i.e., HCHO + methyl vinyl
5 ketone [CH3C(O)CH=CH2] and HCHO + methacrolein [CH2=C(CH3)CHO], arising from
6 initial OH radical addition to the CH2=CH- and CH2=C < bond, respectively) do noi
7 account for the entire reaction pathways. The product yields obtained from the studies of
8 Tuazon and Atkinson (1990a) and Paulson et al. (1992a) are (Atkinson, 1993): methyl vinyl
9 ketone, 34%; methacrolein, 24%; 3-methylfuran, 5%; organic nitrates, «12%; and
10 unidentified carbonyl compounds, **25%. The HCHO yield was consistent with being a
11 co-product formed with methyl vinyl ketone and methacrolein (Tuazon and Atkinson, 1990a),
12 Aerosol formation for isoprene photoxidation has been shown to be of negligible importance
13 under atmospheric conditions (Pandis et al.5 1991; Zhang et al., 1992).
14 To date, few quantitative product studies have been carried out for the monoterpenes
15 (Arey et al., 1990; Hatakeyama et al., 1991; Hakola et al., 1993a,b). Arey et al. (1990) and
16 Hakola et al. (1993a,b) have observed the C7-C10 carbonyl compounds expected by analogy
17 with the reaction scheme shown above for propene., but with total carbonyl formation yields
18 of £50%. These data (Arey et al., 1990; Hakola et al., 1993a,b) indicate the formation of
19 other products in significant, and often dominant, yields. Hatakeyama et al. (1991) used
20 Fourier transform infrared (FTIR) absorption spectroscopy and reported carbonyl compounds
21 to be formed in high yield from a-pinene and /?-pinene, in apparent disagreement with the
22 data of Arey et al. (1990) and Hakola et al. (1993b). While Hatakeyama et al. (1991)
23 ascribed these carbonyl products to those expected from oxidative cleavage of the >C=C<
24 bonds, it is possible that the yields reported for these carbonyls included contributions by
25 other, as yet unidentified, carbonyl-containing products.
26
27 Nitrate Radical Reactions
28 The NO3 radical reactions proceed by reaction schemes generally similar to the OH
29 radical reactions, except that when NO3 radicals are present, NO concentrations are low (see
30 above) and RO2 + RQ2 and RO2 + HO2 radical reactions are expected to dominate over
31 RO2 + NO reactions. For propene the initial reaction is (Atkinson, 1991),
December 1993 3-29 DRAFT-DO NOT QUOTE OR CITE
-------
N0
and CHjCHtONO^CHj, (3-50)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
followed by a series of reactions that are expected (Atkinson, 1991) to lead to the formation
of, among others, carbonyls and mtrato-carbonyls (for example, ECHO, CHjCHO,
CH3CH(ONO2)CHO, and CH3C(O)CH2ONO2 from propene). Few data are presently
available concerning the products and detailed mechanisms of NC^-alkene reactions
(Atkinson, 1991, 1993 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 ah, 1990; Hjorth et ah, 1990; Skov
etal., 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 [ ] ,
03 +
JQIQ + [CH^CHOO]
The energy-rich biradicals, [CH2OO] and [CH3CHOO] , undergo collisional stabilization or
decomposition:
decomposition.
(3-51a)
(3-5Ib)
19
December 1993
3-30 DRAFT-DO NOT QUOTE OR OTE
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1 There are still significant uncertainties concerning the reactions of the energy-rich biradicals
2 (see, for example, Hone and Moortgat, 1991; Atkinson, 1990, 1993), with recent studies
3 showing the production of OH radicals in high yields for several alkenes (Niki et al., 1987;
4 Paulson et al., 1992a; Atkinson et al., 1992b; Paulson and Seinfeld, 1992b; Atkinson and
5 Aschmann, 1993),
6 For isoprene, the major products are methacrolein and methyl vinyl ketone (Kamens
7 et al., 1982; Niki et al., 1983; Paulson et al,, 1992b). Paulson et al. (1992b) derived
8 OH radical and O(3P) atom formation yields of 0.68 ± 0.15 and 0.45 ± 0.2, respectively,
9 from the O3 reaction with isoprene, indicating the dominance of secondary reactions.
10 However, Atkinson et al. (1992b) derived a significantly lower OH radical formation yield of
11 0.27 (uncertain to a factor of «1.5). Clearly, further studies of this important reaction are
12 needed.
13 The only quantitative studies of the gas-phase O3 reactions with the monoterpenes are
14 those of Hatakeyama et al. (1989) for a- and 0-pinene and Hakola et al. (1993a,b) for a
15 series of monoterpenes. Additionally, Atkinson et al, (1992b) derived OH radical formation
16 yields from these reactions under atmospheric conditions.
17 Several groups (Gab et al., 1985; Becker et al., 1990, 1993; Simonaitis et al., 1991;
18 Hewitt and Kok, 1991) have reported the formation of H2O2 and organic peroxides from
19 O3 reactions with alkenes. However, there are significant disagreements in the quantitative
20 results reported by Becker et al. (1990, 1993) and Simonaitis et al. (1991).
21
22 3,2.4.1.3 Aromatic Hydrocarbons
23 The most abundant aromatic hydrocarbons in urban atmospheres are benzene, toluene,
24 the xylenes, and the trimethylbenzenes (Grosjean and Fung, 1984; California Air Resources
25 Board, 1992). As shown in Table 3-2, the only tropospherically important loss process for
26 benzene and the alkyl-substituted benzenes is by reaction with the OH radical. For the alkyl-
27 substituted benzenes, the OH radical reactions proceed by two pathways: H-atom abstraction
28 from the C-H bonds of the alkyl substituent group(s) and OH radical addition to the aromatic
29 ring, as shown here, for p-xylene,
30
December 1993 3-31 DRAFT-DO NOT QUOTE OR CITE
-------
1
2
3
4
5
CH,
CH,
(3-52a)
(3-52b)
with the OH radical addition pathway being reversible above « 325 K (Atkinson, 19&9).
The radical formed in Reaction 3-52a reacts analogously to an alkyl radical (Atkinson,
1993), leading in the presence of NO to aromatic aldehydes and organic nitrates:
0
M
™
1 NO
NO
a,
CH3C6H4QIQ + HO2
(p-tolualdehyde)
1 The OH-containing radical formed in Reaction 3-52b can undergo reaction with both NO2
2 and O2- Knispel et al. (1990) reported rate constants for the reactions of NC>2 and O2 with
3 the OH-containing radicals formed from benzene and toluene. The magnitude of the rate
4 constants they obtained implies that in the troposphere the major reactions of these radicals
5 will be with C>2. However, laboratory studies conducted under a range of NO2
1993 3-32 DRAFT-DO NOT QUOTE OR CITE
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1 concentrations such that the expected reactions varied from being mainly with O2 to being
2 mainly with NO2 showed no obvious change in the major ring-addition product yields
3 (Atkinson et al.s 1989). The data of Atkinson et al. (1989), obtained from the OH radicai-
4 initiated reactions of benzene and toluene, may indicate that the product yields from the
5 O2 and NO2 reactions with these radicals are fairly similar. The present uncertainties
6 concerning the fate of these radicals need to be resolved before a detailed mechanism of the
7 tropospheric degradation of aromatic hydrocarbons can be constructed.
8 Despite these uncertainties, however, products from the OH radical addition pathway
9 have been identified and their formation yields determined (Atkinson, 1993, and references
10 therein). The major products identified from the OH radical addition pathway are phenolic
11 compounds (for example, phenol from benzene and o-, m- and p-cresol from toluene) and
12 a-dicarbonyls (glyoxal, methylglyoxal, and 2,3-butanedione) arising from cleavage of the
13 aromatic ring (see, for example, Atkinson, 1990, 1993, and references therein). Significant
14 fractions (S50% for benzene, toluene, and the xylenes) of the reaction products are,
15 however, still not accounted for.
16
17 3.2.4.1.4 Carbonyl Compounds
18 As noted above, the OH radical reactions with the alkanes, alkenes, and aromatic
19 hydrocarbons lead, often in large yield, to the formation of carbonyl compounds. Likewise,
20 carbonyls are formed during the reactions of NO3 radicals and O3 with alkenes. As a first
21 approximation, the carbonyl compounds of tropospheric interest are: formaldehyde (see
22 Section 3.2.3.3), acetaldehyde, and the higher aliphatic aldehydes; benzaldehyde; acetone,
23 2-butanone, and the higher ketones; and simple dicarbonyls such as glyoxal, methylglyoxal,
24 and 2,3-butanedione.
25 The tropospheric photooxidation of isoprene leads to the formation of methyl vinyl
26 ketone (CH3C(O)CH=CH2) and methacrolein (CH3C(CHO)=CH2). The OH radical-
27 initiated reactions of these two carbonyl compounds in the presence of NOX have been
28 studied by Tuazon and Atkinson (1989, 1990b).
29 The tropospherically important loss processes of the carbonyls not containing > C=C <
30 bonds are photolysis and reaction with the OH radical. As shown in Tables 3-2, photolysis
31 is a major tropospheric loss process for the simplest aldehyde (HCHO) and the simplest
December 1993 3.33 DRAFT-DO NOT QUOTE OR CITE
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1 ketone (CH3C(O)CH3), as well as for the dicarbonyls. For the higher aldehydes and
2 ketones, the OH radical reactions are calculated to be the dominant gas-phase loss process
3 (Table 3-2). For acetaldehyde, the reaction proceeds by H-atom abstraction from the -CHO
4 group to form the acetyl (CH3CO) radical,
5
11
OH + CH3CHO -» HjO + CH.CO,
6
7 which rapidly adds O2 to form the acetyl peroxy radical:
CHjCO + O2 ^ CH3C(0)00. (3-54)
8 This O2 addition pathway is in contrast to the reaction of O2 with the fonnyl (HCO) radical
9 formed from HCHO, which reacts by an H-atom abstraction pathway (Reaction 3-30). The
10 acetyl peroxy radical reacts with NO and
CH3C(O)OO + NO^ CH3C(O)O + NO2 (3-55)
[fast
I
CH3 + CO2;
M
NO2 ** CH3C(O)OONO2! (3-56, -3-56)
12 with the NO2 reaction forming the thermally unstable peroxyacetyl nitrate (PAN). The
13 higher aldehydes also lead to PANs (Roberts, 1990); for example, propionaldehyde reactions
14 lead to the formation of peroxypropionyl nitrate (PPN). While the rate constant at
15 atmospheric pressure for the thermal decomposition of PAN (Atkinson et-al., 1992a) is such
16 that the lifetime of PAN with respect to thermal decomposition is *30 min at 298 K in the
17 lower troposphere, the thermal lifetime of PAN is calculated to be several hundred years in
18 the upper troposphere. Reaction with OH radicals or photolysis, or both, will therefore
19 dominate as the PAN loss processes in the upper troposphere (Atkinson et al., 1992a).
December 1993 3-34 DRAFT-DO NOT QUOTE OR CITE
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1 The transport of PAN out of urban areas into colder air masses (for example, to higher
2 altitude) leads to PANs becoming a temporary reservoir of NOX and allowing for the long-
3 range transport of NOX to less polluted areas. Release of NQj in these less polluted areas
4 via reaction (-3-56), with subsequent photolysis of NO2, then leads to O3 formation and the
5 pollution of remote areas.
6 Since the CH3 radical formed from the NO reaction with the acetyl peroxy radical leads
7 to HCHO formation, the OH radical reaction with acetaldehyde forms formaldehyde. The
8 same process occurs for propionaldehyde, which reacts to form CH3CHO and then HCHO.
9 Benzaldehyde appears to behave as a phenyl-substituted aldehyde with respect to its OH
10 radical reaction, and the analog to PAN is then peroxybenzoyl nitrate, PBzN
11 (C6H5C(O)OONO2).
12 The formation of formaldehyde from acetaldehyde, and of acetaldehyde and then
13 formaldehyde from propionaldehyde, are examples of "cascading", in which the
14 photochemical degradation of emitted VOCs leads to the formation of further VOCs,
15 typically containing fewer carbon atoms than the precursor VOC. This process continues
16 until the degradation products are removed by wet and dry deposition or until CO or CO2 are
17 the degradation products. The reactions of each of these VOCs (i.e., the initially emitted
18 VOC and its first-, second-, and successive-generation products) in the presence of NO
19 (high-NO conditions) can lead to the formation of O3.
20 As discussed in Section 3.2.3.3 for formaldehyde, the photolysis of carbonyl
21 compounds can lead to the formation of new radicals that result in enhanced photochemical
22 activity. The OH radical reactions of the ketones are generally analogous to the reaction
23 schemes for the alkanes and aldehydes.
24
25 3.2.4.1.5 Alcohols and Ethers
26 A number of alcohols and ethers are used in present-day and reformulated gasolines and
27 in alternative fuels. The alcohols include methanol, ethanol, and tert-butyl alcohol, and the
28 ethers include methyl tert-butyl ether and ethyl /erf-butyl ether. Table 3-2 shows that in the
29 troposphere these VOCs react only with the OH radical. These OH radical reactions proceed
30 by H-atom abstraction from the C-H bonds (and to a minor extent from the O-H bonds in the
31 alcohols). For example, for methanol
December 1993 3-35 DRAFT-DO NOT QUOTE OR CITE
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OH + CHjOH -* Hp + O^O (15%) (3-57a)
OH + CH3OH - Hp + CHjOH (85%) (3-57b)
1
2 In the troposphere, both of the CH3O and CH2OH radicals react only with 0^ to form
3 formaldehyde.
4
CH30 + O2 - HCHO + HO2 (3-58)
5
CHjQH + O2 - HCHO + HO2 (3-59)
6
7 The overall reaction is then:
8
OH + CH3OH + O2 -* HjO + HCHO + HO2 (3-60)
9
10 The reaction sequence for ethanol is similar (Atkinson, 1993). Product studies of the OH
11 radical-initiated reactions of methyl ten-butyl ether and ethyl te/t-butyl ether in the presence
12 of NOX have been carried out by Taper et al. (1990), Smith et al. (1991, 1992), Tuazon et al.
13 (1991), and Wallington and Japar (1991). The major products from methyl te/t-butyl ether
14 are ten butyl formate, formaldehyde, and methyl acetate [CH3C(O)OCH3]; and from ethyl
15 ten-butyl ether, rm-butyl formate, ten-butyl acetate, formaldehyde, acetaldehyde, and ethyl
16 acetate. The available product data and the reaction mechanisms have been reviewed by
17 Atkinson (1993) and that reference should be consulted for further details.
18 In addition to the use of alcohols and ethers in gasolines and alternative fuels,
19 unsaturated alcohols have been reported as emissions from vegetation (Arey et al., 1991a;
20 Goldan et al., 1993), and kinetic and product studies have begun to be reported for these
21 biogenic VOCs (Grosjean et al., 1993).
22
23
December 1993 3-36 DRAFT-DO NOT QUOTE OR CITE
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1 3.2.4.1.6 Primary Products and Areas of Uncertainty for the Tropospkeric Degradation
2 Reactions of VOCs
3 The tropospheric degradation reactions of the alkanes, alkenes (including those of
4 biogenic origin), aromatic hydrocarbons, carbonyls (often formed as products of the
5 degradation reactions of alkanes, alkenes, and aromatic hydrocarbons) and other oxygenates
6 have been briefly discussed above. The "first-generation" products of the alkanes, alkenes,
7 and aromatic hydrocarbons are as follows (unfortunately, complete product distributions have
8 not been obtained for most of the VOCs studied):
9
10 Alkanes
11 • Carbonyl compounds (i.e., aldehydes and ketones) are formed as major
12 products for the smaller (^C4 alkanes).
13
14 • Alkyl nitrates are formed from the >C3 alkanes studied to date. The yields
15 increase with the size of the alkane from =4% for propane to =30% for
16 n-octane.
17
18 • 5-Hydroxycarbonyls are expected to be formed after the alkoxy radical
19 isomerization reaction. To date, no direct evidence for the formation of
20 these compounds exists. For the larger alkanes, the formation yields of
21 these compounds could be high.
22
23 • AIM hydroperoxides are formed under low-NO conditions.
24
25 • Alkyl peroxynitrates (ROONO^ are formed but have short lifetimes (a few
26 seconds at 298 K) due to thermal decomposition.
27
28 • Alcohols are formed from the combination reactions of the peroxy radicals
29 under low-NO conditions. These compounds are expected to be formed in
30 low overall yield in the troposphere.
31
32 The major uncertainties in the atmospheric chemistry of the alkanes concern the
33 formation of alkyl nitrates from the reactions of the peroxy radicals with NO
34 (Reaction 3-44b) and the reactions of the alkoxy radicals in the troposphere. These
35 uncertainties affect the amount of NO to NO^ conversion occurring and hence the amounts of
36 O3 which are formed during the NOx-air photooxidations of the alkanes.
37
38
December 1993 3.37 DRAFT-DO NOT QUOTE OR CITE
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1 Alkenes
2 • Carbonyl compounds (aldehydes and ketones) are formed as major products
3 of the OH radical, NO3 radical, and 03 reactions.
4
5 • Organic acids are formed from the O3 reactions, but possibly in low yield.
6
7 • Hydroxynitrates and nitratocarbonyls are formed from the OH radical
8 reactions and NO3 radical reactions, respectively. The hydroxynitrates are
9 formed in low yield from the OH radical reactions, while the
10 nitratocarbonyls may be major products of the NQj radical reactions.
11
12 • Hydroxycarbonyls and carbonyl-acids are also expected to be formed,
13 although few, if any, data exist to date.
14
15 • Decomposition products are produced from the initially energy-rich
16 biradicals formed in the 03 reactions; these include CO, CO2, esters,
17 hydroperoxides, and, in the presence of NOX, peroxyacyl nitrates
18 (RC(O)OONO2; PANs).
19
20 The major areas of uncertainty concern the products and mechanisms of the
21 O3 reactions (in particular, the radical yields from these reactions that affect the
22 03 formation yields from the NOx-air photooxidations of the alkenes) and the reaction
23 products and mechanisms of the OH radical reactions with the alkenes containing more than
24 four carbon atoms.
25
26 Aromatic Hydrocarbons
27 • Phenolic compounds, such as phenol and cresols, have been observed as
28 major products of the atmospheric reactions of the aromatic hydrocarbons
29 under laboratory conditions.
30
31 • Aromatic aldehydes, such as benzaldehyde, are formed in £10% yield.
32
33 • a-Dicarbonyls, such as glyoxal, methylglyoxal, and biacetyl, are formed in
34 faniy high (10 to 40%) yields. These dicarbonyls photolyze rapidly to form
35 radicals and are therefore important products with respect to the
36 photochemical activity of the aromatic hydrocarbons.
37
38 • Unsaturated carbonyl or hydroxycarbonyl compounds, or both, are formed,
39 although there is little direct information concerning the formation of these
40 products.
41
December 1993 3-38 DRAFT-DO NOT QUOTE OR
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1 There is a lack of knowledge as to the detailed reaction mechanisms and reaction
2 products for the aromatic hydrocarbons under tropospheric conditions, (i.e., for the NOX
3 concentration conditions encountered in urban and rural areas.). It is possible that the
4 products observed in laboratory studies, and their formation yields, are not representative of
5 the situation in the troposphere. This then leads to an inability to formulate detailed reaction
6 mechanisms for the atmospheric degradation reactions of the aromatic hydrocarbons, and the
7 chemical mechanisms used in urban airshed models must then rely heavily on environmental
8 (or "smog") chamber data.
9
10 Oxygenated Compounds
11 • The products observed from the atmospheric photooxidations of oxygenated
12 organics are carbonyls, organic acids (RC(O)OH), esters, alcohols, and, in
13 the presence of NOX, PANs.
14
15
16 The major area of uncertainty concerns the importance of photolysis of carbonyl
17 compounds in the troposphere, and the products formed. In particular, there is a lack of
18 information concerning the absorption cross-sections and photoodissociation quantum yields
19 for most of the aldehydes and ketones other than formaldehyde, acetaldehyde, and acetone.
20
21 3.2.4.2 Chemical Formation of Ozone in Polluted Air
22 3.2.4,2.1 Major Steps in Ozone Formation
23 As discussed earlier, NOX, and VOCs interact under the influence of sunlight to form
24 O3 and other photochemical air pollutants. The major steps hi this process are the
25 conversion of NO to NO2 by peroxy radicals, with the photolysis of NO2 leading to
26 O3 production. In the absence of a VOC, Reactions 3-1 through 3-3,
27
NO + 03 - N02 + 02 (3-3)
N02 + h? 2? NO + 03 (3-1, 3-2)
28
December 1993 3.39 DRAFT-DO NOT QUOTE OR CITE
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1 lead to no net formation of O3. The reaction of a VOC with the OH radical, or its
2 photolysis, leads to the formation of HO2 and organic peroxy (RO^ radicals, which react
3 with NO under high-NO conditions:
VOC(+ OH, h?) -H? RO,
(3-61)
RO2 + NO -* RO + NO2
NO,
+ hv 5? NO + O,
(3-44a)
(3-1, 3-2)
4
5
6
7
8
9
Net:
VOC(+ OH, hv) -»2 RO, + O3
(3-62)
with the alkoxy (RO) radical producting further HO2 or RO2 radicals, or both, and, hence,
further production of O3. This process is shown schematically in Figure 3-4.
VOC
Figure 3-4. Major steps in production of ozone in ambient air (R — H, alkyl or
substituted alkyl, or acyl).
December 1993
3-40 DRAFT-DO NOT QUOTE OR CITE
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1
2
3
4
5
6
7
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 following reactions:
(a) The conversion of NO to NO2 occurs through the oxidation reactions,
organic! + OH, hi>, O3) -* RO2
RO3 + NO -+ RO + NO2
RO 2? carbonyl * HO2
(3-63)
(3-44a)
(3-64)
8
9
10
0.60,
0.50
0.40*'
0.30
0.20
0.10C1
0.00 g
O Ozone A NO
D NO2 o Propene
X PAN
3.0
Tlme(h)
Figure 3-5. Time-concentration profiles for selected species during irradiations of an
NOx-propene-air mixture in an indoor chamber with constant light
intensity.
December 1993
3-41
DRAFT-DO NOT QUOTE OR CITE
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0.80,
0.6CH
0.00
O Ozone A NO
D NO2+PAN O Propene
X PAN
8.0
10.0
12.0
Time(h)
14.0
16.0
18.0
Figure 3-6. Time-concentration profiles for selected species during irradiations of an
NOx-propene-air mixture in an outdoor chamber with diumally varying
light intensity.
1
2
3
4
HO2 + NO -» OH * NO2
(3-32)
(b) The maximum concentration of NO2 is less than the initial NO + NO2 concentration
because NO2 is removed through the reaction,
OH + NO2 NO3
(3-15)
5
6
7
8
9
10
11
(c) The O3 concentration increases with the NO2/NO concentration ratio, and O3 formation
ceases when NO2 (and hence NOX) has been removed by reaction.
(d) Formation of PAN occurs by Reaction 3-56. Because of Reactions 3-55 and 3-56, the
PAN concentration also increases with the NOj/NO concentration ratio, and PAN formation
also ceases when NOX has been depleted.
December 1993
3-42 DRAFT-DO NOT QUOTE OR CITE
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1 (e) The removal processes for NOX are by reaction of NO2 with the OH radical to form
2 nitric acid (Reaction 3-16), the formation of organic nitrates from the ROO + NO reaction
3 pathway 3-42b, and the formation of PAN through Reaction 3-56, The initially present NOX
4 is converted to organic nitrates, nitric acid, and thermally unstable PAN(s). At ambient
5 temperature, the PAN(s) will gradually thermally decompose to yield NO2 and the
6 acylperoxy radicals; hence the ultimate fate of NOX will be to form nitric acid and organic
7 nitrates.
8
9 3.2.4.2.2 Effects of Varying Initial Nitrogen Oxide and Nonmethane VOC
10 Concentrations
11 As discussed above, NOX and VOC interact in sunlight to form O3 and other
12 photochemical air pollutants. The formation of O3 from the NOX and VOC precursors is
13 nonlinear with respect to the precursor emissions (or ambient concentrations) and, as
14 discussed in detail in Section 3.6, computer models incorporating emissions, meteorology,
15 and chemistry are necessary for a full understanding of the complexities of the NOX-VOC-O3
16 system. The effects of high versus low VOC/NOX ratios and of VOC versus NOX emission
17 reductions are also discussed in Section 3.6.
18
19 3.2,4.2.3 Effects of Biogenic Nonmethane VOC Emissions
20 Biogenic VOC emissions can be important in urban and rural areas (Trainer et al.,
21 1987; Chameides et al., 1988, 1992; Roselle et al., 1991) and can contribute to O3 formation
22 in much the same way as anthropogenic VOCs. Modeling simulations in which urban
23 biogenic VOC emissions are first included and then excluded from the calculations generally
24 indicate little effect of the biogenic emissions on the predicted 03 levels; this is not
25 unexpected from the shape of the O3 isopleths at high VOC/NOX ratios (see, for example,
26 Chameides et al., 1988, and Section 3.6). However, results of modeling studies in which
27 anthropogenic VOC emissions are removed from the simulations (but anthropogenic NOX
28 emissions are left unaltered) suggest that anthropogenic NOX together with biogenic VOCs
29 may form sufficient O3 to exceed the National Ambient Air Quality Standards (NAAQS), at
30 least in certain areas (Chameides et al., 1988). Thus, as discussed for the Atlanta, GA?
31 region (Chameides et al., 1988), NOX control may be more favorable than VOC control in
32 urban areas with substantial biogenic NMOC emissions.
December 1993 3.43 DRAFT-DO NOT QUOTE OR CITE
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1 While it is known that isoprene is reactive with respect to the formation of
2 63 (Section 3.2.4.3) and that the monoteipenes react rapidly with OH radicals, NO3 radicals,
3 and O3, the ozone-forming potentials of the various monoterpenes emitted into the
4 atmosphere are not known.
5
6 3.2.4.3 Hydrocarbon Reactivity with Respect to Ozone Formation
7 As discussed in Section 3.2.4, VOCs are removed and transformed in the troposphere
8 by photolysis and by chemical reaction with OH radicals, NO3 radicals, and O3. In the
9 presence of sunlight, the degradation reactions of the VOCs lead to the conversion of NO to
10 NO2 and the formation of O3 and various organic products. However, different VOCs react
11 at differing rates in the troposphere because of their differing tropospheric lifetimes
12 (Table 3-2). The lifetimes of most VOCs with respect to reaction with OH radicals and
13 O3 are in the range »1 h to =10 years. In large part because of these differing
14 tropospheric lifetimes and rates of reaction, VOCs exhibit a range of reactivities with respect
15 to the formation of 03 (Altshuller and Bufalini, 1971, and references therein).
16 A number of "reactivity scales" have been developed over the years (see, for example,
17 Altshuller and Bufalini, 1971, and references therein; Daniall et al., 1976), including the rate
18 of VOC disappearance in NOx-VOC-air irradiations, the rate of NO to NO2 conversion in
19 NOx-VOC-air irradiations, 03 formation in NOx-single VOC-air irradiations, eye irritation,
20 and the rate constant for reaction of the VOC with the OH radical. It appears, however, that
21 a useful definition of "reactivity" is that of "incremental reactivity" (IR), defined as the
22 amount of O3 formed per unit of VOC added or subtracted from the VOC mixture in a given
23 air mass under high-NO conditions (Carter and Atkinson, 1987, 1989b):
24
IR = A[03]/AfVOC] (3-65)
25
26 at the limit of A [VOC] -* 0. The concept of incremental reactivity and some further details
27 of this approach are illustrated by the general reaction mechanism for the photooxidation of
28 an alkane, RH:
OH + RH: -* R.O + R C3-42)
29
December 1993 3-44 DRAFT-DO NOT QUOTE OR CITE
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R * O7 -» RO2 (3-43)
RO2 - NO -» RO * NO2 (3-44a)
RO - carbonyl + HO2
HO2 + NO -» OH + NO2 (3-32)
4
5 The net reaction,
6 can be viewed as involving the two separate reaction sequences;
7
8 (1) Formation of organic peroxy (RO^) radicals from the reactions,
OH + RH ^ H2O + R
R + O2 -* RO2
Net: OH + RH ' -*• RO2
9
10 and (2) Conversion of NO to NO2 and the formation of O3 and other products,
11
NO -* RO + NO2
RO ^ carbonyl + H02
HO2 +• NO -* OH + NO2
OH + RH + 2 NO ? carbonyl + 2 NO2 * OH, (3-66)
Net: ROj + 2 NO -+- carbonyl + 2 NO2 + OH
12
December 1993 3.45 DRAFT-DO NOT OUOTE OR CITF
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1 The photolysis of NO2 then leads to O3 formation (Reactions 3-1 and 3-2). The first reaction
2 sequence determines how fast RO^ radicals are generated from the VOC, and has been
3 termed the "kinetic reactivity" (Carter and Atkinson, 1989b). For the case given above,
4 where the only reaction of the VOC is with the OH radical, the kinetic reactivity depends
5 solely on the OH radical reaction rate constant. The second reaction sequence, leading to
6 NO to NO2 conversion, regeneration of OH radicals, and the formation of product species
7 determines the efficiency of formation of O3 from the ROj radicals formed from the first
8 reaction sequence, and has been termed the "mechanistic reactivity" (Carter and Atkinson,
9 1989b). The second reaction sequence can be represented as:
10
RO2 + aNO -» 0 NO2 + 7OH + 6 products. (3-67)
11
12 In general, the faster a VOC reacts in the atmosphere, the higher the incremental
13 reactivity. However, the chemistry subsequent to the initial reaction does affect the ozone-
14 forming potential of the VOC. Thus, the existence of NOX sinks in the reaction mechanism
15 (low values of 0 or values of a-0 > 0) lead to a decrease in the amount of 03 formed.
16 Examples of NOX sinks are the formation of organic nitrates and PANs (which are also sinks
17 for radicals). The generation or loss of radical species can lead to a net formation or net loss
18 of OH radicals (7 > 1 or < 1, respectively). This in turn leads to an enhancement or
19 suppression of radical concentrations in the air parcel and to an enhancement or suppression
20 of the overall reactivity of all VOCs in that air parcel by affecting the rate of formation of
21 RQz radicals.
22 These effects vary in importance depending on the VOC/NOX ratio. Nitrogen oxides
23 sinks are most important at high VOC/NOX ratios (NOx~limited), affecting the maximum
24 ozone formed; while the formation or loss of OH radicals is most important at low
25 VOC/NOX ratios, affecting the initial rate at which ozone is formed (Carter and Atkinson,
26 1989b). In addition to depending on the VOC/NOX ratio (Table 3-3), incremental reactivity
27 depends on the composition of the VOC mixture and on the physical conditions encountered
28 by the air mass (including the dilution rate, light intensity, and spectral distribution (Carter
29 and Atkinson, 1989b; Carter, 1991).
December 1993 3-46 DRAFT-DO NOT QUOTE OR CITE
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TABLE 3-3. CALCULATED INCREMENTAL REACTIVITIES OF SELECTED
VOCs AS A FUNCTION OF THE VOC/NO, RATIO FOR AN EIGHT-COMPONENT
x
VOC MIXTURE" AND LOW-DILUTION CONDITIONS
VOC/NOX Ratio (ppm C/ppm)
NMOC
CO
Ethane
n-Butane
rt-Octane
Bthene
Propene
ftww-2-Butene
Benzene
Toluene
m-Xylene
Fonnaldehyde
Acetaldehyde
Methanol
Ethanol
Urban Mixa
4
0,011
0.024
0.10
0.068
0.85
1.28
1.42
0,038
0.26
0.98
2.42
1 34
0.12
0.18
0.41
8
0.022
0.041
0.16
0.12
0.90
1.03
0.97
0.033
0.16
0.63
1.20
0.83
0.17
0,22
0.32
16
0.016
0.018
0.069
0.027
0.33
0.39
0,31
-0.002
-0.036
0.091
0.32
0.29
0.066
0.065
0.088
40
0.005
0.007
0,019
-0.031
0.14
0.14
0.054
-0.002
-0.051
-0.025
0.051
0.098
0.029
0.006
0.011
"Eight-component VOC mixture used to simulate NMOC emissions in an urban area.
Source: Carter and Atkinson (1989b).
1 3.2.5 Photochemical Production of Aerosols
2 The chemical processes involved in the formation of O3 and other photochemical
3 pollutants from the interaction of NOX and VOCs lead to the formation of OH radicals and
4 the formation of oxidized VOC reaction products that are often of lower volatility than the
5 precursor VOC. The OH radicals that oxidize the VOCs and lead to the generation of RO2
6 radicals and conversion of NO to NC^ (with subsequent photolysis of NO2 form O3) also
7 react with NO2 and SO2 to form nitric and sulfuric acids, respectively, which become
8 incorporated into aerosols as particulate nitrate and sulfate. The low-volatility VOC reaction
9 products can condense onto existing particles in the atmosphere to form secondary organic
10 aerosol matter. Hence ozone formations acid formation, and secondary aerosol formation in
December 1993 3.47 DRAFT-DO NOT OUOTR OP rrm
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1 the atmosphere are so related that controls aimed at reducing O3 levels will impact
2 (positively or negatively) acid and secondary aerosol formation in the atmosphere.
3
4 3.2.5.1 Phase Distributions of Organic Compounds
5 Chemical compounds are emitted into the atmosphere in both gaseous and particle-
6 associated forms. The emissions from combustion sources (for example, vehicle exhaust) are
7 initially at elevated temperature, and compounds that may be in the particle phase at ambient
8 atmospheric temperature may be in the gas phase when emitted. In addition, atmospheric
9 reactions of gas-phase chemicals can lead to the formation of products that then condense
10 onto particles (or self-nucleate) (Pandis et al., 1991; Wang et al,, 1992; Zhang et al., 1992).
11 Measurements of ambient atmospheric gas- and particle-phase concentrations of several
12 classes of organic compounds indicate that the phase distribution depends on the liquid-phase
13 vapor pressure, PL (Bidleman, 1988; Pankow and Bidleman, 1992). The available
14 experimental data and theoretical treatments show that, as a rough approximation, organic
IS compounds with liquid-phase vapor pressures > 10 torr at ambient temperature are mainly
16 in the gas phase (Bidleman, 1988). As expected, the gas-particle phase distribution in the
17 atmosphere depends on the ambient temperature, with the chemical being more particle-
18 associated at lower temperatures. The gas-to-particle adsorption-desorption process can be
19 represented as,
A + TSP
-------
1 Gaseous and paniculate species in the atmosphere are subject to wet and dry deposition,
2 Dry deposition refers to the uptake of gases and particles at the earth's surface by vegetation,
3 soil, ami water, including lakes, rivers, oceans, and snow-covered ground. Wet deposition
4 refers to the removal of gases and particles from the atmosphere through incorporation into
5 rain, fog, and cloud water, followed by precipitation to the earth's surface. These processes
6 are discussed further in Section 3.6.
7 For gases, dry deposition is important primarily for HNOj, SO2, and H2C>2 as well as
8 for O3 and PAN; while wet deposition is important for water-soluble gases such as HNOj,
9 H202, phenols, and, under atmospheric conditions, SO2. Dry deposition of particles depends
10 on the particle size; those of mean diameter =0.1 to 2.5 pm have lifetimes with respect to
11 dry deposition of »10 days (Graedel and Weschler, 1981; Atkinson, 1988), sufficient for
12 long-range transport. However, particles are efficiently removed from the atmosphere by
13 wet deposition (Bidleman, 1988).
14 Particles can form in the atmosphere by condensation or by coagulation, occurring
15 generally by the latter in urban and regional areas. The photooxidation reactions of VOCs
16 generally lead to the formation of more oxidized and less volatile product species. When the
17 vapor pressures exceed the saturated vapor pressure, or the vapor pressure is < 10"6 Torr,
18 the products will become particle-associated (Pandis et al., 1991, 1992), Accumulation-size
19 particles are in the size range 0.08 to 2.5 pm diameter (Whitby et al., 1972).
20 In urban areas, the major sources of paniculate matter (Larson et al., 1989; Solomon
21 et al., 1989; Wolff et al., 1991; ffildemann et al., 1991a,b; Rogge et al., 1991, 1993; Chow
22 etal., 1993) are:
23
24 • Direct emissions of elemental carbon from, for example, diesel-powered vehicles
25 (Larson et al., 1989);
26
27 • Direct emissions of primary organic carbon from, for example, meat cooking
28 operations, paved road dust, and wood-burning fireplaces and other combust! «n
29 sources (ffildemann et al., 1991a,b; Rogge et al., 1991, 1993);
30
31 • Secondary organic material formed in the atmosphere from the atmospheric
32 photooxidations of gas-phase NMOC (Turpin and Huntzicker, 1991; Pandis et al.,
33 1992);
34
December 1993 3.49 DRAFT-DO NOT QUOTE OR CITE
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1 • The conversion of NO and NO2 to nitric acid, followed by neutralization by
2 ammonia or through combination with other cations to form aerosol nitrates:
3
NH3(gas) + HNO3(gas) -* NH4NO3(aerosol);
4
5 • The conversion of SO2 (and other sulfur-containing species) to sulfuric acid, which
6 has sufficiently low volatility to go to the aerosol phase; and
7
8 • Emission into the atmosphere of "fine dust", for example, of crustal material.
9
10 Because the fine-particle size range is the same magnitude as the wavelength of visible
11 light, paniculate matter present in the atmosphere leads to light scattering and absorption,
12 and hence to visibility reduction (see, for example, Larson et al., 1989; Eldering et al.,
13 1993).
14
15 3.2.5.2 Acid Deposition
16 As noted above, the chemical processes involved in the formation of O3 and other
17 photochemical pollutants from the interaction of NMOC and NOX also lead to the formation
18 of acids in the atmosphere. The two major acidic species in ambient air are nitric acid and
19 sulfuric acid, arising from the atmospheric oxidation of NOX and SO2, respectively. Reduced
20 sulfur compounds emitted from biogenic sources and certain anthropogenic sources may also
21 lead to SC>2 or sulfonic acids, or bom (Tyndall and Ravishankara, 1991).
22 The major sulfur-containing compound emitted into the atmosphere from anthropogenic
23 sources is sulfur dioxide, SO2. In the troposphere, the important loss processes of S02 are
24 dry deposition (Atkinson, 1988, and references therein), reactions within cloud water, and
25 gas-phase reaction with the OH radical. The rate constant for the reaction of SO2 with the
26 OH radical is such that the lifetime of SO2 with respect to gas-phase reaction with the OH
27 radical is «15 days. The reaction proceeds by (Stockwell and Calvert, 1983; Atkinson
28 etal., 1992a),
29
M
OH + S02 ™ HOS02 (3-70)
30
December 1993 3-50 DRAFT-DO NOT QUOTE OR CITE
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HOSO2 - Oj -» HO2 + SO3 (3-71)
1
SO3 - H2O - H2SO4. (3-72)
2
3 The reaction of SO3 with water vapor is slow in the gas-phase (Atkinson et al,5 1992a) and
4 hence this may be a heterogeneous reaction. Because of its low vapor pressure, H2SO4
5 exists in the aerosol or particle phase in the atmosphere,
6 Dry deposition is an important atmospheric loss process for SO2, since SO2 has a fairly
7 long lifetime due to gas-phase chemical processes and has a high deposition velocity.
8 A lifetime in relation to dry deposition of 2 to 3 days appears reasonable (Schwartz, 1989).
9 Sulfur dioxide is not very soluble in pure water (Schwartz, 1989). However, the
10 presence of pollutants such as H2O2 or O3, or both, in the aqueous phase displaces the
11 equilibrium and allows gas-phase SO2 to be incorporated into cloud, rain, and fog water,
12 where it is oxidized rapidly (Schwartz, 1989; Pandis and Seinfeld, 1989, and references
13 therein):
SO2(gas) ?* SO2(aqu) (3-73)
14
SO2(aqu) + H2O ** HSO3" + H+ *± SO32" + 2 H+ (3-74)
15
HSO3" + HjO2 -* SQ42~ * H* + H/) (3-75)
16
SOj~ - O3 -* SO42" + O2 (3-76)
17
18 In addition, aqueous sulfur can be oxidized in a process catalyzed by transition metals such
19 as iron(m) [Fe3+] and manganese(n) [Mn2^] (Graedel et al., 1986b; Weschler et al., 1986;
20 Pandis and Seinfeld, 1989).
21
SO?' - 1/2 02 - SOf (3-77)
22
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1 The oxidation rate of aqueous sulfur by O3 decreases as the pH decreases (i.e., as the acidity
2 increases) and this oxidation route is therefore self-limiting and generally of minor
3 importance in the atmosphere. The oxidation of SO2 by H2O2 appears to be the dominant
4 aqueous-phase oxidation process of SO2 (Chandler et al., 1988; Gervat et al.s 1988;
5 Schwartz, 1989; Pandis and Seinfeld, 1989; Fung et al., 1991b), although the transition
6 metal-catalyzed oxidation of SO2 may also be important (Jacob et al., 1989). It should be
7 noted that aqueous-phase H2O2 arises, in part, from the absorption of HO2 radicals and H2O2
8 into the aqueous phase, with HO2 radicals being converted into H2O2 (see also Zuo and
9 Hoigne, 1993).
10 The oxidation of SO2 to sulfate in clouds and fogs is often much faster than the
11 homogeneous gas-phase oxidation of SO2 initiated by reaction with the OH radical. The gas-
12 phase oxidation rate is * 0.5 to 1 % h"1, while the aqueous-phase (cloud) oxidation rate may
13 be as high as 10 to 50% h"1 (Schwartz, 1989).
14 The oxidation of NOX to nitric acid and nitrates was discussed in Section 3.2.3 above.
15 During daylight hours, oxidation occurs by the gas-phase reaction of NO2 with the OH
16 radical:
17
OH * NO2 ^ HNO3 (3-15)
18
19 with the lifetime of NO2 due to Reaction 3-15 calculated to be »1.4 days. Nitric acid is
20 removed from the troposphere by wet and dry deposition, with wet deposition being efficient.
21 During nighttime hours, NO2 can be convened into NO3 radicals and N2O5:
22
23
24
N02 + O3-> N03 + 02 (3-12)
M
N03 - N0: ^ N205, (3-13, -3-13)
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1 with N205 undergoing wet or dry deposition, or both. The reader is referred to Schwartz
2 (1989) for- further discussion of the conversion of NOX to nitrate and nitric acid and acid
3 deposition.
4
5
6 3.3 METEOROLOGICAL PROCESSES INFLUENCING OZONE
7 FORMATION AND TRANSPORT
8 Day-to-day variability in ozone (O3) concentrations is, to a first approximation, the
9 result of day-to-day variations in meteorological conditions. This section presents a succinct
10 overview of those atmospheric processes that affect the concentrations of ozone and other
11 oxidants in urban and rural areas. Included in this list of processes are the vertical structure
12 and dynamics of the planetary boundary layer (PEL); transport processes, including
13 thermally-driven mesoscale circulations such as lake and sea breeze circulations; complex
14 terrain effects on transport and dispersion; vertical exchange processes; deposition and
15 scavenging; and meteorological controls on biogenic emissions and dry deposition.
16
17 3.3.1 Meteorological Processes
18 3.3.1.1 Surface Energy Budgets
19 Knowledge of the surface energy budget is fundamental to an understanding of the
20 dynamics of the planetary boundary layer (PEL). The PEL is defined as that layer of the
21 atmosphere in contact with the surface of the earth and that is directly influenced by the
22 surface characteristics. In combination with synoptic winds, it provides the forces for the
23 vertical fluxes of heat, mass and momentum. The accounting of energy inputs and outputs
24 provides a valuable check on modeled PEL dynamics.
25 Figure 3-7 illustrates the surface radiation budget for short-wave (wavelength roughly
26 <0.4 fjtm) and long-wave radiation. The radiation budget for the surface can be described in
27 terms of its components as:
28
Q8fc = Ki - KT + LI - Lf + QH + QE (3-78)
29
December 1993 3-53 DRAFT-DO NOT QUOTE OR CITE
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Latent
and
Sensible
Heat
Figure 3-7. Surface radiation budget for short-wave (7 > 0.4 /im) 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. That amount that remains can
be absorbed or reflected at the surface. The reflected light (3) can also be
scattered back to the surface (4). The short-wave 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).
1 where Kl is the incoming short-wave radiation, Kt is the outgoing short-wave radiation,
2 Li is the incoming long-wave radiation from the atmosphere, Lt is the outgoing long-wave
3 radiation, and QH and QE are the heat flux and latent heat flux to the soil, respectively,
4 On a global annual average, Qsfc is assumed to be near zero (i.e., the planet is not heating or
5 cooling systematically, an assumption clearly being questioned with the growing debate on
6 climatic change). On a day-to-day basis, however, Qsfc will certainly vary from zero and
7 will cause changes in surface temperature. Cloud cover, as an example, will reduce the
8 amount of short-wave radiation reaching the surface and will modify all the subsequent
9 components of the radiation budget. Moreover, the redistribution of energy through the PEL
December 1993
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1 creates thermodynamic conditions that influence vertical mixing. The treatment of energy
2 budgets has been attempted on the scale of individual urban areas. These studies are
3 summarized by Oke (1987).
4 For many of the modeling studies of the photochemical production of ozone, the
5 vertical mixing has been parameterized by a single, well-mixed layer. However, because
6 this is a great simplification of a complex structure, and because the selection of rate and
7 extent of vertical mixing may influence local control options, it behooves future modeling
8 and observational studies to address the energy balances so that more realistic simulations can
9 be made of the structure of the PEL.
10
11 3.3.12 Planetary Boundary Layer
12 The concentration of an air pollutant depends significantly on the degree of mixing that
13 occurs between the time a pollutant or its precursors are emitted and the arrival of the
14 pollutant at the receptor. Atmospheric mixing is the result of either mechanical turbulence,
15 often associated with wind shear, or thermal turbulence, associated with vertical
16 redistribution of heat energy. The potential for thermal turbulence can be characterized by
17 atmospheric stability. The more stable the air layer the more work is required to move air
18 vertically.
19 As air is moved vertically through the atmosphere, as might happen in a convective
20 thermal, its temperature will decrease with height as the result of adiabatic expansion. It is
21 the comparison of how the temperature should change with height in the absence of external
22 heating or cooling against the actual temperature change with height that is a measure of
23 atmospheric stability. Those layers of the atmosphere where temperature increases with
24 height (inversion layers) are the most stable as air, cooling as it rises, then becomes denser
25 than its new wanner environment. In an atmospheric layer with relatively low turbulence,
26 pollutants do not redistribute vertically as rapidly as they do in an unstable layer. Also,
27 because a stable layer has a relatively low rate of mixing, pollutants in a lower layer will not
28 mix through it to higher altitudes.
29 The stability of the atmosphere is often measured through computation of potential
30 temperature, q, as
31
December 1993 3-55 DRAFT-DO NOT QUOTE OR CITE
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-R/c.
r>
e =
i
2 where 9 is the virtual potential temperature, P is the pressure of the air parcel, P0 is the
3 reference pressure to which the air parcel will be moved (usually 1,000 mb), R is the gas law
4 constant, and cp is the specific heat of air at constant pressure. The faster 6 increases with
5 height, the less the potential for mixing.
6 A stable layer can also act as a trap for air pollutants lying beneath it. Hence, an
7 elevated inversion is often referred to as a "trapping" inversion. On the other hand, if
8 pollutants are emitted into a stable layer aloft, such as might occur from an elevated stack,
9 the lack of turbulence will keep the effluents from reaching the ground while the inversion
10 persists.
11 Traditionally, atmospheric mixing has been treated through use of a mixing height,
12 which is defined as the base of an elevated inversion layer. In this model, the ozone
13 precursors are mixed uniformly through the layer below the mixing height. As this layer
14 grows it both entrains remnant ozone from previous days and redistributes fresh emissions
15 aloft. The vertical mixing profile through the lower layers of the atmosphere is assumed to
16 follow a typical and predictable cycle on a generally clear day. In such a situation a
17 nocturnal surface inversion would be expected to form during the night as Lt exceeds Li.
18 This surface layer inversion persists until surface heating becomes significant, probably two
19 or three hours after sunrise. Pollutants initially trapped in the surface inversion may cause
20 relatively high, local concentrations, but these concentrations will decrease rapidly when the
21 surface inversion is broken by surface heating. The boundary formed between the rising,
22 cooling air of the growing mixing layer and that of the existing PEL is often sharp and can
23 be observed as an elevated temperature inversion.
24 Elevated temperature inversions, when the base is above the ground, are also common
25 occurrences (Hosier, 1961; Holzworth, 1974). This condition can form simply as the result
26 of rapid vertical mixing from below, but is exacerbated in regions of subsiding air when the
27 sinking air warms to a point such that it is warmer than the rising (and cooling) underlying
28 air. Since these circumstances are associated with specific synoptic conditions, they are less
29 frequent than the ubiquitous nighttime radiation inversion. An elevated inversion is
December 1993 3-56 DRAFT-DO NOT QUOTE OR CITE
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1 nevertheless a very significant air pollution feature, because it may persist throughout the day
2 and thus restrict vertical mixing.
3 When compared to a source near the surface and the effects of a radiation (surface)
4 inversion, the pollutant dispersion pattern is quite different for an elevated source plume
5 trapped in a layer near the base of an elevated inversion. This plume will not be in contact
6 with the ground surface in the early morning hours because there is no mixing through the
7 surface radiation inversion. Thus, the elevated plume will not affect surface pollutant
8 concentrations until the mixing processes become strong enough to reach the altitude of the
9 plume. At that time, the plume may be mixed downward quite rapidly in a process called
10 fumigation. During fumigation, surface ozone concentrations will increase if the morning
11 ozone concentration is higher aloft than at the ground, and if insufficient scavenging by NO
12 occurs at ground-level. In fact, the rapid rise in ozone concentrations in the morning hours
13 is often the result of vertical (downward) transport from an elevated reservoir of ozone.
14 After this initial increase, surface concentrations can continue to increase as a result of
15 photochemistry or transport of ozone-rich air to the receptor, or both.
16 When surface heating decreases in the late afternoon and early evening, the surface
17 inversion will form again under most conditions. The fate of the elevated inversion is less
18 clear, however. While ozone and its precursors have been mixed vertically, the reduction of
19 turbulence and mixing at the end of the daylight hours leaves ozone in a remnant layer that is
20 often without a well-defined thermodynamic demarcation. This layer is then transported
21 through the night, often to regions far removed from pollution sources, where its pollutants
22 can influence concentrations at remote locations the next morning as mixing entrains the
23 elevated remnant layer. This overnight transport can be aided by the development of a
24 nocturnal jet that forms many nights at the top of the surface inversion layer.
25 Geography can have a significant impact on the dispersion of pollutants (e.g., along the
26 coast of an ocean or one of the Great Lakes). Near the coast or shore, the temperatures of
27 land and water masses can be different, as can the temperature of the air above such land and
28 water masses. When the water is warmer than the land, there is a tendency toward reduction
29 in the frequency of surface inversion conditions inland over a relatively narrow coastal strip
30 (Hosier, 1961). This in turn tends to increase pollutant dispersion in such areas. The
31 opposite condition also occurs if the water is cooler than the land, as in summer or fall.
December 1993 3.57 DRAFT-DO NOT QUOTE OR CITE
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1 Cool air near the water surface will tend to increase the stability of the boundary layer in the
2 coastal zone, and thus decrease the mixing processes that act on pollutant emissions. These
3 conditions occur frequently along the New England coast (Hosier, 1961). Similarly,
4 pollutants from the Chicago area have been observed to be influenced by a stable boundary
5 layer over Lake Michigan (Lyons and Olsson, 1972), This has been observed especially in
6 summer and fall when the lake surface is most likely to be cooler than the air that is carried
7 over it from the adjacent land.
8 Sillman et al. (1993) investigated abnormally high concentrations of O3 observed in
9 rural locations on the shore of Lake Michigan and on the Atlantic coast in Maine, at a
10 distance of 300 km or more from major anthropogenic sources. A dynamical-photochemical
11 model was developed that represented formation of O3 in shoreline environments and was
12 used to simulate case studies for Lake Michigan and the northeastern United States. Results
13 suggest that a broad region with elevated O3, NOX, and VOC forms as the Chicago plume
14 travels over Lake Michigan, a pattern consistent with observed O3 at surface monitoring
15 sites. Near-total suppression of dry deposition of Oj and NOX over the lake is needed to
16 produce high O3. Results for the east coast suggest that the observed peak O3 can only be
17 reproduced by a model that includes suppressed vertical mixing and deposition over water,
18 2-day transport of a plume from New York, and superposition of the New York and Boston
19 plumes. Hence, the thermodynamics associated with the water bodies seem to play a
20 significant role in some regional-scale episodes of high ozone concentrations.
21 There is concern that the strict use of mixing height unduly simplifies the complex
22 atmospheric processes that redistribute pollutants within urban areas. There is growing
23 evidence that some ozone precursors may not be evenly redistributed over some urban areas
24 in cases where the sources are relatively close to the urban area and atmospheric mixing is
25 not strong enough to redistribute the material over a short travel time. In these cases, it is
26 necessary to treat the turbulent structure of the atmosphere directly and acknowledge the
27 vertical variations in mixing. Methods that are being used to investigate these processes
28 include the use of a diffusivity parameter to express the potential for mixing as a function of
29 height. A simple expression of how the mean concentration, x, changes with time, t, in an
30 air parcel, assuming all concentrations are homogeneous in the horizontal, is:
December 1993 3-58 DRAFT-DO NOT QUOTE OR CITE
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1 where w 'x ' is the vertical turbulent eddy flux of pollutant. The term on the right hand side
2 of the equation changes mean concentration through flux divergence; i.e., turbulence either
3 disperses the pollutant to or from the point being considered. The problem with this
4 representation is that the flux divergence term is virtually impossible to measure directly.
5 The turbulent eddy flux needed to understand the vertical distribution of ozone and its
6 precursors is often parameterized in photochemical models, if included at all, through use of
7 eddy diffusivity. The eddy diffusivity is set using an analogy to mixing length theory as
(3-81)
8 which allows estimation of flux divergence from measured or estimated vertical gradients in
9 concentration and estimation of the eddy diffusivity. The selection of diffusivity is often
10 somewhat arbitrary, but can be related to the eddy diffusivity for heat or momentum, or
11 both, depending on circumstances. Large values result in rapid mixing. Thus, the
12 appropriate selection of eddy diffusivity is necessary to simulate whether elevated plumes
13 will enter an urban airshed.
14 Another method uses a technique called "large-eddy simulation" to recreate the
15 probability of redistribution within the mixing height.
16 Both these techniques require meteorological information that is not normally available
17 from the National Weather Service, but that is now becoming available as part of several
18 ozone field experiments.
19
20 3.3.1.3 Cloud Venting
21 Vertical redistribution of ozone out of the PEL is achieved by the venting of pollutants
22 in clouds. Clouds represent the top-most reaches of thermals of air rising through the PEL
23 and can act simply as chemical reactors for soluble pollutants, returning the "processed" air
24 to the PEL; or if convection is sufficiently vigorous, they can result in physical redistribution
December 1993 3-59 DRAFT-DO NOT QUOTE OR CITE
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1 of ozone and its precursors from the PEL (Greenhut, 1986; Dickerson et al., 1987). Clouds
2 also act to influence photolysis rates and chemical transformation rates.
3 Greenhut (1986) showed that the net ozone flux in the cloud layer was a linear function
4 of the difference in ozone concentration between the boundary layer and the cloud layer.
5 Ozone fluxes between clouds were usually smaller than those found within clouds, but the
6 slower rate is at least partially offset by the larger region of cloud-free air relative to cloudy
7 air.
8 Large clouds, such as cumulonimbus, offer considerably more potential for
9 redistribution of ozone and its precursors. Additionally, the cumulonimbus clouds are also
10 associated with precipitation, a scavenger of pollutants, and with lightning, a potential source
11 for nitrogen oxides. Using carbon monoxide (CO) as a tracer, Dickerson et al. (1987) and
12 Pickering et al. (1990) have illustrated the redistribution potential of cumulonimbus cloud
13 systems. Lyons et al. (1986) provided an illustration of the potential for groups of
14 cumulonimbus clouds to vent the polluted boundary layer.
15 The role of cloud venting is thought to be largely a cleansing process for the boundary
16 layer, although a portion of the material lifted into the free troposphere could be entrained
17 back to the surface in subsequent convection. Aircraft observations have documented
18 frequently the occurrence of relatively high ozone concentrations above lower-concentration
19 surface layers (e.g., Westberg et al., 1976). This is a clear indication that ozone is
20 essentially preserved in layers above the surface and can be transported over relatively long
21 distances even when continual replenishment through precursor reactions is not a factor, such
22 as at night.
23
24 3.3.1.4 Stratospheric-Tropospheric Ozone Exchange
25 The fact that O3 is formed in the stratosphere, mixed downward, and incorporated into
26 the troposphere, where it forms a more or less uniformly mixed background concentration,
27 has been known in various degrees of detail for many years (Junge, 1963). The mechanisms
28 by which stratospheric air is mixed into the troposphere have been examined by a number of
29 authors, as documented previously by EPA (U.S. Environmental Protection Agency, 1986,
30 and references therein).
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1 There is little evidence that ozone from the stratosphere contributes in any substantial
2 way to either peak urban ozone values or regional episodes of elevated ozone levels (Johnson
3 and Viezee, 1981; Ludwig et al., 1977; Singh et al., 1980; and Viezee et al., 1979).
4 Johnson and Viezee (1981) concluded that the ozone-rich intrusions studied sloped downward
5 toward the south. In terms of dimensions, the average crosswind width (north to south) at an
6 altitude of 5.5 kilometers (ca. 18,000 feet or 3.4 miles) for six spring intrusions averaged
7 226 kilometers (364 miles), and for four fall tropopause fold systems, 129 kilometers
8 (208 miles). Ozone concentrations at 5.5 kilometers (ca 18,000 feet or 3.4 miles) average
9 108 ppb in the spring systems and 83 ppb in the fall systems. From this and other research
10 described in the previous criteria document for O3 and other photochemical oxidants (U.S.
11 Environmental Protection Agency, 1986), Viezee and coworkers (Viezee and Singh, 1982;
12 Viezee et al., 1983) concluded that (1) direct ground-level impacts by stratospheric O3 may
13 be infrequent, occurring < 1 % of the time; (2) that such ground-level events are short-lived
14 and episodic; and (3) that they are most likely to be associated with Qj concentrations
15 ^0.1 ppm. (See U.S. Environmental Protection Agency [1986] for additional details).
16 Using the Be-to-O3 ratio as an indicator of O3 of stratospheric origin and sulfate
17 (SO4=) concentrations as a tracer for anthropogenic sources, Altshuller (1987) estimated
18 stratospheric contributions of ozone in the range 0 to 40 ppb (0 to 95% of observed 63) at
19 ground level at Whiteface Mountain, NY, for July 1975 and mid-June to mid-July 1977.
20 Monthly average stratospheric contributions were estimated at 5 to 10 ppb. He also
21 examined extant Be and O3 data for a number of lower-elevation rural locations in the
22 western, midwestern, and southeastern United States, and calculated stratospheric or upper
23 tropospheric contributions at 6 to 8 ppb. He concluded that his calculated values for such
24 contributions should be viewed with caution and regarded probably as upper limits because of
25 scatter in the 7Be and corresponding O3 data that hindered definition of the 7Be-to-O3 ratio.
26 He also concluded that removal and dilution processes result in the loss of most stratospheric
27 O3 before it reaches ground level (Altshuller, 1987).
28
29 3.3.2 Meteorological Parameters
30 This section focuses on analyses of data from previous and ongoing measurement
31 programs to address two driving questions: (1) are there meteorological parameters which
December 1993 3-61 DRAFT-DO NOT QUOTE OR CITE
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1 are systematically associated with ozone levels; and (2) are relationships between ozone and
2 meteorological parameters sufficiently strong such that meteorological fluctuations can be
3 filtered from the data to allow examination of longer-term trends?
4 The meteorological factors that theoretically could influence surface ozone levels
5 include ultraviolet radiation, temperature, wind speed, atmospheric mixing and transport, and
6 surface scavenging. The following examines the theoretical basis for each of these and
7 identifies to what degree empirical evidence supports the hypotheses.
8
9 3.3.2.1 Sunlight
10 Ultraviolet (UV) radiation plays a key role in initiating the photochemical processes
11 leading to ozone formation. Sunlight intensity (specifically the UV portion of sunlight)
12 varies with season and latitude but the latter effect is strong only during the winter months,
13 The importance of photolysis to the formation of O3 provides a direct link between O3 and
14 time of year. However, during the summer, the maximum UV intensity is fairly constant
15 throughout the contiguous United States and only the duration of the solar day varies to a
16 small degree with latitude.
17 The effects of light intensity on individual photolytic reaction steps and on the overall
18 process of oxidant formation have been studied in the laboratory (Peterson, 1976; Demerjian
19 et al., 1980). Early studies, however, employed constant light intensities, in contrast to the
20 diurnally varying intensities that occur in the ambient atmosphere. The diurnal variation of
21 light intensity was subsequently studied as a factor in photochemical oxidant formation (e.g.,
22 Jeffries et al., 1975, 1976). Such studies showed that the effect of this factor varies with
23 initial reactant concentrations. Most important was the observation that similar NMOC/NOX
24 systems showed different oxidant-forming potential depending on whether studies of these
25 were conducted using constant or diurnal light. This led to incorporation of the effects of
26 diurnal or variable light into photochemical models (TUden and Seinfeld, 1982).
27 There is little empirical evidence in the literature, however, linking day-to-day
28 variations in observed UV radiation levels with variations in ozone levels. Samson et al.
29 (1988) illustrated that the number of O3 concentrations exceeding 120 ppb did not track well
30 with potential solar radiation, as shown in Figure 3-8. Although variations in day-to-day
31 concentrations could well be influenced by cloud cover or attenuated by haze, the seasonal
December 1993 3-62 DRAFT-DO NOT QUOTE OR CITE
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Annual Variation
in Solar
ts Radiation
TOO
600
500
u
400
o
1
300
200 O
100
0_ itkffi^maipmiipiipaiapaHfflaH^^ I 0
1 I J 1 IT in T 1 T V t 1 plTT^m j i^ rmT^ ( rpij v
14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
Week of Year
Figure 3-8. The number of reports of ozone concentrations > 120 ppb at the 17 cities
studied in Samson et al. (1988). (1 April = week 14, 1 May = week 18,
1 June = week 22, 1 July = week 27,1 August = week 31, 1 September =
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 et al. (1988).
1
2
3
4
5
6
7
peak in ozone 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 a demonstrable association between tropospheric ozone concentrations and
tropospheric temperature. Numerous studies done over more than a decade have reported
that successive occurrences or episodes of high temperatures characterize seasonally high
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1 ozone years (e.g., Clark and Karl, 1982; Kelly et al., 1986). The relationship has been
2 observed for the South Coast Air Basin of California (Kuntasal and Chang, 1987), and in
3 New England (Wolff and Lioy, 1978; Atwater, 1984; Wackier and Bayly, 1988), as well as
4 elsewhere.
5 Figures 3-9 and 3-10 show the daily maximum ozone concentrations versus maximum
6 daily temperature for summer months (May to October) 1988 to 1990, for, respectively,
7 Atlanta, Georgia, and New York City, New York; and for Detroit, Michigan, and Phoenix,
8 Arizona. There appears to be an upper-bound on ozone concentrations that increases with
9 temperature. Likewise, Figure 3-11 shows that a similar qualitative relationship exists
10 between ozone and temperature even at a number of rural locations.
11
8
•a
D
|
I
240
210
150
120
90
60
30
0
Atlanta, Georgia
10
15
20
25
I
E
I
New York, New York
' o"«8cr
30
35
40
10 15 20 25 30 35
Maximum Temperature, "C
40
Figure 3-9. A scatter plot of maximum daily ozone concentration in Atlanta, Georgia,
and New York, New York, versus maximum daily temperature.
December 1993
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240
.£ 210
*«
120
90
80
30-
210
180
150
120
BO
80
30
0
[Detroit, Michigan)
10
15
Phoenix, Arizona
20
25
•w
'a o "o"
*er—r--
35
40
•ff-8-.
o
10
15
20
25
40
Maximum Temperature. °C
figure 3-10. A scatter plot of maximum daily ozone concentration in Detroit, Michigan,
and Phoenix, Arizona, versus maximum daily temperature.
• Ann Arbor, MI
Wllllamsport, PA
Mammoth Cave, KY o McKensle City , NO
210
160-
150-
a>
I
|
1
120
90-
eo
30
TJ~V£l3Li' «i
• dK« t_>- nil a
> -.»
10 15 20 25 30 35
Maximum Daily Temperature, °C
40
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.
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1 The notable trend in these plots is the apparent upper-bound to ozone concentrations
2 because a function of temperature. It is clear that at a given temperature there is a wide
3 range of possible concentrations because other factors, (e.g., cloudiness, precipitation, wind
4 speed) can reduce the ozone production. The upper bound presumably represents the
5 maximum concentration achieved under the most favorable conditions. Table 3-4 lists the
6 results of a statistical regression performed on the paired O3-temperature data used in
7 Figures 3-9 and 3-10 with separate slopes listed for temperatures above and below 30 °C.
8 Results show that for T > 30 °C the (^-temperature relationship is statistically significant at
9 all sites. The rate of increase is 3 to 5 ppb/°C at rural sites and ranges from 7 to 12 ppb/°C
10 at the three eastern U.S. urban sites (New York, Detroit and Atlanta). At two western sites,
11 Williston, North Dakota, and Billings, Montana, there is a much weaker dependence on
12 temperature, possibly reflecting the lower level of anthropogenic activity. At a third western
13 site, Medford, Oregon, the O3-temperature relationship is comparable to that at rural eastern
14 sites.
15 Relationships between peak O3 and temperature have also been recorded by Wunderli
16 and Gehrig (1991) for three locations in Switzerland. At two sites near Zurich, peak
17 O3 increased 3 to 5 ppb/°C for diurnal average temperatures between 10 and 25 °C, and
18 little change in peak O3 occurred for temperatures below 10 °C. At the third site, a high-
19 altitude location removed from anthropogenic influence, showed much less variation of
20 Oj with temperature was observed.
21 The hypotheses for this correlation include, but are not necessarily limited to:
22 1. Reduction in photolysis rates at low temperatures;
23
24 2. Reduction in I^O concentrations at low temperatures
25
26 3. Thermal decomposition of PAN and its homologues;
27
28 4. Increased anthropogenic emissions of reactive hydrocarbons or NOX or
29 both;
30
31 5. Increased natural emissions of reactive hydrocarbons; and
32
33 6. Relationships between high temperatures and stagnant circulation patterns.
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TABLE 3-4. RATES OF INCREASE OF PEAK OZONE WITH DIURNAL
MAXIMUM TEMPERATURE (ppb/°C) FOR T < 300 K (27 °C) AND T > 300 K,
BASED ON MEASUREMENTS FOR APRIL 1 TO SEPTEMBER 30, 1988
Location
Urbanized Regions:
Nonurban Sites;
NY-NJ-CT
Detroit
Atlanta
Phoenix
Southern California
Williamsport, PA
Saline, MI
Mammoth Cave, KY
Kentucky, cleanest site 3
Williston, ND
Billings, MT
Medford, OR
T <
A03/AT
1.5
1.4
3.2
—
11.3
1.2
0.8
0.1
0.3
0.2
0.1
0.5
300 K
T-statistic
-5.2
-6.4
-4.2
__
-8.9
-5
-3.5
-0.3
-0.7
-1
-0.5
-2.6
T >
A03/AT
8.8
4.4
7.1
1.4
_
4
3.1
4.4
3.4
0.8
0.7
3.3
300 K
T-statistic
-:.4
-6.3
-5.9
-4.1
—
-7.4
-4.9
-7.3
-6.6
-3.7
-2.2
-13.7
Source:
1 The relationship with temperature is well known, but not yet reproduced by air quality
2 models. While it has been argued that this striking relationship with temperature is an
3 indirect result of the stagnant synoptic meteorological conditions that lead to higher ozone
4 levels, the correlation is not strong with other parameters of stagnation, notably wind speed,
5 as is discussed later.
6
7 3.3.2.2.1 Reduction in Photolysis Rates
8 It is possible that on a seasonal scale the correlation between temperature and ozone
9 may be an indirect correlation with UV radiation variability. This is insufficient, however,
10 to explain the day-to-day correlation between the two variables.
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1 Changes in photolysis rates and in H2O concentrations are related in that both are
2 linked to the supply of OH radicals which determines the rate of ozone production in polluted
3 environment. A reduction in either photolysis rates or H2O would reduce the source of OH
4 radicals. Calculations by Sillman and Samson (1993) showed that the difference between
5 summer and fall photolysis rates (at 40° N latitude) has a significant impact on the rate of
6 ozone production in urban photochemical simulations, roughly equal to the impact of PAN
7 thermal decomposition (discussed below). However the impact of photolysis rates and of
8 water vapor was much lower in simulations for polluted rural environments. In the
9 simulations by Silknan et al. (1993) ozone production in urban environments was limited
10 largely by the supply of OH radicals to react with hydrocarbons; whereas in rural
11 environments the limiting factor was the source of NOX. Consequently photolysis rates and
12 H2O had less impact on ozone production in rural environments.
13
14 3.3.2.2.2 Thermal Decomposition of Peroxyacetyl Nitrate
15 Temperature-dependent photochemical rate constants provide a link between 03 and
16 temperature (Sillman et al., 1990a; Cardelino and Chameides, 1990). The reason for the
17 decline in O3 in rural areas when the PAN decomposition rate decreases is that PAN
18 apparently represents a major sink for NOX in rural environments. When the rate of PAN
19 decomposition is decreased NOX drops sharply while OH and HO2 remain largely unaffected.
20 Consequently, the rate of the important HO^ + NO reaction (see Section 3.2) shows a
21 substantial decrease.
22 The photochemical response in an urban environment is fundamentally different,
23 although the final result, a decrease in O3 with temperature, is similar. The impact of PAN
24 in urban environments is attributable to its role as a sink for odd hydrogen rather than to its
25 effect on NOX (Cardelino and Chameides, 1990). Sillman et al. (1990a) have shown that the
26 well-known division of ozone photochemistry into NOx-sensitive and VOC-sensitive regimes
27 is associated with the relative magnitude of odd-hydrogen sinks. In the NOx-sensitive
28 regime, typical of rural areas, the major sink of odd hydrogen consists of formation of
29 peroxides. Ozone formation is relatively insensitive to the magnitude of odd hydrogen
30 sources since the peroxide sink varies with the square of the HO2 concentration and provides
31 partially buffers the effect of a change in sources. At higher NOX concentrations or lower
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1 VOC/NOX ratios, the dominant sink for odd hydrogen is formation of HNO3. The rate of
2 odd hydrogen formation assumes a much greater importance since the buffering effect of
3 peroxide formation and other OH-HC>2 interactions is lost. Formation of Oj becomes
4 strongly sensitive to odd hydrogen formation rates, and to VOC concentrations as sources of
5 odd hydrogen.
6 Sillman and Samson (1993) found that the thermal decomposition of PAN was enough
7 to explain an increase of 1 to 2 ppb peak ozone per degree C increase in temperature in rural
8 locations in the eastern United States, based on photochemical simulations. This increase
9 represents a significant fraction of the observed increase in peak ozone with temperature
10 (3 ppb per degree, Figure 3-11) but is significantly less than the observed increase.
11
12 3.3.2.2.3 Increased Anthropogenic Emissions
13 It has recently been suggested that emission rates for anthropogenic hydrocarbons
14 (VOC) also increase with temperature (U.S Environmental Protection Agency, 1989; Stump
15 et al., 1992). Increased VOC emissions might be expected to cause increased rates of ozone
16 production only in urban areas where ozone is sensitive to VOC, and would be less lively to
17 have impact on rural areas. However, NOx-sensitive urban areas and most rural areas would
18 also show increased ozone production with temperature if NOX emissions also were to
19 increase with temperature. There is no direct evidence for an increase in NOX emissions
20 with temperature but power plant loads tend to be highest when temperatures are high.
21 Because power plants are a major source of NOx, the increased power plant load would also
22 lead to increased NOX emissions. Quantitative estimates are needed to determine the impact
23 of this effect.
24
25 3.3.2.2.4 Increased Natural Emissions
26 Fjnissions of biogenic hydrocarbons increase sharply with temperature (Lamb et al.,
27 1987). In ambient temperatures from 25 to 35 °C the rate of natural hydrocarbon emissions
28 from isoprene-emitting deciduous trees increased by about a factor of four. From coniferous
29 trees the increase was on the order of one and a half times.
30 Recently Jacob et al. (1933) found that the photochemistry of ozone production in a
31 polluted rural environment (Blue Ridge Mountains, VA) is significantly different in
December 1993 3.59 DRAFT-DO NOT QUOTE OR CITE
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1 September and October when natural emissions from deciduous forests have ceased. The
2 difference in chemistry between summer and fall leaf production may also have an impact on
3 the ozone-temperature correlation.
4
5 3.3.2.2.5 Correlation with Stagnation
6 Recently Jacob et al. (1993) found that model-simulated ozone formation in the rural
7 United States shows a tendency to increase with temperature based solely on the difference in
8 atmospheric circulation between relatively warm and relatively cool days. The model-
9 simulated ozone-temperature correlation was less than observed but large enough to represent
10 a significant component of the observed correlation. However, the temperature-meteorology
11 correlation identified by Jacob et al. (1993) was based on simulated meteorology from a
12 General Circulation Model rather than on direct observations. It would be interesting to see
13 whether the correlation between ozone, temperature and atmospheric circulation predicted by
14 Jacobs et al. (1993) can be verified in terms of meteorological observations.
15
16 3.3.2.3 Wind Speed
17 Ozone is expected to be influenced by wind speed because lower wind speeds should
18 lead to reduced ventilation and the potential for greater buildup of ozone and its precursors.
19 Abnormally high temperatures are frequently associated with high barometric pressure,
20 stagnant circulation, and suppressed vertical mixing resulting from subsidence (Mukammal
21 et al., 1982), all of which may contribute to elevated 03 levels. However, in reality this
22 relationship varies from one part of the country to another. Figure 3-12 shows the frequency
23 of 24-h trajectory transport distances to southern cities on days with resulting concentrations
24 of O3 S 120 ppb (Samson et al., 1988). The frequency for southern cities is biased toward
25 lower wind speeds. A similar plot for cities in the northeast United States (Figure 3-13)
26 shows an opposite pattern, in which the bias is toward higher wind speeds than normal. It is
27 unclear how much meteorological information is needed in order to perform accurate
28 urban-area ozone simulations using advanced photochemical models. To understand the
29 significance of variations between upper-air wind measurements during the Southern Oxidant
30 Study (SOS) 1992 Atlanta Intensive, an intercomparison test of the precision of upper-air
31 measurements was conducted. Collocated measurements were made at an SOS measurement
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50 150250350 «0 6SO 6SO 750
Distance traveled, km
•1,000
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 et al. (1988).
I D Provictonai m Portland, m Boston
i ME
i • New Haven ^ AIM? atiss
16
10
SO 1S02XI3504SC5608607508S0950 >1JX)0
Distance traveled, km
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 et al. (1988).
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1 site using a boundary-layer lidar; a wind profiler; and a rawinsonde balloon. There was
2 generally good agreement between the profiler and rawinsonde although some large outliers
3 existed. Figure 3-14 illustrates that the root mean square difference (RMSD) varied with
4 altitude. The RMSD reached a minimum near 1,200 m AGL of about 2 ml sec, rising to
5 over 3 m/sec near the surface and above 1,200 m AGL. Figure 3-15 illustrates the RMSD
6 for the lidar comparison with CLASS observations. There is slightly greater RMSD at all
7 heights than for the profiler-rawinsonde comparison, with a relative minimum observed at
8 about 1,200 m.
9
2,000
••• Maximum
RMSD
6 9
RMSD, m/sec
Figure 3-14. The root-mean-square-difference (RMSD) between CLASS observations
and profiler observations as a function of height above ground level.
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2,000
6 s
RMSD, m/sec
Figure 3-15. The root-mean-square-difference (RMSD) between CLASS observations
and lidar observations as a function of height above ground level.
1
2
3
4
5
6
1
8
9
10
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 for reasons stated before. The profiler obtained values biased slightly
higher than the CLASS system (+0.2 m/s), while 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 (based
on S) must be larger than about 3 m/s to be considered significant.
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1 3.3.2.4 Air Mass Characteristics
2 In meteorology, an "air mass" is a region of air, usually of multistate dimensions, that
3 exhibits similar temperature, humidity and/or stability characteristics. Air masses are created
4 when air becomes stagnant over a "source region" and subsequently takes on the
5 characteristics of the source region. Similarly, when dealing with air pollution meteorology
6 it is possible to identify a "chemical air mass" as a region of air that has become stagnant
7 over an emissions source area. Air that was stagnant over, say, the center of Canada will
8 exhibit relatively cold, dry conditions and will be relatively devoid of pollutants. Air that
9 resides over the industrial regions of the midwestern United States will exhibit low visibility
10 and, often, high ozone levels on a regional scale. Meteorological processes play an
11 important role in determining the amount of "accumulation" of ozone and its precursors that
12 occurs under such stagnant conditions.
13 Episodes of high ozone concentrations in urban areas are often associated with high
14 concentrations of ozone in the surroundings. This accumulated ozone forms under the same
15 atmospheric conditions that lead to high ozone levels in urban areas, and exacerbates the
16 urban problem by supplying relatively high ozone and precursor concentrations to the urban
17 area from upwind. The transport of ozone and its precursors beyond the urban scale
18 (^50 km) to neighboring rural and urban areas has been well documented (e.g., Wolff
19 et al., 1977a,c; Wolff and Lioy, 1978; Clark and Clarke, 1982; Sexton, 1982; Wolff et al.,
20 1982). A summary of these reports was given in the 1986 ozone criteria document (U.S.
21 Environmental Protection Agency, 1986) and will not be reiterated here. The phenomena of
22 high nonurban ozone levels was long-ago illustrated by Stasiuk and Coffey (1974) for
23 transport within New York State; by Ripperton et al. (1977), for sites in the Middle Atlantic
24 States; and by Samson and Ragland (1977) for the midwestern United Stales.
25 These areas of ozone accumulation are characterized by:
26 1. Synoptic-scale subsidence of air in the free troposphere, resulting in
27 development of an elevated inversion layer;
28
29 2. Relatively low wind speeds associated with the weak horizontal pressure
30 gradient around a surface high pressure system;
31
32 3. Lack of cloudiness; and
33
34 4. High temperatures.
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1 On occasion, ozone at levels greater than 120 ppb can occur in rural areas far removed
2 from urban or industrial sources. Ozone levels at the summit of Whiteface Mountain
3 exceeded this value during the summer of 1988 when ozone accumulated across a wide
4 expanse of the eastern United States at levels 5:120 ppb. Nonetheless, even when the
5 regional accumulation is at a level below the current ozone NAAQS, the increment needed to
6 bring the level above the NAAQS in an urban area is not large.
7 The identification, and understanding of the transport of photochemical ozone and other
8 oxidants and their precursors by weather systems represent a significant advance in
9 comprehending photochemical air pollution and the potential extent of its effects.
10 Considerable progress has been made in the development of long-range photochemical
11 modeling techniques so that the likely impact of synoptic systems can be anticipated. Such
12 tools are very much in the research stage, however, because the local impact of ozone and
13 other oxidants results from a complex interaction of distant and local precursor sources,
14 urban plumes, mixing processes, atmospheric chemical reactions, and general meteorology.
15
16 3.3.3 Normalization of Trends
17 The degree to which meteorological factors can be "normalized" out of the ozone
18 concentration and "trends" data depends in large part on the strength of the relationships
19 between ozone and meteorological components. As part of the Southern Oxidants Study
20 (SOS) Atlanta Intensive field campaign, an attempt was made to model statistically the ozone
21 levels in Atlanta to build a predictive tool for forecasting days of specialized measurement.
22 Figure 3-16 shows the fit of the data used to create the model to the model simulations.
23 Figure 3-17 shows the fit obtained from independent data collected in 1992.
24 This model was used successfully to predict next-day ozone levels in Atlanta. Ozone
25 levels in a number of American cities should be analyzed using regression tools such as this
26 to normalize meteorological variability. Through such analyses, it is possible that trends, if
27 any, represented as systematic deviations from the model may become observable.
28 A summary of other techniques for removing meteorological variability is contained in the
29 recent monograph from the National Research Council (1991). Table 3-5 lists a sample of
30 studies aimed at evaluation of ozone trends.
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200
80 120
Og (observed), ppb
Figure 3-16. Model of ozone levels using regression techniques. The use of wind speed,
temperature and previous-day ozone provided a means to forecast ozone
levels.
200
40 80 120
Oj (observed), ppb
200
Figure 3-17. Simulated versus observed ozone levels using regression techniques on an
independent data set obtained in summer 1992 in Atlanta, Georgia.
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TABLE 3-5. RECENT STUDIES EXAMINING TRENDS IN OZONE DATA
AFTER REMOVAL OF VARIABILITY ASSOCIATED
WITH METEOROLOGICAL FACTORS
Study
Variables
Approach
Jones et al. (1988)
Pollock et al. (1988)
Kuntasal and Chang (1987)
Wakim (1989)
Chock et al, (1982)
Kumar and Chock (1984)
Korsog and Wolff (1991)
Surface Temperature
Surface Temperature
850 mh Temperature
Surface Temperature
Surface Temperature, windspeed,
relative humidity, sky cover, wind
direction, dew point temperature,
sea level pressure, precipitation.
Surface Temperature, windspeed,
relative humidity, sky cover, wind
direction, dew point temperature,
sea level pressure, precipitation.
Surface Temperature, windspeed,
relative humidity, sky cover, wind
direction, dew point temperature,
sea level pressure, precipitation.
Compared number of days with
ozone concentrations above
120 ppb to days with temperature
above 30 °C,
Compared number of days with
ozone concentrations above
105 ppb to days with temperature
above 30 °C,
Regression of ozone versus
temperature for southern
California.
Regression of ozone versus
temperature for Houston, New
York and Washington, DC.
Regression versus a variety of
meteorological parameters.
Regression versus a variety of
meteorological parameters.
Regression versus a variety of
meteorological parameters.
Source: National Research Council (1991).
1 3.4 PRECURSORS OF OZONE AND OTHER OXIDANTS
2 3.4.1 Sources and Emissions of Precursors
3 3.4.1.1 Introduction
4 As described earlier in Section 3,2, 03 is formed in the atmosphere through a series of
5 chemical reactions that involve volatile organic compounds (VOC) and the oxides of nitrogen
6 (NO and NO^ = NOX). Control of 03 depends on reducing emissions of VOC or NOX or
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1 both. In addition, models used to determine reductions needed require accurate emission
2 inventories. Thus, it is important to understand the sources and source strengths of these
3 precursor species in order to devise the most appropriate oxidant control strategies. In the
4 following sections, anthropogenic and biogenic NOX and VOC sources will be described and
5 best estimates of their current emission levels and trends will be provided. Confidence levels
6 for the assigned source strengths will be discussed.
7 Both English and metric units have been utilized in emission inventories. Thousands or
8 millions of short tons are the common scales in the English system. The metric unit most
9 often employed is millions of metric tons, which is equivalent to teragrams (Tg). To convert
10 English tons to teragrams, multiply English tons by 0.907 x 10"6. For consistency,
11 teragrams have been employed throughout the ensuing discussion.
12
13 3.4.1.2 Nitrogen Oxides
14 3,4.1.2.1 Manmade Emission Sources
15 Anthropogenic oxides of nitrogen sources are associated with combustion processes.
16 The primary pollutant is nitric oxide, which is formed from nitrogen and oxygen atoms that
17 are produced at high combustion temperatures when air is present. In addition, NOX is
18 formed from nitrogen contained in the combustion fuel. Major NOX source categories
19 include transportation, stationary source fuel combustion, industrial processes, solid waste
20 disposal and some miscellaneous combustion related activities. Table 3-6 provides a more
21 detailed summary of each of these source categories. The transportation category includes
22 gasoline- and diesel-powered motor vehicles, aircraft, railroads, vessels, and off-highway
23 vehicles. Electric utilities, industrial boilers, commercial/institutional boilers, and industrial
24 furnaces and space heaters comprise the Stationary Source Fuel Combustion Category.
25 Industrial processes include petroleum refining and paper, glass, steel, chemical, and cement
26 production. The incineration and open burning of wastes leads to the emissions of NOX in
27 the solid waste disposal category. The miscellaneous sources category includes prescribed
28 forest slash burning, agricultural burning, coal refuse burning, and structure fires. It should
29 be noted at this point that, even though NO is the primary pollutant, oxides of nitrogen
30 emission inventories are quantified relative to NO2 (mol. wt = 46).
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o
8
1
h- '
VO
VO
u>
TABLE 3-6.
Transportation
Highway vehicles
Gasoline-powered
Passenger cars
Light trucks - 1
Light trucks - 2
Heavy duty vehicles
Motorcycles
SOURCE CATEGORIES USED TO INVENTORY NITROGEN OXIDES EMISSIONS
Stationary Source Fuel
Combustion
Coal
Electric utilities
Industrial
Commercial-institutional
Residential
Industrial Processes
Pulp mills
Organic chemicals
Ammonia
Nitric acid
Petroleum refining
Glass
Cement
Lime
Iron and steel
Solid Waste Disposal Miscellaneous
Incineration Forestries
Open burning Other burning
0
o
I
I
Diesel-powered
Passenger cars
Light trucks
Heavy duty vehicles
Aircraft
Railroads
Vessels
Farm machinery
Construction machinery
Industrial machinery
Other off-highway vehicles
Fuel oil
Electric utilities
Industrial
Commercial-institutional
Residential
Natural gas
Electric utilities
Industrial
Commercial-institutional
Residential
Wood
Industrial
Residential
Other Fuels
Industrial residential
Source: U.S. Environmental Protection Agency (1992).
-------
1 Quantifying NOX emissions in all of these categories generally requires multiplying an
2 emission factor and an activity level. Nitrogen oxides emission factors are obtained from
3 Compilation of Air Pollution Factors, AP-42 (U.S. Environmental Protection Agency, 1985),
4 and from the current mobile source emission factor model (e.g., MOBILES) recommended
5 by the U.S. Environmental Protection Agency. Activity levels are derived from information
6 sources that provide consumption levels. This takes the form of fuel type and amount
7 consumed for stationary sources and, for transportation sources, the number of vehicle miles
8 traveled (VMT). Point source emissions are tallied at the individual plant level. These
9 plant-by-plant NOX emissions are first summed at the state level and then state totals are
10 added to arrive at the national emissions total. Data on VMT are published for three road
11 categories—highways, rural roads, and urban streets.
12 Table 3-7 provides a summary of the most recent estimate of NOX emissions from the
13 various categories mentioned previously (U.S. Environmental Protection Agency, 1993b).
14 The 1991 total is 21.39 Tg of NOX emissions in the United States. Slightly less than half of
15 the emissions (10,36 Tg) is associated with the stationary source fuel combustion category.
16 Transportation-related activities are the second largest source, accounting for about 45% of
17 the national total. The remaining 7% of emissions is divided between the industrial
18 processes, solid waste disposal, and miscellaneous sources categories. The two largest single
19 NOX emission sources are electric power generation and highway vehicles.
20 Because of the dominance of the electric utility and transportation sources, the
21 geographical distribution of NOX emissions is related to areas with a high density of power
22 generating stations and urban regions with high traffic densities. Figure 3-18 shows the
23 location of the 50 largest electric power generating sources of NOX in the United States. The
24 majority of these power plants are concentrated in the upper Mississippi-Ohio River corridor.
25 Because of this congregation of large point sources, 69% of U.S. NOX emissions occur
26 within U.S. Environmental Protection Agency Regions 3, 4, 5, and 6 (Figure 3-19). It is
27 interesting to compare the annual NOX emissions from a large electrical generating plant with
28 the yearly transportation-related emissions in a major metropolitan region. The largest utility
29 plants currently release between 0.06 and 0,09 Tg of NOX annually, which compares to
30 approximately 0.12 Tg of NOX emitted by transportation sources in the Atlanta urban area
31 (U.S. Environmental Protection Agency, 1993b).
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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 9.7
Highway Vehicles 7,20
Off-Highway Vehicles 2.51
Stationary Fuel Combustion 10.6g
Electric Utilities 6.74
Industrial 3.27
Other 0.68
Industrial Processes 0.80
Solid Waste Disposal 0.07
Miscellaneous 0.12
Forest Burning
Other Burning
Miscellaneous Organic Solvents
Total of All Sources 21.39
Source: U.S. Environmental Protection Agency (1993b).
1 3.4,1.2.2 Trends in Nitrogen Oxides Emissions
2 Estimates of NOX emissions date back to 1900, when approximately 2.3 Tg were
3 emitted into the atmosphere in the United States (U.S. Environmental Protection Agency,
4 1992). Figure 3-20 summarizes the growth in NOX emissions at 10-year intervals since the
5 1940s. Emissions grew rapidly until the 1970s and then leveled off at about 20 Tg/year.
6 Currently, greater than 90% of the national NOX emissions result from transportation
7 activities and stationary fuel combustion. Figure 3-21 illustrates the growth in each of these
8 categories over the last 50 years. Transportation-related NOX emissions grew steadily until
9 the 1980s and then exhibited a moderate decrease. Emissions of NOX from fuel combustion
10 sources have increased continually from 1940 to the present time.
11 Recent trends in the major NOX emission categories are shown in Table 3-8. Between
12 1987 and 1991, the most recent 5 years for which NOX emission estimates are available,
13 transportation-related emissions have remained essentially constant, while the stationary
14 source NOX emissions have increased about 10%.
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Legend
to 100 Tg/year
45 to 65 Tg/year
& 35 to 45 Tg/year
* 25 to 35 Tg/year
Figure 3-18. The 50 largest sources of nitrogen oxides (power plants) in the United
States.
Source: U,S, Environmental Protection Agency (1992).
Figure 3-19. Nitrogen oxides emissions (Tg) from manmade sources in the 10 U.S.
Environmental Protection Agency regions of the United States, 1991.
Source: U.S. Environmental Protection Agency (1993b).
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20
15 +
10 +
1940 1950 1980 1970 1980 1990
Years
Figure 3-20. Changes in nitrogen oxides emissions from manmade sources in the United
States, 10-year intervals, 1940 through 1990.
Source: U.S. Environmental Prelection Agency (1992).
12
10
1940
1950
1960 1970 1980 1990
Years
Figure 3-21. Growth in nitrogen oxides emissions from stationary source fuel
combustion (SF) and transportation (TR) from 1940 through 1990.
Source: U.S. Environmental Protection Agency (1992).
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TABLE 3-8. RECENT TRENDS IN NITROGEN OXIDES EMISSIONS FOR MAJOR
MANMADE SOURCE CATEGORIES (Tg)
Year
1991
1990
1989
1988
1987
Transportation
9,7
9.9
9.7
9.9
9.7
Stationary Source
Fuel Combustion
11.0
10.7
10.7
10.6
10.1
Source: U.S. Environmental Protection Ageney (1993).
1 Transportation and stationary source fuel combustion will likely show downward trends
2 in their NOX emissions during the next 20 years., This will result from new provisions in the
3 Clean Air Act passed in 1990. Emission limits for electric utility boilers have been
4 prescribed to reduce acidic deposition; automobile tailpipe emission standards will be
5 tightened; and current technology-based applications will be required for industrial boilers
6 (non-utility) in 03 nonattainment areas. In addition, the average grams of NOX per mile
7 from passenger cars is expected to decrease because of new on-board diagnostic systems and
8 expanded inspection and maintenance requirements.
9 As a result of new emission limits and revised performance standards, NOK emissions
10 from electric utilities are expected to decrease by 16% by the year 2000. Control
11 requirements in the industrial non-utility sector are expected to reduce NOX emissions by
12 10% during the 1990 to 2000 time span. Projections based on vehicle miles travelec and
13 emission factors from the MOBILE model suggest nearly a 50% decrease in NOX emissions
14 from highway vehicles between 1990 and 2000 (U.S. Environmental Protection Agency,
15 1992).
16
17 3.4.1.2.3 Uncertainty of Anthropogenic Nitrogen Oxides Emission Estimates
18 Because a large proportion of the U.S NOX emissions are derived from distinct point
19 sources, it is generally believed that published estimates are very reliable. For example, the
20 NAPAP NOX inventory for U.S. emissions in 1985 (18.6 Tg) was assigned a 90% relative
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1 confidence interval in the range of 6 to 11 % (Placet et al., 1991). This confidence level was
2 based on judgments used to assign uncertainty to component inputs of emission models and
3 on statistical assumptions used to aggregate uncertainty values.
4 Sources of error are associated with both the emission factors and the activity levels
5 utilized in the inventorying process. Emission factors provide quantitative estimates of the
6 average rate of emissions from many sources. Consequently, these factors are best applied
7 to a large number of sources over relatively long time periods. In other words, an NOX
8 emission estimate for a single point source on a particular day in 1990 may be highly
9 inaccurate; but the emission value for this same source for the entire year of 1990 could be
10 very good. It appears that the emission factors assigned to the transportation sectors may be
11 the most uncertain. This results from their having been derived from mobile source models
12 that require multiple inputs. This type of model requires information on temperatures,
13 vehicle speeds, gasoline volatility, and several other parameters.
14 Recent attempts to validate NOX emission factors or inventories, or both, have involved
15 comparing ambient NOX concentrations with values predicted using emissions-based models.
16 These have generally taken one of two forms: (1) comparisons between NOX concentrations
17 measured in a tunnel and those predicted from emission factors, activity levels, and dilution
18 factors in the tunnel; or (2) whole-city integration procedures in which ambient NOX
19 concentrations are compared to ambient NOX levels that have been predicted using a model
20 such as the Urban Airshed Model. The latter approach has been applied in the South Coast
21 Air Basin (Fujita et al., 1992). It was reported that measured and predicted NOX
22 concentrations agreed within 20% for a 2-day period in August 1987. Likewise, the results
23 from tunnel studies have shown good agreement between predicted and measured NOX
24 concentrations. It is important to keep in mind that ambient NOX levels predicted using a
25 modeling method cannot be assigned true value status. There could be as much or more
26 uncertainty in the model outputs as there is in the emission inputs that are being tested. The
27 fact, however, that an emissions-based model predicts ambient concentrations that are close
28 to those measured tends to lend credence to the NOX emission estimates.
29 In addition, NOX inventory validation has involved comparing annual emission estimates
30 reported by different groups. Table 3-9 shows several annual U.S. NOX emission estimates.
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TABLE 3-9. COMPARISON OF ESTIMATES OF NITROGEN OXIDES EMISSIONS
FROM MANMAPE SOURCES IN THE UNITED STATES
Emissions/year (Tg)
Inventory* 1982 1985
NAPAP — 18.6
EPA 19.6 19.8
MSCET 18.8 18.2
EPRI 20/7 —
*NAPAP = National Acid Precipitation Assessment Program.
EPA = U.S. Environmental Protection Agency.
MSCET = Month and state current emissions trends.
EPRI = Electric Power Research Institute.
Source: U.S. Environmental Protection Agency (1993a),
1 In 1982, the estimates vary by less than 12% and this decreases to about 9% in the 1985
2 comparison.
3
4 3.4,1.2.4 Natural Emissions
5 Natural sources of NOX include lightning, soils, wildfires, stratospheric intrusion, and
6 the oceans. Of these, lightning and soils are the major contributors. The custom is to
7 include emissions from all soils in the biogenic or natural category even though cultivated
8 soil emissions are in a sense anthropogenic; cultivated soils also appear to produce higher
9 emissions than those from undisturbed forest and prairie soils, as discussed later. Although
10 NOX emitted from large wildfires can be significant on a regional scale, overall this source is
11 considered to be of minor importance for the United States. Injection of NOX into the upper
12 troposphere via subsidence from the stratosphere is estimated at less than 0.1 Tg/year for all
13 of North America. Because of the relatively short lifetime of NOX (1 to 3 days) and a small
14 flux out of sea water, transport of NOX from oceans is thought to be a negligible source in
15 the United States.
16
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1 Lightning. Lightning produces high enough temperatures to allow N2 and O2 io be
2 converted to nitric oxide. Two methods have been employed to estimate the NOX source
3 strength from lightning:
4 (1) Multiply the frequency of lightning flashes by the energy dissipated per flash and
5 the NO production per unit of energy dissipated; or
6
7 (2) Relate NOX production to nitrate deposition in remote areas where lightning-
8 produced NOX is thought to be the dominant nitrate precursor.
9
10 Method (1) yields an annual NOX production of approximately 1.2 Tg for North America
11 (Placet et al., 1991). The deposition-based estimate (Method 2) gives a somewhat larger
12 value of 1.7 Tg/year (Placet et al,, 1991). The NAPAP inventory included lightning-
13 produced NOX on a gridded 10° x 10° latitude-longitude scale. Most of the continental
14 United States fits within 30 to 50° N latitude and 80 to 120° W longitude. The estimated
15 annual lightning-produced NOX for this region (continental United States) is about 1.0 Tg.
16 Roughly 60% (0.6 Tg) of this NOX is generated over the southern tier of states (30 to 40° N
17 latitude; 80 to 120° W longitude).
18
19 Soils. Both nitrifying and denitrifying organisms in the soU can produce NOX. The
20 relative importance of these two pathways is probably highly variable from biome to biome.
21 Nitric oxide is the principal NOX species emitted from soils, with emission rates depending
22 mainly on fertilization levels and soU temperature. Several reports have noted a large
23 increase in NOX emissions from agricultural soils treated with nitrate-containing fertilizers
24 (Johansson and Granat, 1984; Kaplan et al., 1988; Johansson, 1984). Measurements of soil
25 NOX emissions have established that the relationship with temperature is exponential,
26 consisting of approximately a two-fold increase for each 10 °C rise in temperature (Williams
27 et al., 1992; Valente and Thornton, 1993).
28 Inventorying soU NOX emissions is difficult because of the large temporal and spatial
29 variability in emissions. The existing inventories have been developed using emission
30 algorithms that are functions of soil temperature and land-use type. Two broad, land-use
31 categories—natural and agricultural—have been assigned. The natural soils are broken down
32 into biome types, and the agricultural soils subdivided according to fertilizer applications.
33 The highest biogenic NO emissions are in corn-growing regions of the midwest (Nebraska,
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1 Iowa, and Illinois) during summer months. Of the total U.S. biogenic emissions of NO from
2 soils, 85% occur during the spring and summer months.
3 Table 3-10 provides a summary of the annual soil NOX emissions from the ten U.S.
4 Environmental Protection Agency regions. Approximately 60% of this NOX is emitted in
5 Regions 5, 7, and 8 (see Figure 3-19), which contain the central U.S. com belt. The total
6 estimate for U.S. soil emissions is 1.2 Tg.
7
TABLE 3-10. ANNUAL NITROGEN OXIDES EMISSIONS (Tg) FROM SOILS
BY U.S. ENVIRONMENTAL PROTECTION AGENCY REGION
U.S. Environmental Protection Agency Region NOX Emissions
1, 2, and 3 0.05
4 0.11
5 0.26
6 0.18
7 0.27
8 0.21
9 0.04
10 0.01
Total 1.2
Source: Placet et al. (1991).
1 3.4.1.2.5 Uncertainty in Estimates of Natural Nitrogen Oxides Emissions
2 As indicated previously, inventorying NOX produced from lightning requires
3 multiplying the number of flashes by average energy factors. No attempt has been made to
4 assign confidence limits to these variables. A measure of the uncertainty associated with
5 lightning-produced NOX is provided, however, by comparing emission estimates generated
6 independently. Two estimates of the amount of lightning-generated, summertime NOX in the
7 southeastern United States (2.4 and 8.5 x 10"2 Tg) varied by approximately a factor of four
8 (Placet etal., 1991).
9 Sources of uncertainty when inventorying NOX emissions from soils include:
10 (1) land-use assignments; (2) soil temperature; and (3) emission algorithm development.
11 Confidence levels assigned to categories 1 and 2 are about ±50%. The emission algorithm
12 is developed from field measurements of NOX emission rates versus temperature for various
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1 land-use categories. Measurement accuracy is approximately ±30%. However, because of
2 the natural variability of NOX emissions within a specific soil category, uncertainty in the
3 exponential relationship that relates emission rate to temperature is estimated to be in the
4 range of a factor of two to four.
5
6 3.4.1.2.6 Comparison of Emissions from Manmade and Natural Sources
7 On an annual basis, natural sources (lightning and soils) contribute approximately
8 2.2 Tg of NOX to the troposphere over the United States. This compares to the 1990
9 anthropogenic emission estimate of 19.4 Tg. Annual NOX emissions from soils (1.2 Tg) are
10 about 6% of the manmade emissions in the United States. This percentage increases to about
11 14% when the comparison includes only summer months (July, August, and September).
12 Even larger biogenic contributions can occur in certain regions of the United States. For
13 example, it is estimated that biogenic NOX emissions from soils account for about 19% of
14 summertime NOX emissions in Tennessee (Valente and Thornton, 1993) and actually exceed
15 emissions from manmade sources during the summer months in the states of Nebraska and
16 South Dakota (Williams et al., 1992).
17
18 3.4.1.3 Volatile Organic Compounds
19 3.4.1.3.1 Manmade Emission Sources
20 Volatile organic compounds are emitted into the atmosphere by evaporative and
21 combustion processes. Many hundreds of different organic species are released from a large
22 number of source types. The species commonly associated with atmospheric O3 production
23 contain from 2 to about 12 carbon atoms. They can be true hydrocarbons, which possess
24 only carbon and hydrogen atoms (e.g., alkanes, alkenes, and aromatics), or substituted
25 hydrocarbons that contain a functional group such as alcohol, ether, carbonyl, ester, or
26 halogens. Methane has been largely ignored because its atmospheric oxidation rate is very
27 slow compared to the higher-molecular-weight organics.
28 In 1991, the total U.S. emissions of VOCs was estimated to be 21.0 Tg (U.S.
29 Environmental Protection Agency, 1993b). The two largest source categories were industrial
30 processes (10.0 Tg) and transportation (7.9 Tg). Lesser contributions were attributed to
31 waste disposal and recycling (2.0 Tg), stationary source fuel combustion (0.7 Tg), and
December 1993 3-89 DRAFT-DO NOT QUOTE OR CITE
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1 miscellaneous area sources (0.5 Tg). Table 3-11 provides a more detailed breakdown of
2 VOC source contributions. Within the industrial category, solvent utilization, petroleum
3 product storage and transfer, and chemical manufacturing are the major contributors.
4 Volatile organic compounds released from highway vehicles account for almost 75 % of the
5 transportation-related emissions.
6
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 6.00
Off-Highway Vehicles 1.87
Stationary Fuel Combustion 0.68
Electric Utilities 0.03
Industrial 0.26
Other 0.39
Industrial Processes 9,97
Chemical Manufacture 1.61
Petroleum and Related Industries 0.68
Solvent Utilization 5.50
Petroleum Product Storage and Transport 1.69
Other 0.49
Waste Disposal and Recycling 2.01
Miscellaneous 0.51
^ TOTAL ALL SOURCES 21.04
Source: U.S. Environmental Protection Agency (1993).
1 Speciated hydrocarbon emissions from manmade sources were reported in the 1985
2 NAPAP Emissions Inventory. Emissions of each main hydrocarbon family exceeded 1 Tg.
3 Alkanes comprised about 33%, aromatics 19%, and alkenes 11 % of anthropogenic VOC
4 emissions in the 1985 inventory (Placet et al., 1991). None of the major oxygenated
5 hydrocarbon groups (e.g., carbonyls, organic acids, phenols) listed in the speciated inventory
6 exceeded 1 Tg. The carbonyl group, which included formaldehyde, higher aldehydes,
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1 acetone, and higher ketones, was the largest contributor of oxygenated hydrocarbons at
2 0.73 Tg.
3
4 3.4.1.3.2 Trends in Emissions
5 Emissions of nonmethane VOCs peaked in the early 1970s and have decreased
6 continually since that time. Emissions of VOCs increased from 15.5 Tg in 1940 to 27.4 Tg
7 in 1970, and now are estimated to be back down to approximately the same level as in 1940
8 (U.S. Environmental Protection Agency, 1992). Figure 3-22 illustrates these changes at
9 10-year intervals from 1940 to 1990. Up until 1970, highway vehicles were the major
10 source of VOC emissions. As more and better emission controls have been adopted on
11 automobiles, however, emissions from the transportation sector have dropped below those
12 from industrial processes, the category which is now the leading contributor of VOC
13 emissions to the atmosphere. Transportation, industrial processes, and the miscellaneous
14 burning and solvent use categories have accounted for 83 to 93% of VOC emissions over the
15 past 50 years. Figure 3-23 shows the emission trends for these three categories. The
16 transportation-related emissions of VOCs are currently estimated to be at about the same
17 level as in 1940. Industrial process VOC emissions nearly tripled between 1940 and 1980,
18 followed by a small decline in more recent years. The miscellaneous category has exhibited
19 a decrease in emissions from 4.5 Tg in 1940 to a 1990 level estimated at 2.8 Tg/year.
20 Trends for the dominant VOC emissions categories over the last 5 years are shown in
21 Table 3-12. Projections for the year 2000 forecast a 62% reduction in VOC emissions from
22 highway vehicles compared to 1990 levels. The major reduction in the transportation area
23 wUl contribute to an overall 25% decrease in total national VOC emissions between 1990 and
24 2000 (U.S. Environmental Protection Agency, 1992).
25
26 3.4.1.3.5 Uncertainty in Estimates of Emissions from Manmade Sources
27 It has proven difficult to determine the accuracy of VOC emission estimates. Within an
28 area source such as an oil refinery, emission factors and activity levels are assigned for
29 thousands of individual sources (e.g., valves, flanges, meters, and processes) and emission
30 estimates for each of these sources are summed to produce the emissions total. Since it
31 would be impractical to determine an emission factor for each of these sources within a
December 1993 3-91 DRAFT-DO NOT QUOTE OR CITE
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1940
1950
1960 1970
Years
1960
1990
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 (1992).
14-
12
10
111
r
s
o
3. 4
'S
I
Transportation i
Industrial Processes |
Mlsceliarwous i
1940
1050
1060 1970
Yaan
1980
1990
Figure 3-23. Changes in emissions of volatile organic compounds from major manmade
sources, 1940 through 1990.
Source: U.S. Environmental Protection Agency (1992).
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TABLE 3-12. RECENT TRENDS IN EMISSIONS OF VOLATILE
ORGANIC COMPOUNDS FROM MAJOR CATEGORIES
OF MANMADE SOURCES (Tg)
Year
1991
1990
1898
1988
1987
Transportation
7.87
8.07
8.26
9.15
9.29
Industrial Processes
7.86
9.96
9.92
10.00
9.65
Waste Disposal
and Recycling
2.01
2.05
2.08
2.10
2.05
Source: U.S. Environmental Protection Agency (1993).
1 refinery individually, average emission factors for the various source categories are utilized.
2 This can lead to substantial error if the individual sources deviate from the assigned average
3 factor. Even more troublesome are area sources that include a large evaporative emissions
4 component. These sources are dependent upon environmental factors such as temperature,
5 which add to the difficulty in establishing reliable emission estimates. Such sources fall into
6 a miscellaneous solvent evaporation category, which includes emissions from processes such
7 as dry cleaning, degreasing, printing, autobody repair, furniture manufacture, and motor
8 vehicle manufacture.
9 Assigning accurate VOC emission estimates to the mobile source category has proven
10 troublesome, as well. Models are used that incorporate numerous input parameters, each of
11 which has some degree of uncertainty. For example, activity models are employed to
12 characterize the mobile source fleet. This includes the number of vehicles in various
13 categories (e.g., gasoline fueled, diesel fueled, catalyst equipped, non-catalyst equipped,
14 etc.), miles accumulated per year for each type of vehicle, and ages of the vehicles. Vehicle
15 registration statistics are employed for category assignment. Errors can arise because
16 registration data are not always up to date and unregistered vehicles are completely omitted.
17 Military vehicles, foreign-owned automobiles, and old "junkers" that are on the highways but
18 not registered do not get included in the inventorying process. The activity models assume
19 vehicles of the same age accumulate mileage at the same rate. This is probably not correct,
December 1993 3.93 DRAFT-DO NOT QUOTE OR CITE
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1 and there is a need to assess the uncertainty in this assumption thorough a systematic
2 collection of vehicle type-age-mileage accumulation statistics. Recent developments in
3 remote sensing have permitted more accurate measurement of hydrocarbon exhaust emissions
4 from on-road vehicles (Stedman et al., 1990). These studies have demonstrated a highly
5 skewed distribution, with the majority of VOC emissions coming from about 20% of the
6 automobiles. Emission factors developed from laboratory dynamometer testing most likely
7 do not properly account for the high-emitting vehicle contribution (Pitchford and Johnson,
8 1993). In many cases, these high emitters are older cars that are poorly maintained.
9 In order to reduce this source of uncertainty, it may be necessary to reassess the life spans
10 assigned to vehicles. Vehicles manufactured more than 25 years prior to the present time
11 (1993) are not included in the inventory. However, these older vehicles are likely to be high
12 emitters, and if they are under-represented in the model, emissions will be underestimated.
13 Activity models provide data in terms of national averages. This can contribute to
14 inaccuracies in emissions estimates if a particular region varies from the national average in
15 terms of vehicle types, age, or vehicle miles traveled.
16 Ambient measurements of VOCs and NOX have been employed in order to better define
17 uncertainty levels in VOC inventories. Some of the earliest work was carried out in the
18 Atlanta area in the 1980s. Using a simple model and measured ambient VOC and NOX
19 concentrations, it was shown that ambient NOX levels were consistent with the urban NOX
20 emission estimates; but measured ambient VOC concentrations were as much as a factor of
21 six greater than predicted (Westberg and Lamb, 1985). More recently, experiments carried
22 out in tunnels have demonstrated a poor relationship between measured VOC emission
23 factors and those derived from automotive emission models. In a study designed to verify
24 automotive emission inventories for the South Coast Air Basin, measurements in the
25 Van Nuys Tunnel indicated that automotive VOC emissions were a factor of four larger than
26 predicted using emission models (Herson et al., 1990). Improvements in mobile source
27 emission models have resulted in somewhat higher emission estimates that have now reduced
28 the discrepancy with ambient data to about a factor of 2.5 (Fujita et al., 1992; Cadle et al.,
29 1993). It is clear that the relationship between emission inventories and ambient
30 concentrations of NOX and VOCs warrants further study. In addition to improving the
31 mobile source emission inventories, it will be necessary to place uncertainty bounds on
December 1993 3-94 DRAFT-DO NOT QUOTE OR CITE
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1 stationary source inventories. Whether stationary source emissions of VOC are
2 underpredicted using current emission inventory methodology is not known (Finlayson-Pitts
3 and Pitts, Jr., 1993).
4
5 3.4.1.3.4 Biogenic Emissions
6 Vegetation emits significant quantities of reactive VOCs into the atmosphere. Many of
7 these biogenic VOCs may contribute to Oj production in urban (Chameides et al., 1988) and
8 rural (Trainer et al., 1987) environments. The VOC emissions of primary interest are
9 isoprene and the monoterpenes (e.g., a-pinene, jS-pinene, myrcene, limonene, etc.), which
10 are hydrocarbons. Recent field measurements have shown that a variety of oxygenated
11 organics are also emitted from plants (Winer et al., 1992). A thorough discussion of
12 biogenic emissions and their implication for atmospheric chemistry has been published
13 recently by Fehsenfeld et al. (1992), who reviewed (1) the techniques used to measure VOC
14 emissions from vegetation; (2) laboratory emissions studies that have been used to relate
15 emission rates to temperature and light intensity; (3) development of emission models; and
16 (4) the use of emission models in the preparation of emission inventories.
17 Over the past 10 years, a number of regional and national biogenic emission inventories
18 have been reported (Zimmerman, 1979; Winer et al., 1983; Lamb et al., 1985; Lamb et al.,
19 1987; Lamb et al., 1993). These inventories are based on algorithms that relate VOC
20 emissions from a particular vegetation class to ambient temperature, land-use, and, in the
21 case of isoprene, photosynthetically active radiation. Most biogenic VOC emissions from
22 vegetation increase exponentially with temperature. Isoprene emissions are light-dependent,
23 being minimal at night and increasing with solar intensity during the day. Deciduous
24 vegetation is the dominant source of isoprene; whereas coniferous trees emit primarily
25 monoterpenes. Other things being equal, isoprene is emitted at a much higher rate than the
26 monoterpenes. For example, in a southern forest of mixed pine and hardwoods, the isoprene
27 emission rate from an oak tree is about 10 times larger than the flux of a-pinene from an
28 adjacent loblolly pine during the midday period.
29 The most recent biogenic VOC emissions estimate for the United States totals
30 29 Tg/year (Lamb et al,, 1993). This estimate includes 5.9,Tg isoprene, 4.4 Tg a-pinene,
31 6.5 Tg other monoterpenes, and 12.3 Tg other VOCs. Table 3-13 provides a summary of
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TABLE 3-13. ANNUAL BIOGENIC HYDROCARBON EMISSION INVENTORY FX)R
THE UNITED STATES (Tg)
Compound
Isoprene
ot-pinene
Other terpenes
Other VOCs
Total
Percent of Total
Oak
Forests
2.31
0.19
0.41
1.12
4.03
13.9
Other
Deciduous
Forests
1.01
0.23
0.44
0.88
2.56
8.8
Coniferous
Forests
0.61
2.07
3.08
2.72
8.48
29.2
Scrub-
lands
1.17
0.78
1.41
2.49
5.85
20.1
Land Use
Grass-
lands
0.49
0.13
0.24
0.45
1.31
4.5
Crop-
lands
0.2
0.85
0.81
4.51
6.37
21.9
Inland
Waters
0.02
0.06
0.06
0.07
0.21
0.7
Urban
Areas
0.08
0.04
0.06
0.08
0.26
0.9
U.S. Total
5.9
4.4
6.5
12.3
29.1
w Source: Lamb et al. (1993).
ON
-------
1 the contributions from the various vegetation categories. In preparing this inventory,
2 algorithms were developed that related VOC emissions to temperature and light for each of
3 the biomass categories shown in the table. On a national scale, coniferous forests are the
4 largest vegetative contributor because of their extensive land coverage. The category, "other
5 VOCs," is the dominant biogenic hydrocarbon contributor to the national total. From the
6 standpoint of inventory accuracy, this is somewhat unfortunate because the identities of most
7 of the "other VOCs" are uncertain. This classification has carried over from the extensive
8 field measurement program conducted by Zimmerman (1979) and coworkers in the
9 mid-1970s. The category, other VOCs, includes peaks that showed up in sample
10 chromatograms at retention times that could not be matched to known hydrocarbons. It is
11 likely that if the Zimmerman study were repeated today, most of the species making up this
12 "other VOCs" category could be identified. Recent field studies have made use of GC-MS
13 techniques that were not available to Zimmerman in the 1970s.
14 Biogenic emissions vary by season because of their dependence on temperature and
15 vegetational growth. In addition, the southern tier of states is expected to produce more
16 biogenic emissions than those in the north because of higher average temperatures.
17 Table 3-14 shows a spatial and temporal breakdown of U.S. biogenic emissions.
18 Summertime emissions comprise more than half of the annual totals in all regions. Federal
19 Regions IV and VI in the southcentral and southeastern United States have the highest
20 biogenic hydrocarbon emission rates.
21
22 3.4.1.3.5 Uncertainty in Estimates of Biogenic Emissions
23 Sources of error in the biogenic inventorying process arise from uncertainties in
24 (1) emission measurements; (2) determination of biomass densities; (3) land-use
25 characterization; and (4) measurement of light intensity and temperature. Within each of
26 these categories the error is relatively small. However, when emission measurements are
27 combined with temperature or light intensity or both into a single algorithm, the uncertainty
28 increases greatly. This results from the fact that temperature and light are only surrogates
29 for the real physiological processes that control biogenic emissions. Emission rate and
30 ambient temperature can be highly correlated for data collected from one tree branch over a
31 24-h period; but, when these data are combined with measurements from other branches and
December 1993 3-97 DRAFT-DO NOT QUOTE OR CITE
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TABLE 3-14. ANNUAL BIOGENIC HYDROCARBON EMISSION INVENTORY BY MONTH AND
U.S. ENVIRONMENTAL PROTECTION AGENCY REGION FOR THE UNITED STATES (Tg)
ff
h— »
$
U)
oo
o
8
I
1
8
U.S. Environmental Protection Agency Region
Month
1
2
3
4
5
6
7
8
9
10
11
12
Total
Source: Lamb
3
0.018
0.017
0.071
0.169
0.206
0.427
0.441
0.439
0.123
0.069
0.066
0.018
2.1
et al. (1993).
4
0.092
0.139
0.428
0.460
0.475
0.874
0.903
0.903
0.461
0.286
0.162
0.080
5.3
5
0.004
0.004
0.067
0.189
0.240
0.550
0.568
0.568
0.137
0.066
0.063
0.004
2.5
6
0.084
0.123
0.519
0.567
0.586
1.146
1.184
1.184
0.561
0.394
0.174
0.073
6.6
7
0.007
0.007
0.078
0.211
0.226
0.508
0.524
0.524
0.136
0.026
0.025
0.007
2.3
8
0.022
0.02
0.108
0.303
0.362
0.809
0.836
0.820
0.280
0.290
0.110
0.022
4.0
9
0.060
0.054
0.113
0.320
0.331
0.710
0.734
0.734
0.357
0.369
0.130
0.060
4.0
10
0.043
0.039
0.102
0.202
0.208
0.424
0.438
0.438
0.212
0.219
0.109
0.043
2.5
Total
0.3
0.4
1.5
2.4
2.6
5.5
5.6
5.6
2.3
1.7
0.8
03
29.1
Percent
of Total
1.1
1.4
5.1
8.3
9.1
18.7
19.3
19.3
7.8
5.9
2.9
UL
-------
1 other trees the correlation is not nearly as good. The uncertainty associated with the
2 algorithms used to generate the U.S. inventory described previously is estimated to be a
3 factor of three (Lamb et al., 1987). Since other sources of error in the inventorying process
4 are much smaller, a factor of three is the current best estimate of the overall uncertainty
5 associated with biogenic VOC inventories. However, this may be a lower limit if it is shown
6 that oxygenated species are emitted in significant quantities by vegetation. Emission
7 measurement methods employed in the past have not been adequate for quantifying polar,
8 oxygenated organics.
9
10 3.4,1.3.6 Comparison of Manmade and Biogenic Emissions
11 The most recent anthropogenic and biogenic VOC emissions estimates for the United
12 States indicate that natural emissions (29 Tg) exceed manmade emissions (21 Tg). However,
13 in a recent National Research Council review it was concluded that emissions from manmade
14 sources are currently underestimated by a significant amount (National Research Council,
15 1991). Since uncertainty in both biogenic and anthropogenic VOC emission inventories is
16 large, it is not possible to establish at this time whether the contribution of emissions from
17 natural or manmade sources of VOCs is larger.
18
19 3.4.1.4 Relationship of Summertime Precursor Emissions and Ozone Production
20 Peak O3 levels are recorded in most regions of the country during the summer months
21 of June, July, and August. From the foregoing discussion, it is obvious that natural
22 emissions of NOX and VOCs peak during this same time frame. Biogenic emissions are very
23 dependent on temperature; and as ambient temperatures rise during the summer months, NOX
24 and VOC emissions reach a maximum. Figure 3-24 clearly demonstrates this for biogenic
25 VOC emissions, and a plot of monthly biogenic NOX emissions would show a similar
26 pattern. Well over 50% of biogenic NQX and VOC emissions occur during the period of
27 maximum photochemical activity.
28 Seasonal changes in anthropogenic emissions of NOX are believed to be relatively small.
29 The transportation sector produces slightly less NOX during the wanner months, but there is
30 probably a small increase from the stationary source category because of higher summertime
31 power demands. Since these are the major U.S. sources of NOX and changes in seasonal
December 1993 3-99 DRAFT-DO NOT QUOTE OR CITE
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Dec.
Nov.
01234
Total NMHC Emissions fTg)
Figure 3-24. Estimated biogenic emissions of volatile organic compounds in tbe
United States as a function of season.
Source: Fehsenfeld et ai, (1992),
1 emissions tend to offset each other, there is no reason to expect that NOX emissions will vary
2 significantly by season on the national level. Evaporative emissions of VOCs are enhanced
3 during the warm summer months. Because evaporation is an important component of
4 anthropogenic VOC emissions, there should be a summertime increase. In 1987, U.S. VOC
5 emissions during June, July, and August were estimated to exceed annual monthly average
6 VOC emissions by about 17% (U.S. Environmental Protection Agency, 1992), This is a
7 very small change relative to the uncertainty associated with VOC emission estimates. In the
8 NAPAP inventory, VOC emissions from manmade sources were considered to be almost
9 independent of season (Placet et al., 1991).
10 Increases in O3 precursor emissions during the peak O3 season will have a tendency to
11 enhance O3 production. Ozone production in rural areas is usually NO^-limited (Fehsenfeld
12 et al,, 1992). Thus, enhanced summertime emissions of NOX from soils and lightning will
December 1993
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1 add NOX to the atmosphere in rural regions, which in turn will lead to the production of
2 more O3. Larger summertime emissions of VOCs will enhance O3 production in urban
3 areas. Biogenic VOC sources in the vicinity of urban areas can contribute significant
4 quantities of reactive hydrocarbons to the urban 63 precursor mix (CardeUno and Chameides,
5 1990).
6
1 3.4.2 Concentrations of Precursor Substances in Ambient Air
8 The volatile organic compounds (VOCs), excluding methane, often are referred to as
9 nonmethane organic compounds (NMOCs). The class of NMOCs most frequently analyzed
10 in air are the nonmethane hydrocarbons (NMHCs). The NMHC measurements often provide
11 an acceptable approximation of the NMOCs. The NMHCs and the nitrogen oxides (NOX)
12 within urban areas tend to have morning concentration peaks. These result from vehicular
13 traffic in combination with limited mixing depths. Later in the morning into the afternoon
14 hours, concentrations of NMHCs and NOX decrease, but to varying extents (Purdue et al.,
15 1992) because of increases in mixing depths and consequent increases in dilution volumes.
16 Photochemical atmospheric reactions also can rapidly convert nitric oxide (NO) to nitrogen
17 dioxide, and hydrocarbons to carbonyls, PANs, and other products (Sections 3.2.4, 3.4.2.1,
18 and 4.9). Late afternoon and early evening peaks might be expected in NMHC and NOX
19 concentrations because of increased vehicular traffic at urban locations, but such increases
20 often are not discernible (Purdue et al., 1992). This effect probably results from the
21 presence of substantial mixing depths in the warmer months that persist through these hours
22 in many urban locations.
23 Because of the emphasis on early morning inputs of NMOCs and NOX for models such
24 as EKMA, most of the measurements available emphasize the 6 to 9 a.m. period. The
25 variations in the concentrations of NMOCs and NOX, their ratios, and the composition of
26 NMOCs are important factors in the generation of O3 and other photochemical products.
27
28 3.4.2.1 Nonmethane Organic Compounds
29 In earlier measurements based on gas chromatographic analyses made during a number
30 of different studies in urban areas over the years between 1969 and 1983, the mean 6 to
31 9 a.m. NMHC concentrations were reported to range from 0.324 to 3.388 ppm C (U.S.
December 1993 3-101 DRAFT-DO NOT QUOTE OR CITE
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1 Environmental Protection Agency, 1986), The highest NMHC concentrations were those
2 measured at sites in Los Angeles.
3 A program for analysis for NMOCs and NOX in the months of June through September
4 was conducted in a considerable number of U.S. cities during the 1980s. The results
5 obtained from measurements made during the 6 to 9 a.m. period at sites in 22 cities in 1984
6 and 19 cities in 1985 have been subjected to statistical analysis and interpretation (Baugues,
7 1986). The total NMOC measurements throughout the June through September periods in
8 these cities were obtained by the cryogenic preconcentration-direct flame ionization method
9 (PDFJD) (McElroy et al., 1986). In addition, during about 15% of the 6 to 9 a.m. periods,
10 canister samples were collected for subsequent gas chromatographic analysis (Seila et al.,
11 1989). In 1984, the lowest median NMOC value obtained was 0.39 ppm C from
12 measurements in Charlotte, NC, while the highest median NMOC value obtained was
13 1.27 ppm C from measurements in Memphis, TN. In 1985, the lowest median NMOC value
14 obtained was 0.38 ppm C from measurements in Boston, MA, while the highest median
15 NMOC value obtained was 1.63 ppm C in Beaumont, TX. The overall median values from
16 all urban sites were approximately 0.72 ppm C in 1984 and 0.60 ppm C in 1985 (Baugues,
17 1986). The gas chromagraphic analyses made on samples collected in 1984, 1985, and 1986
18 have been reported (Seila et al., 1989). The more abundant individual hydrocarbons include
19 C2~Cg alkanes, C2-C5 alkenes, Cg-Cp aromatics, and acetylene. Based on the 48 most
20 abundant concentrations, the overall median concentrations by class of hydrocarbon
21 (NMHCs) were as follows: paraffins, 0.266 ppm C, 60% of total; aromatics, 0,166 ppm C,
22 26% of total; olefins, 0.047 ppm C, 11 % of total; and acetylene, 0.013 ppm C, 3 % of the
23 48 hydrocarbons measured (Seila et al., 1989). Additional individual NMHCs summing to
24 about 0.100 ppm C were detected at concentrations £0.002 ppm C each. Most of these
25 compounds were identified by class but not by structure.
26 Detailed hydrocarbon analyses for C2-C10 NMHCs were obtained during the
27 17 intensive days of the Southern California Air Quality Study (SCAQS) in 1987 (Lonneman
28 et al., 1989; Rasmussen, 1989; Stockberger et al., 1989). The average percentage ambient
29 composition from eight southern California sites during 11 intensive sampling days of the
30 summer of 1987 by class of NMHCs were as follows: paraffins, 53.4; aromatics, 27.2;
31 olefins, 12.1; carbonyls, 7.7 (Main and Lurmann, 1992).
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1 In Atlanta, GA, during the summer of 1990, hydrocarbon concentrations were
2 measured at six sites with automated gas chromatographs. Results were reported on
3 54 hydrocarbons, with 24-h average concentrations ranging from 0.186 ppm C to
4 0.397 ppm C (Purdue et al., 1992).
5 A comparison of NMHC measurements made by gas chromatographic analyses over a
6 period of years in Los Angeles and in the New York City area has been reported (Lonneman
7 and Seila, 1993). In the Los Angeles area, the NMHC concentrations averaged 2.81 ppm C
8 in 1968, compared to 1.02 ppm C in 1987. In the New York City area, the NMHC
9 concentrations averaged about 1.1 ppm C in 1969, compared to 0.62 ppm C from 1986 to
10 1988. In both the Los Angeles and New York areas, there were significant decreases in
11 NMHC concentrations as well as compositional changes in NMHCs during these years, with
12 increases observed in the percentage of paraffin hydrocarbons and decreases in the
13 percentage of aromatic hydrocarbons and acetylene (Lonneman and Seila, 1993).
14 Aldehydes and ketones occur in urban air as ozone-oxidant precursors from emissions
15 such as vehicular exhaust, and as products of reactions of OH radicals with NMHCs,
16 reactions of alkenes with Oj, and, at night, reactions with NOj radicals. Early morning
17 aldehyde concentrations have been predicted to result to a greater extent from atmospheric
18 reactions of alkenes than from emission of vehicular exhaust (Altshuller, 1993). During the
19 day, aldehydes and ketones are rapidly produced from reactions of OH radicals with aliphatic
20 and aromatic hydrocarbons and of alkenes with O3. Carbonyl concentrations tend to increase
21 through the daytime hours (Grosjean, 1982, 1988; Grosjean et al., 1993).
22 Measurements of ambient air concentrations of carbonyls indicate the total loading of
23 aldehydes and ketones from all processes. Ambient urban air concentrations of formaldehyde
24 and total aldehydes were tabulated for the 1960 to 1981 period (Altshuller, 1983a).
25 Subsequent studies by DNPH-HPLC techniques (Section 3.5.2.3.4) have consistently shown
26 that formaldehyde and acetaldehyde are the most abundant aldehydes; however, a number of
27 other carbonyls—including propanal, acrolein, acetone, butanal, crotonaldehyde, methyl ethyl
28 ketone, pentanal, hexanal, benzaldehyde, and tolualdehyde—also have been measured (Fung,
29 1989; Grosjean 1982, 1988, 1991; Kalabokas et al., 1988; Zweidinger et al., 1988). The
30 ratios of formaldehyde to acetaldehyde concentrations (ppbv) can vary from less than 0.5 in
31 cities in Brazil, where there is high use of ethanol fuels, up to 4.0 to 5.0 at a few urban sites
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1 (Grosjean et al., 1993). However, at most urban sites, the ratios of formaldehyde to
2 acetaldehyde concentrations occur in the 1.0 to 3.0 range.
3 A compilation of the maximum, average range of formaldehyde concentrations from
4 many studies in Southern California carried out between 1960 and 1989 is available
5 (Grosjean, 1991). A downward trend in formaldehyde concentrations occurs, probably
6 because of decreased production from precursor alkenes and decreased emission in vehicular
7 exhaust (Sigsby et al,, 1987; Dodge, 1990). For example, the maximum formaldehyde
8 concentrations decreased from above 100 ppbv in the 1960s down to the 10 to 30 ppbv range
9 during the last decade (Grosjean, 1991). In other U.S. cities in the early 1980s, the
10 maximum formaldehyde concentrations ranged from 5 to 45 ppb (Salas and Singh, 1986),
11 Several studies have reported concurrent morning hydrocarbon and carbonyl
12 concentrations in downtown Los Angeles, CA (Grosjean and Fung, 1984); Raleigh, NC
13 (Zweidinger et al., 1988); and Atlanta, GA (Shreffler, 1992; Grosjean et al., 1993). The
14 average percentage of carbonyls relative to total NMHCs were reported as follows; Los
15 Angeles, 3%; Raleigh, 2%; and Atlanta, ^2% (formaldehyde + acetaldehyde
16 concentrations) at two different sampling sites. In SCAQS, carbonyls were measured at eight
17 sites in summer and five in fall of 1987 (Fung, 1989; Fujita et al., 1992). The average
18 percentage of Cj to C6 carbonyls relative to NMHCs in summer was 7.6% and in fall was
19 3.7%.
20 Compilations of NMHC concentrations of nonurban and remote locations are available
21 (U.S. Environmental Protection Agency, 1986; AltshuUer, 1989a). Total NMHC
22 concentrations reported ranged from less than 0.01 to 0.14 ppm C. At remote locations over
23 the Pacific, NMHC concentrations generally were less than 0.01 ppm C. Over both
24 continental and oceanic locations there can be contributions from biogenic sources of
25 NMHCs.
26 Interest in the contribution of biogenic hydrocarbons has existed for many years and
27 earlier work has been reviewed (Altshuller, 1983b). Photochemical modeling in the United
28 States predicts significant effects of biogenic hydrocarbons on O3 production (Chameides
29 et al., 1988; Roselle et al., 1991). Similar modeling of the effect of biogenic hydrocarbons
30 on O3 production within urban plumes over southeastern England predicted a 2 to 8 ppb
31 increase in plume and background O3 concentrations (MacKenzie et al., 1991). Because of
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1 lower emissions of biogenic and lower overall NMOC/NOX ratios, O3 production over
2 southeastern England is predicted to be limited by the availability of anthropogenic
3 hydrocarbons,
4 Compilations of results of earlier measurements of isoprene and terpene concentrations
5 are available (Altshuller, 1983b; U.S. Environmental Protection Agency, 1986). Average
6 concentrations of isoprene ranged from 0.001 to 0.020 ppm C and terpenes from 0.001 to
7 0.030 ppm C. When concurrent measurements of biogenic and anthropogenic NMHCs
8 were available, the biogenic NMHCs usually constituted much less than 10% of the total
9 NMHCs (Altshuller, 1983b).
10 Among more recent studies are two investigations of terpene and isoprene emissions in
11 the central valley of California and in Louisiana (Arey et al., 1991; Khalil and Rasmussen,
12 1992). Both studies reported a large number of individual terpenes as measured using
13 enclosure methods. When ambient air measurements were made, most of the terpenes
14 measured in the enclosures were not detectable (Khalil and Rasmussen, 1992). In ambient
15 air, isoprene was the predominate hydrocarbon, accounting on average for 70% of the
16 biogenic species and 36% of NMOCs. It is concluded that the bag enclosure method can
17 lead to large overestimates in biogenic emissions (Khalil and Rasmussen, 1992).
18 In two other recent studies in deciduous forests, the isoprene oxidation products were
19 measured as well as isoprene itself (Pierotti et aL, 1990; Martin et al., 1991). Both studies
20 report the ambient concentrations of methacrolein and methyl vinyl ketone. In the
21 investigation in a central Pennsylvania deciduous forest in the summer of 1988, average
22 midday concentrations of isoprene were in the 0.005 to 0.010 ppm C range; whereas the
23 corresponding concentrations of methacrolein and methyl vinyl ketone were in the 0.001 to
24 0.002 ppm C range (Martin et al., 1991). In the study conducted in California forests with
25 samples collected between 1200 and 1600 LT in late spring and summer, the upper quartile
26 of isoprene concentrations was within the 0.010 to 0.025 ppm C range, whereas methacrolein
27 concentrations were within the 0.001 to 0.003 ppm C range, and methyl vinyl ketone
28 concentrations were within the 0.0005 to 0.0015 ppm C range (Pierotti et al., 1991).
29 Higher-molecular-weight semivolatile carbonyls have been measured in a number of
30 rural-remote areas (Juttner, 1986; Yokouchi et al., 1990; Nordek et al., 1992; Ciccioli et al.,
31 1993). The compounds identified include C5-C12 aliphatic aldehydes, aliphatic ketones, and
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1 aromatic aldehydes. Comparisons of the measurement of these carbonyls relative to aromatic
2 hydrocarbons in two studies indicated higher carbonyl concentrations and much lower
3 aromatic hydrocarbon concentrations in the rural-remote sites compared to the urban areas
4 (Yokouchi et al., 1990; Ciccioli et al., 1993). Widely varying natural sources have been
5 associated with these carbonyls, including emissions from forest species (Nordek et al.,
6 1992) and short vegetation (Ciccioli et al., 1993) and as secondary products of natural
7 emissions of terpenes (Ciccioli et al., 1993) or oleic acid (Yokouchi et al., 1990). Among
8 other oxygenates reported to be of natural origin are higher-molecular-weight alcohols
9 (Juttner, 1986; Nordek et al., 1992; Goldan et al., 1993). These oxygenates contribute to
10 the "other VOCs" category in the biogenic emissions inventory (Section 3.4.1.3,4).
11 In an urban-scale study in Atlanta, GA, during the summer of 1990 (as part of the
12 Southern Oxidant Study), isoprene concentrations rose in late morning and into the afternoon,
13 with early evening peaks observed at residential and rural-residential sites (Purdue et al.,
14 1992). A similar diurnal profile for isoprene was observed at a Pennsylvania forest site
IS (Martin et al., 1991). The median concentration at the sampling sites in Atlanta early in the
16 evening ranged from 0.006 to 0.020 ppm C. The isoprene as a percentage of total NMHCs
17 in the early evening ranged among the sites from 2 to 12% (Shreffler, 1992).
18
19 3.4.2.2 Nitrogen Oxides
20 Measurements of NOX were obtained with continuous NOX analyzers at sites in 22 and
21 19 U.S. cities during the months of June through September of 1984 and 1985, respectively.
22 These results have been evaluated and the 6 to 9 a.m. values tabulated (Baugues, 1986).
23 In 1984, the lowest median NOX concentration of 0.010 ppm was obtained from
24 measurements in West Orange, TX; while the highest median NOX concentration of
25 0.088 ppm was obtained from measurements in Memphis, TN. In 1985, the lowest median
26 NOX concentration of 0.005 ppm was obtained from measurements in West Orange, TX;
27 while the highest median NOX concentration of 0.100 ppm was obtained from measurements
28 in Cleveland, OH. The median NOX concentration values for sites in most of these cities in
29 1984 and 1985 ranged between 0.02 and 0.08 ppm. Because of high vehicular emission rates
30 and shallow mixing depths, the median 6 to 9 a.m. concentration values in many of these
31 cities exceeded the annual average NOX values of 0.02 to 0.03 ppm in U.S. metropolitan
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1 areas between 1980 and 1989 (U.S. Environmental Protection Agency, 199la). In the 1990
2 Atlanta study, average summer NOX concentration values at the six study sites ranged from
3 0.011 to 0.026 ppm (Purdue et al., 1992).
4 At nonurban sites, NOX concentrations have been reported as mean 24-h seasonal or
5 annual NOX values. The available results have been compiled for work reported through
6 1983 (Altshuller, 1986). The average seasonal or annual NOX concentrations ranged from
7 less than 0.005 to 0.015 ppm. At remote sites in the earlier investigations, monthly average
8 NOX concentrations were less than 0.001 ppm. In more recent work, the statistics on NOX
9 concentrations have been reported for several relatively remote U.S. sites (Fehsenfeld et al.,
10 1988). The 24-h average NOX concentrations and the range in the central 90% of values
11 were as follows: Point Arena, CA, spring 1985, 0.0004 ppm, 0.0007 to 0.001 ppm; Niwot
12 Ridge, CO, summer 1985, 0.0005 ppm, 0.0001 to 0.002 ppm; and Scotia, PA, summer
13 1986, 0.002 ppm, 0.0007 to 0.009 ppm. It should be noted that each of these sites can be
14 subject to anthropogenic influences, thus accounting for the higher NOX values. For
15 example, at Niwot Ridge, CO, with upslope flow from the Denver-Boulder, CO, urban area,
16 higher NOX concentrations are measured. Nitrogen oxide concentrations at or below
17 0.0001 ppm occur at other remote surface locations (Fehsenfeld et al., 1988).
18
19 3.4.2.3 Ratios of Concentrations of Nonmethane Organic Compounds and Nitrogen
20 Oxides
21 The ratios of 6 to 9 a.m. NMOC/NOX have been obtained from the measurements in
22 the U.S. cities discussed above (Baugues, 1986). In 1984, the lowest median NMOC/NOX
23 ratio of 9.1 was obtained in Cincinnati, OH, and the highest median NMOC/NOX ratio of
24 37.7 was obtained in Texas City, TX. In 1985, the lowest median NMOC/NOX ratio of
25 6.5 was obtained in Philadelphia, PA, whereas the highest median NMOC/NOX ratio of
26 53.2 was obtained in Beaumont, TX. The range in daily 6 to 9 a.m. NMOC/NOX ratios
27 within a given city is large, with 10th percentile to 90th percentile NMOC/NOX ratios
28 varying usually by factors of 2 to 4 and at several sites by factors of 5 to 10 (Baugues,
29 1986). There appears to be a tendency for higher NMOC/NOX ratios in the cities included in
30 the southeastern (9) and southwestern (15) United States than in the northeastern (7) and
31 midwestem United States (7) (Altshuller, 1989b). The NMOC-to-NOx ratios at rural sites
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1 tend to be higher than the mean NMOC-to-NOx ratios in urban locations, with mean values
2 at several rural sites ranging between 20 and 40 (AJtshuller, 1989b).
3 In SCAQs, the ambient NMOC (NMHCs + carbonyl)/NOx ratios averaged 8.8 in
4 summer and 6.9 in the fall of 1987 (Fujita et al., 1992). However, the six intensive days in
5 fall between November 11 and December 11 were not characterized by elevated
6 03 concentrations (Zeldin, 1992). These ambient ratios were 2 to 2.5 times higher than the
7 corresponding emission inventory ratios. Discrepancies as large or larger have been
8 previously discussed for urban and rural NMHC/NOX ambient-to-emission ratios in the
9 eastern United States (Allshuller, 1989b).
10 A trend analysis of NMHC/NOX ratios in the South Coast Air Basin is available for the
11 1976 to 1990 period (Fujita, 1992). The ratios were consistently higher in summer than fall.
12 These ratios started decreasing slowly during the 1980s from maximum ratios of about 12 in
13 summer and 9 in fall down to 8.5 in summer and 7 in fall by 1990. The ambient-to-emission
14 inventory ratios over this period ranged from as high as 3.4 in summer to 1.7 in winter
15 (Fujita, 1992).
16 Interest in the 6 to 9 a.m. NMOC/NOX ratios is associated with their use in the EKMA
17 type of trajectory model (Section 3.6.1.2). The analysis at 10 eastern and midwestera sites
18 of upper-quartile O3 days relative to other O3 days indicated a significant difference
19 (p ^ 0.10) by the two-sample Wilcoxon Rank Sum test at four of the 10 sites with
20 NMOC/NOX ratios (Wolff and Korsog, 1992). However, the correlation of NMOC/NOX
21 ratios with maximum 1-h O3 concentrations was very weak. It was concluded that the use of
22 the 6 to 9 a.m. NMOC/NOX ratio in EKMA will not provide sufficient information to
23 distinguish among NMOC, NOX, or combined VOC-NOX strategies as optimum strategies for
24 urban areas.
25
26 3.4.3 Source Apportionment and Reconciliation
27 3.4.3.1 Source Apportionment
28 Source apportionment refers to determining the quantitative contributions of sources to
29 ambient air pollutant concentrations. In principle, it includes two fundamentally different
30 approaches, source-oriented and receptor-oriented. In the source-oriented approach, a
31 mathematical dispersion model is applied to an emissions inventory and meteorological data
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I to produce an estimate of ambient pollutant concentrations that can be expected at a specified
2 point in space and time. In contrast, the receptor-oriented approach depends on simultaneous
3 ambient concentration measurements of a variety of pollutant species, and a knowledge of the
4 relative amounts of the species (source profiles) that are present in the emissions of the
5 sources that are potential contributors. A mathematical receptor model operates on the
6 source profile and ambient species concentration information to deconvolute the ambient
7 concentrations into their source contributions, without the need of emissions inventory or
8 meteorological information. Indeed, the desire to avoid the latter two kinds of information,
9 whose acquisition is often problematical, has been an important motivation in the
10 development of the receptor-oriented approach.
11 Although source apportionment in its general sense embraces both approaches, in recent
12 years it has come to be regarded as synonymous with the receptor-oriented approach
13 (receptor modeling). The equivalence of source apportionment and receptor modeling is
14 assumed in the following. The most recent review of the field of receptor modeling has been
15 given by Gordon (1988).
16 Because tropospheric O3 is a secondary pollutant, the natural role of receptor modeling
17 is in determining the quantitative source contributions of the VOC precursors of O3.
18 Historically, receptor modeling was first developed in the 1970s for the apportionment of
19 ambient aerosol, and aerosol applications since then have been more extensive than VOC
20 applications. The aerosol and VOC areas of receptor modeling application have more
21 similarities than differences, however, so that much of the mathematical apparatus that has
22 been developed for aerosol problems is readily adaptable to VOCs.
23 For reasons that will become apparent, the separation of emissions sources into
24 anthropogenic and biogenic classes is a natural division for VOC receptor modeling and is
25 used in the following.
26
27 3.4.3.1.1 Manmade Sources of Volatile Organic Compounds
28 A principal approach for receptor modeling of anthropogenic VOC sources is that of
29 "mass balance". In this approach, a particular linear combination of source profiles is sought
30 that best approximates (in a linear least-squares sense) the profile of VOC species
31 concentrations measured in an ambient sample. Here a VOC source profile is defined as the
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1 set of numbers giving the fractional amounts (abundances) of individual species in the
2 emissions from the source. The profile may be normalized to the sum of the abundances of
3 all VOC species emitted by the source or to a sum over some arbitrary subset of species.
4 For the linear combination of profiles that gives the best fit, the coefficients are the source
5 strengths (in the same units as the measured ambient concentrations) associated with each of
6 the included source profiles.
7 Early efforts to use various versions of the mass balance approach include Ehrenfekl
8 (1974), in Los Angeles; Mayrsohn and Crabtree (1976) and Mayrsohn et al. (1977), in
9 Los Angeles; and Nelson et al. (1983), in Sydney, Australia.
10 Of these studies, the work of Mayrsohn et al. (1977) is the most comprehensive—
11 900 samples from eight sites collected during June to September, 1974. The average results
12 were: automotive exhaust, 53%; whole gasoline evaporation, 12%; gasoline headspace
13 vapor, 10%; commercial natural gas, 5%; geogenic natural gas, 19%; liquefied natural gas,
14 1 %. The percentages are for NMHCs through C10 (i.e., not all of the total VOCs).
15 The estimates for the first three vehicle-related sources together account for 75% of the
16 ambient NMHCs, which is the approximate percentage estimated in the other studies listed.
17 Geogenic natural gas is obviously not anthropogenic but is included here for completeness.
18 Its strength (19 %) is striking. It seems unlikely that a contribution this large would be
19 typical of other locales lacking a petroleum-related geology. In any case, accounting for the
20 urban atmospheric concentrations of ethane and propane (the main NMHC constituents of
21 natural gas) has remained an unsatisfactorily resolved problem, so that the 19% result for
22 geogenic natural gas has to be regarded skeptically.
23 Although old, these early studies are of more than just historical interest. In one
24 respect, they are superior to more recent studies in their recognition of two distinctly
25 different kinds of gasoline evaporation: (1) headspace vapor, which represents the partial
26 evaporation of gasoline in situations such as storage tank evaporation or vehicle diurnal
27 evaporation, and is characterized by an enrichment of high volatility species; and (2) whole
28 gasoline emissions, which can arise from spillage, leakage, and vehicle hot-soak emissions,
29 and has a composition resembling liquid gasoline itself. The implications of this are
30 discussed below.
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1 In the mid-1980s, a useful degree of standardization was incorporated into the mass
2 balance approach by the introduction of EPA's Chemical Mass Balance (CMS) software.
3 The current version, CMB7 (Watson et al., 1990), embodies a comprehensive treatment of
4 error (including uncertainty in both ambient data and source profiles) and many diagnostics
5 (including profile collinearity), and has been used frequently in recent VOC receptor
6 modeling studies.
7 Recent studies include Wadden et al. (1986), in Tokyo; O'Shea and Scheff (1988), in
8 Chicago; Aronian et al. (1989), in Chicago; Sweet and Vermette (1992), in Chicago and East
9 St. Louis, IL; Harley et al. (1992), in Los Angeles; Kenski et al. (1993), in Chicago;
10 Beaumont, TX; Detroit; Atlanta; and Washington, DC; Spicer et al. (1993), in Colombus,
11 OH; and Lewis et al. (1993), in Atlanta.
12 The source categories covered by these studies taken together include vehicle exhaust,
13 gasoline evaporation (whole gasoline and headspace vapor), industrial emissions (refineries,
14 coke ovens, chemical plants), architectural coatings, dry cleaning, wastewater treatment, auto
15 painting, industrial solvents/degreasers, graphic arts (printing), and natural gas. Each study
16 gives estimates for the percentage contributions to measured ambient VOC (or related
17 quantity) for a selected subset of these source categories. The one exception is the work of
18 Sweet and Vermette (1992), which estimates the percentage source contributions to individual
19 species, rather than to total VOC. Such species apportionment is always available from the
20 CMB calculations, but is often not explicitly reported.
21 Usually the source profiles used were generic; that is, from compilations (e.g., U.S.
22 EPA, 1991) of source measurements taken elsewhere. The work of Lewis et al. (1993) is
23 unique in the use of profiles extracted from the ambient air data themselves.
24 Generally, for these urban-based studies, vehicle exhaust is found to be the dominant
25 contributor to ambient VOC. Exceptions are the Tokyo results of Wadden et al. (1986),
26 which show an unreasonably small average contribution of 7%, and the Beaumont results of
27 Kenski et al. (1993) at 14%. For all the rest, the average vehicle exhaust results fall in the
28 range (45 ± 15%).
29 The results for gasoline evaporation contribution estimates are much less satisfactory.
30 This is because the recent studies, with the exceptions of Harley et al. (1992) and Lewis
31 et al. (1993), included a gasoline headspace vapor profile but not a whole gasoline profile in
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1 their calculations. The latter two studies suggest that this omission is a serious error. For
2 example, Harley et al. (1992) find a remarkably large whole gasoline contribution (nearly the
3 same as that of vehicle exhaust); and Lewis et al. (1993) find a whole gasoline contribution
4 that is about 20% that of vehicle exhaust. Both, however, find a whole gasoline contribution
5 about four times greater than the headspace contribution. Because vehicle exhaust and whole
6 gasoline profiles are quite similar (except for the very light species that are absent in gasoline
7 but present in exhaust as combustion products), excluding the whole gasoline profile will
8 tend to overestimate the exhaust contribution. Although this error may not greatly affect the
9 total mobile source-related emissions estimate, it is misleading with regard to implied control
10 strategies.
11 Beyond the ubiquitous vehicle-related contributions, other anthropogenic source
12 contribution estimates tend to be smaller or locale-specific.
13
14 3.4.3.1.2 Biogenic Sources of Volatile Organic Compounds
15 The possible role of biogenic VOC emissions in O3 formation is being considered much
16 more seriously now (Chameides et al., 1988) than was the case a decade ago. Because of
17 the severe experimental problems in accurately measuring biogenic emissions directly,
18 receptor modeling approaches are of considerable interest. Compared with anthropogenic
19 sources, however, the application of receptor modeling methodology to biogenic sources has
20 been very limited. The principal reason is that it has not been possible to find VOC species
21 that are simultaneously (1) distinctive components of biogenic emissions, (2) emitted in an
22 approximately fixed proportion to the total VOC biogenic emissions, and (3) relatively
23 unreactive. Without these conditions, the construction of a credible stable biogenic source
24 profile is not possible, and, consequently, the CMB approach is unusable.
25 In this situation, a crude form of receptor modeling has been used in which the ambient
26 concentration of a VOC species, whose only source is thought to be biogenic, is divided by
27 the estimated abundance of the species in the total VOC biogenic emissions. Typical
28 candidates include isoprene (deciduous emission) and the terpenes a- and j8-pinene
29 (coniferous emission), 6-caranene, and limonene. Because these are all highly reactive, any
30 such estimate can only be regarded as a lower limit of the contribution that biogenic
31 emissions make to total ambient VOC, if the loss resulting from atmospheric transformation
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1 is not taken into account. As an example, Lewis et al. (1993) used isoprene, the most
2 prominent biogenic species measured in downtown Atlanta during summer 1990, to infer a
3 lower limit of 2 % (24-h average) for the biogenic percentage of total ambient VOC at that
4 location. Isoprene emissions have a strong diurnal dependence. Lower limits for biogenic
5 emissions at other hours, inferred from average isoprene concentrations, were: 1 % at 8 am,
6 5% at noon, 6% at 4 pm, 2% at 9 pm.
7 The recent review article by Fehsenfeld et al. (1992) lists other prominent biogenic
8 species, and calls attention to the newly recognized importance of alcohols such as methanol
9 as biogenic primary emissions. Goldan et al. (1993) have reported the C5 alcohol,
10 2-methyl~3-buten-2-ol ("methyl butenol"), to be the most abundant VOC of biogenic origin
11 present in a predominantly lodgepole pine forest in Colorado.
12 A more sophisticated form of biogenic receptor modeling involves the radiocarbon
13 isotope 14C. The approach depends on the fact that 14C constitutes a nearly fixed fraction
1 *t t A
14 (approximately 10" ) of all carbon present throughout the biosphere. In contrast, the C in
15 dead organic material older than 40,000 years, certainly the case for fossil fuels, has been
16 reduced by at least 99 % through radioactive decay. This leads to a simple estimate of the
17 biogenic fraction of a carbon-containing sample given by fg/f0 , where fg is the C fraction
18 in the sample, and f0 is the 14C fraction in living material. Besides its conceptual simplicity,
19 the approach is appealing for VOC apportionment because 14C retains its identity in the
20 reaction products that may result from atmospheric transformation of reactive VOC. The
21 method appears to be reliable for paniculate phase organics (Lewis et al., 1988; Lewis et al.,
22 1991a), but is still under development for VOC applications (Klouda et al., 1993).
23
24 3.4.3.2 Source Reconciliation
25 Source reconciliation refers to the comparison of measured ambient VOC concentrations
26 with emissions inventory estimates of VOC source emission rates for the purpose of
27 validating the inventories. Because concentrations and emission rates are specified in
28 different units, the comparisons are done in terms of percentages: the percentage of a
29 source's contribution to ambient total VOC as estimated by receptor modeling versus the
30 source's emission rate as a percentage of the inventory's total VOC emission rate.
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1 Nearly all the receptor modeling studies listed above have included such a percentage
2 comparison. Typically, the agreement is quite good for vehicle exhaust, generally the
3 dominant VOC source in urban airsheds. Gasoline evaporation comparisons are much less
4 consistent, at least partly for the reasons already indicated. Typically, there is at least
5 qualitative agreement for the other anthropogenic sources: They are small in the inventory,
6 and the receptor-estimated contributions are small, An interesting exception is refinery
7 emissions in Chicago (Scheff and Wadden, 1993), for which the receptor estimate was 7%,
8 five times greater than the inventory estimate. Another is the significant (5 to 20%) natural
9 gas/propane contribution estimated in Los Angeles, Columbus, and Atlanta but not reflected
10 in their inventories. The few biogenic source estimates provided by receptor modeling are
11 generally smaller than those given in emissions inventories, at least partly because of the
12 reactivity problem already referred to. Credible C measurements on VOC samples would
13 be extremely helpful in validating the magnitude of the biogenic component of emissions
14 inventories.
IS Lewis et al. (1993) has noted that comparisons based on percentages are quite
16 insensitive for dominant source components, and the comparisons are more dependent on
17 how "total VOC" is defined than is often appreciated (the definition varies for the studies
18 listed). Thus, unfortunately, the generally good agreement (receptor versus inventory
19 estimates) found for vehicle exhaust does not translate into a definitive judgment on the
20 current concern that this source component may be significantly underestimated in existing
21 inventories. For example, if the emission rate of vehicle exhaust in a typical inventory were
22 arbitrarily doubled, the resulting change in the percentage of this component in the inventory
23 is well within the range of what can be produced in the receptor estimate by merely choosing
24 a different definition of "total VOC" from plausible alternatives. Such alternatives relate to
25 questions such as which subset of hydrocarbons are summed. Whether unidentified
26 chromatographic components are included in the sum, etc. In the future, this situation can
27 be improved by more consistency in the total VOC definition and by transforming the
28 receptor modeling results from a concentration-based representation to an emission-rate one.
29 This unavoidably involves introducing some limited meteorological information (Lewis et al.,
30 1991b).
31
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1 3.5 ANALYTICAL METHODS FOR OXIDANTS AND THEIR
2 PRECURSORS
3 3.5.1 Sampling and Analysis of Ozone and Other Oxidants
4 3.5.1.1 Ozone
5 3.5.1.1.1 Introduction
6 The measurement of O3 in the atmosphere has been a subject of research for decades
7 because of the importance of this compound in atmospheric chemistry and because of its
8 potential and demonstrated effects on human health and welfare.
9 Because of the importance of 03 in the air of populated regions, widespread
10 O3 monitoring networks have been operated for many years, and the development of
11 measurement and calibration approaches for O3 has been extensively reviewed (e.g., U.S.
12 Environmental Protection Agency, 1986). This section focuses on the measurement of ozone
13 in the ambient atmosphere at ground level, and summarizes the current state of ambient
14 O3 measurement and calibration. No attempt is made here to cover the full history of
15 development of these methods, since that has been documented elsewhere (e.g., U.S.
16 Environmental Protection Agency, 1978, 1986). Instead, this section concentrates on those
17 methods currently used and on new developments and novel approaches to O3 measurement.
18 Although no method is totally specific for 03, current methods for 03 must be
19 distinguished from earlier methods that measured "total oxidants". The wet chemical
20 methods used earlier for total oxidants have been replaced for essentially all ambient
21 measurements by two more specific instrumental methods based on the principles of
22 chemiluminescence and ultraviolet (UV) absorption spectrometry. These two approaches are
23 described below. In addition, recent developments in spectroscopic measurements, in other
24 chemical approaches, and in passive sampling devices for O3 are described.
25
26 3.5.1.1.2 Chemiluminescence Methods
27 Gas-Phase Chemiluminescence. The most common chemiluminescence method for
28 O3 is direct gas-phase reaction of O3 with an olefin to produce electronically excited
29 products, which decay with the emission of light. This approach was first used nearly
30 30 years ago for chemical analysis by Nederbragt (Nederbragt et al., 1965), and development
31 of a portable monitor (Warren and Babcock, 1970) and application to atmospheric
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1 measurements (Stevens and Hodgeson, 1970) followed soon after. Typically, an O3 monitor
2 based on this approach functions by mixing a constant flow of about 1 L/min of sample air
3 with a small constant flow (»50 cm3/min) of ethylene. Mixing occurs in a small inert
4 reaction chamber fitted with a sealed window through which light can pass to the
5 photocathode of a photomultiplier tube. Electronically excited formaldehyde molecules,
6 generated by a small fraction of the O3-ethylene reactions, produce a broad band of emission
7 centered at 430 nm. The emission intensity is linearly proportional to the O3 concentration
8 over the range of 0.001 ppm to at least 1 ppm. Calibration of the monitor with a known
9 ozone source provides the relationship between monitor response and ozone concentration.
10 Detection limits of 0.005 ppm and a response time of less than 30 s are easily attained, and
11 are typical of currently available commercial instruments.
12 Although no interference has been found from common atmospheric pollutants, a
13 positive interference from atmospheric water vapor has been reported (California Air
14 Resources Board, 1976; Kleindienst et al., 1993 and references therein) and has recently
15 been confirmed (Kleindienst et al., 1993). The recent results indicate a positive interference
16 of about 3% per percent H2O by volume at 25 °C. The results of Kleindienst et al. (1993)
17 were obtained at ozone concentrations of 0.085 to 0.32 ppm, and at I^O concentrations of
18 1 to 3% (i.e., dew point temperatures of 9 to 24 °C). It has been estimated that the
19 interference of water in ethylene chemiluminescent measurements at 30 °C and 60% relative
20 humidity could be as high as 13 ppbv of Oj, or 11 % of the O3 reading at 120 ppbv
21 (Kleindienst et al., 1993). Calibration with known O3 concentrations in air of temperature
22 and humidity similar to that of the sample air can minimize this source of error.
23 A separate potential problem with the ethylene chemiluminescent method is leakage of
24 the pure ethylene reagent gas. Because ozone and hydrocarbon measurements are often
25 co-located for monitoring purposes, leakage of ethylene could cause difficulty in obtaining
26 valid measurements of total nonmethane hydrocarbons (TNMHC) in ambient air.
27 The measurement principle set forth by EPA for compliance monitoring for O3 is the
28 chemiluminescence method using ethylene (Federal Register, 1971). Methods of testing and
29 the required performance specifications that commercial O3 monitors must meet to be
30 designated a reference or equivalent method are documented (Federal Register, 1975).
31 A monitor may be designated a reference method if it employs gas-phase chemiluminescence
December 1993 3-116 DRAFT-DO NOT QUOTE OR CITE
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1 with ethylene as the measuring principle and achieves the required performance
2 specifications. An equivalent method must show a consistent relationship with the reference
3 method and must meet the required performance specifications. Table 3-15 shows those
4 specifications for Oj monitors. Note that ethylene chemiluminescence monitors typically have
5 response times far superior to that required in Table 3-15.
6
TABLE 3-15. PERFORMANCE SPECIFICATIONS FOR AUTOMATED
^ METHODS OF OZONE ANALYSIS
Performance Parameter Units Specification
Range ppm 0 to 0.5
Noise ppm 0.005
Lower detectable limit ppm 0.01
Interference equivalent
Each interference ppm ±0.02
Total interference ppm 0.06
Zero drift, 12 and 24 h ppm ±0.02
Span drift, 24 h
20% of upper range limit % ±20.0
80% of upper range limit % ±5,0
Lag time min 20
Rise time min 15
Fall time min 15
Precision
20% of upper range limit ppm 0.01
80% of upper range limit ppm 0.01
Source: Federal Register (1975); Code of Federal Regulations (1975); cited in U.S. Environmental Protection
Agency (1986).
1 The list of commercial O3 monitors designated as reference or equivalent methods by
2 EPA is shown in Table 3-16 (updated as of February 8, 1993). Details on three monitors not
3 described in the 1986 EPA criteria document for ozone and other oxidants are presented in
4 Table 3-17. All of the reference methods are ethylene chemiluminescence instruments, as
5 required by the definition of a reference method. The equivalent methods are based on either
6 gas-solid chemiluminescence or UV absorption analyzer measurements. Those methods are
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TABLE 3-16. REFERENCE AND EQUIVALENT METHODS FOR OZONE
DESIGNATED BY THE U.S. ENVIRONMENTAL PROTECTION AGENCY8
Method
(Principle)
Reference Methods
(Ethylene Chemiluminescence)
Beckman 950A
Bendix 8002
CSI2000
McMillan 1100-1
McMillan 1100-2
McMillan 1100-3
Meloy OA325-2R
Meloy OA350-2R
Monitor Labs 841 OE
Equivalent Methods
(UV Absorption)
Advanced Pollution Instr. 400
Dasibi 1003- AH, -PC, -RS
Dasibi 1008-AH,-PC,-RS
Environics 300
Lear-Siegler ML9810
Monitor Labs 8810
PQ Ozone Corp. LC-12
Thenno Electron 49
Equivalent Methods (Gas/Solid CL)
Philips PW9771
Designation
Number
RFOA-0577-020
RFOA-0176-007
RFOA-0279-036
RFOA-1076-014
RFOA-1076-015
RFOA-1076-016
RFOA-1075-003
RFOA-1075-004
RFOA-1 176-017
EQOA-0992-087
EQOA-0577-019
EAOA-0383-056
EQOA-0990-078
EQOA-0193-091
EQOA-0881-053
EQOA-0382-055
EQOA-0880-047
EQOA-0777-023
Method
Code
020
007
036
514
515
016
003
004
017
087
019
056
078
091
053
055
047
023
aAs of February 1993.
1
2
3
4
5
described below. A gas-liquid chemiluminescence analyzer for O3, which was submitted for
EPA equivalency during 1993, is also described below.
Gas-Solid Chemiluminescence, The reaction of O3 with Rhodamine-B adsorbed on
activated silica gel produces chemiluminescence in the red region of the visible spectrum.
December 1993 3-118 DRAFT-DO NOT QUOTE OR CITfi
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TABLE 3-17. LIST OF DESIGNATED REFERENCE AND EQUIVALENT METHODS FOR OZONEa
1
cr
CD
vo
0
o
I
I
3
Federal Register
Designation
Number
Identification
Source
Manual
or Auto
Ref. or
Equiv.
Vol.
Page
Notice
Date
EQOA-0990-078
~ EQOA-0992-087
"Environics Series 300 Computerized Ozone
Analyzer," operated on the 0-0.5 ppm range, with
the following parameters entered into the analyzer's
computer system:
Absorption Coefficient = 308 ± 4
Flue Time = 3 Integration Factor = 1
Offset Adjustment = 0.025 ppm
Ozone Average Time = 4
Signal Average + 0
Temp/Press Correction = On
and with or without the RS-232 Serial Data
Interface
"Advanced Pollution Instrumentation, Inc. Model
400 Ozone Analyzer," operated on any full-scale
range between 0-0.1 ppm and 0-1 ppm, at any
temperature in the range of 5 to 40 °C, with the
dynamic zero and span adjustment features set
OFF, with a 5-micron TFE filer element installed
in the rear-panel filter assembly, and with or
without any of the following options:
Internal Zero/Span (IZS)
Rack Mount with Slides
RS-232 with Status Outputs
Zero/Span Valves
Environics, Inc.
165 River Road
West Willington, CT
06279
Auto
Equiv.
55
38386 09/18/90
Advanced Pollution
Instrumentation, Inc.
8815 Production Avenue
San Diego, CA 92121-
2219
Auto
Equiv.
57
44565 09/28/92
n
-------
TABLE 3-17 (cont'd). LIST OF DESIGNATED REFERENCE AND EQUIVALENT METHODS FOR OZONE*
Federal Register
Designation
Number
Identification
Source
Manual
or Auto
Ref. or
Equiv.
Vol.
Page
Notice
Date
EQOA-0193-091 "Lear Siegler Measurement Controls Corporation
Model ML9810 Ozone Analyzer," operated on any
full-scale range between 0-0.050 ppmb and 0-1.0
ppm, with auto-ranging enabled or disabled, at any
temperature in the range of 15 °C to 35 °C, with a
5-micron Teflon filter element installed in the Miter
assembly behind the secondary panel, the service
switch on the second panel set to the In position;
with the following menu choices selected:
Calibration; Manual or Timed: Diagnostic Mode:
Operated; Filter Type: Kalman; Pres/Temp/Flow
Comp: On; Span Comp: Disabled;
With the 50-pin I/O board installed on the rear
panel configured at any of the following output
range settings:
Voltage, 0.1 V, IV, 5V, 10V;
Current, 0-20 mA, 2-20 mA, 4-20 mA; and with
or without any of the following options: Valve
Assembly for External Zero/Span (EZS) Rack
Mount Assembly
Internal Floppy Disk Drive
Lear Siegler Measurement
Controls Corp.
74 Inverness Drive East
Englewood, CO
80112-5189
Auto
Equiv.
58
6964 02/03/93
"Designated since publication of the 1986 EPA criteria document for ozone and other photochemical oxidants.
Users should be aware that designation of this analyzer for operation on any full-scale range less than 0.5 ppm is based on meeting the same absolute
performance specifications required for the 0-0.5 ppm range. Thus, designation of any full-scale range lower than the 0-0.5 ppm range does not imply
commensurably better performance than that obtained on the 0-0.5 ppm range.
-------
1 This was the first chemiluminescence method ever developed for ambient O3 measurement
2 (Regener, 1960, 1964). The emitted light intensity is linearly related to the
3 63 concentration, and the detection limit can be as low as 0.001 ppm. No direct
4 interferences from other gas-phase pollutants are known; however, decay of the sensitivity
5 because of surface aging can occur (Hodgeson et al., 1970). Addition of gallic acid to the
6 surface stabilizes the response characteristics, apparently by allowing direct reaction of
7 03 with the gallic acid, rather than with the Rhodamine-B (Bersis and Vassiliou, 1966).
8 A commercial analyzer (Phillips Model PW9771) based on this approach has been designated
9 an equivalent method for ambient ozone (see Table 3-16), but gas-solid chemUuniinescence is
10 currently rarely used for ambient measurements.
11
12 Gas-Liquid Chemiluminescence. A recently developed commercial monitor uses the
13 chemiluminescent reaction of ozone with the dye eosin-Y in solution (Topham et al., 1993).
14 The monitor functions by exposing a fabric wick, wetted with the eosin-Y solution, to a flow
15 of sample air within view of a red-sensitive photomultiplier tube. The monitor, designated
16 the LOZ-3, is compact, portable, and requires no reagent gases. The LOZ-3 provides very
17 fast response: a lag time of 2 s, rise time of 3 s, and fall time of 2 s, all relative to a step
18 change of 400 ppbv ozone, are reported (Topham et al., 1993). Instrument noise at zero and
19 at 382 ppbv ozone is 0.05 ppbv or less, calculated as the standard deviation of 25 successive
20 2-min averages. The precision of the LOZ-3 is reported to be 0.80 ppbv at 100 ppbv ozone,
21 and as 1.87 ppbv at 400 ppbv ozone, both calculated as one standard deviation of six
22 repeated measurements at these levels (Topham et al., 1993). The instrument provides linear
23 response up to 200 ppbv, with a gradually decreasing slope of the response curve above that
24 level. Temperature and pressure sensitivity are corrected by internal circuitry (Topham
25 et al., 1993). An initial large positive interference from SQj is reported, which becomes
26 smaller and negative as the eosin solution ages; and a positive interference from CC^ is also
27 present. Topham et al. (1993) report that a pretreatment technique applied to the eosin
28 reagent solution minimizes both of these interferences. Several of the performance
29 characteristics of the LOZ-3 are impressive, but verification of the reported interference
30 levels and the effectiveness of temperature and pressure corrections appears to be needed.
31 This method was submitted for EPA equivalency certification during 1993.
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1 3.5.1,1.3 VUmviolet Photometry
2 This method is based on the fact that 03 has a reasonably strong absorption band with a
3 maximum near 254 nm, coinciding with the strong emission line of a low-pressure mercury
4 lamp. The molar absorption coefficient at the mercury line is well known, the accepted
5 value being 134 (±2) M^cm"1 in base 10 units at 0 °C and 1 atmosphere pressure (Hampson
6 et al., 1973). Ultraviolet absorption has frequently been used to measure O3 in laboratory
7 chemical and kinetics studies. Ultraviolet photometry was also used for some of the first
8 atmospheric O3 measurements, but the early instruments suffered from poor precision
9 because of the small absorbances being measured (U.S. Department of Health, Education,
10 and Welfare, 1970).
11 Modern digital electronics have now solved the precision problems resulting from
12 measurement of small absorbances, and several commercial O3 monitors now employ
13 UV photometry. Several instruments based on this principle have been designated by EPA
14 as equivalent methods for ambient ozone (Tables 3-16 and 3-17). Ultraviolet photometry is
15 now the predominant method for assessing compliance with the NAAQS for Oj. The
16 commercial monitors use pathlengths of 1 m or less, and operate in a sequential single-beam
17 mode. Transmission of 254 nm light through the sample air is averaged over a short period
18 of time (as short as a few seconds), and is compared to a subsequent transmission
19 measurement on the same air stream from which O3 has been selectively removed by a
20 manganese dioxide scrubber. The electronic comparison of the two signals can be converted
21 directly into a digital readout of the O3 concentration. The method is in principle absolute,
22 since the absorption coefficient and pathlength are accurately known and the measured
23 absorbance can be converted directly to a concentration.
24 Commercial UV photometers for ambient ozone measurements have detection limits of
25 approximately 0.005 ppm. Time response depends on the averaging time used, but is
26 typically < 1 min. Long-term precision can be within ±5%. The method has the advantage
27 of requiring no gas supplies, and commercial instruments are compact and reasonably
28 portable. Sample air flow control is not critical, within the limitations of the MnO2
29 scrubber. Since the measurement is absolute, UV photometry is also used to assay
30 O3 calibration standards as described below (Section 3.5.1.1.5). Ambient air monitors using
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1 UV photometry are generally calibrated with standard Oj mixtures to account for losses of
2 O3 in sampling lines.
3 A potential disadvantage of UV photometry is that any atmospheric constituent that
4 absorbs 254 nm light and is removed fully or partially by the manganese dioxide scrubber
5 will be a positive interference in O3 measurements. Potential interferents include aromatic
6 hydrocarbons, mercury vapor, and sulfur dioxide. A recent study (Kleindienst et al., 1993)
7 demonstrated that toluene and possibly aromatic reaction products, such as benzaldehyde,
8 produce positive interferences in UV photometric ozone measurements. This result was
9 found using photochernically reactive mixtures of toluene and NOX, at concentrations a factor
10 of two to five higher than those expected in polluted urban air. Consideration of the relative
11 absorption coefficients of O3 and the aromatics indicates that at higher humidities toluene can
12 cause an interference of 0.1 ppbv O3 per ppbv of toluene, whereas benzaldehyde may cause
13 an interference as high as 5 ppbv O3 per ppbv benzaldehyde (Kleindienst et al,, 1993). This
14 interference may be humidity dependent. t In earlier work at very low humidities, no
15 interference was observed with toluene ai^d only a very small interference was observed with
16 benzaldehyde (Grosjean and Harrison, 19J85). However, even at very low humidities these
17 investigators observed significant interferences from styrene, cresols, and nitrocresols.
18 Evaluation of aromatic interference is limited by a lack of appropriate absorption spectra in
19 the 250 nm range, and by a lack of ambient measurements of most of the aromatic
20 photochemical reaction products. The us^ of ethylene chemiluminescence monitors in areas
21 where aromatic concentrations are substantial is suggested (Kleindienst et al., 1993).
22 The same study found no consistent]effect of ambient water vapor on measured
23 O3 concentrations using UV photometry, |in contrast to the effect noted using ethylene
24 chemiluminescence (Kleindienst et al., 1993). However, short-term disturbances in UV
25 photometric O3 readings were observed when the humidity of the sample air was changed
26 substantially within a few seconds. This finding corroborates the observations of Meyer
27 et al. (1991) in an earlier study that indicated microscopic irregularities in the UV cell
28 windows as the cause of such disturbances. This effect should be absent in UV photometric
29 measurements of ambient O3 at the ground, but could be important in other applications,
30 such as measuring vertical O3 profiles from an aircraft (Kleindienst et al., 1993).
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1 A different approach to evaluating potential interferences in ozone measurements has
2 been taken by Leston and OUison (1993), These investigators examined ambient ozone data
3 from instruments of different measurement principles co-located at monitoring sites. The
4 focus of their study is the ozone "design value", the fourth highest daily maximum hourly
5 value from a monitoring station within an urban area, which is established in the 1990 Clean
6 Air Act Amendments as the basis for classification of the area relative to attainment of the
7 NAAQS for ozone. Leston and OUison (1993) examined hourly ozone concentration data
8 from co-located UV and ethylene chemiluminescence instruments, from 1989 and 1990 at a
9 site in Madison, CT, and from shorter periods at sites in East Hartford, CT, and
10 Mobile, AL. They also examined 11 winter days of simultaneous ozone data from UV and
11 Luminox LQZ-3 instruments, from Long Beach, CA. Leston and OUison (1993) reported
12 positive biases in the UV data of 20 to 40 ppbv Oj during "hot, humid, hazy conditions
13 typical of design value days," They proposed that most ozone data and all design values are
14 biased high by known and suspected interferences, and that those interferences are
15 exacerbated by water vapor. Leston and OUison (1993) argue that the interference in UV
16 measurements from benzene derivatives (e.g., styrene, cresols, benzaldehyde, nitro-
17 aromatics) is poorly accounted for. For example, of these compounds, only styrene is
18 measured in the PAMS VOC monitoring network (Leston and Ollison, 1993).
19 Interferences of the magnitude suggested by Leston and Ollison (1993) clearly would
20 have serious implications for monitoring of ambient ozone. It is difficult to estimate whether
21 interferences in the UV method could be as high as suggested, in part because data are
i
22 lacking on the ambient levels of potential interferents. Many potential interferents are
23 photochemically reactive, and it is questionable whether such compounds could co-exist with
24 ozone in sufficient quantities to constitute a significant interference. What is clear is that full
25 evaluation of interferences in UV and ethylene chemiluminescence methods will require
26 simultaneous measurements of ozone, humidity, temperature, and speckled organic
27 compounds, and perhaps of other meteorological parameters and potential interferents.
28
29 3.5.1.1.4 Spectroscopie Methods for Ozone
30 Spectroscopie methods have the potential to provide direct, sensitive, and specific
31 measurements representative of broad areas, rather than of single monitoring sites. This
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1 potential has led to investigation of spectroscopic approaches, primarily differential optical
2 absorption spectrometry (DOAS), for ozone measurement. Differential optical absorption
3 spectrometry measures the absorption through an atmospheric path (typically 0.5 to 1.5 km)
4 of two closely spaced wavelengths of light from an artificial source. One wavelength is
5 chosen to match an absorption line of the compound of interest, and the other is close to but
6 off that line, and is used to account for atmospheric effects. Platt and Perner (1980) reported
7 measurements of several atmospheric species, including ozone, by DOAS, and various
8 investigators have applied the technique since then (Stevens et al., 1993, and references
9 therein). Stevens et al. (1993) describe testing of a commercial DOAS instrument in North
10 Carolina in the fall of 1989. Ozone was measured using wavelengths between 260 and
11 290 nm, over a 557-m path. A detection limit for ozone of 1.5 ppbv was reported, based on
12 a 1-min averaging time (Stevens et al., 1993). Comparison of DOAS results to those from a
13 UV absorption instrument showed (DOAS ozone) = 0.90 x (UV ozone) — 2.5 ppbv, with a
14 correlation coefficient (r2) of 0.89, at ozone levels up to 50 ppbv. The sensitivity, multiple
15 analytical capability, stability, and speed of response of the DOAS method are attractive,
16 though further intercomparisons and interference tests are recommended (Stevens et al.,
17 1993).
18
19 3.5,1.1.5 Passive Samplers for Ozone
20 A passive sampler is one that depends on diffusion of the analyte in air to a collecting
21 or indicating medium. In general, passive samplers are not adequate for compliance
22 monitoring purposes because of limitations in specificity and averaging time. However,
23 passive sampling devices (PSDs) for O3 are of value as a means of obtaining personal human
24 exposure data for O3 and as a means of obtaining long-term O3 measurements in areas where
25 the use of instrumental methods is not feasible. Estimation of long-term population exposure
26 and ecological monitoring for vegetation effects of ozone in remote areas are examples of me
27 latter application. Passive sampling devices have the advantages of simplicity, small size,
28 and low cost, but may also present disadvantages, such as poor precision, loss of
29 effectiveness during use or storage, and interference from other atmospheric constituents.
30 Passive samplers for measuring O3 at ambient concentrations are now commercially
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1 available. No PSD has been fully validated, however, to the point of acceptance as an
2 equivalent method for O3.
3 The Ogawa PSD for O3 (Ogawa, Inc., Pompano Beach, FL) contains 0.1 mL of a
4 solution of sodium nitrite and sodium carbonate in glycerine on glass fiber filter paper. The
5 nitrite ion reacts with O3 to form nitrate. Following exposure, the PSDs are analyzed by
6 extraction of the nitrate with deionized water, followed by ion chromatographic analysis.
7 In a comparative ambient O3 study over 24 weeks, this PSD demonstrated agreement within
8 about 10% with the weekly real-time measurements taken by a UV O3 monitor (Mulik et al.,
9 1991). Extension of these measurements to a full year produced similar results (Mulik et al.,
10 1991). The standard deviation of weekly average measurements by three collocated PSD
11 samplers ranged from about ±1 to ±6 ppb, at weekly average Oj levels of 12 to 45 ppb
12 (Mulik et al. 1991). The Ogawa PSD was also used in a study of personal exposure to
13 indoor and outdoor O3, showing a correlation of r = 0.91, and relative errors of 15 %
14 (daytime) and 25% (nighttime) relative to UV photometric data (Liu et al., 1992).
15 Another PSD for O3 has been developed that is based on the use of a colorant that
16 fades when exposed to O3 (Grosjean and Hisham, 1992; Grosjean and Williams, 1992). The
17 plastic badge-type PSD contains a diffusion barrier and a colorant-coated filter as the
18 O3 trap. The colorant used is indigo carmine (5,5'-disulfonate sodium salt of indigo, X max
19 = 608 nm). With a plastic grid or Teflon filter as the diffusion barrier, detection limits of
20 30 ppb-day and 120 ppb-day, respectively, are achieved. Interferences from NO2,
21 formaldehyde, and PAN are 15, 4, and 16%, respectively, of the ambient interferant
22 concentrations. For sampling ambient O3 in most locations, these interferences are probably
23 negligible (Grosjean and Hisham, 1992). Following sampling, the color change is measured
24 by reflectance spectroscopy and no chemical analysis is required. The reported shelf life is
25 3 mo prior to O3 exposure and 12 mo after 63 exposure (Grosjean and Hisham, 1992).
26 Field tests of the indigo carmine PSD were conducted at five forest locations in
27 California in the summer of 1990 (Grosjean and Williams, 1992). During these tests,
28 ambient ozone ranged up to 250 ppbv; 3-day average ozone values at the sites ranged from
29 40 to 88 ppbv. The precision of the measurements was ±12% based on 42 sets of collocated
30 samplers, over sampling durations of 3 to 30 days. The color change in the PSD was highly
31 correlated (R = 0.99) with ozone dose as measured by UV photometry. No effect of
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1 ambient temperature or humidity variations was observed, and the total interference caused
2 by other pollutants (NC^, PAN, aldehydes) was less than 5 %.
3 A third PSD for ozone has also been recently developed, based on color formation from
4 the reaction of O3 with an aromatic amine (Kirollos and Attar, 1991). The ChromoSense™
5 direct-read passive dosimeter is a credit-card-sized device that changes color proportionally to
6 the integrated dose of exposure of the specific toxic material for which it was designed (U.S.
7 Patent 4,772,560). The dosimeter consists of an outer polyester pouch that encloses a
8 polymeric plate with a sorbant and membrane. A filtering layer is coated on the membrane
9 to reduce the sensitivity of the detection process to nitrogen dioxide. The chromophoric
10 layer, consisting of an aromatic amine that can react with ozone and form color, is
11 encapsulated so as to create a very high surface area. A polymeric barrier separates the
12 chromophore from a UV-absorbing layer to reduce their interaction. The UV absorber (in a
13 polymeric matrix) helps stabilize the chromophore toward intense light exposure when the
14 device is used outdoors. The transparent polymeric plate keeps the wafer flat and allows
15 uninterrupted optical viewing of the color of the reference and the sample area.
16 An electronic reading device measures color on both the exposed (sample) and unexposed
17 (reference) areas, and displays a digital reading that is proportional to the log of the 03 dose.
18 Visible color is formed at doses as low as 20 ppb-h. No interference from NC^ is observed
19 at NO2 concentrations up to 350 ppb, and only a small effect of ambient humidity has been
20 reported (Kirollos and Attar, 1991). No data on precision have yet been reported.
21
22 3.5.1.1.6 Calibration Methods for Owm
23 Since it is an unstable molecule and cannot be stored, O3 must be generated at the time
24 of use to produce calibration mixtures. Electrical discharges in air or oxygen readily
25 produce O3, but at concentrations far too high for calibration of ambient monitors.
26 Radiochemical methods are expensive and require the use of radioactive sources, with
27 associated safety requirements. For calibration purposes, low levels of 63 are nearly always
28 generated by photolysis of oxygen at wavelengths < 200 nm. Placing a mercury lamp near a
29 quartz tube through which air is flowing produces small amounts of O3 in the airstream.
30 Commercial O3 sources based on this approach typically adjust the lamp current to control
31 the amount of light transmitted, and thus the Oj produced.
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1 Once a stable, low concentration of O3 has been produced from a photolytic generator,
2 that 03 output must be established by measurement with an absolute reference method. The
3 original reference calibration procedure promulgated by EPA in 1971 (Federal Register,
4 1971) was an iodometric procedure, employing 1 % aqueous neutral buffered potassium
5 iodide (NBKI). A large number of studies conducted in the 1970s revealed several
6 deficiencies with KI methods, the most notable of which were poor precision or
7 interlaboratory comparability and a positive bias of NBKI measurements relative to
8 simultaneous absolute UV absorption measurements.
9 Following investigations of problems with the NBKI method, EPA evaluated four
10 potential reference calibration procedures and selected UV photometry on the basis of
11 superior accuracy and precision and simplicity of use (Retime et al., 1981). In 1979, UV
12 photometry was designated the reference calibration procedure by EPA (Federal Register,
13 1979).
14 The measurement principle of UV O3 photometers used as reference standards is
15 identical to that of O3 photometers used for ambient measurements (see Section 3.5.1.1.3).
16 A laboratory photometer used as a reference standard will typically contain a long-path cell
17 (1 to 5 m) and employ sophisticated digital techniques for making effective double-beam
18 measurements of small absorbances at low O3 concentrations.
19 A primary reference standard is a UV photometer that meets the requirements set forth
20 in the 1979 revision designating UV photometry as the reference method (Federal Register,
21 1979). Commercially available O3 photometers that meet those requirements may function as
22 primary standards. The EPA and the National Institute of Standards and Technology (NIST,
23 formerly National Bureau of Standards [NBS]) have established a nationwide network of
24 Standard Reference Photometers (SRPs) that are used to verify local primary standards and
25 transfer standards. A secondary or transfer standard is a device or method that can be
26 calibrated against the primary standard, and then moved to another location for calibration of
27 03 monitors. Commercial UV photometers for Oj are often used as secondary or transfer
28 standards, as are commercial photolytic ozone generators and apparatus for the gas-phase
29 titration of O3 with nitric oxide (NO).
30 The latter method, gas-phase titration (GPT) of 03 with NO (NO + O3 -* NO2 + O2),
31 is a direct and absolute means of determining Oj, provided NO is in excess so that no side
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1 reactions occur. Under such conditions, GPT has the advantage that measurement of the NO
2 or O3 consumed or the NO2 produced gives a simultaneous measurement of the other two
3 species. All three modes have been used, and this method is often used for calibration of
4 NO/NOX analyzers. Gas-phase titration has been compared to UV photometry in several
5 studies. The most detailed study is that of Fried and Hodgeson (1982), who used an NBS
6 primary standard UV photometer, highly accurate flow measurements, photoacoustic
7 detection of NC^, and NBS (now NIST) Standard Reference Materials as sources of NO and
8 NO2, That study showed that decreases in O3 as measured by the UV method averaged
9 3.6% lower than the corresponding decrease in NO and increase in NO2 measured
10 independently. Because of uncertainty about the origin of the small bias relative to UV
11 photometry, GPT is used as a transfer standard but not as a primary reference standard.
12
13 3.5.1.2 Peroxyacetyl Nitrate and Its Homologues
14 During laboratory organic photooxidation studies, Stephens et al. (1956a,b) determined
15 the presence of a number of alkyl nitrates and an unidentified species called "Compound X".
16 The presence of "Compound X" in the atmosphere of Los Angeles was confirmed by Scott
17 et al, (1957). In later work (Stephens et al., 1961) its structure was determined and
18 "Compound X" was named peroxyacetyl nitrate (PAN). Since the discovery of PAN much
19 effort has been directed toward its atmospheric measurement. In the following subsections
20 PAN measurement and calibration techniques are described. The discussion on measurement
21 techniques includes a summary description, identifies limits of detection, specificity
22 (interferences), reproducibility and accuracy of each method. The relative merits of each
23 method are also presented. The subsection on calibration techniques includes those methods
24 most often employed during ambient air measurement studies.
25
26 3.5.1.2.1 Measurement Methods
21 Two methods have been generally employed to make atmospheric measurements of
28 PAN. These methods are infrared spectroscopy (IR) and gas chromatography (GC).
29 Infrared spectroscopy permits the sampling and analysis to be conducted in real time. Since
30 PAN is very reactive in the gas phase and exhibits surface adsorptive effects, the minimal
31 contact time offered by IR makes this method very attractive. However, IR instrumentation
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1 is expensive and complex and requires a good deal of space. On the other hand, gas
2 chromatography is inexpensive and requires minimal space and operator training. A PAN
3 GC can be set up to automatically sample and analyze air every 10 to 15 min. Application
4 of these methods for obtaining ambient concentrations of PAN has recently been reviewed by
5 Roberts (1990).
6
7 Infrared Spectroscopy. Conventional long-path infrared spectroscopy and FI1R have
8 been used to detect and measure atmospheric PAN. Sensitivity is enhanced by the use of
9 FUR. The most frequently used IR bands have been assigned and the absorptivities reported
10 in the literature (Stephens, 1964; Bruckmann and WiUner, 1983; Holdren and Spicer, 1984;
11 Niki et al., 1985; Tsatkani et al., 1989) permit the quantitative analysis of PAN without
12 calibration standards. Tuazon et al. (1978) have described an FTIR system operable at
13 pathlengths up to 2 km for ambient measurements of PAN and other trace constituents. This
14 system employed an eight-mirror multiple reflection cell with a 22.5-m base path. The
15 spectral windows available at pathlengths of 1 km were 760 to 1,300, 2,000 to 2,230 cm"1.
16 Thus, PAN could be detected by the bands at 793 and 1,162 cm"1. The 793 cm"1 band is
17 characteristic of peroxynitrates, while the 1,162 cm"1 band is reportedly caused by PAN only
18 (Stephens, 1969; Hanst et al., 1982), Tuazon et al. (I981a,b) reported ambient
19 measurements with this system during a smog episode in Claremont, California, in 1978.
20 Maximum daily PAN concentrations ranged from 6 to 37 ppb over a 5-day episode.
21 A detection limit for PAN was 3 ppb at a pathlength of ~ 1 kilometer. Hanst et al. (1982)
22 modified the FTIR system used by Tuazon by changing it from an eight-mirror to a
23 three-mirror cell configuration and by considerably reducing the cell volume. A detection
24 limit for PAN was increased to 1 ppb at a similar pathlength.
25 The limited sensitivity (-1 ppb) and the complexity of the above FITR systems have
26 generally limited their field use to urban areas such as Los Angeles. More recently,
27 cryogenic sampling and matrix-isolation FITR has been used to measure PAN in 15-L
28 integrated samples of ambient air. The matrix isolation technique has a theoretical level of
29 detection of -50 ppt (Griffith and Schuster, 1987).
30
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1 Gas Chromatography-Electron Capture Detection. Peroxyacetyl nitrate is normally
2 measured by using a gas chromatograph coupled to an electron capture detector (GC/ECD).
3 The method was originally described by Darley et al. (1963) and has subsequently been
4 refined and employed by scientists over the years. Key features of the method remain
5 unchanged. The column and detector temperatures are kept relatively low (—50 ° and
6 100 °C, respectively) to minimize PAN thermal decomposition. Short columns of either
7 glass or Teflon are generally used (1 to 5 ft in length). Finally, column packing normally
8 includes a Carbowax stationary phase coated onto a deactivated solid support. Using packed
9 columns, detection limits of 10 ppt have been reported using direct sampling with a 20-mL
10 sample loop (Vierkorn-Rudolph et al., 1985). Detection limits were further extended to 1 to
11 5 ppt using cryogenic enrichment of samples (Vierkorn-Rudolph et al., 1985; Singh and
12 Salas, 1983). These studies have found only slight overall losses of PAN (10 to 20%)
13 associated with cryosampling, provided samples are warmed only to room temperature during
14 desorption.
15 Recently, improved precision and sensitivity have been reported using fused-silica
16 capillary columns instead of packed columns (Helmig et al., 1989; Roberts et al., 1989).
17 Signal-to-noise enhancement of 20 has been claimed (Roberts et al., 1989).
18
19 Gas Chromatography-AUernate Detection. As noted earlier, PAN is readily reduced to
20 NO in the gas phase. To separate PAN, NO, and NO2, Meyrahn et al. (1987) have coupled
21 a GC with a molybdenum converter; and used a chemiluminescent analyzer to measure PAN
22 as NO. Using a 10 mL sample loop, a detection limit of 10 ppb was reported.
23 A luminol-based detector has also shown sensitivity to PAN. Burkhardt et al. (1988)
24 used gas chromatography and a commercially available luminol-based instrument (i.e.,
25 Scintrex LMA-3 Lummox) to detect both NO2 and PAN. Using a sampling interval of 40 s,
26 linear response was claimed from 0,2 to 170 ppb NO2 and from 1 to 65 ppb PAN. Although
27 the PAN calibration was nonlinear below 1 ppb, a detection level of 0.12 ppb was reported.
28 Drummond et al. (1989) have slightly modified the above approach by converting the PAN
29 from the GC column to NO2 and measuring the resulting NO2 with a luminol-based
30 instrument.
31
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1 3.5.1.2.2 Peroxyacetyl Nitrate Stability
2 Peroxyacetyl nitrate is an unstable gas and is subject to surface-related decomposition as
3 well as thermal instability. Peroxyacetyl nitrate exists in a temperature-sensitive equilibrium
4 with the peroxyacetyl radical and NO2 (Cox and Roffey, 1977). Increased temperature
5 favors the peroxyacetyl radical and NO2 at the expense of PAN. Added NO2 should force
6 the equilibrium toward PAN and enhance its stability. In the presence of NO, peroxyacetyl
7 radicals react rapidly to form NO2 and acetoxy radicals, which decompose in O2 to radicals
8 that also convert NO to NO2. As a result, the presence of NO acts to reduce PAN stability
9 and enhance its decay rate (Lonneman et al., 1982). Stephens (1969) reported that
10 appreciable PAN loss in a metal sampling valve was traced to decomposition on a silver-
11 soldered joint. Meyrahn et al. (1987) reported that PAN decayed according to first-order
12 kinetics at a rate of 2 to 4%/h in glass vessels and they suggested first-order decay as the
13 basis for a proposed method of in-field PAN calibration. In contrast, Holdren and Spicer
14 (1984) found that without NO2 added, 20 ppb PAN decayed in Tedlar bags according to
15 first- order kinetics at a rate of 40%/h. The addition of 100 ppb NO2 acted to stabilize the
16 PAN (20 ppb) in the Tedlar bags.
17 A humidity-related difference in GC-ECD response has been reported (Holdren and
18 Rasmussen, 1976). Low responses observed at humidities below 30% and PAN
19 concentrations of 10 and 100 ppb, but not 1,000 ppb, were attributed to sample-column
20 interactions, A humidity effect was alluded to by Nieboer and Van Ham (1976) but details
21 were not given. No humidity effect was observed by Lonneman (1977). Watanabe and
22 Stephens (1978) conducted experiments at 140 ppb and did not conclude that the reduced
23 response was from faults in the detector or the instrument. They concluded that there was no
24 column-related effect, and they observed surface-related sorption by PAN at 140 ppb in dry
25 acid-washed glass flasks. They recommended that moist air be used to prepare PAN
26 calibration mixtures to avoid potential surface-mediated effects.
27 Another surface-related effect has been reported for PAN analyses of remote marine air
28 (Singh and Viezee, 1988). Peroxyacetyl nitrate concentrations were found to increase by
29 20 to 170 ppt, an average factor of 3, when the sample was stored in a glass vessel for 1 to
30 2 min prior to analysis. This effect remains to be explained.
31
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1 3.5.1.2,3 Preparation and Calibration
2 Because PAN is unstable, the preparation of reliable calibration standards is difficult.
3 The more promising methods are described here. The original method used the photolysis of
4 ethyl nitrite in pure oxygen (Stephens, 1969). When pure PAN is desired, the reaction
5 mixture must be purified, usually by chromatography, to remove the major by-products,
6 acetaldehyde and methyl and ethyl nitrates (Stephens et al.s 1965). For GC calibration,
7 purification is unnecessary; the PAN concentration in the reactant matrix is established from
8 the IR absorption spectrum and subsequently diluted to the parts-per-billion working range
9 needed for calibration purposes (Stephens and Price, 1973).
10 Static mixtures of molecular chlorine, acetaldehyde, and NO2 in the ratio of 2:4:4 can
11 be photolyzed in the presence of a slight excess NO2 to give a near-stoichiometric yield of
12 PAN (Gay et al., 1976). This method was adapted by Singh and Salas (1983) and later by
13 Grosjean et al. (1984) using photolytic reactors to provide continuous PAN calibration units
14 at concentrations between 2 and 400 ppb. In the former approach, the PAN concentration is
IS established by measuring the change in acetaldehyde concentration across the reactor. In the
16 latter, the PAN concentration is established by measuring the acetate in an alkaline bubbler
17 where PAN is hydrolyzed.
18 A static technique involving the photolysis of acetone hi the presence of NO2 and air at
19 250 nm has been reported to produce a constant concentration of PAN (Meyrahn et al.,
20 1987; Wameck and Zerbach, 1992). A Penray mercury lamp is inserted into a mixture of
21 10 ppm NO2 and 1 % acetone and irradiated for 3 min to yield 8.9 ± 0.3 ppm PAN.
22 Peroxyacetyl nitrate can be synthesized in the condensed phase by the nitration of
23 peracetic acid in hexane (Helmig et al., 1989), heptane (Nielsen et al., 1982), octane
24 (Holdren and Spicer, 1984), or /z-tridecane (Gaffney et al., 1984). Purification of PAN in
25 the liquid phase is needed using the first two methods. The resulting PAN-organic solution
26 can be stored at -20 to -80 °C with losses of less than 3.6%/mo and can be injected directly
27 into a vessel containing air to produce a calibration mixture. The PAN concentration is
28 normally established by FTIR analysis of the solution or the resulting PAN-air mixture.
29 Peroxyacetyl nitrate readily disassociates to NO, and chemiluminescence NOX analyzers
30 have near-quantitative response to PAN. Thus under some circumstances, chemiluminescent
31 NOX response can be used for PAN calibration. One method uses the difference in NOX
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1 signal measured upstream and downstream of an alkaline bubbler (Grosjean and Harrison,
2 1985a). Joos et al. (1986) have coupled a chemiluminescence NOX analyzer with a GC
3 system to permit calibration of the BCD response by reference to the chemiluminescence
4 NOX analyzer that has been calibrated by traditional methods.
5 As noted previously, NO in the presence of PAN is converted to NO2. Approximately
6 four molecules of NO can react per molecule of PAN. Lonneman et al. (1982) devised a
7 PAN calibration procedure based on the reaction of PAN with NO in the presence of
8 benzaldehyde, which is added to control unwanted radical chemistry and improve precision.
9 Using this approach and an initial NO-to-PAN ratio between 10 and 20 to 1, the change in
10 NO concentration is monitored with a chemiluminescence NO analyzer, the change in PAN
11 GC-ECD response is monitored, and the resulting ratio (i.e., ANO/APAN) is divided by the
12 stoichiometric factor of 4.7 to arrive at & calibration factor for the BCD.
13 Peroxyacetyl nitrate and n-propyl nitrate (NPN) have similar BCD responses. Serial
14 dilution of the more stable compound, NPN, has been used for field operations
15 (Vierkorn-Rudolph et al., 1985). This approach is not recommended for primary calibration,
16 however, because it does not permit verification of quantitative delivery of PAN to the
17 detector (Stephens and Price, 1973).
18
19 3.5.1.3 Gaseous Hydrogen Peroxide
20 Although O3 has long been considered to be the primary oxidant affecting air quality,
21 atmospheric chemists recently have identified H2O2, a photochemical reaction product as
22 another oxidant that may also play a significant role in diminishing air quality. In order to
23 assess the role of atmospheric H2O2, good measurement methods are needed. Early
24 measurements in the 1970s reported H2O2 concentrations ranging from 10 to 180 ppb (Gay
25 and Bufalini, 1972 a,b; Kok et al., 1978 a,b). However, these measurements are in error
26 because of artifact formation of H2O2 from reactions of absorbed gaseous 03 (Zika and
27 Saltzman, 1982; Heikes et al., 1982, Heikes, 1984). Modeling results also indicate that
28 H2O2 atmospheric concentrations should be on the order of 1 ppb (Chameides and Tan,
29 1981; Logan et al., 1981).
30 In the following section, the discussion focuses on those sampling and analytical
31 methods most frequently used within the last decade to determine atmospheric levels of
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1 H2O2. The measurement techniques are described and limits of detection, specificity
2 (interferences) reproducibility and accuracy are discussed.
3
4 3.5.1.3.1 Measurement Methods
5 In-situ measurement methods that have been employed for determining gaseous H2O2
6 include both FUR and tunable diode laser absorption spectrometry (TDLAS). Four methods
7 involving sample collection via wet chemical means and subsequent analysis via
8 chemiluminescent or fluorescent detection have also been frequently used. These methods
9 are the (1) luminol; (2) peroxyoxalate; (3) enzyme-catalyzed (peroxidase); and (4) benzoic
10 acid-fenton reagent methods. Application of most of these methods for obtaining ambient
11 concentrations of H2O2 has recently been reviewed by Sakugawa et al. (1990) and Gunz and
12 Hoffmann (1990).
13
14 In-Situ Methods, Fourier transform infrared spectroscopy was employed in the early
15 1980's for atmospheric measurements (Tuazon et al., 1980; Hanst et al., 1982). Even
16 though the FTIR is very specific for H2O2, it saw limited use because of the high detection
17 level of -50 ppb when using a 1-km path length. The TDLAS also has very high specificity
18 for H2O2 and was subsequently evaluated and shown to have a much improved detection
19 limit of 0.1 ppb when using scan averaging times of several minutes (Slemr et al., 1986;
20 MacKay and Schiff, 1987; Schiff et al., 1987).
21
22 Wet Chemical Methods. Numerous wet chemical techniques for measuring H2O2 have
23 been reported. However, discussion in this section is limited to the four approaches most
24 frequently used by researchers.
25
26 Luminol Method. Hydrogen peroxide concentrations in the atmosphere have been
27 determined by the chemiluminescent response obtained from the catalyzed oxidation of
28 luminol (5-amino-2,3-dihydro-l,4-phthalazinedione) by H2O2. Copper (II) (Armstrong and
29 Humphreys, 1965; Kok et al., 1978 a,b; Das et al., 1982), as well as hem in, a blood
30 component (Yoshizumi et al., 1984), have been reported as catalysts for the luminol-based
31 H2O2 oxidation. Method sensitivity of —0.01 ppb has been achieved. Interference from
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1 O3, SO2, metal ions and high pH have been reported along with ways to mitigate these
2 effects (Heikes et al., 1982; Zika and Saltzman, 1982; Ibusuki, 1983; Lazaras et al., 1985;
3 Aoyanagi and Mitsushima, 1987; Hoshino and Hinze, 1987).
4
5 Peroxyoxalate Method. The peroxyoxalate chemiluminescence method has also been
6 employed by a number of researchers (Rauhut et al., 1967; Scott et al., 1980; Klockow and
7 Jacob, 1986). Hydrogen peroxide reacts with bis(2,4,5-trichloro-6-phenyl)-oxalate to form a
8 high-energy dioxetanedione (Stauff and Jaeschke, 1972). The chemiluminescenee is
9 transmitted to the fluorophore, perylene, which emits light upon return to the ground state.
10 Method sensitivity of —0.01 ppb is achieved and no interferences are observed from O3 and
11 metal ions. A signal depression has been reported for trace levels of nitrite (> 10* M),
12 sulfite (> 10"4 M), and formaldehyde (> 10"3 M) (Klockow and Jackob, 1986).
13
14 Enzyme-Catalyzed Method (Peroxidase). This general method involves three
15 components: a substrate that is oxidizable; the enzyme, horseradish peroxidase (HRP); and
16 hydrogen peroxide. The production or decay of the fluorescence intensity of the substrate or
17 reaction product is measured as it is oxidized by H2O2, catalyzed by HRP. Some of the
18 more widely used chromogenic substrates have been scopoletin (6-methoxy-7-hydroxy-l,2-
19 benzopyrone) (Andreae, 1955; Perschke and Broda, 1961); 3-(p-hydroxphenyl)propionic acid
20 (HPPA) (Zaitsu and Okhura, 1980); leuco crystal violet (LCV) (Mottola et al., 1970); and
21 p-hydroxyphenylacetic acid (POPHA) (Guilbault et al., 1968).
22 Of the chromogens used, POPHA is one of the better indicating substrates. Hydrogen
23 peroxide oxidizes the peroxidase and is itself reduced by electron transfer from POPHA.
24 The POPHA radicals form a dimer that is highly fluorescent. Since the chemical reaction is
25 sensitive to both H2O2 and organic peroxides, a dual channel system with a H2O2 removal
26 step (use of catalase) is used to distinguish H2O2 from organic peroxides (Lazarus et al.,
27 1985; Wei and Weihan, 1987; Dasgupta and Hwang, 1985; Kok et al., 1986).
28 The peroxidase-POPHA-fluorescence technique has been used by several groups to
29 measure gas-phase H2O2 concentrations (Lazarus et al., 1986; Tanner et al., 1986; Heikes
30 et al., 1987; Van Valin et al., 1987; Dasgupta et al., 1988). Method detection levels range
31 from 0.01 to 0.1 ppb. However, artifact formation does occur as a result of the reaction of
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1 dissolved O3 in the collection devices (Staehelin and Hoigne, 1982; Heikes, 1984; Gay et al,,
2 1988). To overcome the O3 interference researchers have used NO to eliminate 03 (Tanner,
3 1984; Tanner et al., 1986; Shen et al., 1988).
4
5 Fenton Reagent-Isomeric Hydroxybenzoic Acids Method. This technique involves the
6 formation of aqueous OH radicals from the reaction of Fenton reagent (Fe(n) complex) with
7 gaseous H2O2. The OH radicals in turn react with benzoic acid (hydroxyl radical scavenger)
8 to form isomeric hydroxybenzoic acids (OHBA). The OHBA fluoresces weakly at the pH
9 necessary to carry out the above reactions. Fluorescence is enhanced by adding NaOH to the
10 product stream (Lee et al., 1990) or by using a low pH Al(in) fluorescence enhancing
11 reagent (Lee et al., 1993).
12
13 3.5.1.3.2 Comparison of Methods
14 The above techniques have been shown to measure H2O2 in the atmosphere with
15 detection levels of «0.1 ppb. Kleindienst et al. (1988) have compared several of these
16 techniques using three sources of H2O2: (1) zero air in the presence and absence of common
17 interferences, (2) steady-state irradiations of hydrocarbon-NOx mixtures, and (3) ambient air.
18 The measurements were conducted simultaneously from a common manifold. For pure
19 samples in zero air, agreement within 23% was achieved among methods over a
20 concentration range of 0.06 to 128 ppb. A negative SO2 interference was caused with the
21 luminol technique. During the irradiation experiment, significant concentrations of organic
22 peroxides were generated and the agreement among techniques for H2O2 was very poor. For
23 ambient measurements, the methods agreed reasonably well with an average deviation of
24 30% from the mean values.
25 Atmospheric intercomparison studies have also been conducted as part of the Carbon
26 Species Methods Comparison Study (Calif, 1986). The results of the study indicated that the
27 wet chemical methods still suffer from sampling artifacts and interferences from other
28 atmospheric constituents (Dasgupta et al., 1990; MacKay et al., 1990; Kok et al., 1990;
29 Sakugawa et al., 1990; Tanner et al., 1990). It is clear from the above studies that further
30 comparisons of techniques are needed to resolve questions of errors and provide improved
31 measurement techniques.
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1 3.5.1.3.3 Calibration Methods
2 The most frequently used method for generating aqueous standards is simply the serial
3 dilution of commercial grade 30% H2O2/water. The dUute solutions of H2O2 as low as 10"4
4 have been found to be stable for several weeks if kept in the dark (Armstrong and
5 Humphreys, 1965). The stock H2O2 solution is standardized by iodometry (Allen et al.,
6 1952; Hochanadel, 1952; Cohen et al., 1967) or, more recently, by using standardized
7 permanganate solution (Lee et al., 1991).
8 Gaseous H2O2 standards are not as easily prepared and stability problems require use of
9 standard mixtures immediately. One method makes use of the injection of microliter
10 quantities of 30% H2O2 solution into a metered stream of air that flows into a Teflon bag.
11 The amount of H2O2 in the gas phase is determined by the iodometric titration method
12 (Cohen and Purcell, 1967). Gas-phase H2O2 standards have also been generated by
13 equilibrating N2 with an aqueous H2O2 solution of known concentration that is maintained at
14 constant temperature. Equilibrium vapor pressures and corresponding gas-phase
15 concentrations are calculated using Henry's law constant (Lee et al., 1991).
16
17 3.5.2 Sampling and Analysis of Volatile Organic Compounds
18 3.5.2.1 Introduction
19 The term volatile organic compounds (VOCs) generally refers to gaseous organic
20 compounds that have a vapor pressure greater than 0.15 mm and generally have a carbon
21 content ranging from Ct through C12. As discussed in Sections 3.2 and 3.4, VOCs are
22 emitted from a variety of sources and play a critical role in the photochemical formation of
23 03 in the atmosphere.
24 The U.S. Environmental Protection Agency (EPA) recently revised the ambient air
25 quality surveillance regulations in Title 40 Part 58 of the Code of Federal Regulations to
26 include, among other activities, the monitoring of volatile organic compounds. The revisions
27 require states to establish additional air monitoring stations as part of their existing State
28 Implementation Plan (SIP) monitoring networks. Authority for requiring the enhanced
29 monitoring is provided for in Title I, Section 182 of the Clean Air Act Amendments of 1990.
30 The term nonmethane organic compounds (NMOC) is also frequently used and refers to
31 a subset of VOCs, since it excludes the compound methane. Numerous sampling, analytical,
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1 and calibration methods have been employed to determine NMOCs in ambient air. Some of
2 the analytical methods utilize detection techniques that are highly selective and sensitive to
3 specific functional groups or atoms of a compound (e.g., formyl group of aldehydes,
4 halogen), while others respond in a more universal manner; that is, to the number of carbon
5 atoms present in the organic molecule. In this overview of the most pertinent measurement
6 methods, NMOC have been arranged in three major classifications: nonmethane
7 hydrocarbons (NMHC), carbonyl species, and polar volatile organic compounds (PVOCs).
8 Measurement and calibration procedures are discussed for each classification.
9
10 3.5.2.2 Nonmethane Hydrocarbons
11 Nonmethane hydrocarbons (NMHC) constitute the major portion of NMOC in ambient
12 air. Traditionally, NMHC have been measured by methods that employ a flame ionization
13 detector (FID) as the sensing element. This detector was originally developed for gas
14 chromatography and employs a sensitive electrometer that measures a change in ion intensity
15 resulting from the combustion of air containing organic compounds. Ion formation is
16 essentially proportional to the number of carbon atoms present in the organic molecule
17 (Sevcik, 1975). Thus, aliphatic, aromatic, alkenic, and acetylenic compounds aU respond
18 similarly to give relative responses of 1.00 ± 0.10 for each carbon atom present in the
19 molecule (e.g., 1 ppm hexane = 6 ppm C; 1 ppm benzene = 6 ppm C; 1 ppm propane =
20 3 ppm C). Carbon atoms bound to oxygen, nitrogen, or halogens give reduced relative
21 responses (Dietz, 1967). Consequently, the FID, which is primarily used as a hydrocarbon
22 measuring method, should more correctly be viewed as an organic carbon analyzer.
23 In the following sections, discussion focuses on the various methods utilizing this
24 detector to measure total nonmethane organics. Methods in which no compound speciation is
25 obtained are covered first. Methods for determining individual organic compounds are then
26 discussed.
27
28 3.5.2,2,1 Nonspeciation Measurement Methods
29 The original EPA reference method for nonmethane organic compounds, which was
30 promulgated in 1971, involves the gas chromatographic separation of methane (CH^ from
31 the remaining organics in an air sample (Federal Register, 1971). A second sample is
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1 injected directly to the flame ionization detector without methane separation. Subtraction of
2 the first value from the second produces a nonmethane organic concentration.
3 A number of studies of commercial analyzers employing the Federal Reference Method
4 have been reported (Reckner, 1974; McElroy and Thompson, 1975; Harrison et al., 1977;
5 Sexton et al., 1982). These studies indicated an overall poor performance of the commercial
6 instruments when either calibration or ambient mixtures containing NMOC concentrations
7 less than 1 ppm C were used. The major problems associated with using these NMOC
8 instruments have been reported in an EPA technical assistance document (U.S.
9 Environmental Protection Agency, 1981). The technical assistance document also suggests
10 ways to reduce the effects of existing problems. Other nonspeciation approaches to the
11 measurement of nonmethane organics have also been investigated. These approaches have
12 been discussed in the 1986 EPA air quality criteria document (U.S. Environmental Protection
13 Agency, 1986). Again, these approaches are also subject to the same shortcomings as the
14 EPA reference method (i.e., poor performance below 1 ppm C of NMHC).
15 More recently, a method has been developed for measuring NMOC directly and
16 involves the cryogenic preconcentration of nonmethane organic compounds and the
17 measurement of the revolatilized NMOCs using flame ionization detection (Cox et al., 1982;
18 Jayanty et al., 1982). This methodology has been formalized and is referred to as Method
19 TO-12 and is published in a compendium of methods for air toxics (Winberry et al., 1988).
20 The EPA recommends this methodology for measuring total NMOC and has incorporated it
21 into tiie Technical Assistance Document far SampUng and Analysis of Ozone Precursors
22 (U.S. Environmental Protection Agency, 1991).
23 A brief summary of the method is as follows, A whole air sample is drawn through a
24 glass bead trap that is cooled to approximately —185 °C using liquid argon. The cryogenic
25 trap collects and concentrates the NMOC, while allowing the methane, nitrogen, oxygen,
26 etc., to pass through the trap without retention. After a known volume of air has been
27 drawn through the trap, carrier gas is diverted to the trap first to remove residual air and
28 methane. When the residual gases have been flushed from the trap, the cryogen is removed
29 and the temperature of the trap is ramped to approximately 100 °C. The revolatilized
30 compounds pass directly to a flame ionization detector (no analytical column). The
31 corresponding signal is integrated over time (several minutes) to obtain a total FID response
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1 from the NMOC species. Water vapor, which is also preconcentrated, causes a positive shift
2 in the FID signal. The effect of this shift is minimized by optimizing the peak integration
3 parameters.
4 The sensitivity and precision of Method TO-12 are proportional to the sample volume.
5 However, ice formation in the trap limits sampling volumes of «>500 cc. The detection level
6 is 0.02 ppm C (with a signal-to-noise ratio, S/N, of 3) and the precision at 1 ppm C and
7 above has been determined to be S 5 %. The instrument response has been shown to be
8 linear over a range of 0 to 10 ppm C. Propane gas certified by the National Institute of
9 Standards and Technology (NISI) is normally used as the calibrant. Accuracy at the method
10 quantitation level (S/N = 10) is ±20%.
11
12 3.5.2,2.2 Speciation Measurement Methods
13 The primary measurement technique utilized for NMOC speciation is gas
14 chromatography (GC). Coupled with flame ionization detection, this analytical method
15 permits the separation and identification of many of the organic species present in ambient
16 air.
17 Separation of compounds is accomplished by means of both packed and capillary GC
18 columns. If high resolution is not required and large sample volumes are to be injected,
19 packed columns are employed. The traditional packed column may contain either (1) a solid
20 polymeric adsorbent (gas-solid chromatography) or (2) an inert support, coated with a liquid
21 (gas-liquid chromatography). Packed columns containing an adsorbent substrate are normally
22 required to separate C2 and C3 compounds. The second type of column can be a support-
23 coated or wall-coated open tubular capillary column. The latter column has been widely
24 used for environmental analysis because of its superior resolution and broader applicability.
25 The wall-coated capillary column consists of a liquid stationary phase coated or bonded
26 (cross-linked) to the specially treated glass or fused-silica tubing. Fused-silica tubing is most
27 commonly used because of its physical durability and flexibility. When a complex mixture is
28 introduced into a GC column, the carrier gas (mobile phase) moves the sample through the
29 packed or coated capillary column (stationary phase). The chromatographic process occurs
30 as a result of repeated sorption-desorption of the sample components (solute) as they move
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1 along the stationary phase. Separation occurs as a result of the different affinities that the
2 solute components have for the stationary phase.
3 As described in the previous ozone criteria document (U.S. Environmental Protection
4 Agency, 1986), the GC-FID technique has been used by numerous researchers to obtain
5 ambient NMOC data. Singh (1980) drew on the cumulative experience of these researchers
6 to prepare a guidance document for state and local air pollution agencies interested in
7 obtaining speciation data. In general, most researchers have employed two gas
8 chromatographic units to carry out analyses of NMOC species in ambient air. The more
9 volatile organic compounds (C2 through C5) are generally measured on one unit using
10 packed-column technology, while the other GC separates the less volatile organics using a
11 capillary column. In typical chromatograms of urban air, all major peaks are identified and,
12 on a mass basis, represent from 65 to 90% of the measurable nonmethane organic burden.
13 Identification of GC peaks is based upon matching retention times of unknown
14 compounds with those of standard mixtures. Subsequent verification of the individual species
15 is normally accomplished with gas chromatographic-mass spectrometric (GC-MS) techniques.
16 Compound-specific detection systems, such as electron capture, flame photometry, and
17 spectroscopic techniques, have also been employed to confirm peak identifications. The peak
18 matching process is far from being a trivial task. Ambient air chromatograms are often very
19 complex (> 200 peaks/run) and require a good deal of manual labor to assure that the peak
20 matching process is being carried out correctly by the resident peak identification/
21 quantification software. Efforts to improve upon the accuracy of peak assignment and
22 diminish the labor hours normally associated with the objective have recently been reported.
23 Silvestre et al. (1988) developed an off-line spreadsheet program that is menu-driven and
24 used to identify and edit a chromatogram containing 200 peaks within 15 minutes. The
25 accuracy of peak assignment was typically better than 95%. Mason et al. (1992) developed a
26 novel algorithm, which is embedded within the Harwell MatchFinder software package, and
27 have demonstrated its potential for enhancing peak identification in complex chromatograms.
28 The authors indicate that the software could be used to batch process large volumes of
29 chromatographic data. A commercial software package from Meta Four Software, Inc., was
30 recently employed during the Atlanta Ozone Precursor Monitoring Study to batch process
31 chromatographic data from over 6,000 GC runs (Purdue et al., 1992). This software was
December 1993 3-142 DRAFT-DO NOT QUOTE OR CITE
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1 also used to validate peak identities from two GC databases and was shown to improve peak
2 identities from the originally processed data by 10 to 20% (Holdren et al,, 1993).
3 Because the organic components of the ambient atmosphere are present at parts-per-
4 billion levels or lower, sample preconcentration is necessary to provide sufficient material for
5 the GC-FDD system. The two primary techniques utilized for this purpose are the use of
6 solid adsorbents and cryogenic collection. The more commonly used sorbent materials are
7 divided into three categories: (1) organic polymeric adsorbents, (2) inorganic adsorbents,
8 and (3) carbon adsorbents. Primary organic polymeric adsorbents used for NMOC analyses
9 include the materials Tenax®~GC and XAD-2®. These materials have a low retention of
10 water vapor and, hence, large volumes of air can be collected. These materials do not,
1 1 however, efficiently capture highly volatile compounds such as C^. to ^5 hydrocarbons, nor
12 certain polar compounds such as methanol and acetone. Primary inorganic adsorbents are
13 silica gel, alumina, and molecular sieves. These materials are more polar than the organic
14 polymeric adsorbents and are thus more efficient for the collection of the more volatile and
15 polar compounds. Unfortunately, water is also efficiently collected, which in many instances
16 leads to rapid deactivation of the adsorbent. Carbon adsorbents are less polar than the
17 inorganic adsorbents and, as a result, water adsorption by carbon adsorbents is a less
18 significant problem. The carbon-based materials also tend to exhibit much stronger
19 adsorption properties than organic polymeric adsorbents; thus, lighter-molecular-weight
20 species are more easily retained. These same adsorption effects result, however, in
21 irreversible adsorption of many compounds. Furthermore, the very high thermal desorption
22 temperatures required (350 to 400 °C) limit their use and also may lead to degradation of
23 labile compounds. The commonly available classes of carbon adsorbents include:
24 (1) various conventional activated carbons; (2) carbon molecular sieves (Spherocarb®,
25 Carbosphere®, Carbosieve®); and (3) carbonaceous polymeric adsorbents (Ambersorb®
26 XE-340, XE-347, SE-348).
27 Although a number of researchers have employed solid adsorbents for the
28 characterization of selected organic species in air, only a few attempts have been made to
29 identify and quantitate the range of organic compounds from C^ and above. Westberg et al.
30 (1980) evaluated several carbon and organic polymeric adsorbents and found that Tenax®-GC
31 exhibited good collection and recovery efficiencies for sC6 organics; the remaining
r»r» xTrvr rvnrvrTJ r\o
-------
1 adsorbents tested (XAD-4®, XE-340®) were found unacceptable for the lighter organic
2 fraction. The XAD-4® retained ^ C2 organic gases, but it was impossible to desorb these
3 species completely without partially decomposing the XAD-4®. Good collection and
4 recovery efficiencies were provided by XE-340® only for organics of C4 and above. Ogle
5 et al. (1982) used a combination of adsorbents in series and designed an automated GC-FID
6 system for analyzing €2 through C10 hydrocarbons. Tenax-GC® was utilized for Cg and
7 above; whereas Carbosieve S® trapped C3 through €5 organics. Silica gel followed these
8 adsorbents and effectively removed water vapor while passing the C2 hydrocarbons onto a
9 molecular-sieve 5A adsorbent. More recently, Levaggi et al. (1992) have used a
10 combination of adsorbents in series for analyzing C2 through C10 hydrocarbons. Tenax GR,
11 Carbotrap, and Carbosieve S-ffl were evaluated. At room temperature collection, excellent
12 recovery efficiencies were obtained for all species except acetylene (breakthrough begins
13 after 220 cc). Smith et al. (1991) evaluated a commercially available GC system
14 (Chrompack, Inc.) and found that a Carbotrap C, Carbopack B, and Carbosieve S-ffl
15 combination was effective for all C2 and above species if the trap temperature was
16 maintained at —30 °C during collection (600 cc). The above researchers also caution that
17 artifact peaks do occur during thermal desorption and recommend closely screening the
18 resulting data.
19 The preferred method for obtaining NMOC data is cryogenic preconcentration (Singh,
20 1980). Sample preconcentration is accomplished by directing air through a packed trap
21 immersed in either liquid oxygen (b.p. -183 °C) or liquid argon (b.p. -186 °C). For the
22 detection of about 1 ppb C of an individual compound, a 250-cc air sample is normally
23 processed. The collection trap is generally filled with deactivated 60/80 mesh glass beads
24 (Westberg et al., 1974), although coated chromatographic supports have also been used
25 (Lonneman et al., 1974). Both of the above cryogens are sufficiently warm to allow air to
26 pass completely through the trap, yet cold enough to collect trace organics efficiently. The
27 use of cryogenic preconcentration for collection of volatile organic compounds in general was
28 automated to allow sequential hourly updates of gas chromatographic data (McClenny et al.,
29 1984), leading to the initial configuration of what are now referred to as "auto GCs" for
30 ozone precursor monitoring. The cryogenic collection procedure also condenses water
31 vapor. An air volume of 250 cc at 50% relative humidity and 25 °C contains approximately
December 1993 3-144 DRAFT-DO NOT QUOTE OR CITE
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1 2.5 rag of water, which appears as ice in the collection trap. The collected ice at times will
2 plug the trap and stop the sample flow; furthermore, water transferred to the capillary
3 column during the thermal desorption step occasionally causes plugging and other deleterious
4 column effects. To circumvent water condensation problems, Pleil et al. (1987) have
5 characterized the use of a Nafion® tube drying device to remove water vapor selectively
6 during the sample collection step. Although hydrocarbon species are not affected, polar
7 organics are partially removed when the drying device is used. Burns et al. (1983) also
8 showed that partial loss or rearrangement of monoterpenes, or both (e.g., a-pinene,
9 limonene), occur when the Nafion® tube is used to reduce water vapor.
10 The EPA has recently provided technical guidance for measuring volatile organic
11 compounds that is based on the above studies as well as emerging and developing technology
12 (U.S. Environmental Protection Agency, 1991). Guidance for the use of automated gas
13 chromatography sampling and analysis for VOCs has been derived from experience gained
14 from application of this technology during an ozone precursor study conducted by the EPA in
15 Atlanta, GA, during the summer of 1990 (Purdue et al., 1992). For that study, an
16 automated GC system developed and manufactured by Chrompack, Inc., and modified for
17 ozone percursor monitoring (McClenny et al., 1991) was used to obtain hourly VOC
18 measurements. The GC system was equipped with a preconcentration adsorption trap, a
19 cryofocusing secondary trap, and a single analytical column. The study was focused on the
20 identification and quantitation of 55 ozone precursor compounds, and resulted in accounting
21 for 65 to 80 % of the total NMOC mass. Sample volumes of 600 cc were used and a
22 detection level of 0.1 ppb C was reported. External auditing indicated accuracy of ±30% at
23 challenge concentrations of 2 ppb C (17-component audit mixture).
24 The study also revealed several weaknesses. First of all, excessive amounts of liquid
25 cryogen were consumed in carrying out the measurements. The inferior quality of the
26 cryogen containers and poor delivery schedules resulted in reduced data capture. Secondly,
27 because of the single-column approach, numerous target species either co-eluted or were
28 poorly resolved. Finally, several significant artifact peaks co-eluted with the target species
29 and therefore biased the reported concentrations of those species as well as the total NMOC
30 (by summation of peaks).
December 1993 l-HW DRAFT-DO NOT nirOTR OB PITR
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1 Based upon these deficiencies, the EPA has challenged commercial GC instrument
2 makers with improving the current state of the art. One result has been the evolution of
3 systems that require no liquid cryogen for operation, yet provide sufficient gas
4 chromatographic resolution of target species (McClenny, 1993; Holdren et al., 1993).
5 A recent comparison study of automated gas chromatographs at Research Triangle Park with
6 five participating vendors has indicated that the newer auto GC designs use cryogens more
7 efficiently (Purdue, 1993).
8 In addition to direct sampling via preconcentration with sorbents and cryogenic
9 techniques, collection of whole air samples is frequently used to obtain NMOC data. Rigid
10 devices such as syringes, glass bulbs, or metal containers and non-rigid devices such as
11 Tedlar* and Teflon® plastic bags are often utilized during sampling. The primary purpose of
12 whole-air collection is to store an air sample temporarily until subsequent laboratory analysis
13 is performed. The major problem with this approach is assuring the integrity of the sample
14 contents prior to analysis. The advantages and disadvantages of the whole air collection
15 devices have been previously summarized in the 1986 air quality criteria document (U.S.
16 Environmental Protection Agency, 1986).
17 The canister-based method is the preferred means for collecting VOCs and is described
18 as part of the "EPA Compendium of Methods for the Determination of Toxic Organic
19 Compounds in Ambient Air" (Compendium Method TO-14). McClenny et al. (1991)
20 recently reviewed the canister-based method and have discussed basic facts about the
21 canisters, described canister cleaning procedures, contrasted the canister collection system
22 versus solid adsorbents, and discussed the storage stability of VOCs in canisters. Although
23 storage stability studies have indicated that many target VOCs can be stored with good
24 integrity over time periods of at least 7 days, there are still many VOCs for which there are
25 no stability data (Pate et al., 1992; Oliver et al., 1986; Holdren et al., 1987; Westberg et al.,
26 1981; Gholson et al., 1990; Westberg et al., 1984). Coutant (1993) has developed a
27 computer-based model for predicting adsorption behavior and vapor-phase losses in
28 multicomponent systems, based on the potential for physical adsorption as well as the
29 potential for dissolution in condensed water for canister samples collected at high humidities,
30 At present, the database for the model contains relevant physicochemical data for
December 1993 3-146 DRAFT-DO NOT QUOTE OR CITE
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1 78 compounds (including water) and provisions for inclusion of up to 120 additional
2 compounds are incorporated in the software,
3
4 3.5.2.2.3 Calibration Methods
5 Calibration procedures for NMOC instrumentation require the generation of dilute
6 mixtures at concentrations expected to be found in ambient air. Methods for generating such
7 mixtures are classified as static or dynamic systems.
8 As described in the previous ozone criteria document (U.S. Environmental Projection
9 Agency, 1986), static systems are generally preferred for quantitating NMOCs. The most
10 commonly used static system is a compressed gas cylinder containing the appropriate
1 1 concentration of the compound of interest. These cylinder gases may also be diluted with
12 hydrocarbon-free air to provide multi-point calibrations. Cylinders of calibration gases and
13 hydrocarbon-free air are available commercially. Also, some standard gases such as propane
14 and benzene, as well as a 17-component ppb mixture, are available from the National
15 Institute of Standards and Technology (NIST) as certified standard reference materials
16 (SRM). Commercial mixtures are generally referenced against these NIST standards. In its
17 recent technical assistance document for sampling and analysis of ozone precursors, EPA
18 recommended propane (or benzene)-in-air standards for calibration (U.S. Environmental
19 Protection Agency, 1991). Some commercially available propane cylinders have been found
20 to contain other hydrocarbons (Cox et al., 1982), so that all calibration data should be
21 referenced to NIST standards.
22 Because of the uniform carbon response of a GC-FID system (±10%) to hydrocarbons
23 (Dietz, 1967), a common response factor is assigned to both identified and unknown
24 compounds obtained from the speciation systems. If these compounds are oxygenated
25 species, an underestimation of the actual concentrations will be reported. Dynamic
26 calibration systems are employed when better accuracy is needed for these oxygenated
27 hydrocarbon species. Dynamic systems are normally employed to generate in situ
28 concentrations of the individual compound of concern and include devices such as permeation
29 and diffusion tubes and syringe delivery systems.
30
31
December 1993 1.1 47 DRAFT-DO NOT niTOTP nw
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1 3.5,2.3 Carfoonyl Species
2 Historically, the major problem in measuring concentrations of carbonyls in ambient air
3 has been to find an appropriate monitoring technique that is sensitive to low concentrations
4 and specific for the various homologues. Early techniques for measuring formaldehyde, the
5 most abundant aldehyde, were subject to many interferences and lacked sensitivity at low
6 parts-per-billion concentrations. The 1986 air quality criteria document described two
7 methods frequently used: the chromotropic acid (CA) method for formaldehyde and the
8 3-methyl-2-benzothiazolone hydrazone (MBTH) technique for total aldehydes (U.S.
9 Environmental Protection Agency, 1986). However, spectroscopic methods, on-line
10 colorimetric methods, and the high-performance liquid chromatography (HPLC) method
11 employing 2,4-dinitrophenylhydrazine (DNPH) derivatization are the preferred methods
12 currently used for measuring atmospheric levels of carbonyl species.
13
14 3.5.2.3.1 Spectroscopic Methods
15 Three spectroscopic methods have been used to make measurements for atmospheric
16 levels of formaldehyde and were recently intercompared at an urban site in California
17 (Lawson et al., 1990). The Fourier Transform Infrared Spectroscopy (FUR) method used
18 gold-coated 30-cm-diameter mirrors and a total optical path of 1,150 m. The 2781.0 cm"
19 "Q-branch" adsorption peak was used to measure HCHO. The limit of detection was 3 ppb,
20 and the measurement errors were within ±3 ppb. The Differential Optical Absorption
21 Spectroscopy (DOAS) method was operated at an 800-m pathlength, and an absorption peak
22 at 339 nm was used to measure HCHO; NO^ and HONO spectral features were subtracted.
23 The limit of detection was 4.5 ppb; the experimental error was ±30%. A Tunable Diode
24 Laser Absorption Spectroscopy (TOLAS) method was operated at a pathlength of 150 m.
25 Laser diodes were mounted in a closed-cycle helium cryocooler with a stabilizing heater
26 circuit for constant temperature control. Radiation from the diode was collected and focused
27 into the sampling by reflective optics. Formaldehyde absorption was measured at
28 1,740 cm"1. The limit of detection was 0.1 ppb and the measurement errors were within
29 ±20 %. Additional information on F1TR and DOAS has been reported by Winer et al.,
30 1987; Atkinson et al., 1988; and Biermann et al., 1988. A more complete description of
31 TOLAS is given by MacKay et al., 1987.
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1 3.5.2,3.2 On-line Colorimetric Method
2 A wet chemical method based upon the derivatization of HCHO in aqueous solution to
3 form a fluorescent product was recently developed by Kelly et al. (1990). The detection of
4 fluorescent product was made more sensitive by using intense 254 nm light from a mercury
5 lamp for excitation. This procedure allowed the use of a simple and efficient glass coil
6 scrubber for collection of gaseous HCHO. A detection limit of 0.2 ppb was obtained when
7 using a response time of 1 min. The instrument is portable and highly selective for HCHO.
8
9 3.5.2.3.3 High-Performance Liquid ChromcUography-2,4~DinUrophenylhydra&ne Method
10 The preferred and most current method for measuring aldehydes in ambient air is one
11 involving derivatization of the aldehydes concurrent with sample collection, followed by
12 analysis using high-performance liquid chromatography (HPLC). This method takes
13 advantage of the reaction of carbonyl compounds with 2,4-dinitrophenylhydraaane (DNPH) to
14 form a 2,4-dinitrophenylhydrazone:
15
RR'C=O + NHyNHC^CNO^ -» Hp + RR'C=NNHC6H3(NO2)2 (3-82)
16
17 Because DNPH is a weak nucleophile, the reaction is carried out in the presence of acid in
18 order to increase protonation of the carbonyl.
19 In this method, atmospheric sampling was initially conducted with micro-impingers
20 containing an organic solvent and aqueous, acidified DNPH reagent (Papa and Turner, 1972;
21 Katz, 1976; Smith and Drummond, 1979; Fung and Grosjean, 1981). After sampling was
22 completed, the hydrazone derivatives were extracted and the extract was washed with
23 deionized water to remove the remaining acid and unreacted DNPH reagent. The organic
24 layer was then evaporated to dryness, subsequently dissolved in a small volume of solvent,
25 and analyzed by reversed-phase liquid chromatographic techniques employing an ultraviolet
26 (UV) detection system (360 nm).
27 An unproved procedure was subsequently reported that is much simpler than the above
28 aqueous impinger method (Lipari and Swarin, 1982; Kuntz et al., 1980; Tanner and Meng,
29 1984). This scheme utilizes a midget impinger containing an acetonitrile solution of DNPH
30 and an acid catalyst. After sampling, an aliquot of the original collection solution is directly
December 1993 3-149 DRAFT-DO NOT QUOTE OR TTTR
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1 injected into the liquid chromatograph. This approach eliminates the extraction step and
2 several sample-handling procedures associated with the DNPH-aqueous solution; and
3 provides much better recovery efficiencies. This method has been formalized by the EPA as
4 Compendium Method TO-5 (Winberry et al,, 1988). The TO-5 Method has been further
5 modified to include the use of DNPH-impregnated solid adsorbent rather than DNPH
6 impinger solutions as the collection medium. This modification and associated sampling
7 conditions are referred to as EPA Method TOIL The methodology can be easily used for
8 long-term (1 to 24 h) sampling of ambient air. Sampling rates of 500 to 1,200 cc/min can be
9 achieved and detection levels of 1 ppbV can be attained with sampled volumes of 100 L.
10 The method currently calls for the use of SepPak® silica gel material as the sorbent material.
11 However, researchers have noted that O3 present in ambient air reacted more easily with
12 carbonyl compounds collected on DNPH-coated silica gel cartridges than on DNPH-coated
13 C18 bonded silica material. To eliminate this interference problem, these researchers used an
14 ozone scrubber (Arnts et al., 1989). The TO11 Method has been included in EPA's
15 Technical Assistance Document for SampUng and Analysis of Ozone Precursors (U.S.
16 Environmental Protection Agency, 1991).
17
18 3.5.2.3.4 Calibration of Carbonyl Measurements
19 Because they are reactive compounds, it is extremely difficult to make stable calibration
20 mixtures of carbonyl species in pressurized gas cylinders. Although gas-phase standards are
21 available commercially, the vendors do not guarantee long-term stability and accuracy.
22 Formaldehyde standards are generally prepared by one of several methods. The first
23 method utilizes dilute commercial formalin (37% HCHO, w/w). Calibration is accomplished
24 by the direct spiking into sampling impingers of the diluted mixture or by evaporation into
25 known test volumes, followed by impinger collection. Formaldehyde can also be prepared
26 by heating known amounts of paraformaldehyde, passing the effluent gases through a
27 methanol-liquid nitrogen slush trap to remove impurities, and collecting the remaining
28 HCHO. Paraformaldehyde permeation tubes have also been used (Tanner and Meng, 1984).
29 For the higher-molecular-weight carbonyl species, liquid solutions can be evaporated or
30 pure vapor can be generated in dynamic gas-flow systems (permeation tubes, diffusion tubes,
31 syringe delivery systems, etc.). These test atmospheres are then passed through the
December 1993 3-150 DRAFT-DO NOT QUOTE OR CITE
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1 appropriate collection system and analyzed A comparison of these data, with the direct
2 spiking of liquid carbonyl species into the particular collection system, provides a measure of
3 the overall collection efficiency.
4
5 3.5.2.4 Polar Volatile Organic Compounds
6 The VOCs discussed earlier in this chapter (Section 3.5.2.2) have included aliphatic,
7 aromatic, alkenic, and acetylenic hydrocarbons. These compounds are relatively nonpolai,
8 nonreactive species, and measurement methods have been easily applied in determining
9 ambient concentrations.
10 Recently, attention has also been directed toward the more reactive oxygen- and
11 nitrogen-containing organic compoundss in part by the inclusion of many of these compounds
12 on a list of 189 hazardous air pollutants specified in the 1990 Clean Air Act Amendments.
13 Many of these compounds are directly emitted from a variety of industrial processes, mobile
14 sources, and consumer products, and some are also formed in the atmosphere by
15 photochemical oxidation of hydrocarbons. However, as indicated earlier in this document,
16 very few ambient data exist for these species. These compounds have been collectively
17 referred to as polar VOCs (PVOCs), although it is their reactivity and water solubility, more
18 than simple polarity, that make their measurement difficult with existing methodology.
19 Two approaches have been utilized in developing analytical methods for PVOCs. One
20 approach has incorporated the use of cryogenic trapping techniques similar to those discussed
21 earlier for the nonpolar hydrocarbon species; the second approach has utilized adsorbent
22 material for sample preconcentration. To be effective for sensitive ppb measurement of
23 PVOCs, both approaches require some type of water management system to mitigate the
24 adverse effects that water has on the chromatography and detector sensitivity and reliability.
25 Several researchers have reported the use of cryogenic trapping with two-dimensional
26 chromatography to selectively remove water vapor from the analytical process (Pierotti,
27 1990; Caidin and Lin, 1991). Although this column "heart cutting" technique has been
28 successful for selected compounds, additional studies are needed to determine its potential
29 use for the wide range of PVOCs. Ogle et al. (1992) developed a novel water management
30 system based upon the condensation of moisture from the saturated carrier gas stream during
31 thermal desorption of a cryogenic trap. The moisture management system was found to be
32 effective for reducing the amount of water delivered to the column during laboratory analyses
December 1993 3-151 DRAFT-DO NOT OUOTK OR PTTR
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1 of spiked mixtures. However, the system has not yet been extended to field monitoring.
2 Gordon et al. (1989) have used cryogenic trapping and gas chromatography/mass
3 spectrometry techniques to demonstrate the potential of chemical ionization (CI) within an ion
4 trap to detect PVOCs. The water vapor present in the sample served as the CI reagent gas
5 and appeared to be an effective reagent gas; however, deleterious chroinatography results
6 were also encountered. The authors concluded that further laboratory work is needed before
7 this methodology can be applied to ambient air monitoring. Martin et al. (1991) also
8 reported the use of cryogenic trapping with a GC-FID system to measure ambient levels of
9 isoprene and two of its oxidation products, methacrolein and methylvinyl ketone (detection
10 level of 0.5 ppb). The water vapor was selectively removed by using a potassium carbonate
11 (I^COj) trap ahead of the cryogenic trap. Frequent replacement of the K2C03 trap was
12 required.
13 The use of solid adsorbents for sample preconcentration of PVOCs has been reported
14 by Kelly et al. (1993). The analytical method was used extensively at two field sites that
15 were formerly used in EPA's Toxic Air Monitoring Study (TAMS). The analytical method
16 consisted of gas chromatographic separation of PVOCs with quantification by a ion trap mass
17 spectrometer. A two-stage adsorbent trap containing Carbopack B and Carbosieve S-ffl
18 (Supelco catalog number 2-0321) was used to separate water vapor from the PVOCs. The
19 optimum room temperature trapping and drying procedure consisted of a 320-cc sample
20 (100 cc/min) followed by a dry nitrogen purge of 1,300 cc (100 cc/min). The trap was then
21 backflushed and thermally desorbed with helium at 220 °C. A 5-min 260 °C trap bakeout
22 followed each collection-analysis cycle. The target list contained 14 PVOCs, including
23 alcohols, ethers, esters, and nitrile species. Individual detection limits ranged from 0.2 to
24 1 ppb.
25
26 3.5.3 Sampling and Analysis of Oxides of Nitrogen
27 3.5.3.1 Introduction
28 The measurement of oxides of nitrogen in ambient air is of interest because of the role
29 that certain of those compounds play as precursors to ozone and because nitrogen dioxide
30 (NO2) has been shown to elicit health effects. The primary nitrogen oxides emitted from
31 combustion sources are nitric oxide (NO) and nitrogen dioxide (NOj). Collectively these
December 1993 3-152 DRAFT-DO NOT QUOTE OR CITE
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1 two compounds are called NOX, They contribute to ozone formation by means of reactions
2 discussed in Section 3.2. As a result, measurement of NOX is important in efforts to
3 understand and control ozone and NO2 in ambient air.
4 The atmospheric photochemistry that produces ozone also results in conversion of NO
5 and NO2 to products such as nitric acid (HNO3), nitrous acid (HONO), peroxyacetyl nitrate
6 (CH3C(O)O2NO2; PAN), organic nitrates, and other species. The total of all of these labile
7 nitrogen species in air, NOX included, is termed NOy. Such compounds may be labile via
8 photolysis (e.g., HONO) or thermal decomposition (e.g., PAN), and may be toxic, irritating,
9 or acidic. However, in general they do not play the same critical role that NO2 and NO play
10 as ozone precursors. For that reason, this section focuses on measurement methods for NO
11 and NO2, as the primary ozone precursors among the nitrogen oxides. The nitrogen oxides
12 other than NOX may be important, however, as interferents in efforts to measure NO and
13 NO2. These non-NOx species are considered in this section in that regard.
14 Measurements of NOX may involve measurements of NO, of NO2, or of the sum of
15 NOX. Nitrogen dioxide, but not NO, is a criteria air pollutant, and thus reference and
16 equivalent methods are specified for NO2 measurements. In this section, the current state of
17 measurement methods for NO and NO2 will be summarized separately. Such methods in
18 some cases rely on measurements of total NOX, or at least an approximation of NOX. This
19 discussion focuses on current methods and on promising new technologies; but no attempt is
20 made here to cover the extensive history of development of these methods. More detailed
21 discussions of such methods may be found elsewhere (U.S. Environmental Protection
22 Agency, 1993; National Aeronautics and Space Administration, 1983). Wet chemical
23 methods are no longer commonly used and are not discussed here; a review of such methods
24 is given by Purdue and Hauser (1980).
25
26 3.5.3.2 Measurement of Nitric Oxide
27 3.5.3.2.1 Gas-phase Chemiluminescence Methods
28 By far the most common method of NO measurement is gas-phase Chemiluminescence
29 (CL) with 03. In this method, excess 03 is added to air containing NO in a darkened,
30 internally reflective chamber viewed by a photomultiplier tube. A small portion of the
31 NO reactions with O3 produce electronically excited NO2 molecules, which decay by
December 1993 3,153 DRAFT-DO NOT OTJOTR rn? rrrn
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1 emission of light of wavelengths longer than 600 nm. The emitted light is detected by a red-
2 sensitive photomultiplier tube, through an optical filter that prevents passage of wavelengths
3 shorter than 600 nm. This optical filtering minimizes interference from chemiluminescence
4 produced by Qj reactions with other species (e.g., hydrocarbons). The excited NO2 is
5 readily quenched in air, so that in typical instruments air and O3 are mixed at reduced
6 pressure (i.e., at least 20 in. of Hg vacuum). The intensity of the emitted light is linearly
7 proportional to the NO content of the sample air over several orders of magnitude in
8 concentration.
9 Commercial CL instruments for continuous measurement of NO are available from
10 several manufacturers. The chemiluminescence approach is also an EPA-designated
11 measurement principle for measuring ambient NO2; it requires a means of converting NO2 to
12 NO for detection. The complexities of this conversion are discussed in Section 3.5.3.3, on
13 NO2 methods. The commercial NO monitors typically are claimed to have detection limits
14 of a few parts per billion by volume in air (ppbv) with response time of a few minutes.
15 Field evaluations of several commercial instruments have indicated that minimum levels of
16 detection for NO2 are 5 to 13 ppbv (Michie et a!., 1983; Holland and McElroy, 1986).
17 However, more recent evaluations have indicated better performance. Rickman et al. (1989)
18 reported detection limits of 0.5 to 1 ppbv, and precision of ±0.3 ppbv, from laboratory and
19 field evaluations of two commercial instruments operated on their 50 ppbv full-scale ranges.
20 Commercial NO analyzers are portable and quite reliable, and are now commonly used in
21 ambient air monitoring networks.
22 Commercial NO analyzers may not have sensitivity sufficient for surface measurements
23 in rural or remote areas, or for airborne measurements. As a result, several investigators
24 have devised modifications to commercial instruments to improve their sensitivity and
25 response time (Delany et al., 1982; Tanner et al., 1983, Dickerson et al., 1984, Kelly et al.,
26 1986). Those modifications include: (1) operating at low pressure and high sample flow
27 rate; (2) using a larger, more reflective reaction chamber that promotes mixing of the
28 reactants close to the photomultiplier tube; (3) increasing the O3 supply; for example, by use
29 of oxygen in the O3 source; (4) cooling of the photomultiplier to reduce noise; (5) adopting
30 photon counting techniques for light detection; and (6) adding a prereactor to obtain a more
31 stable and appropriate background signal. Commercial instruments modified in these ways
December 1993 3-154 DRAFT-DO NOT QUOTE OR CITE
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1 are generaUy reported to have detection limits of 0,1 ppbv or less, with response times of
2 30 s or less.
3 Research-grade NO instruments specially designed for ultra-high sensitivity have also
4 been built for use in remote ground-level or airborne applications (e..g, Ridley and Hewlett,
5 1974; Kley and McFarland, 1980; Kelly et al., 1980; Helas et al., 1981; Drummond et al.,
6 1985; Tones, 1985; Kondo et al., 1987; Parrisn et al., 1990). These instruments typically
7 have detection limits of 10 ppt (i.e., 0.01 ppbv) or less, with response times from a few
8 seconds to 1 min.
9 A number of studies indicate that the chemiluminescence method is essentially specific
10 for NO. Operation at reduced pressure prevents interference resulting from quenching by
11 water vapor (Michie et al., 1983; Drummond et al., 1985). In air samplings no significant
12 interferences have been found in NO detection from sulfur-, chlorine-, and nitrogen-
13 containing species (Joshi and Bufalini, 1978; Sickles and Wright, 1979; Grosjean and
14 Harrison, 1985; Fahey et al., 1985). However, H2S and possibly other sulfur-containing
15 compounds from seawater have been reported to give false NO signals (Zafiriou and True,
16 1986). This effect should not be important for ambient air measurements. Fahey et al.
17 (1985) and Drummond et al. (1985) also reported no significant NO interference from a
18 variety of other nitrogen-containing species, including NO2, HNO3, PAN, N2O5, NH3,
19 HCN, N2O, and HO2NO2; as well as no interference from methane, propylene, and
20 hydrogen peroxide.
21 Several ambient air intercomparisons have been done of chemiluminescence NO
22 instruments (Walega et al., 1984; Hoell et al., 1987; Fehsenfeld et al., 1987; Gregory et al.,
23 1990), These studies have focused on high-sensitivity research instruments, rather than the
24 commercial instruments used for widespread ambient air measurements. These studies have
25 shown excellent agreement among the CL NO instruments, even at NO levels in the low ppt
26 range (Hoell et al., 1987; Gregory et al., 1990). These results support the validity of the CL
27 approach for NO. Good agreement has also been found between CL measurements and
28 spectroscopic NO measurements in these studies (see Section 3.5.3.2.2).
29
30
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1 3.5.3.2.2 Spectroscopic Methods for Nitric Oxide
2 Direct Spectroscopic methods for NO include two-photon laser-induced fluorescence
3 (TPIIF), tunable-diode laser absorption spectroscopy (TOLAS), and two-tone frequency-
4 modulated spectroscopy (TTFMS). The primary characteristics of these methods are their
5 very high sensitivity and selectivity for NO, For example, a detection limit of 10 ppt has
6 been quoted for TPLIF with a 30-s integration time, with no significant interferences from
7 atmospheric species (Davis et al., 1987). An accuracy of ±16% as a 90% confidence limit
8 has been calculated for NO measurement by TPLIF from an aircraft (Davis et al., 1987).
9 The TOLAS method is similarly highly selective for NO and achieves a detection limit of
10 0.5 ppbv (Schiff et al., 1983). The response time of the TOLAS instrument is about 1 min
11 for NO, and is limited by stabilization of concentrations with the large surface area of the
12 multi-pass White cefl. The newest method is TTFMS, which appears in laboratory studies to
13 be very sensitive, fast, and selective. With a 100-m path length in a 20-torr multiple-pass
14 cell, and a 1-min averaging time, the detection limit of NO is estimated to be 4 ppt (Hansen,
15 1989).
16 Spectroscopic methods have compared well with the CL method for NO in ambient
17 measurements. Walega et al. (1984) reported good agreement between CL and TOLAS
18 results for NO in laboratory air, in ambient air, and even in downtown Los Angeles air.
19 Gregory et al. (1990) reported comparisons of TPLIF and CL NO methods in airborne
20 measurements. Agreement at levels below 20 ppt was within the expected accuracy and
21 precision of the instruments (i.e., within 15 to 20 ppt).
22 The major drawbacks of these Spectroscopic methods are their complexity, size, and
23 cost. Although possessing remarkable characteristics, these methods are restricted to
24 research applications. The TTFMS approach, in fact, is still hi the laboratory development
25 stage.
26
27 3.5.3.2.3 Passive Samplers
28 At present no passive sampler exists that directly measures NO. Instead, passive
29 samplers developed for NO2 have been adapted for NO measurement, using an oxidizing
30 material that converts NO to NO2. Palmes tubes (Palmes and Tomczyk, 1979) have been
31 adapted for NO measurement by using two tubes in parallel. One tube collects NO2 on a
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1 triethanolamine (TEA)-coated grid, while the other collects NO2 on a TEA grid, plus NO
2 oxidized by a chromic acid-coated surface. The grids are then extracted and analyzed for
3 NO2 ion. Nitric oxide is determined by difference between the two results, after accounting
4 for the different diffusivities of NO and NO2. The sampling rates depend on temperature
5 and air velocity. The tubes cannot be used for periods longer than 24 h, and are intended for
6 use at ppm NO levels important in the workplace (e.g., 2 to 200 ppm • h). Applicability to
7 ambient NO levels has not been demonstrated.
8 A more sensitive passive sampler for NO has been reported (Yanagisawa and
9 Nishimura, 1982) that uses the same TEA chemistry, with CrO^ as the NO oxidizer.
10 A detection limit of 70 ppbv-h has been reported. As with any currently available passive
1 1 sampler, the disadvantages of the method are the potential for interferences, relatively poor
12 precision, and low sensitivity for ambient air measurements.
13
14 3.5J3J Measurements for Nitrogen Dioxide
15 3.5,3.3,1 Gas-phase Chemiluminescence Methods
16 In 1976, the gas-phase chemiluminescence approach described above for NO detection
17 was designated as the measurement principle on which U.S. EPA reference methods for
18 ambient NO2 must be based. The CL method thus filled the vacancy left by withdrawal of
19 the Jacobs-Hocnheiser method, because of technical problems, in 1973. To be designated as
20 a reference method, an NO2 detection method must use the CL approach and be calibrated
21 by the specified methods (gas-phase titration of NO with O3, or use of an NO2 permeation
22 device). In addition the instrument must meet the performance specifications shown in
23 Table 3-18. An equivalent method, either manual or automated, must meet certain
24 requirements for comparability with a reference method when measuring simultaneously in a
25 real atmosphere. Those comparability requirements are shown in Table 3-19. An automated
26 equivalent method must also meet the performance requirements shown in Table 3-18.
27 The selection of the ozone CL method as the reference measurement principle for
28 ambient NO2 was the result of comparison tests of CL and wet chemical methods.
29 Chemiluminescence analyzers were found superior to the wet chemical methods in response
30 time, zero and span drift, and overall operation, although agreement among all the methods
31 tested was good, at the NO2 spike levels provided (Purdue and Hauser, 1980). Table 3-20
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TABLE 3-18. PERFORMANCE SPECIFICATIONS FOR NITROGEN
DIOXIDE AUTOMATED METHODS
Performance Parameter Units NO2
Range ' ppm 0-0.5
Noise
0% upper range limit ppm 0.005
80% upper range limit ppm 0.005
Lower detectable limit ppm 0,01
Interference equivalent
Each interferant (SO2,NO,NH3,H2O ppm ±0.02
Total interferant ppm ,<.0.04
Zero drift, 12 and 24 hours ppm ±0.02
Span drift, 24 hours
20% upper range limit % ±20.0
80% upper range limit % ±5.0
Lag time min 20
Rise time min 15
Fall time min 15
Precision
20% upper range limit ppm 0.02
80% upper range limit ppm 0.03
Source: Code of Federal Regulations, Ambient Air Monitoring Reference and Equivalent Methods,
C.F.R. Title 40, Part 53.
TABLE 3-19. COMPARABILITY TEST SPECD7ICATIONS
FOR NITROGEN DIOXIDE
Nitrogen Dioxide Maximum Discrepancy
Concentration Range (ppm) Specification (ppm)
Low 0.02 to 0.08 OG2
Medium 0.10 to 0.20 0.02
High 0.25 to 0.35 0.03
1 lists the methods currently designated (as of February 1993) by U.S. EPA as reference and
2 equivalent methods for ambient NQj. Three wet chemical methods are shown as equivalent
3 methods, but these are rarely used for ambient air measurements,
4 The ozone chemiluminescence method does not measure NO2 directly, because the
5 chemiluminescence is produced by reaction of NO with O3. As a result, NO2 must first be
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TABLE 3-20. REFERENCE AND EQUIVALENT METHODS FOR
NITROGEN DIOXIDE DESIGNATED BY U.S. EPAa
Method
Reference Methods (Continuous CL Analyzers)
Advanced Pollution Instrumentation 200
Beckman952A
Bendix 8101-B
Bendix 8101-C
CSI1600
Dasibi 2108
Lear Siegler ML9841
Meloy NA53OR
Monitor Labs 8440E
Monitor Labs 8840
Monitor Labs 8841
Philips PW9762/02
Thermo Electron 14B/E
Thermo Electron 14D/E
Thermo Environmental 42
Equivalent Methods (Wet Chemical)
Sodium arsenite
Sodium arsenite/Technicon n
TGS-ANSAb
Designation
Number
RFNA-0691-082
RFNA-01 79-034
RFNA-0479-038
RFNA-0777-022
RFNA-0977-025
RFNA-1 192-089
RFNA-1292-090
RFNA-1078-031
KFNA-0677-021
RFNA-0280-042
RFNA-0991-083
RFNA-0879-040
RFNA-0179-035
RFNA-0279-037
RFNA-1289-074
EQN-1277-026
EQN-1277-027
EQN-1277-028
Method
Code
082
034
038
022
025
089
090
031
021
042
083
040
035
037
074
084
084
098
aAs of February 1993.
Triethanolamine-guajacol-sulfite with 8-amino-l-naphthalene-suIfonic acid ammonium salt.
1 reduced to NO for detection. In principle, such a reduction should readily result in
2 measurement of NO + NO2 (i.e., NOX), and allow indirect measurement of NC«2 by
3 difference between NO and NOX responses, measured either sequentially, or simultaneously
4 by separate detectors. In practice, however, selective measurement of NOX by this approach
5 has proven very difficult.
6 Several methods have been employed to convert NO2 to NO, including catalytic
7 reduction with heated molybdenum or stainless steel, reaction with CO over a gold catalyst
8 surface, reaction with ferrous sulfate (FeSO4) at room temperature, reaction with carbon at
9 200 °C, and photolysis of NO2 at wavelengths of 320 to 400 nm (Kelly et al., 1986). It has
December 1993 1-1 SO DRAFT-DO MOT nrTrm; no
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1 been found in many separate investigations that the heated converters reduce NC>2 to NO
2 effectively, but also reduce other NOy species as well (e.g., Winer et al., 1974; Cox, 1974;
3 Joseph and Spicer, 1978; Grosjean and Harrison, 1985; Fahey et al., 1985). Efficiencies of
4 conversion near 100% are reported in these studies for N^ and for NOy species such as
5 HNO3, HONO, PAN, and organic nitrates. This finding is particularly important for
6 widespread monitoring networks that use commercial instruments, because such instruments
7 without exception use heated catalytic converters (typically molybdenum). Thus, such
8 instruments measure not NO and NOX, but more nearly NO and total NOy. The NO2 value
9 inferred from such measurements may be significantly in error (see below), and may in turn
10 affect the results of models of ambient ozone. The completeness of the measured NO value
11 is also questionable because, for example, HN03 is readily lost to surfaces, and, in ambient
12 sampling, may be removed within the sampling system before reaching the heated converter.
13 Other conversion methods for NO2 have been tried in an effort to achieve higher
14 selectivity. Ferrous sulfate (FeSO4) has been used for ambient NO2 measurements using
15 high-sensitivity research grade CL instruments (e.g., Kelly et al., 1980; Helas et al., 1981;
16 Dickerson et al,, 1984). This material is an efficient reducer of NO2, but has also been
17 found to convert a portion of PAN, and possibly a portion of HONO and organic nitrates
18 (Fehsenfeld et al., 1987). Memory effects and reduction in efficiency can occur because of
19 humidity effects (Fehsenfeld et al., 1987). As a result of these characteristics, use of FeSO4
20 has given high readings in comparison with spectroscopic instruments and the photolytic NO2
21 converter, and likely results in overestimating ambient NOX by a significant amount
22 (Fehsenfeld et al., 1987; Ridley et al., 1988a; Gregory et al., 1990). Ferrous sulfate has
23 never been used in commercial NOX instruments, and is no longer used in research
24 measurements.
25 The most specific method for converting NO2 to NO is photolysis (Hey and
26 McFarland, 1980). In this approach, ambient NO2 is photolyzed to NO by a xenon arc
27 lamp. The method does not produce NO from the major potential interferents present in air
28 (i.e., HNO3, PAN, and organic nitrates), but less abundant NOy species such as HONO or
29 HO2NO2 may interfere. A detailed description of steps to minimize such interferences is
30 given by Ridley et al. (1988b). As currently used, the photolytic converter appears to be
31 essentially specific for NO2. However, it does not provide complete conversion of NO2.
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1 Conversion efficiencies are 50 to 60% with a new lamp, and may decline to 20% over the
2 course of several weeks (Parrish et al,, 1990). Thus the conversion efficiency must be
3 repeatedly calibrated. This approach has not been implemented with commercial NO
4 detectors, but has been implemented with research-grade CL NO instruments for studies of
5 NOX and NOy chemistry at a variety of locations (e.g., Parrish et al.s 1990; Trainer et al.,
6 1991; Parrish et al., 1992). The photolytic method has compared well with other techniques,
7 including spectroscopic methods, even at NO2 levels as low as 0.05 ppbv (Gregory et al.,
8 1990).
9 As noted above, the commercial CL analyzers used for most ambient air NO and NOX
10 measurements actually measure more nearly NO and NOy. The magnitude of the resulting
1 1 overestimation of NO2 determined by difference obviously depends on the portion of NOy
12 that is NOX. The smaller the portion of NOy that is NOX, the greater will be the error in the
13 NO2 determined by difference. In remote areas, where NOX has undergone extensive
14 conversion to other products during transport from a source region, NOX may contribute a
15 small fraction of NOy, In urban areas, close to sources, NOX may comprise nearly all of
16 NOy. For example, in measurements at Point Arena, California, Parrish et al. (1992) report
17 NOx/NOy ratios averaging 0.3 in air of marine origin, and 0.75 in air subject to continental
18 influence. Clearly, although the commercial CL instruments are designated as reference
19 methods for NO2, the great majority of existing ambient air data for NOj or NOX are biased
20 high, because of the inclusion of some portion of other NOy species. The magnitude of this
21 bias may not be large in urban areas, but in any case it is unknown at this time,
22
23 3.5.3.3.2 Luminol Chemiluminescence Method
24 This approach is based on the chemiluminescent reaction of gaseous NO2 with the
25 surface of an aqueous solution of luminol (5-amino-2,3-dihydro-l ,4-phthaJazinedione).
26 Emission occurs primarily between 380 and 520 nm. In commercial instruments, luminol
27 solution flows down a fabric wick that lies vertically on a clear window viewed by a
28 photomultiplier tube. Nitrogen dioxide in sample air passing over the wick produces light,
29 the intensity of which is proportional to the NO2 concentration. Commercial instruments
30 using this approach are compact, light, and relatively inexpensive, and can provide detection
31 limits as low as 0.01 ppbv with response times below 30 s. The instrument has the
December 1993 3.1 fit DRAFT-DO NOT nTTfYrn m>
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1 advantage of detecting NO2 directly. However, several difficulties have had to be dealt with
2 in developing the method.
3 Original reports of the approach (Maeda et al., 1980) indicated positive interferences
4 from O3 and SO2 and a negative one from CO2. Reformulation of the luminol reagent
5 solution has minimized, though not fully eliminated, those interferences (Wendel et al,,
6 1983; Schiff et al., 1986). Reported effects include a slight negative response from NO, and
7 sensitivity to PAN, HONO, and O3 (Wendel et al., 1983; Schiff et al., 1986; Rickman et al.,
8 1989; Kelly et al., 1990; Spicer et al., 1991). Response to NO2 may be nonlinear at low
9 concentrations (Kelly et al., 1990), although recent reformulation of the reagent has
10 apparently reduced this behavior (Busness, 1992). Evaluation of the luminol NOj monitor
11 indicates that great care must be taken in using and calibrating the instrument in order to
12 achieve good precision and accuracy in ambient measurements (Kelly et al., 1990). The
13 monitor has been widely used as a research tool, but has not been widely used in ambient air
14 monitoring and has not been designated an equivalent method for NCX^
15 An 03 scrubber is available to eliminate the 03 interference noted above, but was also
16 found to remove a portion of the NO2 (Kelly et al., 1990). The lumdnol approach has also
17 been modified to measure NO, by using a CrO3 converter that oxidizes NO to NO^ for
18 detection. Thus NO is detected by difference. This method has the potential for
19 measurement of total NOX; however, evaluations of the CrO^ converter arc still underway at
20 several laboratories. Given the known interferences in the luminol approach, careful
21 evaluation of this method must be completed before it gains acceptance as an NO
22 measurement method.
23 An adaptation of the commercial luminol NO^ detector has been reported to provide
24 measurements of total NOy, NO2, and NOX (Drummond et al., 1993). This adaptation,
25 called the LNC-3M, uses a commercial luminol instrument for NO2 detection, with a
26 CrO3 converter for NOX detection. The NOX measurement must be corrected for the few
27 percent of the ambient NO2 that is lost in the CrO3 converter (Drummond et al., 1993). The
28 NOy measurement is achieved using a stainless steel converter maintained at 400 °C. Tests
29 indicate that this converter provides a more complete conversion of alkyl nitrates, and
30 consequently a more complete measurement of NOys than is provided by either the heated
31 molybdenum converters used in commercial ozone CL NOX detectors or the gold converters
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1 with CO addition used in research instruments (Druramond et al., 1993), The LNC-3M adds
2 a small amount of NO2 to the sample to eliminate the nonlinearity at low concentrations, and
3 uses a zeroing scrubber that greatly reduces the interference from PAN. However, this
4 scrubber must be replaced weekly when in continuous use (Drummond et al., 1993).
5
6 3,5.3.3.3 Spectroscopic Methods
1 Several spectroscopic approaches to NO2 detection have been developed: TDLASS
8 TTFMS, differential optical absorption (DOAS), and differential absorption lidar (DIAL) are
9 absorption methods that have been used. The TOLAS method is probably the most
10 commonly used spectroscopic NO2 method. It can provide high selectivity for NO2, with a
11 detection limit of 0.1 ppbv, accuracy of ±15 percent, and a response time on the order of
12 1 min because of the White cell (Mackay and Schiff, 1987). The DOAS method is an
13 open-path long-pathlength system. The detection limit for NO2 with a 0.8-km pathlength and
14 12-min averaging time has been reported as 4 ppbv, with measurement accuracy reported as
15 ±10% (Biermann et al., 1988). However, recent improvements have resulted in a
16 commercial DOAS instrument capable of an NO2 detection limit of 0.6 ppbv, based on a
17 557-m path and a 1-min averaging time (Stevens et al., 1993). The detection limit for NO2
18 by the DIAL technique has been reported as 10 ppbv with a 6-km pathlength (Staehr et al.,
19 1985). The novel TTFMS method noted above for NO is reported to have an NC^ detection
20 limit of 0.3 ppt, but is not fully proven for ambient measurements.
21 Fluorescence methods have also been used for NO^, including photofragmentation
22 TPLJF (PF/TPUF) (Davis, 1988). This method uses two cells hi which NO is measured by
23 TPLIF. In one of the cells, an excimer laser emitting at 353 nm photolyzes NO2 to NO for
24 detection. Thus NO2 is ultimately measured, by difference, as NO, but the NO is formed
25 directly by photolysis of NO2. With a 2-min integration time, an NO2 detection limit of
26 12 ppt is reported. The method is highly selective for NO2, since an interferant would have
27 to photolyze to produce NO. Several potential atmospheric species have been ruled out in
28 this regard (Davis, 1988).
29 The drawbacks of most of these methods are, as noted earlier, complexity, size, and
30 cost. At present these factors outweigh the obvious advantages of the sensitivity and
31 selectivity of these spectroscopic methods, and have largely restricted the use of these
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1 NO2 methods to specific research applications or to their use as reference methods in
2 intercomparisons. In such intercomparisons, absorption measurements have been most
3 commonly used. The TOLAS method has been used in ground-level comparisons with
4 O3 CL and luminol instruments to provide specific NO2 measurements (Walega et al., 1984;
5 Sickles et al., 1990; Fehsenfeld et al., 1990), and in an airborne comparison with PF/TP1IF
6 and O3 CL instruments (Gregory et al., 1990b), A finding of these studies was that the
7 TDLAS consistently read higher than other established methods at very low NO2 levels (i.e.,
8 <0.4 ppbv) (Fehsenfeld et al., 1990; Gregory et al., 1990b).
9 The spectroscopic NO2 method most fully developed beyond the research stage is the
10 DOAS technique. Stevens et al. (1993) report testing of a commercial DOAS instrument in
11 North Carolina over 17 days in the fall of 1989. The DOAS measured NO2 using
12 wavelengths between 400 and 460 nm? and achieved a detection limit of 0.6 ppbv, as noted
13 above. Simultaneous measurements of ozone, sulfur dioxide, formaldehyde, and nitrous acid
14 were also provided by the DOAS instrument. Comparison of the DOAS NC^ results to those
15 from a commercial CL detector showed (DOAS NO^ = 1.14 X (CL NO^ + 2.7 ppbv,
16 with a correlation coefficient (r2) of 0.93, at NC^ levels up to 50 ppbv (Stevens et al., 1993).
17 The sensitivity, stability, response time, and multicomponent capability are the primary
18 advantages of the DOAS approach. Further intercomparisons and interference tests are
19 recommended (Stevens et al, 1993).
20
21 3.5.3.3,4 Passive Samplers
22 Passive samplers are attractive, inexpensive, and simple means to obtain long-term or
23 personal exposure data for NO2 or NOX. The simplest passive sampler for NO2 is the
24 nitration plate, which is essentially an open dish containing filter paper impregnated with
25 TEA. Nitrogen dioxide diffuses to the paper, and is extracted later as NO2~ for analysis.
26 No diffusion barrier exists in this approach, or in a similar approach using a candle-shaped
27 absorber (Kosmus, 1985); consequently, results are very subject to ambient conditions and
28 give at best a qualitative indication of NO2 or NOX.
29 Addition of a diffusion barrier to the nitration plate concept has led to badge-type
30 passive samplers for NO2 (e.g., Mulik and Williams, 1986, 1987; Mulik et al., 1989, 1991).
31 In general, such devices use perforated screens, plates, or filters as diffusion barriers on the
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1 chemically reactive material, which may be exposed on one or both sides, depending on the
2 application. Extraction of the sorbent then allows measurement of the NO2 collected,
3 typically as NO2" ion. Such a device using TEA as the active material gave very good
4 agreement relative to a CL analyzer in laboratory tests with NO2 at 10 to 250 ppbv (Mulik
5 and Williams, 1987). However, interferences from PAN and HONO (the latter both in
6 outdoor and indoor air) are expected (Sickles and Michie, 1987). Comparison of ambient
7 NO2 results in the 5 to 25 jig/m range (i.e., about 2.5 to 12.5 ppbv) from the passive
8 device to those from TDLAS showed good agreement on average values, but a correlation
9 coefficient (r) of only 0.47 on daily values (Mulik et al., 1989).
10 Badge-type personal samplers for NO2 have also been developed by Yanagisawa and
11 Nishimura (1982) (YN) and by Cadoff and Hodgeson (1983) (CH). Triethanolamine is used
12 as the active collecting medium in both samplers, and both use colorimetry as the analytical
13 method for detection of NO2 , The samplers differ in that the YN device uses TEA coated
14 on a cellulose filter with a Teflon® filter as a diffusion barrier; whereas the CH sampler uses
15 TEA coated on a glass fiber filter with a polycarbonate filter as a diffusion barrier.
16 Detection limits are reported to be 0.07 ppm-h (Yanagisawa and Nishimura, 1982) and
17 0,06 pprn-h (Cadoff and Hodgeson, 1983). Interferences from PAN and HONO are expected
18 (Sickles and Michie, 1987); likewise, the devices are sensitive to the speed of ambient air
19 movement.
20 Palmes tubes have been developed for NO2 measurement and adapted to NO
21 measurement as described above. The device has been used for workplace and personal
22 exposure monitoring (Wallace and Ott, 1982), but not for ambient air measurements.
23 A detection limit of 0.03 ppm-h can be achieved if ion chromatography is used to determine
24 the extracted NO2" (Mulik and Williams, 1986). Adsorption of NO2 to the tube walls may
25 raise this limit considerably (Miller, 1988), but this effect can be counteracted by use of
26 stainless steel tubes. The device is sensitive to temperature and wind speed; and PAN and
27 HONO are likely interferences (Sickles and Michie, 1987). In a comparison with two
28 commercially produced NO2 passive samplers, the Palmes tube showed reasonable accuracy
29 and precision at loadings of 1 to 80 ppm-h. However, the commercial devices were designed
30 for use at relatively high loadings; therefore, this comparison does not support the use of
31 Palmes tubes for ambient air monitoring. The Palmes tubes have the same disadvantages as
December 1993 3-165 DRAFT-DO MITT nTTrvrc ou
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1 other passive devices for NO2, namely, poor precision, insufficient sensitivity, temperature
2 dependence, and considerable interferences in ambient sampling.
3
4 3.5.3.4 Calibration Methods
5 Calibration of NO measurement methods is done using standard cylinders of NO in
6 nitrogen. Typical NO concentrations in such cylinders are 1 to 50 ppmv. Dilution of such
7 standards with clean air using mass flow controllers can accurately provide NO
8 concentrations in the ambient (i.e., 1 to 100 ppbv) range for calibration. Nitric oxide
9 standards are available as Standard Reference Materials (SRMs) from the National Institute
10 of Standards and Technology (NIST), and as commercially available Certified Reference
11 Standards. Commercially available certified NO standards have been shown to be stable and
12 accurate in the certified concentrations.
13 Standard cylinders of N02 in nitrogen or air are sometimes used for NO2 calibration.
14 These standards are commercially available, and are readily diluted to ppbv levels in the
15 same manner as for NO standards. However, instability of the N02 levels in such standards
16 has been reported, and caution must be used in relying on NO2 standards as the primary
17 means of calibration.
18 Two calibration methods for NO2 are specified in the Code of Federal Regulations
19 (1987) for calibration of ambient NO2 measurements. Those methods are permeation tubes
20 and gas-phase titration.
21 An NO2 permeation tube is an inert enclosure, generally of Teflon®, glass and Teflon®,
22 or stainless steel and Teflon®, that contains liquid NO2. As long as liquid NO2 is present,
23 NO2 will permeate through the Teflon® at a rate that depends on the temperature of the tube.
24 Maintaining the permeation tube at a constant temperature (i.e., ±0.1 °C) results in
25 permeation of NO2 at a constant rate. Dilution of the emitted NO2 with a flow of dry air or
26 N2 results in known low NO2 concentrations for calibration. Nitrogen dioxide permeation
27 tubes are supplied as SRMs by NIST, and tubes are commercially available with a wide
28 range of permeation rates. Permeation tubes are small, simple, reliable, and relatively
29 inexpensive, although constant temperature ovens and dilution systems are required to obtain
30 good results. Nitrogen dioxide permeation tubes are susceptible to moisture, and changes in
31 permeation rate or emission of other species (HNO3, HONO, NO) may occur if they are not
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1 kept dry. As with NO2 cylinder standards, the NO2 permeation tube requires care as a
2 calibration method for NO2.
3 Gas-phase titration (GPT) uses the rapid reaction of NO with 03 to produce NO2 with
4 1:1 stoichiometry. In practice, excess NO generated from a standard cylinder containing
5 50 to 100 ppmv NO is reacted with Oj from a stable source. The resultant decrease in NO
6 concentration, usually measured on the NO channel of a chemiluminescence NOX analyzer,
7 equals the concentration of NO2 generated. Varying amounts of NO2 can be produced by
8 varying the amount of O3.
9
10
11 3.6 OZONE AIR QUALITY MODELS
12 To plan control strategies to achieve compliance with the National Ambient Air Quality
13 Standard (NAAQS) for ozone at some future date it is necessary to predict how ozone
14 concentrations change in response to prescribed changes in source emissions of precursor
15 species; the oxides of nitrogen (NOX) and volatile organic compounds (VOCs). This
16 assessment requires an air quality model, which in the case of ozone prediction is often
17 called a photochemical air quality model. The model in effect is used to determine the
18 emission reductions needed to achieve the ozone air quality standard. For at least a decade,
19 the U.S. Environmental Protection Agency (EPA) has offered guidelines on the selection of
20 air quality modeling techniques for use in State Implementation Plan (SIP) revisions, new
21 source reviews, and studies aimed at the prevention of significant deterioration of air quality.
22 Ozone air quality models provide the ability to address "what if' questions, such as
23 what if emissions of VOCs or NOX or both are reduced? The model can be used as an
24 experiment that cannot be run in the atmosphere. Sensitivity questions can be asked, such as
25 how important is emissions change A relative to emissions change B, or what is the effect of
26 an X% uncertainty in a certain chemical reaction rate constant on the ozone levels predicted.
27 Models are the ultimate integrators of our knowledge of the comprehensive chemistry
28 and physics of the atmosphere. As such, they are an indispensable tool for understanding the
29 complex interactions of transport, transformation, and removal in the atmosphere. Models
30 assist in the design of field measurement programs and are essential in the interpretation of
31 data from such programs.
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1 The purpose of Section 3,6 is to review briefly the main elements of ozone air quality
2 models, to describe several of the current models, to discuss how one evaluates the
3 performance of these models, and to present examples of the use of the models for
4 determining VOC and NOX control strategies.
5
6 3.6.1 Definitions, Description, and Uses
7 Air quality models are mathematical descriptions of the atmospheric transport,
8 diffusion, removal, and chemical reactions of pollutants. They operate on sets of input data
9 that characterize the emissions, topography, and meteorology of a region and produce outputs
10 that describe air quality in that region. Mathematical models for photochemical air pollution
11 were first developed in the early 1970s and have been improved, applied, and evaluated since
12 that time. Much of the history of the field is described in reviews by Tesche (1983),
13 Seinfeld (1988), and Roth et al. (1989).
14 Photochemical air quality models include treatments of the important physical and
15 chemical processes that contribute to ozone formation in and downwind of urban areas.
16 In particular, such models contain a representation of the following phenomena (Roth et al.s
17 1989):
18 • Precursor emissions. The spatial and temporal characteristics of reactive
19 hydrocarbon, carbon monoxide (CO), and NOX emissions sources must be
20 supplied as inputs to the model. Hydrocarbon emissions are generally
21 apportioned into groups (e.g., alkanes, alkenes, aromatics, etc.) according to the
22 speciation requirements of the chemical kinetic mechanism embedded in the
23 model.
24
25 • Pollutant transport. Once the ozone precursors are emitted into the atmosphere,
26 they are transported by the wind. When ozone is formed, it is also subject to
27 transport by the wind. Grid-based models require the preparation of three-
28 dimensional, time-varying fields of the wind speed and direction. These values
29 must be specified for each grid cell. Cloud venting and cloud mixing processes
30 that are important on the regional scale can also be included in the pollutant
31 transport description.
32
33 • Turbulent diffusion. Ozone and its precursors are also subject to turbulence-
34 related dispersion processes that take place on a subgrid scale. These turbulent
35 diffusion effects are usually represented in grid-based models by the so-called
36 gradient transport hypothesis, where the pollutant flux is assumed to be
37 proportional to the spatial gradient in the concentration field. The turbulent
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1 diffusivities employed in the model are dependent on atmospheric stability and
2 other meteorological variables,
3
4 • Chemical reactions. Ozone results from chemical transformations involving
5 reactive organics and NOX (See Section 3.2). A chemical kinetics mechanism
6 representing the important reactions that occur in the atmosphere is employed to
7 estimate the net rate of change of each pollutant simulated by the model,
8 Description of chemical reactions requires actinic flux, cloud cover, temperature,
9 and relative humidity.
10
11 • Removal processes. Pollutants are removed from the atmosphere via interactions
12 with surfaces at the ground, so-called "dry deposition," and by precipitation,
13 called "wet deposition."
14
15 Guidelines issued by U.S. EPA (U.S. Environmental Protection Agency, 1986a)
16 identify two kinds of photochemical model: The grid-based Urban Airshed Model (UAM) is
17 the recommended model for modeling ozone over urban areas and the trajectory model
18 EKMA (empirical kinetics modeling approach) is identified as an acceptable approach. The
19 1990 Clean Air Act Amendments mandate that three-dimensional, or grid-based, air quality
20 models, such as UAM, be used in SIPs for ozone nonattainment areas designated as extreme,
21 severe, serious, or multistate moderate (U.S. Environmental Protection Agency, 1991b).
22
23 3.6.1.1 Grid-Based Models
24 The basis for grid-based air quality models is the atmospheric diffusion equation, which
25 expresses the conservation of mass of each pollutant in a turbulent fluid in which chemical
26 reactions occur (Seinfeld, 1986). The region to be modeled is bounded on the bottom by the
27 ground, on the top by some height that characterizes the maximum extent of vertical mixing,
28 and on the sides by east-west and north-south boundaries. The choice of the size of the
29 modeling domain will depend on the spatial extent of the ozone problem, including the
30 distribution of emissions in the region, the meteorological conditions, and, to some extent,
31 the computational resources available. This space is then subdivided into a three-dimensional
32 array of grid cells. The horizontal dimensions of each cell are usually a few kilometers for
33 urban applications up to tens of kilometers for regional applications. Some older grid-based
34 models assumed only a single, well-mixed vertical cell extending from the ground to the
35 inversion base; current models subdivide the region into layers. Vertical dimensions can
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1 vary, depending on the number of vertical layers and the vertical extent of the region being
2 modeled. A compromise generally must be reached between the better vertical resolution
3 afforded by the use of more vertical layers and the associated increase in computing time.
4 Although aerometric data, such as the vertical temperature profile, that are needed to define
5 the vertical structure of the atmosphere are generally lacking, it is still important to use
6 enough vertical layers so that vertical transport processes are accurately represented.
7 There are practical and theoretical limits to the minimum horizontal grid cell size,
8 Increasing the number of cells increases computing and data acquisition effort and costs.
9 In addition, the choice of the dimension of a grid cell implies that the input data information
10 about winds, turbulence, and emissions, for example, are resolved to that scale. The spatial
11 resolution of the concentrations predicted by a grid-based model corresponds to the size of
12 the grid cell. Thus, effects that have spatial scales smaller than those of the grid cell cannot
13 be resolved. Such effects include the depletion of ozone by reaction with nitric oxide (NO)
14 near strong sources of NOX like roadways and power plants.
15 Several grid-based photochemical air quality models have been developed to simulate
16 ozone production in urban areas or in larger regions. They differ primarily in their treatment
17 of specific atmospheric processes, such as chemistry, and in the numerical procedures used
18 to solve the governing system of equations, They will be reviewed in Section 3.6.3.
19
20 3.6.1.2 Trajectory Models
21 In the trajectory model approach, a hypothetical air parcel moves through the area of
22 interest along a path calculated from wind trajectories. Emissions are injected into the air
23 parcel and undergo vertical mixing and chemical transformations. The data requirements for
24 trajectory models include: (1) initial concentrations of all relevant pollutants and species;
25 (2) rates of emissions of VOC and NOX precursors into the parcel along its trajectory;
26 (3) meteorological characteristics such as wind speed and direction needed to define the path
27 of the air parcel through the region; (4) mixing depth; and (5) solar ultraviolet radiation.
28 Basic limitations of trajectory models include neglect of horizontal wind shear, and neglect of
29 cell volume changes resulting from convergence and divergence of the wind field (liu and
30 Seinfeld, 1975).
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1 Trajectory models provide a dynamic description of atmospheric source-receptor
2 relationships that is simpler and less expensive to derive than that obtained from grid models.
3 Trajectory models are designed to study the photochemical production of ozone in the
4 presence of sources and vertical diffusion of pollutants; otherwise the meteorological
5 processes are highly simplified.
6 A simple trajectory model is used in the empirical kinetic modeling approach
7 (BKMA)(Dodge, 1977a). This modeling approach relates the maximum level of ozone
8 observed downwind of an urban area to the levels of VOC and NOX observed in the urban
9 area. It is based on the use of a simple, one-cell moving box model. As the box moves
10 downwind, it encounters emissions of organics and NOX that are assumed to be uniformly
1 1 mixed within the box. The height of the box is allowed to expand to account for the breakup
12 of the nocturnal inversion layer. As the height of the box increases, pollutants above the
13 inversion layer are transported into the box. The model is first used to generate a series of
14 constant ozone lines (or isopleths) as depicted in Figure 3-25. The isopleths show the
15 downwind, peak 1-h ozone levels as a function of the concentrations of VOC and NOX for a
16 hypothetical urban area. These isopleths were generated by carrying out a large number of
17 model simulations in which the initial concentrations and anthropogenic emissions of VOC
1 8 and NOX were varied systematically while all other model inputs were held constant. When
19 it was first conceived, EKMA employed a very simple, highly empirical chemical mechanism
20 and the isopleths generated were for a hypothetical situation in Los Angeles.
21 As understanding of the chemical processes responsible for ozone formation increased, the
22 EKMA model was updated to include more complete representations of atmospheric
23 chemistry. Although EKMA has employed the CBM-IV mechanism, the same mechanism
24 that is currently being used in several grid-based models, the most recent version allows the
25 input of any mechanism. The EKMA method is now used to generate city-specific isopleth
26 diagrams using information on emissions, transport, and dilution that are appropriate to the
27 particular city being modeled.
28 City-specific ozone isopleths can be used to estimate the reduction in nonmethane
29 hydrocarbon (NMHC) or NOX levels, or both, needed to achieve the NAAQS for ozone in a
30 specific urban area. The first step is to determine the early-morning NMHC/NOX ratio for
31 the urban area in question and the maximum 1-h downwind O3 concentration. Both the
December 1993 3-17! DRAFT-DO NOT nunTP ni? r-rrc
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0.28
0.24 -
0.20 -
0.16 -
0.12
0.08 -
0.04
0 0.2 0.4 0.6 0.6 1.0 1.2 1.4 1.6 1.8
Nonmethane Hydrocarbon Concentration, ppm
Figure 3-25. Example of EKMA diagram for high-oxidant urban area.
Source: Derived from U.S. Environmental Protection Agency (1986b).
0.28
0.24
0.20
- 0.16
- 0.12
- 0.08
0.04
1 NMHC/NOX ratio and the peak ozone concentration are obtained from air monitoring data.
2 These two values define a point on the isopleth surface and from this point, the percentage
3 reductions in NMHC or NOX, or both, needed to achieve the ozone NAAQS can be
4 determined.
5 As examination of Figure 3-25 reveals, for an NMHC concentration of 0.6 ppmC, for
6 example, increasing NOX leads to increased Qj until NMHC/NOX ratios of about 5:1 to 6:1
7 are reached; further NOX increases, leading to lower NMHC/NOX ratios, inhibit
8 O3 formation. Thus, in this example, there is a "critical" ratio (in the range of 5:1 to 6:1) at
9 which the NOX effect on 03 changes direction. Besides this "critical" ratio, an "equal
10 control" NMHC/NOX ratio also exists, above which the reduction of NOX is more beneficial
December 1993
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1 in terms of O3 reduction than an equal percentage reduction in NMHC. This ratio, for the
2 isopleths shown in Figure 3-25, is roughly 8:1 to 9:1 for low levels of control, and as high
3 as 20:1 for the levels of control needed to reduce O3 to 0,12 ppm. Thus, for this particular
4 case (Figure 3-25), the chemical mechanism modeling evidence suggests that (1) NOX control
5 will increase the peak downwind O3 concentration at NMHC/NOX ratios of between 5:1 and
6 6:1 or lower; (2) both NOX control and NMHC control will be beneficial at somewhat higher
7 ratios, with control of NMHC being more effective; and (3) for ratios above 20:1, NOX
8 control is relatively more effective in reducing O3 to attain the ozone NAAQS.
9 The EKMA-based method for determining control strategies has some limitations, the
10 most serious of which is that predicted emissions reductions are critically dependent on the
11 initial NMHC/NOX ratio used in the calculations. This ratio cannot be determined with any
12 certainty and it is expected to be quite variable in an urban area. Another limitation is that
13 trajectory models have limited spatial and temporal scopes of application. They are generally
14 1-day models, simulating only one cell at a time. Another problem with the use of morning
15 NMHC/NOX ratios is the failure to account for photochemical evolution as urban emissions
16 are carried downwind. As demonstrated in simulations by Milford et al. (1989) and in smog
17 chamber studies by Johnson and Quigley (1989), an urban plume that is in the VOC-
18 controlling regime (low NMHC/NOX ratio) near city center can move increasingly into the
19 NOx-controUing regime (high NMHC/NOX ratio) as the air parcels age and move downwind.
20 This progression occurs because NOX is photochemically removed from an aging plume more
21 rapidly than VOC, causing the VOC/NOX ratio to increase. As demonstrated by Milford
22 et al. (1989), the implication of this evolution is that different locations in a large urban area
23 can show very different ozone sensitivities to VOC and NOX changes. Because of this and
24 other drawbacks, the 1990 Clean Air Act Amendments require that grid-based models be
25 used in most ozone nonattainment areas.
26
27 3.6.2 Model Components
28 3.6.2.1 Emissions Inventory
29 The spatial and temporal characteristics of VOC and NOX emissions must be supplied
30 as inputs to a photochemical air quality model. Emissions from area and point sources are
31 injected into ground-level grid cells, and emissions from large point sources are injected into
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1 upper-level cells. Total VOC emissions are generally apportioned into groups of chemically
2 similar species (e.g., alkanes, alkenes, aromatics, etc.) according to the requirements of the
3 chemical mechanism. This apportionment may be accomplished using actual emission
4 sampling and analysis or be based on studies of similar emission sources. Recognition of
5 potential undercounting in existing inventories has spurred efforts to improve the accuracy of
6 emissions inventories. In fact, at present the emissions inventory is the most rapidly
7 changing component of photochemical models. It has been recognized that both mobile and
8 stationary source components have been highly uncertain, and there is significant ongoing
9 effort to improve the accuracy of emissions inventories.
10 Some emissions terminology is as follows (Tesche, 1992):
11 • Emissions data - the primary information used as input to emissions models,
12
13 • Emissions model - the integrated collection of calculational procedures, or
14 algorithms, properly encoded for computer-based computation.
15
16 • Emissions estimates - the output of emissions models; used as input to
17 photochemical models.
18
19 • Emissions inventory - the aggregated set of emissions estimate files.
20
21 • Emissions model evaluation - the testing of a model's ability to produce accurate
22 emissions estimates over a range of source activity and physicochemical and
23 meteorological conditions.
24
25 Emissions input requirements for the UAM, for example, include:
26 • Spatial allocation of precursor emissions (VOC, NOX, CO):
27 — Actual location of individual point sources;
28 — Spatial allocation by gridding surrogates;
29 — Assignment of surrogates to other categories.
30
31 • Stack parameters for point sources:
32 — Temperature, height, diameter, exit velocity.
33
34 • Speciation of VOC emissions for CBM-IV mechanism:
35 — Region-specific speciation profiles;
36 — EPA default speciation profiles,
37
38 • Temporal allocation of precursor emissions:
39 — Operating schedules for individual point sources;
40 — Assignment of diurnal profiles for area and mobile sources.
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1 The emissions inventory component of modeling is moving in the direction of the use
2 of emissions models rather than inventories. Emissions models are being developed for the
3 Lake Michigan Oxidant Study (LMOS) and the San Joaquin Valley Air Quality Study
4 (SJVAQS) and the Atmospheric Utility Signatures, Predictions, and Experiments (AUSPEX),
5 designated as the SJVAQS/AUSPEX Regional Model Adaptation Project (SARMAP) studies.
6 The consistency of existing inventories was improved in 1990 when U.S. EPA released the
7 Emissions Preprocessor System (EPS) as a component of the UAM (U.S. Environmental
8 Protection Agency, 1990d). The EPS was updated in 1992 to EPS Version 2 (EPS2). It is
9 an emissions model that considers spatial and temporal disaggregation factors, speciation
10 data, and meteorological data to convert daily emissions estimates for each point source and
11 for area source categories and mobile source emissions factors computed by the EPA
12 MOBILES model into hourly, gridded speciated estimates needed by a photochemical grid
13 model.
14 A step beyond the EPS is the Emissions Modeling System (EMS) (Tesche, 1992), The
15 EMS1 utilizes emissions estimation and information processing methods to provide gridded,
16 temporally resolved, and chemically speciated base year emissions estimates for all relevant
17 source categories; to provide flexibility in forecasts of future year emissions rates; and to
18 provide modular code design facilitating module updating and replacement. The EMS
19 provides for easy substitution of alternative assumptions, theories, or input parameters (e.g.,
20 emissions factors, activity levels, spatial distributions) and facilitates sensitivity and
21 uncertainty testing.
22
23 3.6.2.2 Meteorological Input to Air Quality Models
24 Grid-based air quality models require, as input, the three-dimensional wind field for the
25 episode being simulated. This input is supplied by a so-called meteorological module.
26 Meteorological modules for constructing wind fields for air quality models Ml into one of
27 four categories (Tesche, 1987; Kessler, 1988):
28
29 • Objective analysis procedures that interpolate observed surface and aloft wind
30 speed and direction data throughout the modeling domain.
31 The EMS has been renamed the GMEP (Geocoded Model of Emissions and Projections).
December 1993 ^.17« rn?APT-nn
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1 • Diagnostic methods in which the mass continuity equation is solved to determine
2 the wind field.
3
4 • Dynamic, or prognostic, methods based on numerical solution of the governing
5 equations for mass, momentum, energy, and moisture conservation along with
6 the thermodynamic state equations on a three-dimensional, finite-difference
7 mesh.
8
9 • Hybrid methods that embody elements from both diagnostic and prognostic
10 approaches.
12 3.6.2.2 .1 Objective Analysis
13 Objective wind-field analysis involves the interpolation and extrapolation of wind speed
14 and direction measurements (collected at a number of unequally spaced monitoring stations)
15 to grid points throughout the region (Goodin et al., 1980). For flat terrain settings away
16 from complex mesoscale forcings, this class of techniques may provide an adequate method
17 for estimating the wind field, provided that appropriate weighting and smoothing functions
18 are used (Haltiner, 1971). For complex terrain or coastal-lake environments, however, it is
19 tenuous to interpolate between and extrapolate from surface observational sites except with an
20 unusually dense monitoring network. In most cases, the routinely available rawinsonde
21 network sounding data are even more severely limited because of the large distances (300 to
22 500 km) between sites and because soundings are made only every 12 h. The limitations of
23 even the best available data sets are most severe above the surface layer, where upper-level
24 observations are less frequent and more expensive to obtain. It will remain economically
25 unfeasible to obtain sufficiently dense atmospheric observations to allow any direct objective
26 analysis scheme to provide the required detail and accuracy necessary for use in advanced,
27 high-resolution photochemical models.
28
29 3.6.2.2.2 Diagnostic Modeling
30 In diagnostic wind modeling, the kinematic details of the flow are estimated by solving
31 the mass conservation equation. Dynamic interactions such as turbulence production and
32 dissipation and the effects of pressure gradients are parameterized. Various diagnostic wind
33 models have been developed, many employing the concepts introduced by Sherman (1978)
34 and Yocke (1981).
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1 In recent years, attempts have been made to combine the best features of objective
2 analysis and pure diagnostic wind modeling. The current release of U.S. EPA's UAM-IV
3 includes the Diagnostic Wind Model (DWM) as the suggested wind-field generator for this
4 urban-scale photochemical model. The DWM (U.S. Environmental Protection Agency,
5 1990c) is representative of this class of hybrid objective-diagnostic models. The DWM
6 combines the features of the Complex Terrain Wind Model (CTWM) (Yocke, 1981) and the
7 objective wind interpolation code developed at the California Institute of Technology (Goodin
8 et al., 1980). In the DWM, a two-step procedure is normally followed. First, a
9 "domain-scale" wind is estimated from available surface and upper-air synoptic data. This
10 initial field consists of a single wind vector (e.g., horizontal homogeneity) for each elevation.
11 The domain-scale wind is adjusted using procedures derived from the CTWM for the
12 kinematic effects of terrain such as lifting, blocking, and flow acceleration.
13 Thermodynamically generated influences such as mountain-valley winds are parameterized.
14 This first step produces a horizontally varying field of wind speed and direction for each
15 vertical layer within the DWM modeling domain. Typically, 10 to 12 vertical layers are
16 used. In the second step, available hourly surface and upper air measurements are
17 objectively combined with the step 1 hourly diagnostic flow fields to produce a resultant
18 wind field that matches the observations at the monitoring points and obeys the general
19 constraints of topography in regions where data are absent. The DWM contains a number of
20 user-specified options whereby different final flow fields may be produced, depending upon
21 selection of various smoothing and weighting parameters. The final output of the DWM is a
22 set of hourly averaged horizontal wind fields for each model layer.
23 Diagnostic models may invoke scaling algorithms that propagate the influence of the
24 surface-flow field into upper levels according to the local height of the inversion and the
25 Pasquill-Gifford-Turner stability category for the hour. Once the winds are created by
26 DWM, they must be "mapped" onto the photochemical model's vertical grid structure. This
27 function is normally accomplished in a two-step process. First, the DWM winds are
28 interpolated onto the photochemical model grid using simple linear interpolation. Second,
29 the three-dimensional divergence is computed in each grid cell and an iterative scheme is
30 used to minimize this divergence to a user-specified level. Typically, the output consists of
December 1993 3-177 DRAFT-DO NOT OTIOTP. OH TTTP
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1 "non-divergent" x- and y-direction wind components for direct input to the photochemical
2 model.
3 Among the advantages of the diagnostic modeling approach are its intuitive appeal and
4 modest computing requirements. The method generally reproduces the observed wind values
5 at the monitoring locations and provides some information on terrain-induced airflows in
6 regions where local observations are absent. In addition, one may calibrate diagnostic model
7 parameters for a particular locale based on site-specific field measurements. However, there
8 are several disadvantages. Diagnostic models cannot represent complex mesoscale
9 circulations, unless these features are well-represented by surface and aloft observations.
10 Often the vertical velocities produced by a diagnostic model are unrealistic and, in regions of
11 complex terrain, local horizontal flow velocities may often be an order of magnitude too high
12 (Tesche et al., 1987). Since the diagnostic model is not time-dependent, there is no inherent
13 dynamic consistency in the winds from one hour to the next. That is, calculation of the flow
14 field at hour 1200, for example, is not influenced by the results of the 1100 hour winds.
IS This is a particular problem in applications involving important flow regimes such as land-
16 sea breezes, mountain-valley winds, eddy circulations, and nocturnal valley jets, that take
17 several hours to develop and whose three-dimensional character is poorly characterized by
18 even the most intensive sampling networks.
19
20 3,6.2,2.3 Prognostic Modeling
21 In prognostic meteorological modeling, atmospheric fields are computed based on
22 numerical solutions of the coupled, nonlinear conservation equations of mass, momentum,
23 energy, and moisture. Derivations of these equations are presented extensively in the
24 literature (see, for example, Haltiner, 1971; Pielke, 1984; Seinfeld, 1986; Cotton and
25 Anthes, 1989). Many prognostic models have been developed for computing mesoscale wind
26 fields, as shown in the recent survey by Pielke (1989); and they have been applied to a
27 variety of problems, including the study of land-sea and land-lake circulations. Available
28 prognostic models range from relatively simple one-dimensional representations to complex
29 three-dimensional codes.
30 Prognostic wind models are attractive because they explicitly address the various
31 physical processes governing atmospheric flows. Consequently, they have the potential for
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1 describing a number of wind regimes that are particularly relevant to air pollution modeling,
2 such as flow reversal, daytime upslope flows, wind shear, and other mesoscale thermally
3 induced circulations. Drawbacks of prognostic models include the need to gather detailed
4 data for model performance testing and the large computational costs. Indeed, prognostic
5 models may require as much or more computer time than regional-scale photochemical
6 models. More intensive data sets are needed to evaluate prognostic models than for
7 diagnostic models, but this is not necessarily a disadvantage; rather, it provides the modeler
8 and decision-maker with a far better basis for judging the adequacy of the model than can be
9 achieved with objective or diagnostic models.
10 Summaries of prognostic models available for use in air quality modeling are presented
11 extensively in the literature (e.g., Pielke, 1989; Benjamin and Seaman, 1985; McNally,
12 1990; Stauffer et al., 1985; Stauffer and Seaman, 1990; Ulrickson, 1988; Wang and Warner,
13 1988; and Yamada et al,, 1989). From these reviews, two models stand out as representing
14 the present state-of-science in applications-oriented prognostic modeling. These are the
15 Mesoscale Model Versions 4 and 5 (MM4/MM5) developed by Pennsylvania State University
16 and the National Center for Atmospheric Research (NCAR) (Anthes and Warner, 1978;
17 Anthes et al., 1987; Zhang et al., 1986; Seaman, 1990; Stauffer and Seaman, 1990), and the
1 8 Coast and Lake Regional Atmospheric Modeling System (CAL-RAMS) (Tripoli and Cotton,
19 1982; Pielke, 1974, 1984, 1989; Lyons et al., 1991).
20 Two ongoing regional ozone modeling programs in the U.S. (i.e., LMOS and
21 SARMAP) are using prognostic models to drive regional ozone models. Part of the U.S.
22 EPA's long-range plan (in the Office of Research and Development) for model development
23 is to construct a "third" generation modeling framework referred to as MODELS 3 (Dennis,
24 1991). This modeling system will consolidate all of the agency's three-dimensional models.
25 The current plan calls for meteorological inputs to the MODELS 3 system to be supplied by
26 prognostic models. The MM4 model (the hydrostatic version of MM5) is presently being
27 examined by U.S. EPA for this purpose.
28 Activities are currently underway in the Lake Michigan Oxidant Study (LMOS) to
29 supply prognostic model fields to U.S. EPA's Regional Oxidant Model (ROM) for use in
30 simulating regional ozone distributions in four multiple-day ozone episodes extensively
31 monitored during the 1991 field program in the midwest. The U.S. EPA will be exercising
December 1993 ^,170 DRAFT-DO MOT nTTrvrc rvo
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1 ROM2.2 (version 2,2) with fields obtained from CAL-RAMS (Lyons et al., 1991) to
2 examine whether prognostic model output gives improved regional ozone estimates (Guinnup
3 and Possiel, 1991).
4 The SARMAP program is the modeling and data analysis component of a multiyear
5 collaboration between two projects, the San Joaquin Valley Air Quality Study (SJVAQS) and
6 the Atmospheric Utility Signatures, Predictions, and Experiments Study (AUSPEX). The
7 major near-term objective of SARMAP is to understand the processes that lead to high ozone
8 concentrations in the San Joaquin Valley of California. An overview of the regional
9 meteorological and air quality modeling approach of SARMAP is described by Tesche
10 (1993). For SARMAP, the MM5 model was chosen as the "platform" prognostic
11 meteorological model because of its broad application history, its demonstrated reliability on
12 large domains requiring spatially and temporally varying boundary conditions, and its
13 capability for four-dimensional data assimilation (FDDA) (see Section 3.6.2.2.4)—needed for
14 longer-range simulations. All of these attributes are crucial to the success of mesoscaie
15 meteorological modeling.
16 Prognostic models are beEeved to provide a dynamically consistent, physically realistic,
17 three-dimensional representation of the wind and other meteorological variables at scales of
18 motion not resolvable by available observations. However, the meteorological fields
19 generated by a prognostic model do not always agree with observational data. Numerical
20 approximations, physical parameterizations, and initialization problems are among the
21 potential sources of error growth in model forecasts that can cause model solutions to deviate
22 from actual atmospheric behavior. Described below are methods that have been devised over
23 the past 20 years to mitigate these problems.
24 "Post-processing" refers to methods whereby output fields from prognostic models are
25 selectively adjusted through a series of objective techniques with the aim of improving the
26 realism of the resultant fields. Examples of this procedure (sometimes referred to as
27 objective combination) are given by Cassmassi et al. (1990) in the Los Angeles Basin,
28 Kessler and Douglas (1989) in the South Central Coast Air Basin, and Moore et al. (1987) in
29 the San Joaquin Valley.
30 Ideally, a prognostic model should be initialized with spatially varying, three-
31 dimensional fields (i.e., wind, temperature, moisture) that represent the state of the
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1 atmosphere at the initial simulation time A prognostic model that is initialized with such
2 fields, however, can generate large non-meteorological "waves" when the initial conditions
3 do not contain a dynamic balance consistent with the model formulation (Hoke and Ant lies,
4 1976; Errico and Bates, 1988). The objective of an initialization procedure is to bring the
5 initial conditions into dynamic balance so that the model can integrate forward with a
6 minimum of noise and a maximum of accuracy (Haltiner and Williams, 1980). Dynamic
7 initialization makes use of a model's inherent adjustment mechanism to bring the wind and
8 temperature into balance prior to the initial simulation time. In this technique, a
9 "pre-simulation" integration of the model equations produces a set of dynamically balanced
10 initial conditions. By allowing the simulation to begin with a balanced initial state, this
11 technique reduces the generation of meteorological noise and thus improves the quality of the
12 simulation.
13
14 3.6.2.2.4 Four-Dimensional Data-Assimilation Techniques
15 Four Dimensional Data Assimilation (FDDA) refers to a class of procedures in which
16 observational data are used to enhance the quality of meteorological model predictions
17 (Harms et al., 1992). The most common use of FDDA today in applications-oriented models
18 is known as Newtonian relaxation, or simply as "nudging". With this method, model
19 estimates at a particular time interval are adjusted toward the observations by adding artificial
20 tendency terms to the governing prognostic equations. The objective of this method is to
21 improve prognostic model estimates through the use of valid, representative observational
22 data. As an example of mis procedure, a linear term is added to the momentum equations to
23 "nudge" the dynamic calculation towards the observed state at each time step in regions
24 where data are available. The FDDA procedures may be thought of as the joint use of a
25 dynamic meteorological model in conjunction with observed data (or analysis fields based on
26 these data) in such a manner that the prognostic equations provide temporal continuity and
27 dynamic coupling of the hourly fields of monitored data (Seaman, 1990).
28 A recent example of the use of FDDA in regional-scale applications with the
29 MM4/RADM model is given by Stauffer and Seaman (1990). Attempts to apply FDDA in
30 support of urban-scale photochemical grid modeling are described by Tesche et al. (1990b)
31 and McNally (1990) for the San Diego Air Basin and by Stauffer et al. (1993) for the Grand
December 1993 3-181 DRAFT-DO NOT QUOTE OR CTTR
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1 Canyon region of Arizona. Currently, FDDA is being used in the CAL-RAMS simulations
2 in the LMOS program (Lyons et al., 1991) and in the MM5 simulations for SARMAP
3 (Seaman, 1992).
4
5 3.6.2.3 Chemical Mechanisms
6 A chemical kinetic mechanism (a set of chemical reactions), representing the important
7 reactions that occur in the atmosphere, is used in an air quality model to estimate the net rate
8 of formation of each pollutant simulated as a function of time.
9 Various grid models employ different chemical mechanisms. Because so many VOCs
10 participate in atmospheric chemical reactions, chemical mechanisms that explicitly treat each
11 individual VOC component are too lengthy to be incorporated into three-dimensional
12 atmospheric models. Lumped mechanisms are therefore used (e.g., Lurmann et al., 1986;
13 Gery et al., 1989; Carter, 1990; Stockwell et ai, 1990). These lumped mechanisms are
14 highly condensed and do not have die ability to follow explicit chemistry because of this
15 lumping. Lumped-molecule mechanisms group VOCs by chemical classes (alkanes, alkenes,
16 aromatics, etc.). Lumped-structure mechanisms group VOCs according to cartxm structures
17 within molecules. In both cases, either a generalized (hypothetical) or surrogate (actual)
18 species represents all species within a class. Organic product and radical chemistry is limited
19 to a few generic compounds to represent all products; thus, chemistry after the first oxidation
20 step is overly uniform. Some mechanisms do not conserve carbon and nitrogen mass. Some
21 molecules do not easily "fit" the classes used in the reduced mechanisms. Because different
22 chemical mechanisms follow different approaches to "lumping," and because the developers
23 of the mechanisms made different assumptions about how to represent chemical processes
24 that are not well understood, models can produce somewhat different results under similar
25 conditions (Dodge, 1989).
26 No single chemical mechanism is currently considered "best." Both UAM-IV and
27 ROM utilize the CBM-IV mechanism, which, along with the SAPRC (Statewide Air
28 Pollution Research Center, University of California, Riverside) and RADM mechanisms, is
29 considered to represent the state-of-the-science (Tesche et al., 1992; National Research
30 Council, 1991). Agreement among mechanisms is better for ozone than for other secondary
31 pollutants (Dodge, 1989, 1990; National Research Council, 1991), raising concern that the
December 1993 3-182 DRAFT-DO NOT QUOTE OR CITE
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1 mechanisms may suffer from compensating errors These mechanisms are at least 5 years
2 old and often tested on much older smog chamber data.
3 The chemical mechanisms used in existing photochemical ozone models contain
4 uncertainties that may limit the accuracy of their predictions. The reactions that are included
5 in these mechanisms generally fall into one of three categories:
6 (1) Reactions for which the magnitude of their rate constants and their product
7 distribution is well known. These include mostly the inorganic reactions and
8 those for the simple carbonyls.
9
10 (2) Reactions with known rate constants and known products but uncertain product
11 yields. These are mostly organic reactions, and the actual product yields
12 assumed may vary among mechanisms.
13
14 (3) Reactions with known rate constants but unknown products. Each mechanism
15 assumes its own set of products for reactions in this class. This class includes
16 aromatic oxidation reactions.
17
18 Most inorganic gas-phase processes are understood. Regarding classes of VOCs the
19 following general comments can be made:
20 • Unbranched alkanes comprise approximately one-half of the carbon emissions in
21 urban areas. Reaction rates are relatively slow. The only important reaction is
22 with the hydroxyl radical. For alkanes C4 or below, the chemistry is well
23 understood and the reaction rates are slow. For C5 and higher alkanes the
24 situation is more complex because few reaction products have been found.
25
26 • Branched alkanes have rates of reaction that are highly dependent on structure.
27 Rate constants have been measured for only a few of the branched alkanes and
28 reaction produces for this class of organics are not well characterized.
29
30 • Alkenes are highly reactive with hydroxyl, ozone, and the NO3 radical. Most
31 rate constants of these reactions are known. Alkenes make up about 15 % of the
32 emitted carbon and constitute about 20 % of the hydrocarbon reactions in urban
33 areas. Ozone reaction products are not well characterized, and the mechanisms
34 are poorly understood. Mechanisms for the NO3 radical are also uncertain.
35
36 • Aromatics constitute about 15% of the carbon compounds emitted and 20% of
37 the hydrocarbons reacting in urban areas. Aromatics have been frequently
38 studied, but only a few reaction products have been well characterized.
39 Aromatics act as strong NOX sinks under low NOX conditions.
40
41 Mechanisms used in photochemical air quality models thus have uncertainties, largely
42 attributable to a lack of fundamental data on products and product yields. The missing
December 1993 3-183 DRAFT-DO Nfvr nun-rc nr»
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1 information necessitates that assumptions be made. Current mechanisms provide acceptable
2 overall simulation of ozone generation in smog chamber experiments. Specific VOCs may,
3 however, be simulated poorly, and products other than ozone may not be accurately
4 simulated. Existing mechanisms are mostly applicable to single-day, high NOX conditions
5 because those are the conditions of almost all smog chamber experiments. Low NOX
6 condition simulations are less well verified. Fundamental kinetic data are needed on the
7 photooxidation of aromatics, higher alkanes, and higher alkenes to fill in areas of uncertainty
8 in current mechanisms. Whereas these uncertainties are important and require continued
9 research to remove, the uncertainties are likely not such that general conclusions about the
10 relative roles of hydrocarbons and NOX in ozone formation will be changed by new data.
11
12 3.6.2.4 Deposition Processes
13 Species are removed from the atmosphere by interaction with ground-level surfaces,
14 so-called dry deposition; and by absorption into airborne water droplets followed by transport
15 of the water droplets, wet deposition. Dry deposition is an important removal process for
16 ozone and other species on both the urban and regional scales and is included in all urban-
17 and regional-scale models as a contribution to the ground-level flux of pollutants. Wet
18 deposition is a key removal process for gaseous species on the regional scale and is included
19 in regional scale acid deposition models. Urban-scale photochemical models have generally
20 not included a treatment of wet deposition as ozone episodes do not occur during periods of
21 significant clouds or rain.
22
23 3.6.2.4.1 Dry Deposition
24 It is generally impractical to simulate, in explicit detail, the complex of multiple
25 physical and chemical pathways that result in dry deposition to individual surface elements.
26 Because of this, the usual practice has been to adopt simple parameterizations mat consolidate
27 the multitude of complex processes. For example, it is generally assumed that the dry
28 deposition flux is proportional to the local pollutant concentration [at a known reference
29 height (zr), typically 10 m], resulting in the expression F = -v^C, where F represents the
30 dry deposition flux (the amount of pollutant depositing to a unit surface area per unit time)
December 1993 3-184 DRAFT-DO NOT QUOTE OR CITE
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1 and C is the local pollutant concentration at the reference height. The proportionality
2 constant, vd> has units of length per unit time and is known as the deposition velocity.
3 It is customary to interpret the dry deposition process in terms of the electrical
4 resistance analogy, where transport of material to the surface is assumed to be governed by
5 three resistances in series: the aerodynamic resistance (ra), the quasi-laminar layer resistance
6 (rb), and the surface or canopy resistance (rs) (Wu et al., 1992). The aerodynamic resistance
7 characterizes the turbulent transport through the atmosphere from reference height zr down to
8 a thin layer of stagnant air very near the surface. The molecular-scale diffusive transport
9 across the thin quasi-laminar sublayer near the surface is characterized by rb. The chemical
10 interaction between the surface and the pollutant of interest once the gas molecules have
11 reached the surface is characterized by rc. The total resistance (rr) is the sum of the three
12 individual resistances, and is, by definition, the inverse of the deposition velocity,
13 M'vd=rt=ra'>rrbJt~rs' Note that the deposition velocity is small when any one of the
14 resistances is large. Hence, either meteorological factors or the chemical interactions on the
15 surface can govern the rate of dry deposition.
16 Dry deposition velocities of HNO3 and SO2 are typically ~2 cm s~ , and those of
17 O3 and peroxyacetyl nitrate (PAN) are generally approximately 0.5 cm s" and «* 1 cm s" ,
18 respectively (see, for example, Dolske and Gate, 1985; Colbeck and Harrison, 1985; Huebert
19 and Robert, 1985; Shepson et al., 1992). With a 1-km-deep inversion or boundary layer, the
20 time-scale for dry deposition is of the order of 1 day for a deposition velocity of 1 cm s"1,
21 and dry deposition is important for those chemicals with high or fairly high deposition
22 velocities and long or fairly long lifetimes ( > 10 days) due to photolysis and chemical
23 reaction (for example, HNO3, SO2> and H2O2, as well as 03 and PAN).
24 A number of researchers have reviewed the deposition literature and provided
25 summaries of deposition velocity data. The rank ordering of deposition velocity values
26 among pollutant species based on several such studies is summarized as follows:
27 McRae and Russell (1984):
28 HNO3 > SO2 > NO2 » O3 > PAN > NO
29 Derwent and Hov (1988):
30 HNO3 > SO2 = O3 > NO2 > PAN
31
December 1993 1-18S DRAFT-DO NOT OTIOTR OT? CITV
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1 McRae et al. (1982b):
2 O, > NO2 > PAN > NO > CO
3 Chang et al. (1987»:
4 HNO3 > H2O2 > NH3 > HCHO > O3 = SO2 = NO2 = NO > RCHO
5
6 There is general agreement that HNO^ is removed at the highest observed rates, which
7 is consistent with the relative deposition rates observed by Huebert and Robert (1985) and
8 which suggests that the surface resistance of HNQij is essentially zero. Most of the surveys
9 are roughly consistent with the relative deposition velocity ordering seen in the experiments
10 of Kin and Chamberlain (1976): diffusion-limited acids > SO2 > NO2 « O3 > PAN >
11 NO > CO. This suggests surface resistance values should be ordered approximately as:
12 CO > NO > PAN > O3 * NO2 > SO2 > HNO3 = 0.
13 There are a significant number of other gases for which there are no surface resistance
14 data and for which values must be estimated using engineering judgment. The values should
IS be consistent with the existing experimental values for vegetative surfaces, and should
16 preserve the apparent rank ordering among the pollutant species (discussed above). For
17 ozone, surface resistance values by land-use type and season recommended by Sheih et al.
18 (1986) and Wesely (1988) are appropriate. For NO, NO2, NH3, H2O2, HCHO, and
19 CH3CHO, the surface resistance values for each land use can be estimated from that for SO2
20 (Wesely, 1988), except that different proportionality factors should be used for NO and NO2.
21
22 3.6.2.4.2 Wet Deposition
23 Wet deposition refers to the removal of gases and particles from the atmosphere by
24 precipitation events, through incorporation of gases and particles into rain, cloud, and fog
25 water followed by precipitation at the earth's surface. Removal of gases and particles during
26 snow falls is also wet deposition. Wet removal of gases arises from equilibrium partitioning
27 of the chemical between the gas and aqueous phases (Bidleman, 1988; Mackay, 1991). This
28 partitioning can be defined by means of a washout ratio, Wg, with Wg - [C\rainl\C\cdr,
29 where [Qrain and \C\air are the concentrations of the chemical in the aqueous and gas
30 phases, respectively. Since Wg is the inverse of the air/water partition coefficient, J5Taw, then
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1 (Mackay, 1991) Wg = RT/H, where R is the gas constant, T is the temperature, and H is the
2 Henry's Law Constant.
3 Particles, and particle-associated chemicals, are efficiently removed from the
4 atmosphere by precipitation events, and the washout ratios for particles, Wp, are typically in
5 the range 104-106 (Eisenreich et al., 1981; Bidleman, 1988). Wet deposition is important for
6 particles (and particle-associated chemicals) and for those gas-phase compounds with washout
7 ratios of Wg S 1(T. Examples of such gaseous chemicals are HNO3, H2O2, phenol, and
8 cresols, all of which are highly soluble in water. Formaldehyde is present in the aqueous
9 phase as the glycol, H2C(OH)2, and has an effective washout ratio of 7 x 103 at 298 K
10 (Betterton and Hoffmann, 1988; Zhou and Mopper, 1990). Note that the importance of wet
1 1 deposition may depend on whether the chemical is present in the gas phase or is particle-
12 associated. For example, the gas-phase alkanes have low values of Wg and are inefficiently
13 removed by wet deposition, while the particle-associated alkanes are efficiently removed by
14 wet deposition (Bidleman, 1988), through removal of the host particles.
15
16 3.6.2.5 Boundary and Initial Conditions
17 When a grid-based photochemical model is applied to simulate a past pollution episode,
18 it is necessary to specify the concentration fields of all the species computed by the model at
19 the beginning of the simulation. These concentration fields are called the initial conditions.
20 Throughout the simulation it is necessary to specify the species concentrationSj called the
21 boundary conditions, in the air entering the three-dimensional geographic domain.
22 Three general approaches for specifying boundary conditions for urban-scale
23 applications can be identified: (1) Use the output from a regional-scale photochemical
24 model; (2) use objective or interpolative techniques with ambient observational data; or,
25 (3) for urban areas sufficiently isolated from significant upwind sources, use default regional
26 background values and expand the area that is modeled.
27 In the ideal case, observed data would provide information about the concentrations for
28 all the predicted species at the model's boundaries. An alternative approach is to use
29 regional models to set boundary and initial conditions. This is, in fact, preferred when
30 changes in these conditions are to be forecast. In any event, simulation studies should use
3 1 boundaries that are far enough from the major source areas of the region that concentrations
December 1993 3-187 DRAFT-DO NOT nTTrvro ru>
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1 approaching regional values can be used for the upwind boundary conditions. Boundary
2 conditions at the top of the area that is being modeled should use measurements taken from
3 aloft whenever they are available. Regional background values are often used in lieu of
4 measurements. An emerging technique for specifying boundary conditions is the use of a
5 nested grid, in which concentrations from a larger, coarse grid are used as boundary
6 conditions for a smaller, nested grid with finer resolution. This technique reduces
7 computational requirements compared to those of a single-size, fine-resolution grid.
8 Initial conditions are determined mainly with ambient measurements, either from
9 routinely collected data or from special studies. Where spatial coverage with data is sparse,
10 interpolation can be used to distribute the surface ambient measurements. Because few
11 measurements of air quality data are made aloft, it is generally assumed that species
12 concentrations are initially uniform in the mixed layer and above it. To ensure that the
13 initial conditions do not dominate the performance statistics, model performance should not
14 be assessed until the effects of the initial conditions have been swept out of the grid.
15
16 3.6.3 Urban and Regional Ozone Air Quality Models
17 Grid-based models that have been widely used to evaluate ozone and acid deposition
IS control strategies are:
19 « The Urban Airshed Model (UAM), developed by Systems Applications, Inc., has
20 been, and is continuing to be, applied to urban areas throughout the country,
21 It is described in Section 3.6.3.1. The current U.S. EPA-approved version is
22 UAM-IV. The UAM-V, which has been developed for the Lake Michigan
23 Oxidant Study (LMOS), is a nested regional-scale model.
24
25 • The California Institute of Technology/Carnegie Institute of Technology (CIT)
26 model has been applied to California's South Coast Air Basin (McRae et al.,
27 1982a,b; McRae and Seinfeld, 1983; Milford et al., 1989; Harley et al., 1993).
28
29 • The Regional Oxidant Model (ROM), developed by U.S. EPA, has been applied
30 to the northeastern and southeastern United States (Schere and Way land,
31 1989a,b). It is described in Section 3.6.3.2.
32
33 • The Acid Deposition and Oxidant Model (ADOM) was developed by ENSR
34 Consulting and Engineering for the Ontario Ministry of the Environment and
35 Environment Canada (Venkatrani et al., 1988) and the German
36 Umweldbundersamdt. Its primary application has been to acidic deposition.
37
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1 • The Regional Acid Deposition Model (RADM) was developed by the National
2 Center for Atmospheric Research (NCAR) and the State University of Nev York
3 for the National Acid Precipitation Assessment Program (NAPAP), The primary
4 objective of RADM applications is the calculation of changes in sutftir and
5 nitrogen deposition over the eastern United States and southeastern Canada
6 resulting from changes in emissions (National Acid Precipitation Assessment
7 Program, 1989). See Section 3.6.3.3 for a description of RADM.
8 A summary of the major applications of the above air quality models, including the
9 Sulfur Transport Eulerian Model (STEM-II) „ is presented in Table 3-21. All of the models
10 are nominally based on a 1-h time resolution. The horizontal spatial resolutions vary from
11 5 to 120 km. Typical spatial resolutions used in past model applications are summarized in
12 Table 3-22. It is important to note that the spatial scale at which a model is applied is
13 governed by the manner in which physical processes are treated and the spatial scale of the
14 inputs. The regional models can have a vertical resolution on the order of 10 to 15 layers
15 extending up to 6 to 10 km in order to treat vertical redistribution of species above the
16 planetary boundary layer. This increased vertical resolution often comes at the expense of
17 decreased horizontal resolution. Urban models typically have two to five layers extending up
18 to 1,000 to 2,000 m. The treatment of meteorological fields by the six models is
19 summarized in Table 3-23. Generally the treatment of meteorology is separate from the air
20 quality model itself, and models can employ wind fields prepared by different approaches as
21 long as consistent assumptions, such as non-divergent wind field, are employed in each
22 model. The regional models, ROM, RADM, ADOM, and STEM-IT, treat the vertical
23 redistribution of pollutants resulting from the presence of cumulus clouds. Table 3-24
24 summarizes the gas-phase chemical mechanisms incorporated into the six models. Generally
25 three chemical mechanisms are used in the models: (1) CBM-IV used in ROM and UAM;
26 (2) versions of the SAPRC mechanism used in ADOM, STEM-H, and CIT; and (3) the
27 RADM mechanism. Of the three chemical mechanisms, RADM is the largest and CBM-IV
28 is the smallest. Aqueous-phase chemistry is currently treated only in the regional models.
29 Cloud processes are treated in the three regional models, RADM, ADOM, and STEM-II
30 (Table 3-25). Cumulus venting and solar attenuation are treated in ROM. Layer 3 depths
31 are also influenced by cloud thickness. At present, only RADM, ADOM and STEM-n treat
32 wet deposition. The treatment of dry deposition in the models is also summarized in
33 Table 3-25.
December 1993 1.1 so DRAFT-T>O NOT OTTOTP nv rrrn
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Model
TABLE 3-21. GRID-BASED URBAN AND REGIONAL AIR POLLUTION MODELS:
OVERVIEW OF THREE-DIMENSIONAL AIR QUALITY MODELS
Major Applications
Major References for
Model Formulation
Selected References for Model Performance
Evaluation and Application
UAM
CIT
ROM
RADM
ADOM
STEM-H
Urban and nonurban areas in the
United States and Europe
Los Angeles Basin
Eastern United States
(E of 99° W longitude)
Eastern North America
Eastern North America and
Northern Europe
Reynolds et al. (1973, 1974, 1979)
Tesche et al. (1992)
U.S. Environmental Protection Agency
(1990a-e)
Scheffe and Morris (1993)
McRae et al, (1982a)
Lamb (1983)
Chang et al. (1987)
Venkatram et al. (1988)
Philadelphia area, Kentucky, and Cannichael et al. (1986)
northeastern United States, central
Japan
Tesche et al. (1992)
McRae and Seinfeld (1983)
Russell et al. (1988a,b)
Harley et al. (1993)
Schere and Wayland (1989a,b)
Meyer et al. (1991)
Middleton et al. (1988, 1993)
Middleton and Chang (1990)
Dennis et al. (1993a)
Cohn and Dennis (1994)
Venkatram et al. (1988)
Macdonald et al. (1993)
Karamchandani and Venkatram (1992)
Cannichael et al. (1991)
Saylor et al. (1991)
-------
If Model
TABLE 3-22. GRID-BASED URBAN AND REGIONAL AIR POLLUTION MODELS:
TREATMENT OF EMISSIONS AND SPATIAL RESOLUTION
Emitted Species
Point-Source Emissions
Area-Source Emissions
Vertical Resolution
UAM SO2, sulfate, NO, NO2, CO,
NH3, and 8 classes of ROG
and PM(4 size classes)
CIT SO2, sulfate, NO, NO2, CO,
NH3, and 6 classes of ROG
and PM(4 size classes)
Released into grid cell in layer
corresponding to plume rise in
UAM; treated with a reactive
plume model in PARIS
Treated with a plume model
with simple NOX and O3
chemistry
Grid-average with resolution
ranging from 4 km x 4 km to
10 km X 10 km in past
applications
Grid-average with 5 km x
5 km resolution in past
applications
Typically, 5-6 layers up to about 1.5 km
Five layers up to about 1.5 km
ROM CO, NO, NO2, and 8 classes Released into grid cell in layer Grid-average with 18.5 km X Three layers up to about
of ROG corresponding to plume rise 18.5 km resolution in present 4 km
applications
.o
5 RADM
SO2, sulfate, NO, NO2, CO, Released into grid cell in layer Grid-average with 80 km X
NH3, and 12 classes of ROG corresponding to plume rise 80 km resolution in past
applications
Fifteen layers up to about 16 km
1
3
ADOM SO2, sulfate, NO, NO2,NH3, Released into grid cell in layer Grid-average with resolution Twelve layers up to about 10 km
and 8 classes of ROG and PM corresponding to plume rise ranging from 60 km X 60 km
to about 120 km X 120 km in
past applications
STEM-n SO2, sulfate, NO, NO2, NH3, Released into grid cell in layer Grid-average with resolution Ten to 14 layers up to about 6 km
and 8 classes of ROG corresponding to plume rise ranging from 10 km X 10 km
to 56 km X 56 km in past
applications
0
-------
Model
TABLE 3-23. GRID-BASED URBAN AND REGIONAL AIR POLLUTION MODELS:
TREATMENT OF METEOROLOGICAL FIELDS, TRANSPORT AND DISPERSION
Meteorology
Transport
Turbulent Diffusion
UAM
CIT
ROM
RADM
ADOM
STEM-n
Constructed through data interpolation or 3-D wind field. Finite difference numerical Vertical turbulent diffusion function of atmospheric
calculated with land-sea breeze or complex technique. stability and friction velocity. Constant horizontal
terrain wind model. turbulent diffusion coefficient.
Constructed through data interpolation with 3-D wind field. Finite element numerical
diagnostic wind model technique.
Constructed through data interpolation.
3-D wind field with vertical transport
through cumulus clouds. Finite difference
numerical technique.
Calculated with Community Climate Model 3-D wind field with vertical transport
(CCM) and MM4 through cumulus clouds. Finite difference
numerical technique
Constructed through data interpolation in
combination with prognostic planetary
boundary-layer model.
Calculated with dynamic wind model
(MASS) or constructed through data
interpolation.
3-D wind field with vertical transport
through cumulus clouds. Cubic spline
numerical technique.
3-D wind field with vertical transport
through clouds. Finite element numerical
technique.
Vertical turbulent diffusion function of atmospheric
stability and friction velocity. Horizontal turbulent
diffusion function of mixing height and convective
velocity scale.
Vertical turbulent diffusion function of atmospheric
stability. Horizontal turbulent diffusion function of
atmospheric stability, convective cloud cover and
velocity scale, and the depths of the boundary layer
and clouds.
Vertical turbulent diffusion function of atmospheric
stability and wind shear. No horizontal turbulent
diffusion.
Vertical turbulent diffusion calculated from planetary
boundary layer model. No horizontal turbulent
diffusion.
Vertical turbulent diffusion function of atmospheric
stability and surface roughness. Horizontal turbulent
diffusion proportional to vertical turbulent diffusion.
-------
I —
Model
TABLE 3-24. GRID-BASED URBAN AND REGIONAL AIR POLLUTION MODELS:
TREATMENT OF CHEMICAL PROCESSES
Gas-Phase Chemistry
Aqueous- Phase Chemistry
U)
UAM
CIT
ROM
RADM
ADOM
- STEM-0
o
Eighty-seven reactions among 36 species including NOX, 03, No treatment of aqueous-phase chemistry.
ROG, and SO2 (CBM-IV) (Gery et al., 1988, 1989)
One hundred-twelve reactions among 53 species including No treatment of aqueous-phase chemistry.
NOX, O3, ROG, and SO2 (Lurmann et al., 1986)
Eighty-seven reactions among 36 species including NOX, O3, No treatment of aqueous-phase chemistry.
ROG, and SO2 (CBM-IV)
One hundred fifty-seven reactions among
59 species including NOX , O3, ROG, and SO2 (Stockwell
et al., 1990)
Forty-two equilibria and five reactions for SO2 oxidation.
One hundred-twelve reactions among 53 species including Fourteen equilibria and five reactions for SO2 oxidation
NOX, O3, ROG, and SO2 (Lurmann et al., 1986)
One hundred-twelve reactions among 53 species including Twenty-six equilibria and about 30 reactions for SO2 and NOX
NOX, O3, ROG, and SO2 (Lurmann et al., 1986) oxidation, radical chemistry, and transition metal chemistry.
I
2
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Model
TABLE 3-25. GRID-BASED URBAN AND REGIONAL AIR POLLUTION MODELS:
TREATMENT OF CLOUD AND DEPOSITION PROCESSES
Cloud Processes
Wet Deposition
Dry Deposition
D
O
I
UAM
CIT
ROM
RADM
ADOM
STEM-n
No treatment of cloud processes.
No treatment of cloud processes.
No treatment of cloud processes, except
vertical transport treatment.
No treatment of wet deposition
No treatment of wet deposition.
No treatment of wet deposition
Treatment of (1) precipitating cumulus Calculated from precipitation rate and
clouds, (2) precipitating stratus clouds and cloud average chemical composition.
(3) fair-weather cumulus clouds based on No below-cloud scavenging.
precipitation amount, temperature, and
relative humidity vertical profiles. Use of
cloud-averaged properties for aqueous
chemistry.
Treatment of (1) cumulus clouds and
(2) stratus clouds based on precipitation
amount (for stratus clouds), temperature,
and relative humidity vertical profiles.
Vertical resolution for cloud chemistry.
Treatment of clouds with the Advanced
Scavenging Module based on cloud base
height, precipitation rate, and surface
temperature.
Calculated from precipitation rate and
vertically weighted cloud average
chemical composition, below-cloud
scavenging included.
Dry deposition velocity approach; function of wind
speed, friction velocity, land type, and species.
Dry deposition velocity approach; function of
atmospheric stability, wind speed, land type, and species.
Resistance transfer approach; function of land type, wind
speed, atmospheric stability, and species.
Resistance transfer approach; function of atmospheric
stability, wind speed, season, land type, insolation,
surface wetness, and species.
Resistance transfer approach; function of atmospheric
stability, wind speed, land type, season, insolation, and
species.
Calculated with the Advanced Scavenging Resistance transfer approach; function of atmospheric
Module. Treats cloud water, rain water, stability, land type, wind speed, and species.
and snow; below-cloud scavenging
included.
8
n
-------
1 More detailed descriptions will now be presented for UAM, ROM, and RADM. The
2 UAM is described as it is officially specified by EPA as a grid-based model for urban-scale
3 ozone control strategy determination. Its regional-scale ozone model, ROM is being used by
4 U.S. EPA to evaluate ozone control measures for the eastern United States and to provide
5 boundary conditions for urban area simulations using UAM. Representative of a
6 comprehensive state-of-the-science ozone/acid deposition model, RADM has been used to
7 evaluate combined ozone and acid deposition abatement strategies for the northeastern United
8 States and Canada.
9 The U.S. Environmental Protection Agency is embarking on a project to produce the
10 next generation of photochemical models, termed MODELS 3 (Dennis et al., 1993b). This
11 group of models will be flexible (scalable grid and domain), will be modular (modules with
12 interchangeable data structure), will have uniform input/output across subsystems, and will
13 contain advanced analysis and visualization features. The models will be designed to take
14 advantage of the latest advances in computer architecture and software.
15
16 3.6.3.1 The Urban Airshed Model
17 The UAM is the most widely applied and broadly tested grid-based photochemical air
18 quality model. The model is described in a number of sources, including a multi-volume
19 series of documents issued by the U.S. Environmental Protection Agency (U.S.
20 Environmental Protection Agency, 1990a,b,c,d,e) and a comprehensive evaluation by Tesche
21 et al. (1992). Current versions include provisions enabling the user to model transport and
22 dispersion within both the mixed and inversion layers. The computer codes have been
23 structured to allow inclusion of up to 10 vertical layers of cells and any number of cells in
24 the horizontal directions.
25 The original UAM developed by Reynolds et al. (1973) simulated the dynamic behavior
26 of six pollutants: reactive and unreactive hydrocarbons, NO, NOj, ozone, and CO. Since
27 1977, the UAM has employed various versions of the Carbon-Bond Mechanism. Currently,
28 the model utilizes the CBM-IV Mechanism (Gery et al., 1988, 1989), which treats
29 36 reacting species. Reactive organic compounds include alkanes, alkenes, aromatics, and
30 aldehydes; while nitrogen-bearing species include nitrous acid (HONO), nitric acid (HNO3),
31 and peroxyacetyl nitrate (PAN).
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1 3.6.3.2 The Regional Oxidant Model
2 The ROM was designed to simulate most of the important chemical and physical
3 processes that are responsible for the photochemical production of 03 over regional domains
4 and for multiple 3-day episodes of up to IS days in duration. These processes include
5 (1) horizontal transport, (2) atmospheric chemistry and subgrid-scale chemical processes,
6 (3) nighttime wind shear and turbulence associated with the low-level nocturnal jet, (4) the
7 effects of cumulus clouds on vertical mass transport and photochemical reaction rates,
8 (5) mesoscale vertical motions induced by terrain and the large-scale flow, (6) terrain effects
9 on advection, diffusion, and deposition, (7) emissions of natural and anthropogenic ozone
10 precursors, and (8) dry deposition. The processes are mathematically simulated in a three-
11 dimensional Eulerian model with three vertical layers, including the boundary layer and the
12 capping inversion or cloud layer. The ROM geographical domains are summarized in
13 Table 3-26 and illustrated in Figure 3-26.
14 Meteorological data are used to objectively model regional winds and diffusion. The
15 top three model layers of ROM are prognostic (predictive) and are free to locally expand and
16 contract in response to changes in the physical processes occurring within them. During an
17 entire simulation period, horizontal advection and diffusion and gas-phase chemistry are
18 modeled in the upper three layers. Predictions from layer 1 are used as surrogates for
19 surface concentrations. Layers 1 and 2 model the depth of the well-mixed layer during the
20 day. Some special features of layer 1 include the modeling of (1) the substantial wind shear
21 that can exist in the lowest few hundred meters above ground in local areas where strong
22 winds exist and the surface heat flux is weak; (2) the thermal internal boundary layer that
23 often exists over large lakes or near sea coasts; and (3) deposition onto terrain features that
24 protrude above the layer. At night, layer 2 represents what remains of the daytime mixed
25 layer. As stable layers form near the ground and suppress turbulent vertical mixing, a
26 nocturnal jet forms above the stable layer and can transport aged pollutant products and
27 reactants considerable distances. At night, emissions from tall stacks and warm cities are
28 injected directly into layers 1 and 2. Surface emissions are specified as a mass flux through
29 the bottom of layer 1. During the day, the top model layer, layer 3, represents the synoptic-
30 scale subsidence inversion characteristic of high ozone-concentration periods; the base of
31 layer 3 is typically 1 to 2 km above the ground. Relatively clean tropospheric air is assumed
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TABLE 3-26. REGIONAL QXTOANT 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 X 126 Grid Cells (columns X rows)
NORTHEAST DOMAIN (NEROXA)
89.00 W to 67.00 W Longitude
35.00 N to 47.00 N Latitude
88 X 72 Grid Cells (columns X rows)
MIDWEST DOMAIN (MIDROXA)
97.00 W to 78.00 W Longitude
35.00 N to 47.00 N Latitude
76 X 72 Grid Cells (columns X rows)
SOUTHEAST DOMAIN (SEROXA)
98.75 W to 76.25 W Longitude
27.67 N to 37.67 N Latitude
90 X 60 Grid Cells (columns x rows)
SOUTHERN DOMAIN (TEXROXA)
99.00 W to 81.00 W Longitude
26.00 N to 37.67 N Latitude
72 X 70 Grid Cells (columns X rows)
NORTHEAST DOMAIN (ROMNET)
85.00 W to 69.00 W Longitude
36,33 N to 45.00 N Latitude
64 X 52 Grid Cells (columns X rows)
NORTHEAST DOMAIN (NEROS1)
84.00 W to 69.00 W Longitude
38.00 N to 45.00 N Latitude
60 X 42 Grid Cells (columns X rows)
SOUTHEAST DOMAIN (SEROS1)
97.00 W to 82.00 W Longitude
28.00 N to 35.00 N Latitude
60 X 42 Grid Cells (columns X rows)
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W97Se9S94939Z01908988S78e8S8A83B28160797S777675747372717068asa7
99 98 97 98 96 M 93 92 91 90 SB 88 87 M 85 84 83 82 Bl 80 79 78 77 78 75 74 73 72 71 70 68 88
Longitude
Figure 3-26. Regional oxidant model superdomain with modeling domains.
Source: R. Wayland, U.S. Environmental Protection Agency (1993).
1 to exist above layer 3 at all times and stratospheric intrusion of O3 is assumed to be
2 negligible. If cumulus clouds are present, an upward flux of Oj and precursor species is
3 injected into the layer by penetrative convection. At night, O3 and the remnants of other
4 photochemical reaction products may remain in this layer and be transported long distances
5 downwind. These processes are modeled in layer 3.
6 When cumulus clouds are present in a layer 3 cell, the upward vertical mass flux from
7 the surface is partially diverted from injection into layer 1 to injection directly into the
8 cumulus cloud of layer 3. In the atmosphere, strong thermal vertical updrafts, primarily
9 originating near the surface in the lowest portion of the mixed layer, feed growing fair-
10 weather cumulus clouds with vertical air currents that extend in one steady upward motion
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1 from the ground to well above the top of the mixed layer. These types of clouds are termed
2 "Mr-weather cumulus" since atmospheric conditions are such that they do not grow to the
3 extent that precipitation forms. The dynamic effects of this transport process and daytime
4 cloud evolution can have significant effects on the chemical fate of pollutants. Within the
5 ROM system, a submodel parameterizes the above cloud flux process and its impact on mass
6 fluxes among all the model's layers. In the current implementation of the chemical kinetics,
7 liquid-phase chemistry is not included, and thus part of the effects from the cloud flux
8 processes are not accounted for in the simulations. The magnitude of the mass flux
9 proceeding directly from the surface layer to the cloud layer is modeled as being proportional
10 to the observed amount of cumulus cloud coverage and inversely proportional to the observed
11 depth of the clouds.
12 Horizontal transport within the ROM system is governed by hourly wind fields that are
13 interpolated from periodic wind observations made from upper-air soundings and surface
14 measurements. During the nighttime simulation period, the lowest few hundred meters of
15 the atmosphere above the ground may become stable as a radiation inversion forms. Wind
16 speeds increase just above the top of this layer, forming the nocturnal jet. This jet is capable
17 of carrying O3, other reaction products, and emissions injected aloft considerable distances
18 downwind. This phenomenon is potentially significant in modeling regional-scale air quality
19 and is implicitly treated by the model, where the definition of layer 1 attempts to account
20 for it.
21 The ROM system requires five types of "raw" data inputs: air quality, meteorology,
22 emissions, land use, and topography.
23 Air quality data required by the ROM include initial conditions and boundary
24 conditions. The model is initialized several (usually 2 to 4) days before the start of the
25 period of interest (called an "episode," usually around 15 days long) with clean tropospheric
26 conditions for all species. Ideally, the initial condition field will have been transported out of
27 the model domain in advance of the portion of the episode of greatest interest. Upwind
28 lateral boundary conditions for O3 are updated every 12 h based on measurements, except for
29 the large superdomain, where tropospheric background values are used. Other species
30 concentrations at the boundaries, as well as all species at the top of the modeling domain, are
31 set to tropospheric clean-air concentrations.
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1 Meteorological data are assimilated by the first stage of preprocessors. These data
2 contain regular hourly observations from U.S. National Weather Service surface stations (and
3 from similar stations in Canada as necessary), including wind speed and direction, air
4 temperature and dew point, atmospheric pressure, and cloud amounts and heights, Twice-
5 daily sounding data from the upper-air observation network are also included in the
6 meteorological database. Upper-air meteorological parameters include atmospheric pressure,
7 wind speed and direction, and air temperature and dew point. Finally, both buoy and
8 Coastal Marine Automated Station data are used; parameters typically reported are wind
9 speed and direction, and air and sea temperatures.
10 Emissions data for the primary species are input to the ROM system as well.
11 Originally these data were provided from the National Acid Precipitation Assessment
12 Program 1985 emissions inventory with 18.5-km spatial resolution. Most recently, the
13 interim regional inventory is being widely used to support current applications of the ROM,
14 It represents an update and improvement to the NAPAP inventory and is being used to
15 support State Implementation Plan modeling until State inventories are approved (U.S.
16 Environmental Protection Agency, 1993a,b). Species included are CO, NO, NO^, and ten
17 hydrocarbon reactivity categories. Natural hydrocarbons are also input, including isoprene
18 explicitly, monoterpenes divided among the existing reactivity classes, and unidentified
19 hydrocarbons. The chemical mechanism in ROM is the CBM-IV as previously described.
20 Land-use input data consist of 11 land-use categories in 1/4-degree longitude by
21 1/6-degree latitude grid cells. The data are more than 20 years old and represent a
22 weakness. Data are provided for the United States and Canada as far as 55° N. The land-
23 use categories are (1) urban land, (2) agricultural land, (3) range land, (4) deciduous forests,
24 (5) coniferous forests, (6) mixed-forest wetlands, (7) water, (8) barren land, (9) nonforested
25 wetland, (10) mixed agricultural land and range land, and (11) rocky, open places occupied
26 by low shrubs and lichens. Land-use data are used to obtain biogenic emissions estimates as
27 a function of the area of vegetative land cover, and for the determination of surface heat
28 fluxes.
29 Topography input data consist of altitude matrices of elevations in a 7.5° x 7.5° grid.
30 The data are obtained from the GRIDS database operated by U.S. EPA's Office of
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1 Information Resources Management. Topography data are used in the calculation of layer
2 heights.
3
4 3.6.3.3 The Regional Acid Deposition Model
5 The Regional Acid Deposition Model was initially developed at the National Center for
6 Atmospheric Research (NCAR) for U.S, EPA, and subsequently refined and improved at the
7 State University of New York at Albany (SUNYA). The model is an Eulerian transport,
8 transformation, and removal model that includes a treatment of the relevant physical and
9 chemical processes leading to acid deposition and the formation of photochemical oxidants.
10 As summarized in Tables 3-21 through 3-25, these processes include atmospheric transport
11 and mixing, gas-phase and aqueous-phase chemical transformations, dry deposition, and
12 cloud mixing and scavenging.
13 Chemical trace species are transported and diffused through the three-dimensional
14 RADM grid using externally specified meteorological data. The RADM uses hourly three-
15 dimensional fields of horizontal winds, temperature, and water vapor mixing ratio calculated
16 by the meteorological model MM4 with four-dimensional data assimilation (FDDA).
17 In addition, RADM requires two-dimensional, hourly fields of surface temperature, surface
18 pressure, and precipitation rates over the model domain. Kuo et al. (1985) found that to
19 calculate accurate mesoscale trajectories, at least 3-h temporal resolution is desirable, and the
20 12-h resolution of upper air observations is inadequate. Recent verification studies with
21 30 meteorological episodes by Stauffer and Seaman (1990) further support the use of MM5
22 data with FDDA. Using meteorology generated from a dynamically consistent
23 meteorological model can introduce errors caused by simulation errors associated with the
24 meteorological model. These uncertainties can be quantified through objective verification
25 studies with observed data (Anthes et al., 1985; Stauffer and Seaman, 1990).
26 The RADM2 chemical mechanism has been described by Stockwell et al. (1990),
27 Chang et al. (1991), Carter and Lurmann (1990), and Stockwell and Lurmann (1989). For
28 RADM2, the VOCs are aggregated into 12 classes of reactive organic species. Each
29 category of VOC is represented by several model species that span the required range for
30 reaction with the OH radical. Most emitted organic compounds are lumped into surrogate
31 species of similar reactivity and molecular weight, although organic chemicals with large
December 1993 V?m DRAFT-DO NOT DITOTR nw rrrn
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1 emissions are treated as separate model species even though their reactivities may be similar.
2 Categories of VOCs with large reactivity differences and complicated secondary chemistries
3 are represented by larger numbers of intermediates and stable species. During the
4 aggregation of organic species, the principle of reactivity weighting is followed to attempt to
5 account for differences in reactivity.
6
7 3.6.4 Evaluation of Model Performance
8 Air quality models are evaluated by comparing their predictions with ambient
9 observations. Because a model's demonstration of attainment of the ozone NAAQS is based
10 on hypothetical reductions of emissions from a base-year-episode simulation, the accuracy of
11 the base-year simulation is necessary, but not sufficient. An adequate model should give
12 accurate predictions of current peak ozone concentrations and temporal and spatial ozone
13 patterns. It should also respond accurately to changes in VOC and NOX emissions, to
14 differences hi VOC reactivity, and to spatial and temporal changes in emissions patterns for
15 future years.
16 Model performance can be evaluated at several levels. The important sub-models, the
17 emissions model, the meteorological model, and the chemical mechanism, can be
18 independently evaluated, and the model as a whole can be evaluated. Evaluation of
19 emissions models can be carried out with special measurements designed to isolate the effects
20 of emissions from a particular source category, such as tunnel studies (Pierson et al., 1990)
21 or on-road surveillance of motor vehicles (Lawson et al., 1990) to evaluate the accuracy of
22 motor vehicle emissions models. Meteorological sub-models can be evaluated from the
23 results of tracer experiments. Chemical mechanisms have traditionally been developed and
24 evaluated on the basis of smog chamber experiments. A question that merits continued
25 attention is how well chemical mechanisms developed with reference to smog chamber data
26 perform when simulating the ambient atmosphere. As noted in this section, comparisons of
27 observed and predicted concentrations for all important precursors, intermediates, and
28 products are important in assessing the accuracy of a chemical mechanism.
29
30
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1 3.6.4.1 Model Performance Evaluation Procedures
2 Specific numerical and graphic procedures have been recommended for evaluation of
3 the accuracy of grid-based photochemical models (Tesche et al., 1990b). The methods
4 suggested include the calculation of peak prediction accuracy; various statistics based on
5 concentration residuals; and time series of predicted and observed hourly concentrations.
6 Four numerical measures appear to be most helpful in making an initial assessment of the
7 adequacy of a photochemical simulation (Tesche et al., 1990b):
8
9 • The paired peak prediction accuracy.
10
11 • The unpaired peak prediction accuracy.
12
13 • The mean normalized bias.
14
15 • The mean absolute normalized gross error,
16
17 Accurate matching of ozone alone may not be sufficient to ensure that a model is
18 performing accurately. The possibility of compensatory errors must be recognized in which
19 two or more sources of error interact in such a way that ozone is predicted accurately, but
20 for the wrong reasons. The inaccuracies offset each other in part. The modeling effort
21 should be designed to minimize the likelihood of the presence of compensatory errors,
22 Evaluation of model performance for precursor and intermediate species as well as for
23 product species other than ozone, when ambient concentration data for these species are
24 available, significantly improves the chances that a flawed model will be identified.
25 Comparisons of observed and predicted concentrations for all important precursors,
26 intermediates, and products involved in photochemical air pollution—such as individual
27 VOCs, nitric oxide, nitrogen dioxide, PAN, ozone, H2O2, nitrous acid (HONO), and HNO3
28 —are useful in model evaluation, especially with respect to the chemistry component of the
29 model (Jeffries et al., 1992). Comparisons of predictions and observations for total organic
30 nitrates (mainly PAN) and inorganic nitrates (HNO3 and nitrate aerosol) can be used to test
31 qualitatively whether the emissions inventory has the correct relative amounts of VOCs and
32 NOX. However, to include HNO3 and nitrate aerosol in the data set for model comparisons,
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1 the model should include an adequate description of the HNO^ depletion process associated
2 with aerosol formation.
3 Adequate model performance for several reactive species increases the assurance that
4 correct ozone predictions arc not a result of chance or fortuitous cancellation of errors
5 introduced by various assumptions. Multispecies comparisons could be the key in
6 discriminating among alternative modeling approaches that provide similar predictions of
7 ozone concentrations.
8 As noted above, photochemical models have the potential to produce nearly the right
9 ozone concentrations when performance is evaluated, but do so because two or more flaws
10 were compensating each other. The existence of compensating errors in many modeling
11 applications is suspected because most applications to date have used emission inventories
12 whose validity is now in question (National Research Council, 1991). Underestimation of
13 VOC emissions from motor vehicles may be responsible for the lack of agreement between
14 inventories and ambient concentration data (Baugues, 1986; Lawson et al., 1990; Pierson
15 et al., 1990; Fujita et al., 1992). Underestimation of emissions from other sources is also a
16 possibility. One potentially underestimated VOC source is vegetation, which naturally emits
17 VOCs. An underestimation of VOC emissions could be compensated for by underestimation
18 of mixing height or wind speed or by overestimation of boundary concentrations of ozone or
19 precursors or by inaccurate chemistry modules. Boundary concentrations (which can be
20 obtained from measurements or regional models or by assuming background concentrations,
21 as discussed in Section 3.6.2.6) are often poorly defined.
22 If only a routine database is available for modeling ozone in an urban area, then there
23 are several areas of concern that require attention (Roth, 1992):
24
25 • Air quality aloft - Most likely these data will not be available. These
26 measurements are important and instrumental for diagnostic analysis of model
27 simulations.
28
29 • Boundary conditions - If the possibility of significant transport into the region
30 exists, and the data are not available, the boundary conditions become a variable
31 that allows the introduction of compensatory errors if the emissions are
32 inaccurate. An approach to circumvent this problem is to define the region in a
33 way so that the boundaries become a much less significant issue.
34
December 1993 3-204 DRAFT-DO NOT QUOTE OR COB
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1 • Ambient VOC data - These are generally not routinely available. In their
2 absence, evaluation of model performance is hampered.
3
4 • Meteorological data aloft - Very often there are only surface measurements and a
5 few soundings from which to extrapolate the needed data.
6
7 If any of these four areas is missing from the database, the performance evaluation and
8 subsequent model application must be adequately planned to minimize the possibility of
9 compensatory errors.
10
11 3.6.4.2 Performance Evaluation of Ozone Air Quality Models
12 3.6.4.2.1 Urban Airshed Model
13 The UAM has been applied to many urban areas in the United States and Europe, and
14 most of these studies have included some form of performance evaluation. (See summary in
15 Tesche et al., 1992, Table 6-2.) Thus, there is a growing body of information concerning
16 the accuracy of the model's predictions. (UAM itself was continuing to undergo revision.)
17 Evaluations of UAM's performance have been carried out for a number of geographic areas.
18 Evaluations carried out since 1985 have indicated mean discrepancies between predicted and
19 measured ozone values of 20 to 40% of the observations, when paired in space and time
20 (Roth et al., 1989). The prediction of peaks exhibits relative errors that are smaller than the
21 average error, with a tendency toward underprediction (Roth et al., 1989). The
22 discrepancies between predicted and measured NO2 in UAM applications are on the order of
23 30 to 50% with no improvement over the history of modeling applications (Roth et al.,
24 1989). Underprediction of NO2 by UAM has been typical, generally on the order of 20 to
25 40% (Roth etal., 1989).
26
27 3.6.4.2.2 Regional Oxidant Model
28 A primary role of the ROM model is to estimate boundary conditions for use by the
29 Urban Airshed Model in evaluating hydrocarbon and NOX reduction strategies for urban
30 areas in the eastern United States, especially in areas where transport is a significant element
31 (U.S. Environmental Protection Agency, 1990f). Analysis of regional ozone abatement
32 strategies is also a major role of the ROM model (Possiel et al., 1990).
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1 The ROM has been used in a U.S, EPA program, the Regional Ozone Modeling for
2 Northeast Transport (ROMNBT) program, to assess the effectiveness of various regional
3 emission control strategies in lowering O3 concentrations to nationally mandated levels for
4 the protection of human health, forests, and crops (Meyer et al., 1991). As part of the
5 ROMNET program, the ROM is also being used to provide regionally consistent initial and
6 upwind boundary conditions to smaller-scale urban models for simulations of future-year
7 scenarios.
8 The most complete testing of ROM2.0 was accomplished in an evaluation with the
9 50-day (My 12 to August 31, 1980) NBROS database (Schere and Wayland, 1989a,b). The
10 model underestimated the highest values and overestimated the lowest. It showed good
11 performance (an overall 2% overproduction) in predicting maximum daily 03 concentrations
12 averaged over aggregate groups of monitoring stations. A key indicator of model
13 performance on the regional scale is the accuracy of simulating the spatial extent and
14 location, as well as the magnitude, of the pollutant concentrations within plumes from
15 significant source areas. In ROM2.0 performance analyses, plumes from the major
16 metropolitan areas of the Northeast Corridor, including Washington, DC; Baltimore;
17 New York; and Boston, could be clearly discerned in the model predictions under episodic
18 conditions. Generally the plumes were well characterized by the model, although there was
19 evidence of a westerly transport bias and underprediction of O3 concentrations near the
20 center of the plume. Using aircraft data, ROM2.0 was found to underpredict the regional
21 tropospheric burden of ozone.
22 The evaluation of ROM2.1 (Pierce et al., 1990), unlike that of ROM2.0, was based on
23 routinely archived data from state and local agency monitoring sites rather than on an
24 intensive field-study period. The evaluation consisted of the comparison of observed and
25 predicted O3 concentrations during selected episodes (totaling 26 days) of high ozone
26 observed during the summer of 1985. Evaluation showed that ROM2.1 underestimated the
27 highest values and slightly overestimated the lowest; underestimates of the upper percentiles
28 tended to be more prevalent in the southern and western areas of the ROMNET domain
29 (Table 3-26). The model showed good performance (an overall 1.4% overprediction) in
30 predicting maximum daily O3 concentrations averaged over aggregate groups of monitoring
31 stations; and it appears to correct for the westerly transport bias of high-ozone plumes in the
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1 Northeast Corridor seen in ROM2.0. As with ROM2.0, model performance degraded as a
2 function of increasingly complex mesoscale wind fields.
3
4 3.6.4.3 Data Base Limitations
5 As previously mentioned, the use of routine air quality and meteorological data requires
6 that a number of assumptions be made about key model inputs. While intensive field studies
7 are desirable during ozone episodes to acquire the full set of data required, three key
8 problems arise: such studies are expensive and, therefore, are limited in number; the time
9 required to cany out field studies usually exceeds the time available; and most field studies
10 have not captured the worst ozone episodes. Since U.S. EPA guidance emphasizes planning
11 to meet worst-case conditions, field data must often be manipulated to approximate highest
12 ozone concentrations. Such adjustments invariably increase uncertainty in model projections.
13 Studies that have, or will, provide data for model evaluation include: the St. Louis
14 Regional Air Pollution Study (RAPS) conducted in 1975-1976; the Northeast Corridor
15 Regional Modeling Project (NECRMP) conducted in 1979 and 1980; the South Central Coast
16 Cooperative Aerometric Monitoring Program (SCCCAMP) conducted in 1985; the South
17 Coast Air Quality Study (SCAQS) conducted in 1987; studies in Sacramento and San Diego
18 in 1990; SJVAQS/AUSPEX conducted in 1990; the Lake Michigan Oxidant Study (LMOS)
19 conducted in 1990 and 1991; the Southern Oxidant Study (SOS) conducted in 1991 and 1992;
20 arid a Gulf Coast study planned for 1993.
21 In most cases, field studies have not coincided with periods in which ozone
22 concentrations have attained values as high as the design values. Given the low probabilities
23 of occurrence of the most adverse meteorological conditions and the fact that field studies
24 typically acquire data for two or three ozone episodes, obtaining a design value concentration
25 during the course of a field study is unlikely.
26 The U.S. EPA recommends that the five highest daily maximum ozone concentrations
27 at a design-value site, selected from the three most recent years, be modeled if EKMA is
28 used for a SIP (U.S. Environmental Protection Agency, 1989b). Because EKMA's data
29 requirements are minimal, it can be applied to the worst episodes. In contrast, the number
30 of episodes available for grid-based modeling is less than desirable in all areas. In addition,
31 any available intensive databases often do not include the worst-case meteorology; intensive
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1 databases typically restrict modeling to two or three ozone episodes having a duration of 2 to
2 3 days each. Moreover, the intensive databases never encompass the fall range of
3 meteorological conditions of interest (if ozone exceedances occur in an area under different
4 meteorological conditions, the relative effectiveness of different control strategies might vary
5 with the different meteorological conditions). The U.S. EPA spells out procedures for
6 episode selection for use with grid-based models (U.S. Environmental Protection Agency.
7 1991b).
8 Because the number of intensive databases is limited both in terms of episodes and
9 regions, U.S. EPA has investigated the feasibility of applying UAM without conducting
10 intensive field studies (Scheffe and Morris, 1990a,b). These studies, known as the Practice
11 for Low-cost Application in Nonattainment Regions (PLANE), were conducted for
12 New York, Philadelphia, Atlanta, Dallas-Fort Worth, and St. Louis. Of the five cities
13 studied, St. Louis, New York, and Philadelphia had intensive databases available.
14 Simulations were carried out using both routine and intensive databases for St. Louis and
15 Philadelphia, Model performance using routine data was much better for St. Louis than for
16 Philadelphia (Scheffe and Morris, 1990a,b). Scheffe and Morris (1990a,b) caution that the
17 differing results may be complicated by the quality of the databases, but they speculate that
18 model performance using routine databases for Philadelphia might have been poorer because
19 of regional transport. Performance statistics for all four applications using routine data were
20 consistent with other UAM applications (Scheffe and Morris, 1990a,b); however, the paucity
21 of data in the routine databases precluded any investigation of the possibility that
22 compensating errors occurred.
23 Scheffe and Morris (1990a,b) note that the PLANE lack of air quality data was
24 addressed by extending the length of the simulations and expanding the upwind boundary,
25 which, in effect, increased the need for accurate emissions inventories (boundary conditions
26 could also be obtained through use of EOM). For PLANE applications, gridded emissions
27 were created from routine county-level emission inventories by utilizing an emissions
28 program that made use of surrogate information, such as population distribution.
29
30
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1 3.6.5 Use of Ozone Air Quality Models for Evaluating Control Strategies
2 Photochemical air quality models are used for control strategy evaluation by first
3 demonstrating that a past episode, or episodes, can be adequately simulated and then
4 reducing hydrocarbon or NOX emissions or both in the model inputs and assessing the effects
5 of these reductions on ozone in the region. Ozone concentrations can be decreased by
6 reducing either VOC or NOX concentrations to sufficiently low levels. Controlling VOC
7 emissions always delays ozone formation and reduces the amount of ozone formed.
8 Controlling highly reactive VOCs in areas exhibiting low VOC-to-NOx ratios delays and
9 reduces ozone formation most effectively. The effects of NOX emissions reductions on ozone
10 concentrations vary because NOX is an atypical precursor: though it is necessary for ozone
11 formation, fresh NO emissions remove ozone, and high concentrations of NOX retard the rate
12 of ozone formation by removing radicals. Control of NOX tends to accelerate the rate of
13 ozone formation; however, its effects on peak ozone concentration depend upon the location
14 and timing of the control and upon ambient concentrations of VOCs and NOX, which vary
15 widely in time and space, even within a single urban area during one day.
16 Grid modeling applications are currently underway by or for State agencies for
17 approximately 20 areas within the United States to support regional ozone SIP revisions.
18 An immediate problem faced for almost all urban areas is that even if an adequate
19 number of episodes exist, the episodes may not include the most adverse ozone levels.
20 An inherent question in using a less adverse episode to develop control strategies is how
21 these strategies extrapolate to a more severe set of conditions. There is no clear answer to
22 this question at this point. At present, control strategies, evaluated using grid-based models,
23 are determined based on available episodes that have the largest amount of data whether or
24 not these episodes contain the highest ozone concentration achieved. Another issue is that
25 the form of the NAAQS for ozone does not correspond with the output from a grid-based
26 model. The model output does not provide a direct answer to whether an area will meet the
27 standard in its current statistically based form.
28 Table 3-27 summarizes a number of recent ozone control strategy evaluations for
29 different areas of the United States. Some general observations can be made concerning
30 issues that have arisen in control strategy exercises, particularly as they relate to problems
31 associated with different areas of the country (Roth, 1992). In California, model results
December 1993 1-20Q DRAFT-DO Wfvr nnrms nr>
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TABLE 3-27. APPLICATIONS OF PHOTOCHEMICAL AIR QUALITY MODELS TO
EVALUATING OZONE
Investigators
Region/Episode
Model Used
Strategies Evaluated
Chu et al. (1992)
Chu and Cox (1993)
Roselle et al. (1992)
Mathur and Schere (1993)
Possiel et al. (1993)
Possiel and Cox (1993)
Milford et al, (1992)
Rao (1987)
Rao et al. (1989)
Rao and Sistla (1993)
Scheffe and Morris
(1990a,b)
Possiel et al. (1990)
Eastern U.S./
July 2-10, 1988
Northeastern U.S./
July 1-12, 1988
Northeastern U.S./
July 2-17, 1988
New York Metropolitan
area/
5 days in 1980
New York
St. Louis
Atlanta
Dallas-Ft. Worth
Philadelphia
Northeastera U.S./
July 2-17, 1988
Roselle and Schere (1990) Northeast U.S./
Roselle et al. (1991) July 12-18, 1980
Dunker et al. (1992a,b)
Milford et al. (1989)
Los Angeles
New York
Dallas-Ft. Worth
South Coast Air Basin
ROM2.2
ROM2.2
ROM
UAM/RQM2A
UAM
ROM
ROM2.1
UAM
Across-the-board NOX/VOC
reductions.
Estimate ozone reductions per
1990 Clean Air Act Amendments
Analysis of effect of NO,
reductions.
Evaluation of 1988 SIPs and
VOC/NOX strategies
Use of UAM for demonstrating
attainment with routinely
available data
Ozone control strategies in
Northeast
Sensitivity of ozone in Northeast
to biogenic emissions
Effects of alternate fuels and
reformulated gasolines on ozone
levels
Effects of systematic VOC and
NOX reductions
Middleton et al. (1993)
Eastern U.S. and
Southeastern Canada
RADM
2010 emissions projections
1 indicate that ozone has been underestimated, most likely because VOC emissions from motor
2 vehicles have been seriously underestimated. The underestimation was hidden by adjusting
3 other model inputs within their range of uncertainty. In Atlanta, it has been estimated that
4 approximately 60% of the VOC inventory is of biogenic origin, and the variation of
5 anthropogenic emissions reductions required to achieve ozone attainment within the
6 uncertainty range of the biogenic emissions is on the order of 20%. The uncertainty range of
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1 the biogenic VOC emissions needs to be reduced to obtain tighter control strategy estimates,
2 Studies with ROM and UAM tend to indicate that NOX control is beneficial in the northeast.
3 While it is unlikely that this general conclusion will be altered by more refined data, there
4 are still significant uncertainties concerning regionwide boundary conditions and the extent of
5 influence of emissions in upwind regions on air quality in the major eastern metropolitan
6 areas.
7
8 3.6.6 Conclusions
9 The 1990 amendments to the Clean Air Act have mandated the use of photochemical
10 grid models for demonstrating how most ozone nonattainment areas can attain the NAAQS.
11 Predicting ozone is a complex problem. There are still many uncertainties in the models;
12 nonetheless models are necessary and essential for regulatory analysis and constitute one of
13 the major tools for attacking the ozone problem. These models have developed considerably
14 in the past 10 years. However, their usefulness is constrained by limited databases for
15 evaluation and from having to rely on hydrocarbon emissions data that may be inaccurate.
16 Comparison of model predictions against ozone measurements, while necessary, is not a
17 robust test of a model's accuracy. Ideally, one should evaluate performance against more
18 extensive sets of species such as individual VOCs, NOX, and NOy. Compensating errors in
19 input information to a model and within the model formulation can cause an ozone model to
20 generate correct ozone predictions for the wrong reasons. Therefore, model evaluation
21 indicators are needed to demonstrate the reliability of a prediction before the model can be
22 effectively used in making control strategy decisions. Models can be effectively used in a
23 relative sense to rank different control alternatives in terms of their effectiveness in reducing
24 ozone and to indicate the approximate magnitude of improvement in peak ozone levels
25 expected under various control strategies. To do sos there must be a sound emissions model
26 and data and an adequate database on which to construct the modeling. Grid-based ozone air
27 quality modeling is superior to the available alternatives for ozone control planning, but if the
28 model is not evaluated sufficiently, one can be misled. The goal is to minimize the chances
29 of its incorrect use.
30
31
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1 3.7 SUMMARY AND CONCLUSIONS
2 3.7.1 Tropospheric Ozone Chemistry
3 3.7.1.1 Ozone in the Unpolluted Atmosphere
4 Ozone is found in the stratosphere, the "free" troposphere, and the planetary boundary
5 layer (PEL) of the earth's atmosphere. In the stratosphere, the uppermost layer, O3 is
6 produced through cyclic reactions that are initiated by the photolysis of molecular oxygen by
7 short-wavelength radiation from the sun and are terminated by the recombination of
8 molecular oxygen and ground-state oxygen atoms.
9 In the "free" troposphere, O3 occurs as the result of incursions from the stratosphere;
10 upward venting from the PEL (which is the layer next to the earth, extending to altitudes of
11 ~ 1 to 2 km) through certain cloud processes; and photochemical formation from precursors,
12 notably methane, carbon monoxide, and nitrogen oxides,
13 Ozone is present in the PEL as the result of downward mixing from the stratosphere
14 and free troposphere and as the result of photochemical processes occurring within the PEL.
15 The photochemical production of O3 and other oxidants found at the earth's surface is the
16 result of atmospheric physical and chemical processes involving two classes of precursor
17 pollutants, reactive volatile organic compounds (VOCs) and (NOX). The formation of O3 and
18 other oxidants from its precursors is a complex, nonlinear function of many factors,
19 including the intensity and spectral distribution of sunlight; atmospheric mixing and related
20 meteorological conditions; the reactivity of the mixture of organic compounds in ambient air;
21 the concentrations of precursor compounds in ambient air; and, within reasonable
22 concentrations ranges, the ratio between the concentrations of reactive VOCs and NC^.
23 In the free troposphere and in relatively "clean" areas of the PEL, methane is the chief
24 organic precursor to in situ photochemical production of O3 and related oxidants. The major
25 tropospheric removal process for methane is by reaction with OH radicals. In the complex
26 cyclic reactions that result in oxidation of methane, there can be a net increase in 03 or a net
27 loss of 03, depending mainly upon the NO concentration.
28
29 3.7.1.2 Ozone Formation in the Polluted Troposphere
30 The same basic processes by which methane is oxidized occur in the atmospheric
31 oxidative degradation of other, even more reactive and more complex VOCs. The only
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1 significant initiator of the photochemical formation of ozone in the troposphere is the
2 photolysis of NO2, yielding NO and a ground-state oxygen atom that reacts with molecular
3 oxygen to form O3. The O3 thus formed reacts with NO, yielding 02 and NO2. These
4 cyclic reactions attain equilibrium in the absence of VOCs, In the presence of VOCs,
5 however, the equilibrium is upset, resulting, through a complex series of chain reactions, in
6 a net increase in O3.
7 The key reactive species in the troposphere is the hydroxyl (OH) radical, which is
8 responsible for initiating the oxidative degradation reactions of almost all VOCs. As in the
9 methane oxidation cycle, the conversion of NO to NO2 during the oxidation of VOCs is
10 accompanied by the production of O3 and the efficient regeneration of the OH radical. The
11 O3 and PANs formed in polluted atmospheres increase with the NO2/NO concentration ratio.
12 At night, in the absence of photolysis of reactants, the simultaneous presence of 03 and
13 NO2 results in the formation of the nitrate radical, NO3. The reaction with nitrate radicals
14 appears to constitute a major sink for alkenes, cresols, and some other compounds, although
15 alkyl nitrate chemistry is not well characterized.
16 Most inorganic gas-phase processes, that is, the nitrogen cycle and its interrelationships
17 with O3 production, are well understood. The chemistry of the VOCs in ambient air,
18 however, is not as well understood. The chemical loss processes of gas-phase VOCs, with
19 concomitant production of O3, include reaction with OH, NO3, O3, and photolysis.
20 The major classes of VOCs in ambient air are: alkanes, alkenes (including alkenes
21 from biogenic sources), aromatic hydrocarbons, carbonyl compounds, alcohols, and ethers.
22 A wide range of lifetimes in the atmosphere, from minutes to years, characterize the VOCs.
23 The only important reaction of alkanes is with OH radicals. For alkanes having
24 carbon-chain lengths of four or less (C5 alkanes the situation is more complex because few reaction
26 products have been found. Branched alkanes (e.g., isobutane) have rates of reaction that are
27 highly dependent on structure. It is difficult to represent reactions of these VOCs
28 satisfactorily in the chemical mechanisms of air quality models. Stable products of alkane
29 photooxidation are known to include carbonyl compounds, alkyl nitrates, and
30 5-hydroxycarbonyls. Major uncertainties in the atmospheric chemistry of the alkanes concern
31 the chemistry of alkyl nitrate formation; these uncertainties affect the amount of NO-to-NO^
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1 conversion occurring and hence the amounts of O3 formed during photochemical degradation
2 of the alkanes.
3 Alkenes react in ambient air with OH and NO^ radicals and with 03. All three
4 processes are important atmospheric transformation processes, and all proceed by initial
5 addition to the >C=C< bond(s). Products of alkene photooxidation include carbonyl
6 compounds, hydroxynitrates and nitratocarbonyls, and decomposition products from the
7 energy-rich biradicals formed in alkene-O3 reactions. Major uncertainties in the atmospheric
8 chemistry of the alkenes concern the products and mechanisms of their reactions with O3,
9 especially the radical yields (which affect the O3 formation yields).
10 The only troposphericaUy important loss process for aromatics (benzene and the alkyl-
11 substituted benzenes) is by reaction with the OH radical, followed by H-atom abstraction or
12 OH radical addition. Products of aromatic hydrocarbon photooxidation include phenolic
13 compounds, aromatic aldehydes, a-dicarbonyls (e.g., glyoxal), and unsaturated carbonyl or
14 hydroxycarbonyl compounds or both. Aromatics appear to act as strong NOX sinks under
15 low NOX conditions. Major uncertainties in the atmospheric chemistry of aromatic
16 hydrocarbons are mainly with regard to reaction mechanisms and reaction products under
17 ambient conditions (i.e., for NOX concentration conditions that occur in urban and rural
18 areas). These uncertainties impact on the representation of mechanisms in models.
19 Tropospherically important loss processes for carbonyl compounds not containing
20 >C=C< bonds are photolysis and reaction with the OH radical; those that contain such
21 bonds can undergo the same reactions as alkenes. Photolysis is the major loss process for
22 HCHO (the simplest aldehyde) and acetone (the simplest ketone), as well as for the
23 dicarbonyls. Reactions with OH radicals are calculated to be the dominant gas-phase loss
24 process for the higher aldehydes and ketones. Products formed and the importance of
25 photolysis are major uncertainties in the chemistry of carbonyl compounds.
26 Alcohols and ethers in ambient air react only with the OH radical, with the reaction
27 proceeding primarily via H-atom abstraction from the C-H bonds in these compounds.
28 It should be noted that the photooxidation reactions of VOCs can lead to the formation
29 of participates in ambient air. The chemical processes involved in the formation of O3 and
30 other photochemical pollutants lead to the formation of OH radicals and the formation of
31 oxidized VOC reaction products that are of low enough volatility to be present as organic
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1 paniculate matter. Hydroxyl radicals that oxidize VOCs also react with NO2 and SO2 to
2 form nitric and sulfuric acids, respectively, which become incorporated into aerosols as
3 participate nitrate and sulfate. Controls aimed at reducing ozone will impact—positively or
4 negatively—acid and secondary aerosol formation in the atmosphere.
5
6 3.7.2 Meteorological Processess Influencing Ozone Formation and
7 Transport
8 3.7.2.1 Meteorological Processes
9 The surface energy (radiation) budget of the earth strongly influences the dynamics of
10 the planetary boundary layer (PEL). In combination with synoptic winds, it provides the
11 forces for the vertical fluxes of heat, mass, and momentum. The redistribution of energy
12 through the PEL creates thermodynamic conditions that influence vertical mixing. Energy
13 balances require study so that more realistic simulations can be made of the structure of the
14 PEL.
15 Day-to-day variability in ozone concentrations depends heavily on day-to-day variations
16 in meteorological conditions. For example, the concentration of an air pollutant depends
17 significantly on the degree of mixing that occurs between the time a pollutant or its
18 precursors are emitted and the arrival of the pollutant at the receptor. Inversion layers
19 (layers in which temperature increases with height above ground level) are prominent
20 determinants of the degree of atmospheric vertical mixing and thus the degree to which ozone
21 and other pollutants will be dispersed or accumulate. Ozone left in a layer aloft, as the result
22 of reduced turbulence and mixing at the end of daylight hours, can be transported through
23 the night, often to areas far removed from pollution sources. Downward mixing on the
24 subsequent day can result in increases in local concentrations from the transported ozone.
25 Growing evidence indicates that the strict use of mixing heights in modeling is an
26 oversimplification of the complex processes by which pollutants are redistributed within
27 urban areas; and that it is necessary to treat the turbulent structure of the atmosphere directly
28 and acknowledge the vertical variations in mixing.
29 Geography can significantly affect the dispersion of pollutants along the coast or shore
30 of oceans and lakes. Temperature gradients between bodies of water and land masses
31 influence the incidence of surface conditions. The thermodynamics of water bodies (e.g.,
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1 Lake Michigan and the Atlantic Ocean off the coast of Maine) may play a significant role in
2 some regional-scale episodes of high ozone concentrations.
3 An "air mass" is a region of air, usually of multistate dimensions, that exhibits similar
4 temperature, humidity, and stability characteristics. Episodes of high ozone concentrations in
5 urban areas are often associated with high concentrations of ozone in the surroundings.
6 The transport of ozone and its precursors beyond the urban scale (^50 km) to
7 neighboring rural and urban areas has been well documented and was described in the 1986
8 EPA criteria document for O3. Areas of ozone accumulation are characterized by:
9 (1) synoptic-scale subsidence of air in the free tropopsphere, resulting in development of an
10 elevated inversion layer; (2) relatively low wind speeds associated with the weak horizontal
11 pressure gradient around a surface high pressure system; (3) a lack of cloudiness; and
12 (4) high temperatures.
13
14 3.7.2.2 Meteorological Parameters
15 Ultraviolet (UV) radiation from the sun plays a key role in initiating the photochemical
16 processes leading to ozone formation and affects individual photolytic reaction steps. There
17 is little empirical evidence in the literature, however, linking day-to-day variations in
18 observed UV radiation levels with variations in O3 levels.
19 An association between tropospheric ozone concentrations and tropospheric temperature
20 has been demonstrated. Plots of daily maximum ozone concentrations versus maximum daily
21 temperature for summer months of 1988 to 1990 for four urban areas, for example, show an
22 apparent upper bound on ozone concentrations that increases with temperature. A similar
23 qualitative relationship exists at a number of rural locations.
24 The relationship between wind speed and ozone buildup varies from one part of the
25 country to another. Research done during the Southern Oxidant Study (in the "Atlanta
26 intensive" field study) indicates that variations in wind speed at a particular level above
27 ground must be larger than about 3 m/s to be considered significant. This may limit the
28 accuracy of urban-area ozone simulations using photochemical models.
29
30
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1 3.7.2.3 Normalization of Trends
2 Statistical techniques (e.g., regression techniques) can be used to help identify real
3 trends in ozone concentrations, both intra-annual and inter-annual, by normalizing
4 meteorological variability. In the Southern Oxidant Study, for example, regression
5 techniques were successfully used to forecast ozone levels to ensure that specialized
6 measurements were made on appropriate days.
7
8 3.7.3 Precursors
9 3.7.3.1 Nitrogen Oxides Emissions
10 Anthropogenic oxides of nitrogen are associated with combustion processes. The
11 primary pollutant emitted is nitric oxide (NO) formed at high combustion temperatures from
12 the nitrogen and oxygen in air and from nitrogen in the combustion fuel. Emissions of NOX
13 in 1991 in the United States totaled 21.39 Tg. The two largest single NOX emission sources
14 are electric power generation and highway vehicles. Emissions of NOX are therefore highest
15 in areas having a high density of electric-power-generating stations and in urban regions
16 having high traffic densities. Between 1987 and 1991, transportation-related emissions
17 remained essentially constant, while stationary source NOX emissions increased about 10%.
18 Natural NOX sources include stratospheric intrusion, oceans, lightning, soils, and
19 wildfires. Lightning and soil emission are the only two significant natural sources of NOX in
20 the United States. The estimated annual lightning-produced NOX for the continental United
21 States is —1.0 Tg, about 60% of which is generated over the southern states. Both
22 nitrifying and denitrifying organisms in the soil can produce NOX, principally NO. Emission
23 rates depend mainly on fertilization levels and soil temperature. Inventorying soil NOX
24 emissions is difficult because of large temporal and spatial variability, but the nationwide
25 total has been estimated at 1.2 Tg/year, of which about 85% is emitted in spring and
26 summer. About 60% of the total soil NOX is emitted in the area of the country containing
27 the central corn belt.
28 Combined natural sources contribute about 2.2 Tg of NOX to the troposphere over the
29 continental United States. Uncertainties in natural NOX inventories are much larger,
30 however, than for anthropogenic NOX emissions. Because a large proportion of
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1 anthropogenic NOX emissions come from distinct point sources, published annual estimates
2 are thought to be very reliable.
3
4 3.7.3.2 Volatile Organic Compound Emissions
5 Hundreds of volatile organic compounds, commonly containing from 2 to about
6 12 carbon atoms, are emitted by evaporative and combustion processes from a large number
7 of source types. Total U.S. VOC emissions in 1991 were estimated at 21.0 Tg. The two
8 largest source categories were industrial processes (10.0 Tg) and transportation (7.9 Tg).
9 Emissions of VOCs from highway vehicles accounted for almost 75 % of the transportation-
10 related emissions; studies have shown that the majority of these VOC emissions come from
11 about 20% of the automobiles in service, many, perhaps most, of which are older cars that
12 are poorly maintained.
13 The accuracy of VOC emission estimates is difficult to determine, both for stationary
14 and mobile sources. Within major point sources, deviations of emission rates from
15 individual sources from assigned average factors can result in error for the entire point
16 source. Evaporative emissions, which depend on temperature and other environmental
17 factors, compound the difficulties of assigning accurate emission factors. In assigning VOC
18 emission estimates to the mobile source category, models are used that incorporate numerous
19 input parameters (e.g., type of fuel used, type of emission controls, age of vehicle), each of
20 which has some degree of uncertainty.
21 Vegetation emits significant quantities of VOCs into the atmosphere, chiefly
22 monoterpenes and isoprene, but also oxygenated VOCs, according to recent studies. The
23 most recent biogenic VOC emissions estimate for the United States showed annual emissions
24 of 29.1 Tg/year. Coniferous forests are the largest vegetative contributor on a national basis,
25 because of their extensive land coverage. Summertime biogenic emissions comprise more
26 than half of the annual totals in all regions because of their dependence on temperature and
27 vegetational growth. Biogenic emissions are, for those reasons, expected to be higher in the
28 southern states than in the northern.
29 Uncertainties in both biogenic and anthropogenic VOC emission inventories prevent
30 establishing the relative contributions of these two categories.
31
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1 3.7.3.3 Concentrations of Volatile Organic Compounds in Ambient Air
2 The VOCs most frequently analyzed in ambient air are the nonmethane hydrocarbons
3 (NMHCs). Morning concentrations (6:00 a.m. to 9:00 a.m.) have been measured most often
4 because of the use of morning data in EKMA and in air quality simulation models. Major
5 field studies in 22 cities in 1984 and 19 cities in 1985 produced NMHC measurements
6 showing median values ranging from 0.39 ppm C to 1.27 ppm C for 1984; and ranging from
7 0.38 ppm C to 1.63 ppm C in 1985. Overall median values from all urban sites were about
8 0.72 ppm C in 1984 and 0.60 ppm C in 1985.
9 Comparative data over two decades (1960's through 1980's) in the Los Angeles and
10 New York City areas showed decreases in NMHC concentrations in those areas.
11 Concomitant compositional changes were observed over the two decades, with increases
12 observed in the percentage of alkanes and decreases in the percentage of aromatic
13 hydrocarbons and acetylene.
14 Concurrent measurements of anthropogenic and biogenic NMHCs have shown that
15 biogenic NMHCs usually constituted much less than 10% of the total NMHCs. For
16 example, average isoprene concentrations ranged from 0.001 to 0.020 ppm C and terpenes
17 from 0.001 to 0.030 ppm C.
18
19 3.7.3.4 Concentrations of Nitrogen Oxides in Ambient Air
20 Measurements of NOX at sites in 22 and 19 U.S. cities in 1984 and 1985, respectively,
21 showed that median NQX concentrations ranged from 0.02 to 0.08 ppm in most of these
22 cities. The 6-to-9 a.m. median concentrations in many of these cities exceeded the annual
23 average NOX values of 0.02 to 0.03 ppm found in U.S. metropolitan areas between 1980 and
24 1989. Nonurban NQX concentrations, reported as average seasonal or annual NOx, range
25 from < 0.005 to 0.015 ppm.
26 Ratios of 6-to-9 a.m. NMOC to NOX are higher in southeastern and southwestern
27 U.S. cities than in northeastern and midwestern U.S. cities, according to data from EPA's
28 multi-city studies conducted in 1984 and 1985. Median ratios ranged from 9.1 to 37.7 in
29 1984; in 1985, median ratios ranged from 6.5 to 53.2 in the cities studied. Rural
30 NMOC/NOX ratios tend to be higher than urban ratios. Morning (6-to-9 a.m.) NMOC/NOX
31 ratios are used in the EKMA-type of trajectory model. Trends from 1976 to 1990 show
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1 decreases in these ratios in the South Coast Air Basin of California. The correlation of
2 NMOC/NOX ratios with maximum 1-h O3 concentrations, however, was weak in a recent
3 analysis.
4
5 3.7.3.5 Source Apportionment and Reconciliation
6 Source apportionment (now regarded as synonymous with receptor modeling) refers to
7 determining the quantitative contributions of various sources of VOCs to ambient air
8 pollutant concentrations. Source reconciliation refers to the comparison of measured ambient
9 VOC concentrations with emissions inventory estimates of VOC source emission rates for the
10 purpose of validating the inventories.
11 Early studies in Los Angeles employing a "mass balance" approach to receptor
12 modeling showed the following estimated contributions of respective sources to ambient air
13 concentrations of NMOCs through CIO; automotive exhaust, 53%; whole gasoline
14 evaporation, 12%; gasoline headspace vapor, 10%; commercial natural gas, 5%; geogenic
15 natural gas, 19%; and liquefied natural gas, 1 %. Recent studies in eight U.S. cities showed
16 that vehicle exhaust was the dominant contributor to ambient VOCs (except in Beaumont,
17 TX, with 14% reported). Estimates of the contributions of gasoline evaporation differ in
18 methodology; the more appropriate methods used result in estimates of large whole gasoline
19 contributions, i.e., equal to vehicle exhaust in one study and 20% of vehicle exhaust in a
20 second study.
21 The chemical mass balance approach used for estimating anthropogenic VOC
22 contributions to ambient air cannot be used for receptor modeling of biogenic sources.
23 A modified approach, applied to 1990 data from a downtown site in Atlanta, indicated a
24 lower limit of 2% (24-h average) for the biogenic percentage of total ambient VOCs at that
25 location (isoprene was used as the biogenic indicator species). The percentage varies during
26 the 24-h period because of the diurnal (e.g., temperature, light intensity) dependence of
27 isoprene concentrations.
28 Source reconciliation data have shown disparities between emission inventory estimates
29 and receptor-estimated contributions. For biogenics, emission estimates are greater than
30 receptor-estimated contributions. The reverse has been true for natural gas contributions
31 estimated for Los Angeles, Columbus, and Atlanta; and for refinery emissions in Chicago.
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1 3.7.4 Analytical Methods for Oxidants and Their Precursors
2 3.7.4.1 Oxidants
3 Current methods used to measure O3 are chemiluminescence (CL), ultraviolet (UV)
4 absorption spectrometry, and newly developed spectroscopic and chemical approaches,
5 including chemical approaches applied to passive sampling devices for 03.
6 The CL method, designated as the reference method by EPA, involves the direct gas-
7 phase reaction of ozone with an alkene (ethylene) to produce electronically excited products,
8 which decay with the emission of light. Detection limits of 0.005 ppm and a response time
9 of less than 30 s are typical of currently available commercial instruments. A positive
10 interference from atmospheric water vapor was reported in the 1970s and has recently been
11 confirmed. Proper calibration can minimize this source of error.
12 Commercial UV photometers for measuring ozone have detection limits of about
13 0.005 ppm, long-term precision within about ±5%, and a response time of < 1 min. Ozone
14 has a fairly strong absorption band with a maximum near 254 nm; its molar absorption
15 coefficient at that wavelength is well known. Since the measurement is absolute, UV
16 photometry is also used to calibrate O3 methods.
17 A potential disadvantage of UV photometry is that atmospheric constituents that absorb
18 254 nm radiation (and that are removed fully or partially by the manganese dioxide scrubber
19 used in UV O3 photometers) will be a positive interference in Oj measurements.
20 Interferences have been reported in two recent studies but assessment of the potential
21 importance of such interferences (e.g., toluene, styrene, cresols, nitrocresols) is hindered by
22 lack of absorption spectra data in the 250 nm range and by lack of ambient measurements of
23 most of the aromatic photochemical reaction products.
24 Differential optical absorption spectrometry (DOAS) has been used to measure ambient
25 O3, but further intercomparisons with other methods and interference tests are recommended.
26 Passive sampling devices (PSDs) permit acquisition of personal human exposure data and of
27 ozone monitoring data in areas where the use of instrumental methods is not feasible. Three
28 PSDs are commercially available. All employ solid absorbents that react with Q$.
29 Calibration of O3 measurement methods (other than PSDs) is done by UV spectrometry
30 or by gas-phase titration (GPT) of Oj with NO. Ultraviolet photometry is the reference
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1 calibration method approved by EPA. Ozone is unstable and must be generated in situ at
2 time of use to produce calibration mixtures.
3 Two methods have been generally employed to measure atmospheric peroxyacetyl
4 nitrate (PAN) and its higher homologues (PANs): infrared spectroscopy (TR) and gas
5 chromatography (GC) using an electron capture detector (BCD). A third method, less often
6 used, couples GC with a molybdenum converter that reduces PAN to NO in the gas phase
7 and subsequently measures the NO with a chemiluminescence analyzer. Peroxyacetyl nitrate
8 and the higher PANs are normally measured by GC-ECD. Detection limits have been
9 extended to 1 to 5 ppt using cryogenic enrichment of samples and specified desorption
10 procedures that limit losses associated with cryosampling. Because PAN is unstable
11 (explosive, and subject to surface-related decomposition), the preparation of reliable
12 calibration standards is difficult. Methods devised to generate calibration standards include
13 photolysis of static concentrations of gases, nitration of peracetic acid in single hydrocarbons,
14 and analysis of PAN as NO under specified conditions of the dissociation of PAN into its
15 precursors.
16 Early measurements of 10 to 80 ppb hydrogen peroxide (I^Oz) reported in the 1970s
17 have been found to be in error because of artifact formation of H2O2 from reactions of
18 absorbed gaseous O3, Modeling results also indicate that lower levels of H2O2 occur in the
19 atmosphere, on the order of 1 ppb.
20 In-situ measurement methods for H2O2 include FUR and tunable diode laser absorption
21 spectrometry (TOLAS). The FITR method is specific for H2O2 but has a high detection
22 level of ~50 ppb (using a 1-km path length). The TOLAS method is also specific and has a
23 detection level of 0.1 ppb over averaging times of several minutes. Four frequently used wet
24 chemical methods for measurement of H2O2 are available. All involve the oxidation of a
25 substrate followed by instrumental detection and quantification of the resulting
26 chemiluminescence or fluorescence. Detection limits are comparable to those of FTIR and
27 TOLAS, but interferences are common and must be obviated or minimized with specified
28 procedures.
29 Calibration of methods for gaseous H202 measurement requires the immediate use of
30 standard mixtures prepared by one of several wet chemical methods.
31
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1 3.7.4.2 Volatile Organic Compounds
2 Increased monitoring of volatile organic compounds is required under Title I, Section
3 182, of the Clean Air Act Amendments of 1990 because of their role as precursors to the
4 formation of O3 and other photochemical oxidants. Volatile organic compounds (VOCs) are
5 those gaseous organic compounds that have a vapor pressure greater than 0.15 mm and
6 generally have a carbon content ranging from Cl through C12. They include methane;
7 nonmethane organic compounds (NMOC); nonmethane hydrocarbons (NMHC); polar,
8 oxygenated hydrocarbons (PVOC); and reactive organic gases (ROG), which can include all
9 of the subcategories listed here.
10 Traditionally, NMHCs have been measured by methods that employ a flame ionization
11 detector (FID) as the sensing element that measures a change in ion intensity resulting from
12 the combustion of air containing organic compounds. The method recommended by EPA for
13 total NMOC measurement involves the cryogenic preconcentration of nonmethane organic
14 compounds and the measurement of the revolatilized NMOCs using FID. The main
15 technique for speciated NMOC/NMHC measurements is cryogenic preconcentration followed
16 by GC-FID, Systems for sampling and analysis of VOCs have now been developed that
17 require no liquid cryogen for operation, yet provide sufficient resolution of species.
18 Stainless steel canisters have become the containers of choice for collection of whole-air
19 samples for NMHC/NMOC data. Calibration procedures for NMOC instrumentation require
20 the generation, by static or dynamic systems, of dilute mixtures at concentrations expected to
21 occur in ambient air.
22 Preferred methods for measuring carbonyl species (aldehydes and ketones) in ambient
23 air are spectroscopic methods; on-line colorimetric methods; and the high-performance liquid
24 chromatography (HPLC) method employing 2,4-dinitrophenylhydrazine (DNPH)
25 derivatization in a silica gel cartridge. The most common method in current use for
26 measuring aldehydes in ambient air is the HPLC-DNPH method. Use of an O3 scrubber has
27 been recommended to prevent interference in this method by O3 in ambient air. Carbonyl
28 species are reactive, making preparation of stable calibration mixtures difficult; but several
29 methods are available.
30 Impetus for the development of methods for measuring the more reactive oxygen- and
31 nitrogen-containing organic compounds has come from their roles as precursors or products
December 1993 3-223 DRAFT-DO NOT QUOTE OR rnr?
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1 of photochemical oxidation but also from the inclusion of many of these compounds on the
2 list of hazardous air pollutants in the 1990 Clean Air Act Amendments. Measurement of
3 these polar volatile organic compounds (PVOCs) is difficult because of their reactivity and
4 water solubility. Methods are still in development.
5
6 3.7.4.3 Oxides of Nitrogen
7 Nitric oxide (NO) and nitrogen dioxide (NO^ comprise the oxides of nitrogen (NOX)
8 involved as precursors to ozone and other photochemical oxidants.
9 The most common method of NO measurement is the gas-phase chemiluminescent
10 (CL) reaction with ozone. The CL method is essentially specific for NO. Commercial NO
11 monitors have detection limits of a few parts per billion by volume (ppbv) in ambient air.
12 Commercial NO analyzers may not have sensitivity sufficient for surface measurements in
13 rural or remote areas, or for airborne measurements. Direct spectroscopic methods for NO
14 exist that have very high sensitivity and selectivity for NO. Major drawbacks of these
15 methods are their complexity, size, and cost, which restrict these methods to research
16 applications. No passive sampling devices (PSDs) presently exist for measurement of NO.
17 Chemiluminescence (CL) analyzers are the method of choice for NO2 measurement,
18 even though they do not measure NO2 directly. Minimum detection levels for NO2 have
19 been reported to be 5 to 13 ppb, but more recent evaluations have indicated detection limits
20 of 0.5 to 1 ppbv. Reduction of N02 to NO is required for measurement. In practice,
21 selective measurement of NOX by this approach has proved difficult. Commercial
22 instruments that use heated catalytic converters to reduce NO2 to NO measure not NO and
23 NOX, but more nearly NO and total NOy. Thus, the NO2 value inferred from such
24 measurements may be significantly in error, which may in turn affect the results of modeling
25 of ambient ozone.
26 Several spectroscopic approaches to NO2 detection have been developed. As noted
27 above for NO, however, these methods have major drawbacks that include their complexity,
28 size, and cost, which outweigh at present the advantages of their sensitivity and selectivity.
29 Passive samplers for NO2 exist, but are still in the developmental stage for ambient air
30 monitoring.
December 1993 3-224 DRAFT-DO NOT QUOTE OR CITE
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1 Calibration of methods for NO measurement is done using standard cylinders of NO in
2 nitrogen. Calibration of methods for NO2 measurement include: use of cylinders of NO2 in
3 nitrogen or air; use of permeation tubes; and gas-phase titration.
4
5 3.7.5 Ozone Air Quality Models
6 3.7.5.1 Definitions, Descriptions, and Uses
7 Photochemical air quality models are used to predict how O3 concentrations change in
8 response to prescribed changes in source emissions of NOX and VOCs. They are
9 mathematical descriptions of the atmospheric transport, diffusion, removal, and chemical
10 reactions of pollutants. They operate on sets of input data that characterize the emissions,
11 topography, and meteorology of a region and produce outputs that describe air quality in that
12 region.
13 Two kinds of photochemical models are recommended in guidelines issued by EPA:
14 The grid-based Urban Airshed Model (UAM) is recommended for modeling O3 over urban
15 areas; and EKMA (the Empirical Kinetics Modeling Approach) is identified as an acceptable
16 approach under certain circumstances. The 1990 Clean Air Act Amendments mandate the
17 use of three-dimensional (grid-based) air quality models such as UAM in developing SIPs
18 (State Implementation Plans) for areas designated as extreme, severe, serious, or multistate
19 moderate.
20 In grid-based air quality models, the region to be modeled (the modeling domain) is
21 subdivided into a three-dimensional array of grid cells. Pertinent atmospheric processes and
22 chemical reactions are represented for each cell.
23 In trajectory models, such as EKMA, a hypothetical air parcel moves through the area
24 of interest along a path calculated from wind trajectories. Emissions are injected into the air
25 parcel and undergo vertical mixing and chemical transformations. Trajectory models provide
26 a dynamic description of atmospheric source-receptor relationships that is simpler and less
27 expensive to derive than that obtained from grid models, but meterological processes are
28 highly simplified in trajectory models
29 The EKMA-based method for determining 03 control strategies has some limitations,
30 the most serious of which is that predicted emissions reductions are critically dependent on
31 the initial NMHC/NOX ratio used in the calculations. This ratio cannot be determined with
December 1993 3-225 DRAFT-DO NOT OUOTR OR rmn
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1 any certainty and is expected to be quite variable in an urban area. Grid-based models have
2 their limitations as well. These are pointed out subsequently.
3
4 3.7.5.2 Model Components
5 Spatial and temporal characteristics of VOC and NOX emissions are major inputs to a
6 photochemical air quality model. Greater accuracy in emissions inventories is needed, for
7 biogenics and for both mobile and stationary source components. Grid-based air quality
8 models also require as input the three-dimensional wind field for the photochemical episode
9 being simulated. This input is supplied by "meteorological modules" which fall into one of
10 four categories: (1) objective analysis procedures; (2) diagnostic methods; (3) dynamic, or
11 prognostic, methods; and (4) hybrid methods that embody elements from both diagnostic and
12 prognostic approaches. Prognostic models are believed to provide a dynamically consistent,
13 physically realistic, three-dimensional representation of the wind and other meteorological
14 variables at scales of motion not resolvable by available observations. Outputs of prognostic
15 models do not always agree with observational data, but methods have been devised to
16 mitigate these problems.
17 A chemical kinetic mechanism (a set of chemical reactions), representing the important
18 reactions that occur in the atmosphere, is used in an air quality model to estimate the net rate
19 of formation of each pollutant simulated as a function of time. Chemical mechanisms that
20 explicitly treat each individual VOC component of ambient air are too lengthy to be
21 incorporated into three-dimensional atmospheric models. " Lumped" mechanisms are
22 therefore used. The chemical mechanisms used in existing photochemical O3 models contain
23 uncertainties that may limit the accuracy of their predictions. Because of different
24 approaches to "lumping" of reactions, models can produce somewhat different results under
25 similar conditions. Both the UAM (UAM-IV) and EPA's Regional Oxidant Model (ROM)
26 use the Carbon-Bond Mechanism-IV (CMB-IV). The CBM-IV and the SAPRC and RADM
27 mechanisms (Statewide Air Pollution Research Center, and Regional Acid Deposition Model,
28 respectively) are considered to represent the state-of-the-science.
29 Dry deposition, the removal of chemical species from the atmosphere by interaction
30 with ground-level surfaces, is an important removal process for ozone on both urban and
31 regional scales; and is included in all urban- and regional-scale models. Wet deposition (the
December 1993 3-226 DRAFT-DO NOT QUOTE OR CITE
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1 removal of gases and particles from the atmosphere by precipitation events) is generally not
2 included in urban-scale photochemical models, since O3 episodes do not occur during periods
3 of significant clouds or rain,
4 Concentration fields of all species computed by the model must be specified at the
5 beginning of the simulation; these concentration fields are called the initial conditions. These
6 initial conditions are determined mainly with ambient measurements, either from routinely
7 collected data or from special studies; but interpolation can be used to distribute the surface
8 ambient measurements.
9
10 3.7.5.3 Evaluation of Model Performance
11 Air quality models are evaluated by comparing their predictions with ambient
12 observations. An adequate model should give accurate predictions of current peak
13 O3 concentrations and temporal and spatial O3 patterns. It should also respond accurately to
14 changes in VOC and NOX emissions, to differences in VOC reactivity, and to spatial and
15 temporal changs in emissions patterns for future years. Likewise, multispecies comparisons
16 could be the key in discriminating among alternative modeling approaches that provide
17 similar predictions of O3 concentrations. Adequate model performance for several reactive
18 species increases the assurance that correct ozone predictions are not a result of chance or
19 fortuitous cancellation of errors introduced by various assumptions.
20 If only a routine database is available for modeling O3 in an urban area, then several
21 concerns require attention relative to model performance evaluation: air quality aloft,
22 boundary conditions, ambient VOC data, and meteorological data aloft. If any of these four
23 areas is missing from the database, the performance evaluation and subsequent model
24 application must be adequately planned to minimize the possibility of compensatory errors.
25
26 3.7.5.4 Use of Ozone Air Quality Model for Evaluating Control Strategies
27 Photochemical air quality models are used for control strategy evaluation by first
28 demonstrating that a past episode, or episodes, can be adequately simulated and then
29 reducing hydrocarbon or NOX emissions or both in the model inputs and assessing the effects
30 of these reductions on O3 in the region. The adequacy of control strategies based on grid-
31 based models depends in part on the nature of input data for simulations and model
December 1993 3-227 DRAFT-DO NOT QUOTE OR CITE
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1 validation, on input emissions inventory data, and on the mismatch between model output
2 and the current form of the NAAQS for Qj.
3 Grid-based models that have been widely used to evaluate control strategies for 03 or
4 acid deposition, or both, are: (1) the Urban Airshed Model; (2) the California Institute of
5 Technology/Carnegie Institute of Technology (CIT) model; (3) the Regional Oxidant Model
6 (ROM); (4) the Acid Deposition and Oxidant Model (ADOM); and (5) the Regional Acid
7 Deposition Model (RADM).
8
9 3.7.5.5 Conclusions
10 Urban air quality models are becoming readily available for application and have been
11 applied in recent years in several urban areas. Significant progress has also been made in the
12 development of regional models and the integration of state-of-the-art prognostic
13 meteorological models as drivers.
14 There are still many uncertainties in photochemical air quality modeling. Prime among
15 these are emission inventories, However, models are essential for regulatory analysis and
16 constitute one of the major tools for attacking the 03 problem. Grid-based 03 air quality
17 modeling is superior to the available alternatives for O3 control planning, but the chances of
18 its incorrect use must be minimized.
19
December 1993 3-228 DRAFT-DO NOT QUOTE OR CITE
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7
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12
13 Winer, A. M.; Arey, J.; Atkinson, R,; Aschmann, S. M.; Long, W. D.; Morrison, C. L.; Olszyk, D. M.
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17 Wolff, G. T.; Korsog, P. E. (1992) Ozone control strategies based on die ratio of volatile organic compounds to
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19
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26 Wolff, G. T.; Lioy, P. J.; Meyers, R. E.; Cederwall, R. T.; Wight, G. D.; Pasceri, R. E.; Taylor, R. S.
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29
30 Wolff, G. T.; Lioy, P. J.; Wight, G. D.; Meyers, R. E.; Cederwall, R. T. (1977b) An investigation of
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33
34 Wolff, G. T.; Lioy, P. J.; Wight, G. D.; Pasceri, R. E. (1977c) Aerial investigation of the ozone plume
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36
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42
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48
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29
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48
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5
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8
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i 4. EPWIRONMENTAL CONCENTRATIONS,
2 PATTERNS, AND EXPOSURE ESTIMATES
3
4
5 4.1 INTRODUCTION
6 The ubiquity and toxicity of ozone (O3) are well documented (U.S. Environmental
7 Protection Agency, 1986a). Its effects on humans, animals, and vegetation have received
8 extensive examination and are discussed later in this document (Chapters 5 through 9).
9 As indicated in the previous O3 criteria document (U.S. Environmental Protection Agency,
10 1986a), most of the human and welfare effects research has focused on evaluating the
11 impacts on health or vegetation of exposure to O3 that mimic ambient O3 exposures (e.g.,
12 matching the occurrence of hourly average concentrations or more prolonged times of
13 exposure). The concentration information extensively monitored in the United States can be
14 useful for both linking anthropogenic emissions of O3 precursors with the protection of health
15 and welfare (i.e., determining compliance with air standards) and also to augment exposure
16 assessment and epidemiology studies. Therefore, as in previous criteria documents, the
17 major emphasis in this chapter will be on characterizing and summarizing the extensive
18 O3 monitoring data collected under ambient conditions. Although most of the O^ air quality
19 data summarized were gathered for compliance and enforcement purposes, the hourly
20 averaged 03 information can be used for determining patterns and trends and as inputs to
21 exposure and health assessments (e.g., U.S. Environmental Protection Agency, 1992a;
22 Lefohn et al., 1990a). In the sections that follow, the hourly averaged ambient 03 data have
23 been summarized in different ways to reflect the interests of those who wish to know more
24 about the potential for O3 to affect humans and their environment.
25 Trend patterns for 03 over several periods of time are described in Section 4.2. The
26 trends for O^ have been summarized by the U.S. EPA (1993) for the period 1983 to 1992.
27 In addition, trends analysis for specific regions of the United States have been performed by
28 several investigators. In some cases, attempts have been made to adjust for meteorological
29 variation. In Section 4.3, the hourly averaged concentration information from several
30 monitoring networks has been characterized for urban and rural areas. The diurnal variation
31 (Section 4.4) occurring at urban and rural locations, as well as seasonal patterns are also
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1 described. Specific focus is provided on O3 monitoring sites that are isolated from
2 anthropogenic sources of ozone precursors because these locations form the "basis for
3 comparison" for 03 concentrations and exposures. In many cases, it is important to know
4 O3 exposure regimes experienced at isolated areas so that the regimes can be compared with
5 those that are used in control treatments in experimental studies. In Section 4.5, the seasonal
6 patterns of exposure are discussed. The hourly average concentration information is used in
7 Section 4.6 to compare the spatial variations that occur in urban areas with those in nonurban
8 areas, as well as in high-elevation locations. For comparing indoor to outdoor O^ exposures
9 or concentrations, information is provided in Section 4.7 on the latest data on indoor/outdoor
10 ratios.
11 Section 4.8 describes efforts to estimate accurately both human and vegetation exposure
12 to O3. Examples are provided on how both fixed-site monitoring information, as well as
13 human exposure models, are used to estimate risks associated with 63 exposure. A short
14 discussion is provided on the importance of hourly average concentrations, used in the human
15 health and vegetation experiments, mimicking as closely as possible "real world" exposures.
16 As indicated in the previous O3 criteria document (U.S. Environmental Protection
17 Agency, 1986a), O3 is the only photochemical oxidant other man nitrogen dioxide that is
18 routinely monitored and for which a comprehensive aeromerric data base exists. Data for
19 peroxyacetyl nitrate (PAN) and hydrogen peroxide (H2O2) have been obtained as part of
20 special research investigations. Consequently, no data on nationwide patterns of occurrence
21 are available for these non-O3 oxidants; nor are extensive data available on the correlations
22 of levels and patterns of these oxidants with those of O3. Sections 4.9 and 4.10 summarize
23 the data available for these other oxidants. Section 4.11 describes the cooccurrence patterns
24 of O3 with nitrogen dioxide, sulfur dioxide, acidic aerosols, acidic precipitation, and acid
25 cloudwater.
26
27 4.1.1 Characterizing Ambient Ozone Concentrations
28 For purposes of using air quality data for assessing human health and vegetation effects,
29 it is important to distinguish among concentration, exposure, and dose. For human health
30 considerations, Sexton and Ryan (1988) provide the following definitions, as described in the
31 Air Quality Criteria for Carbon Monoxide (U.S. Environmental Protection Agency, 1991):
December 1993 4-2 DRAFT-DO NOT QUOTE OR OTfi
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1 1. The concentration of a specific air pollutant is the amount of that material
2 per unit volume of air. Air pollution monitors measure pollutant
3 concentrations, which may or may not provide accurate exposure estimates.
4
5 2. The term exposure is defined as any contact between an air contaminant of
6 a specific concentration and the outer (e.g., skin) or inner (e.g., respiratory
7 tract epithelium) surface of the human body. Exposure implies the
8 simultaneous occurrence of the two events.
9
10
11 The concentration of an airborne contaminant that is measured in an empty room is not
12 exposure. However, a concentration measured in a room with people present is considered
13 to be a measurement of exposure. A measured concentration is a surrogate for exposure
14 only to the degree to which it represents concentrations actually experienced by individuals.
15 Exposure is defined as the pollutant concentration at the point of contact between die body
16 and the external environment, while dose is defined as the amount of pollutant that actually
17 crosses one of the body's boundaries and reaches the target tissue.
18 For vegetation, similar to human health considerations, concentrations of airborne
19 contaminants are considered to represent exposure when contacts with them are experienced
20 by a plant. As indicated in Chapter 5 (see Section 5.5), dose has been defined historically by
21 air pollution vegetation researchers as ambient air quality concentration multiplied by time
22 (O'Gara, 1922). However, a more rigorous definition is required. Runeckles (1974)
23 introduced the concept of "effective dose" as the amount or concentration of pollutant that
24 was adsorbed by vegetation, in contrast to that present in the ambient air. Fowler and Cape
25 (1982) developed this concept further and proposed that the "pollutant adsorbed dose" be
2
26 defined in units of g m" (of ground area or leaf area) and could be obtained as the product
27 of concentration, time, and stomatal (or canopy) conductance for the gas in question. Taylor
28 et al. (1982) suggested internal flux (mg m" If1) as a measure of the dose to which plants
29 respond. For the purposes of vegetation, this chapter has adopted the concept that dose is
30 the amount of pollutant absorbed by the plant.
31 In order to characterize the specific doses responsible for affecting human health and
32 vegetation, there has to be a linkage between exposure and the actual dose. Unfortunately, it
33 is difficult to predict this relationship without detailed modeling. For example, the sensitivity
34 of vegetation as a function of time of day or period of growth, as well as edapiric conditions,
35 may result in plants being exposed to high O3 concentrations with little resultant injury or
December 1993 4.3 DRAFT-DO NOT QUOTE OR CITE
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1 damage, while more moderate levels of O3 exposures result in injury (Showman, 1991).
2 Because (1) not enough is known to quantify the links between exposure and dosage and
3 (2) routine monitoring for O3 is summarized as hourly average concentrations (i.e., potential
4 exposure), most of the information provided in this chapter is characterized in terms of
5 concentration and exposure.
6 As indicated in the human health and vegetation chapters (Chapters 7 and 5), for many
7 years, air pollution specialists have explored alternative mathematical approaches for
8 summarizing ambient air quality information in biologically meaningful forms that can serve
9 as surrogates for dose. At present, for human health effects purposes, 03 is usually
10 characterized in terms of the daily maximum (i.e., the highest hourly average concentration
11 for the day). In addition, recent human health concern about 63 exposures for extended
12 periods has resulted in the summarization of hourly average concentrations of O3 in terms of
13 4- to 8-h daily maximum concentrations (see Chapter 7 for further discussion).
14 For vegetation, as indicated in Chapter 5 (Section 5.5), extensive research has focused
15 on identifying indicators of exposure with a firm foundation on biological principles. Many
16 of these indicators have been based on research results indicating that the magnitude of
17 vegetation responses to air pollution is determined more as a function of the magnitude of the
18 concentration than the length of the exposure (U.S. Environmental Protection Agency,
19 1986b; U.S. Environmental Protection Agency, 1992b). Short-term, high concentration
20 O3 exposures have been identified by many researchers as being more important than
21 long-term, low concentration exposures for induction of effects on vegetation (see Chapter 5
22 for further discussion). Similarly, for human health considerations, results using controlled
23 human exposures have shown the possible importance of concentration in relation to duration
24 of exposure and ventilation rate. The product of O3 concentration multiplied by exposure
25 duration was shown to be an imprecise indicator of O3 toxicity when the rate of O3
26 inhalation was increased (DeLucia et al., 1978). Folinsbee et al. (1978) and Silverman et al.
27 (1976) observed that O3 toxicity was better represented by an effective dose expressed as the
28 product of concentration, exposure duration, and ventilation. Adams et al. (1981) examined
29 the effective dose concept and concluded that O3 concentration was of greater importance in
30 affecting pulmonary function decrement than either ventilation or exposure duration.
31 Pulmonary function research results reported by Hazucha et al. (1990) have also revealed
December 1993 4-4 DRAFT-DO NOT QUOTE OR CITE
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1 that concentration may be more important than duration. Horstman et al. (1990) reported
2 that O3 concentration was slightly more influential than exposure duration for inducing
3 FEVi o responses. Lung function modelling results reported by Larsen et al. (1991) appears
4 to agree with previous reports that concentration is one of the most important components of
5 exposure and that the higher hourly average concentrations should be weighted differently
6 than the lower ones.
7 Long-term average concentrations initially were used to describe O^ exposures when
8 assessing vegetation effects (Heck et al., 1982). However, as evidence began to mount that
9 higher concentrations of O3 should be given more weight than lower concentrations (see
10 Section 5.5 for further details), the following specific concerns about the use of a long-term
11 average to summarize exposures of Qs began appearing in the literature: (1) the use of a
12 long-term average failed to consider the impact of peak concentrations, as well as duration;
13 (2) a large number of hourly distributions within a 7-h (0900 to 1559 h) window could be
14 used to generate the same 7-h seasonal mean; and (3) high hourly average concentrations
15 (e.g., values greater than 0.10 ppm) occurred outside of a fixed 7-h window.
16 In summarizing the hourly average concentrations in this chapter, specific attention is
17 given to the relevance of the exposure indicators used. For example, for human health
18 considerations, concentration (or exposure) indicators such as the daily maximum 1-h average
19 concentrations, as well as the number of daily maximum 4-h or 8-h average concentrations
20 are used to characterize information hi the population-oriented locations. For vegetation,
21 several different types of exposure indicators are used. For example, much of the National
22 Crop Loss Assessment Network (NCLAN) exposure information is summarized in terms of
23 the 7-h average concentrations. However, because peak-weighted, cumulative indicators
24 (i.e., exposure parameters that sum the products of hourly average concentrations multiplied
25 by time over an exposure period) have shown considerable promise in relating exposure and
26 vegetation response (see Section 5,5), several exposure indicators that use either a threshold
27 or a sigmoidally weighting scheme are used in this chapter to provide insight concerning the
28 O3 exposures that are experienced at a select number of rural monitoring sites in the United
29 States. The peak-weighted cumulative exposure indicators used in this chapter are SUM06
30 (the sum of all hourly average concentrations equal to or greater than 0.06 ppm), SUM08
31 (the sum of all hourly average concentrations equal to or greater than 0.08 ppm), and W126
December 1993 4-5 DRAFT-DO NOT QUOTE OR CITE
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1 (the sum of the hourly average concentrations that have been weighted according to a
2 sigmoid function [see Lefohn and Runeckles, 1987] that is theoretically based on a
3 hypothetical vegetation response) are used.
4 The exposure indicators used for human health considerations are in concentration units
5 (i.e., ppm), whereas the indicators used for vegetation are in both ppm (e.g., 7-h seasonal
6 average concentrations) and ppm-h (e.g., SUM06, SUM08, and W126). The magnitude of
7 the peak-weighted cumulative indicators at specific sites can be compared with those values
8 experienced at pristine areas. In some cases, to provide more detailed information about the
9 distribution patterns for specific O3 exposure regime, the percentUe distribution of the hourly
10 average concentrations (in units of ppm) is given. For further clarification of the
11 determination and rationale for the exposure indicators that are used for assessing human
12 health and vegetation effects, the reader is encouraged to read Chapters 5 (Section 5.5)
13 and 7.
14
15 4.1.2 The Identification and Use of Existing Ambient Ozone Data
16 Information is readily available from the database supported by a network of monitoring
17 stations that were established to determine the compliance with the National Ambient Air
18 Quality Standard for O3. Most of the data presented in this chapter were obtained from data
19 stored in the EPA's computerized Aerometric Information Retrieval System (AERS) and were
20 collected after 1978. As pointed out in the previous criteria document for O3 and other
21 photochemical oxidants (U.S. Environmental Protection Agency, 1986a), there was some
22 difficulty in interpreting the Og data obtained at most sites across the United States prior to
23 1979 because of calibration problems.
24 In the United States, O3 hourly average concentrations are routinely monitored through
25 the National Air Monitoring Network, consisting of three types of sites. The National Air
26 Monitoring Station (NAMS) sites are located in areas where the concentrations of O$ and
27 subsequent potential human exposures are expected to be high. Criteria for these sites have
28 been established by regulation to meet uniform standards of siting, quality assurance,
29 equivalent analytical methodology, sampling intervals, and instrument selection to assure
30 consistency among the reporting agencies. For O3, NAMS sites are located only in urban
31 areas with populations exceeding 1 million. The other two types of sites are States and Local
December 1993 4-6 DRAFT-DO NOT QUOTE OR CITE
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1 Air Monitoring Stations (SLAMS) and Special Purpose Monitors, which meet the same rigid
2 criteria for the NAMS sites out may be located in areas which do not necessarily experience
3 high concentrations in populated areas.
4 For O3, the reporting interval is 1 h, with the instruments operating continuously and
5 producing an integrated hourly average measurement. In many cases, the U.S.
6 Environmental Protection Agency summarizes air quality data by an 03 "season." Table 4-1
7 summarizes the O3 "season" for the various states in the United States.
8 In this chapter, data are analyzed for the purpose of providing focus on specific issues
9 of exposure-response relationships that are considered in later effects chapters. The analyses
10 proceed from a national picture of peak annual averages in Metropolitan Statistical Areas,
11 through national 10-year and 3-year trends, to characteristic seasonal and diurnal patterns at
12 selected stations, and a brief examination of the incidence of episodic 1-h levels. Although
13 there are O3 data collected from monitoring stations not listed in AIRS, the major source of
14 information was derived from ambient air concentrations from monitoring sites operated by
IS the State and local air pollution agencies who report their data to AIRS. Because
16 meteorology affects the identification of trends, methodologies which adjust for meteorology
17 are described.
IS To obtain a better understanding of the potential for ambient O3 exposures to affect
19 human health and vegetation, hourly average concentration information was summarized for
20 urban versus rural (forested and agricultural) areas in the United States. A land use
21 characterization of "rural" does not imply that any specific location is isolated from
22 anthropogenic influences. For example, Logan (1989) has noted that hourly average
23 03 concentrations above 0.08 ppm are common in rural areas of the eastern United States in
24 spring and summer, but are unusual at remote western sites. Consequently, for comparing
25 exposure regimes that may be characteristic of clean locations in the United States with those
26 that are urban influenced (i.e., located in either urban or rural locations), this chapter
27 characterizes data collected from those stations whose locations appear to be isolated from
28 large-scale anthropogenic influences.
29 For the most part, research on O^ concentrations is clearly divided between ambient air
30 environments and indoor air environments, although some exposure studies use personal
31 monitors to measure continuous O3 concentrations in both situations. Long-term
December 1993 4.7 DRAFT-DO NOT QUOTE OR COTE
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TABLE 4-1. OZONE MONITORING SEASON BY STATE
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
D.C.
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
aAQCR numbers 4,
bAQCR numbers 1,
Begin
March
April
January
March
January
March
April
April
April
January
March
January
April
April
April
April
April
April
January
April
April
April
April
April
March
April
5, 7, 10, and
2, 3, 6, 8, 9,
End
November
October
December
November
December
September
October
October
October
December
November
December
October
October
October
October
October
October
December
October
October
October
October
October
November
October
n.
and 12.
State
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texasa
Texasb
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
J3egin
June
April
January
April
April
January
April
April
May
April
March
April
April
April
April
June
April
January
March
May
April
April
April
April
April
April
End
September
October
December
October
October
December
October
October
September
October
November
October
October
October
October
September
October
December
October
September
October
October
October
October
October
October
December 1993
4-8
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(multiple-year) patterns and trends are available only from stationary ambient monitors; data
1 on indoor concentrations are collected predominantly in selected settings during
2 comparatively short-term studies. Data from the indoor and outdoor environments are
3 reviewed here independently.
4
5
6 4.2 TRENDS IN OZONE CONCENTRATIONS
7 4.2.1 Trends in Ambient Ozone Concentrations
8 Ozone concentrations and thus, exposure, change from year to year. Ambient trends
9 during the 1980s were heavily influenced by varying meteorological conditions (U.S.
10 Environmental Protection Agency, 1993). High Og levels occurred in 1983 and 1988 in
11 some areas of the United States. These levels were more than likely attributable in part to
12 hot, dry, stagnant conditions. However, 03 levels in 1992 were the lowest of the 1983 to
13 1992 period (U.S. Environmental Protection Agency, 1993). These low levels may have
14 been due to less favorable meteorological conditions for O3 formation, as well as recently
15 implemented control measures. Nationally, the summer of 1992 was the third coolest
16 summer on record (U.S. Environmental Protection Agency, 1993). The U.S. EPA (1993)
17 has recently reported a 21 % improvement in Oj levels between 1983 and 1992, which in part
18 may be attributed to relatively high levels in 1983, compared to the low Qj exposure year
19 from the period 1989 through 1992. However, new statistical techniques accounting for
20 meteorological influences have been used by the U.S. EPA and they appear to suggest an
21 improvement (independent of meteorological considerations) of 10% for the 10-year period,
22 1983 to 1992.
23 The U.S. Environmental Protection Agency summarizes trends for the National
24 Ambient Air Quality Standards (NAAQS) for the most current 3- and 10-year periods.
25 In order to be included in the 10-year trend analysis in the annual National Air Quality and
26 Emissions Trend Report (U.S. Environmental Protection Agency, 1993) a station must report
27 valid data for at least eight of the last ten years. A companion analysis of the most recent
28 three years requires valid data in all three years. Analysis in the above report covers the
29 periods 1983 to 1992 and 1990 to 1992, respectively; 509 sites met the 10-year period
December 1993 4.9 DRAFT-DO NOT QUOTE OR CITE
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1 criteria, 672 sites are included in the 1990 to 1992 data base. The NAMS sites comprise
2 196 of the long-term trends sites and 222 of the sites in the 3-year data base.
3 Figure 4-1 displays the 10-year composite average trend for the second highest daily
4 maximum hourly average concentration during the O^ season for the 509 trend sites and the
5 subset of 196 NAMS sites. The 1992 composite average for the 509 trend sites is 21 %
6 lower than the 1983 average and 20% lower for the subset of 196 NAMS sites. The 1992
7 value is the lowest composite average of the past ten years (U.S. Environmental Protection
8 Agency, 1993). The 1992 composite average is significantly less than all the previous nine
9 years, 1983 to 1991. As discussed in U.S. EPA (1992a), the relatively high
10 O3 concentrations in 1983 and 1988 were likely attributable in part to hot, dry stagnant
11 conditions in some areas of the country that were especially conducive to O3 formation.
p
t
c
o
1
I
vs. 10
0.16-
0.14-
04 O
.If.
0.10-
0.08-
0.06-
0.04
0.02-
nnn
^SiL ''.3J^lx.
^^**«<3p"— *.- .--ii^'^V NAAQS
* "1 £•§— •*" •5k
1 ~f*-| :^""--~~^_
A All Sites (509) • NAMS Sites (196)
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992
Figure 4-1. National trend in the composite average of the second highest maximum
1-h ozone concentration at both NAMS and all sites with 95% confidence
intervals, 1982 to 1991.
Source: U.S. Environmental Protection Agency (1993).
December 1993
4-10
DRAFT-DO NOT QUOTE OR CITE
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1 Between 1991 and 1992, the composite mean of the second highest daily maximum
2 1-h O3 concentrations decreased 7% at the 672 sites and 6% at the subset of 222 NAMS
3 sites. Between 1991 and 1992, the composite average of the number of estimated
4 exceedances of the O3 standard decreased by 23% at the 672 sites, and 19% for me
5 222 NAMS sites. Nationwide VOC emissions decreased 3% between 1991 and 1992 (U.S.
6 Environmental Protection Agency, 1993),
7 The composite average of the second daily maximum concentrations decreased in eight
8 of the ten EPA Regions between 1991 and 1992, and remained unchanged in Region vn.
9 Except for Region vn, the 1992 regional composite means are lower than the corresponding
10 1990 levels.
11 Investigators have explored methods for investigating techniques for adjusting Q3 trends
12 for meteorological influences (Stoeckenius and Hudischewskyj, 1990; Wakim, 1990; Shively,
13 1991; Korsog and Wolff, 1991; Lloyd et al., 1990; Davidson, 1993; Cox and Chu, 1993).
14 Stoeckenius and Hudischewskyj (1990) used a classification method to group days into
15 categories according to the magnitude of O3 and similarity of meteorological conditions
16 within each defined group. Adjusted 63 statistics for each year were computed from the
17 meteorologically grouped data and the yearly frequency of occurrence of each group relative
18 to its long-term frequency was described. Wakim (1990) used standard regression analysis to
19 quantify the effect of daily meteorology on 63. Adjusted O3 statistics were calculated by
20 adding the expected O3 statistic for a year with typical meteorology to the average of the
21 regression residuals obtained for the adjusted year. Shively (1991) described a model in
22 which the frequency of exceedance of various 03 thresholds was modeled as a non-
23 homogeneous Poisson process where the parameter is a function of time and meteorological
24 variables. Kolaz and Swinford (1990) categorized 03 days as "conducive" or "non-
25 conducive" based on selected meteorological conditions within the Chicago area. Within
26 these categories, the meteorological intensity of days conducive to daily exceedances of the
27 NAAQS for O3 was calculated and used to establish long-term trends in the annual
28 exceedance rate.
29 Recently, Cox and Chu (1993) have modeled the daily maximum 03 concentration
30 using a Weibull distribution with a fixed shape parameter and a scale parameter, the
31 logarithm of which varies as a linear function of several meteorological variables and a
December 1993 4-11 DRAFT-DO NOT QUOTE OR CITE
-------
1 yearly index. The authors tested for a statistically significant trend term to determine if an
2 underlying meteorologically adjusted trend could be detected. Overall the measured and
3 modeled predicted percentiles tracked closely in the northern latitudes but performed less
4 well in southern coastal and desert areas. The results suggested that meteorologically
5 adjusted upper percentiles of the distribution of daily maximum 1-h O3 are decreasing in
6 most urban areas over the period 1981 through 1991. The median rate of change was
7 —1.1% per year, indicating that 03 levels have decreased approximately 11 % over this time
8 period. The authors reported that trends estimated by ignoring the meteorological component
9 appear to underestimate the rate of improvement in Qj primarily because of the uneven year-
10 to-year distribution of meteorological conditions favorable to Oj formation.
11 Lefohn et al. (1993a) focused on a potentially useful method for identifying monitoring
12 sites whose improvement in Qj exposures may be attributed more to the implementation of
13 abatement control strategies than meteorological changes. As has been pointed out
14 previously, meteorology plays an important role in affecting the O3 concentrations that are
IS contained in the tail of the 1-h distributions, as indicated by the successful predictive
16 application of the exponential-tail model to distributions (California Air Resources Board,
17 1992), Because meteorology plays such an important role in affecting the tail of the 1-h
18 distribution at a specific site, changes in "attainment" status would be expected not to affect
19 changes in the entire distribution pattern and thus, the average diurnal pattern. Lefohn et al.
20 (1993b) investigated the change in the annual averaged diurnal pattern as changes in
21 O3 levels occurred. The authors reported that although the amplitude of the diurnal patterns
22 changed, there was little evidence for consistent changes in the shape of the annual diurnal
23 patterns (Figure 4-2). In a follow-up to this analysis, Lefohn et al. (1993a) reported that
24 25 of the 36 sites that changed compliance status across years showed no statistically
25 significant change in the shape of the average diurnal profile (averaged by Qj season).
26 In addition, the authors reported that for 71 % (10 out of 14) of the sites in southern
27 California and Dallas-Fort Worth, Texas, that showed improvement in 03 levels (i.e.,
28 reductions in the number of exceedances over the years), but still remained in
29 "nonattainment," a statistically significant change in the shape of the seasonally averaged
30 diurnal profile occurred (Figure 4-3). Thus, the authors noted that for the southern
31 California and Dallas-Fort Worth sites, which showed improvements in O3 levels, changes
December 1993 4-12 DRAFT-DO NOT QUOTE OR CITE
-------
Annual
1 3 5 7 9 11 13 15 17 19 21 23
-1987 -»-1988--*-1989
-1990
0.08 1
Louisville, KY
Annual
1 3 5 7 9 11 13 15 17 19 21 23
-1987 -*-1988 -e-1989
-1990
Annual
1 3 57 9 11 13 15 17 19 21 23
-1987 -«-198B -0-1989
-1990
Dade Co., FL
(d) Annual
1 3 57 9 11 13 15 17 19 21 23
-1987-^1988 -0-1989
-1990
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).
1
2
3
4
5
6
7
were observed in the seasonally averaged diurnal profiles, while for the sites moving between
"attainment" and "nonattainment" status, such a change in shape was generally not observed.
Lefohn et al. (1993a) pointed out that it was possible that meteorology played a more
important role in affecting attainment status than changes in emission levels.
Historically, the long-term O3 trends in the United States characterized by the U.S.
Environmental Protection Agency have emphasized air quality statistics that are closely
December 1993
4-13
DRAFT-DO NOT QUOTE OR CITE
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0.08
3 57 911 1'3 1'5 17 1'9 2?1
Hour
1980-*- 1981-0- 1990 -^ 1991
19BO 19&2 10&4 19'86 1968 1990
Year
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: Lefbhn et al. (1993a).
1 related to tile NAAQS. A recent report by the National Academy of Sciences (NAS) (NRC,
2 1991) stated that the principal measure currently used to assess 03 trends is highly sensitive
3 to meteorological fluctuations and is not a reliable measure of progress in reducing O3 over
4 several years for a given area. The NAS report recommended that "more statistically robust
5 methods be developed to assist in tracking progress in reducing ozone." The NAS report
6 points out that most of the trends analyses are developed from violations of standards based
7 on lower concentration cutoffs or using percentile distributions. Because of the interest by
8 the EPA in tracking trends in the quality of air people breathe when outdoors, most of the
9 above measures have some association with the existing NAAQS, in the form of either
10 threshold violations or O3 concentrations.
11 Several of the alternative examples provided in the NAS report were described
12 previously by Curran and Frank (1990). Several of the examples mentioned in the NAS
13 report involved threshold violations: the number of days on which the maximum
14 O3 concentration was above 0.12 ppm (Jones et al., 1989; Kolaz and Swinford, 1990;
15 Wakiin, 1990); the number of times during the year that the daily summary statistics
December 1993 4-14 DRAFT-DO NOT QUOTE OR CITE
-------
1 exceeded 0.08 ppm or 0.105 ppm (Stoeckenius, 1990); and the number of days in California
2 when the O3 concentration exceeded 0,20 ppm (Zeldin et al., 1990). Several other
3 O3 concentration measures are described in the NAS (NRC, 1991) report.
4 As an alternative to the way in which the U.S. EPA historically implemented its trends
5 analysis, the EPA (1992a) used percentiles in the range from 50th percentile (or median) to
6 the 95th percentile. The U.S. EPA (1992a) reported that the pattern for the 10-year trends
7 (1982 to 1991) using the various alternative O3 summary statistics were somewhat similar,
8 There was a tendency for the curves to become flatter in the lower percentiles. The peak
9 years of 1983 and 1988 were still evident in the trend lines for each indicator. The increase
10 of 8% recorded in the annual second-highest daily maximum 1-h concentration between 1987
11 and 1988 was also seen in the 95th and 90th percentile concentrations. The lower percentite
12 indicators had smaller increases of 3 to 4%. The percent change between 1982 and 1991 for
13 each of the summary statistics follows: annual daily maximum 1-h concentration (—11 %),
14 annual second daily maximum 1-h concentration (-8%), 95th percentile of the daily
15 maximum 1-h concentrations (-5%), 90th percentile (-4%), 70th percentile (-1%), 50th
16 percentile, or median of the daily maximum 1-h concentrations (+1 %), and the annual mean
17 of the daily maximum 1-h concentrations (-1 %).
18 Besides the U.S. Environmental Protection Agency, additional investigators have
19 assessed trends at several locations in the United States (e.g., Kuntasal and Chang, 1987),
20 (Gallopoulos et al., 1988; Korsog and Wolff, 1991; Lloyd et al., 1990; Rao et al., 1992;
21 Davidson, 1993). For example, Kuntasal and Chang (1987) performed a basin-wide air
22 quality trend analysis for the South Coast Air Basin of California using multi-station
23 composite daily maximum-hour average ambient concentrations for the third quarter from
24 1968 to 1985. Basin-wide ambient Oj concentrations appeared to show downward trends for
25 the period 1970 to 1985, but because of high fluctuations, it was difficult to delineate trends
26 for shorter periods. The meteorology (850 mb temperature)-adjusted O3 showed a more
27 consistent downward trend than did unadjusted O3. Korsog and Wolff (1991) examined
28 trends from 1973 to 1983 at eight major population centers in the northeastern United States,
29 using a robust statistical method. The 75th percentile was shown to be a good statistic for
30 determining trends and was used for analysis of the trends. The surface temperature and
31 upper air temperature variables were found to be the best predictors of (>3 behavior. Two
December 1993 4-15 DRAFT-DO NOT QUOTE OR CITE
-------
1 regression procedures were performed to remove the variability of meteorological conditions
2 conducive to high O3 (i.e,, O3 concentrations >0.08 ppm). The results of the analysis
3 showed that there has been a decrease of a few ppb on a yearly basis for the majority of the
4 sites investigated by the authors.
5 Lloyd et al. (1989) investigated the improvement in 03 air quality from 1976 to 1987 in
6 the South Coast Air Basin of California. The authors reported that when the trend in total
7 exceedance hours of a consistent set of Basin air monitoring stations was considered, the
8 "improvement over the period of investigation was substantial. The authors reported that the
9 number of station hours at or above the Stage I Episode Level (0.20 ppm, 1-h average) had
10 decreased by about two-thirds over the period 1976 to 1987. Davidson (1993) reported on
11 the number of days on which O3 concentrations at one or more stations in the South Coast
12 Air Basin exceeded the federal standard and the number of days reaching Stage I episode
13 levels, for the months of May through October in the years from 1976 to 1991. The author
14 reported that the number of Basin days exceeding the federal standard declined at an average
15 annual rate of 2.27 days per year over the period. In addition, the number of Basin days
16 with Stage I episodes declined at an average annual rate of 4.70 days per year over the
17 period 1976 to 1991. Rao et al. (1992) demonstrated the use of some statistical methods for
18 examining trends in ambient O3 air quality downwind of major urban areas. The authors
19 examined daily maximum 1-h O3 concentrations measured over New Jersey, metropolitan
20 New York City, and Connecticut for the period 1980 to 1989. The analyses indicated that
21 although there has been an improvement in O3 air quality downwind of New York City,
22 there has been little change in O3 levels upwind of New York City during this 10-year
23 period.
24 Lefhon and Runeckles (1987) proposed a sigmoidal weighting function that was used in
25 developing a cumulative integrated exposure index (W126):
26
w = * . . , (4-1)
[1 + M x exp('A x **}
27 where: M and A are positive arbitrary constants,
28 Wj = weighting factor for concentration i, and
29 Cj = concentration i.
December 1993 4-16 DRAFT-DO NOT QUOTE OR CITE
-------
1 Lefohn et al. (1988) reported the use of the sigmoidally weighted index with constants,
2 M and A, 4,403 and 126 ppm , respectively. The authors referred to the index as W126.
3 The values were subjectively determined to develop a weighting function that (1) included
4 hourly average concentrations as low as 0.04 ppm, (2) had an inflection point near
5 0.065 ppm, and (3) had an equal weighting of 1 for hourly average concentrations at
6 approximately 0.10 ppm and above. To determine the value of the index, the sigmoidal
7 weighting function at CL was multiplied by the hourly average concentration, Cj, and summed
8 over all relevant hours. The index included the lower, less biologically effective
9 concentrations in the integrated exposure summation. The weighting function has been used
10 to describe the relationship between 03 exposure and vegetation response (e.g., Lefohn
11 et al., 1988; Lefohn et al., 1992a).
12 Lefohn and Shadwick (1991), using the W126 sigmoidally weighted exposure index,
13 assessed trends in O3 exposures at rural sites in me United States over 5- and 10-year periods
14 (1984 to 1988 and 1979 to 1988, respectively) for forestry and agricultural regions of the
15 United States. Although their analysis did not explore the effects on trends of the lower
16 O3 exposure period 1989 to 1992, their analysis did reflect the effect of the higher
17 03 exposure years (1983 and 1988). To compare the exposure index values across years, a
18 correction for missing data was applied for each pollutant. The corrections were determined
19 for each site on a monthly basis. The Kendall's K statistic (Mann-Kendall test) was used to
20 identify linear trends. Estimates of the rate of change (slope) for the index were calculated.
21 Table 4-2 summarizes the results of the analysis. For sites distributed by forestry regions,
22 there were more positive than negative slope estimates for the 5-year analysis of sites in the
23 Southern, Midwest, and Mid-Atlantic regions. For the 10-year analysis, the above was true
24 except for the Mid-Atlantic seasonal analysis, where there was one positive and one negative
25 significant trend. In the Southern region, 38% of the sites showed significant trends. For the
26 sites in the Northeastern region, few sites showed a significant trend. There were
27 considerably fewer sites in the remaining regions than in the four forestry regions above.
28 Hence, for these regions, no significance was assigned to the differences in the number of
29 negative and positive slope estimates in the tables. Similar to the results reported for the
30 forestry regions, most of the sites in the agricultural regions showed no Oj trends.
December 1993 4.17 DRAFT-DO NOT OTTOTR OP rim?
-------
TABLE 4-2. SUMMARY BY FORESTRY AND AGRICULTURAL REGIONS FOR
OZONE TRENDS USING THE W126 EXPOSURE PARAMETER ACCUMULATED
ON A SEASONAL BASIS8
Forestry
Five- Year Trends
Not Significant
South
Midwest
West
Pacific
Northwest
Plains
Northeast
Mid-Atlantic
Rocky
Mountains
All
Pacific
Mountain
Northern
Plains
Lake States
Cora Belt
Northeast
Appalachian
Southeast
Delta State
Southeastern
Plains
All
53
38
10
4
3
14
12
5
139
Not
14
5
3
10
20
26
27
16
9
9
139
(16)
(1)
(0)
(2)
(0)
(0)
(0)
(2)
(21)
Five-Year
Significant
(2)
(2)
(0)
(0)
(1)
(0)
(9)
(5)
(0)
(2)
(21)
Significant
-
0
0
0
0
0
1
0
0
1
Trends
+
14
7
3
0
0
0
3
1
28
Agricultural
Significant
-
0
0
0
0
0
1
0
0
0
0
1
+
3
1
0
1
3
3
14
1
2
0
28
Ten- Year Trends
Not Significant
13
20
4
2
2
7
4
2
54
Ten-Year
Not Significant
6
2
2
5
11
11
8
4
4
1
54
Significant
-
1
1
2
0
0
1
1
0
6
Trends
+
7
6
1
0
0
1
1
1
17
Significant
-
2
0
0
0
1
2
0
1
0
0
6
+
1
1
0
1
2
2
8
0
1
1
17
aNumbers in parentheses in the "Not Significant" column under "Five-year trends" are the number of sites with
exactly 3 years of data.
Source: Lefhon and Shadwick, 1991.
December 1993
4-18
DRAFT-DO NOT QUOTE OR CITE
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1 However, in the Appalachian agricultural region, as many as 50% of the sites showed a
2 pronounced indication of a trend. A predominance of positive significant trends for both the
3 5- and 10-year analyses was observed. In the other agricultural regions, there were
4 approximately an equal number of positive and negative significant 5- and 10-year trends.
5 The O3 results produced patterns that were not pronounced enough to draw more than
6 tentative conclusions for the 10-year analysis. For the 5-year analysis, there was still not a
7 strong indication of an O3 trend. However, when significant trends were observed, they
8 were almost always positive. This can be attributed to eastern O3 levels that were generally
9 higher in 1988 than in previous years.
10
11
12 4.3 SURFACE OZONE CONCENTRATIONS
13 4.3.1 Introduction
14 Ozone is an omnipresent compound that is measured at levels above the minimum
15 detectable level at all monitoring locations in the world (Lefohn et al., 1990b). Stratospheric
16 sources of O3 play an important role in determining the O3 concentrations at remote sites that
17 are isolated from either local generation of O3 or the transport of 03 or its precursors (Singh
18 et al., 1978). The occasional occurrence of stratospheric injection of O3, at specific times
19 and in certain locations, is accepted and may be responsible for some of the rare occurrences
20 of elevated levels that have been observed at both some high- and low-elevation remote sites.
21 Altshuller (1989) attributes approximately 10 ppb of surface-level Q^ concentration to
22 stratospheric intrusion.
23 For purposes of comparing how Oj levels have changed over time, it would be
24 interesting to know how current levels compare to natural background levels. However,
25 estimations of natural background O3 concentrations are difficult to make. The definitions of
26 natural background and use of the information are subject to much uncertainty. It is
27 difficult, if not impossible, to determine whether any geographic location on Earth is free
28 from human influence (Finlayson-Pitts and Pitts, 1986). Finlayson-Pitts and Pitts (1986)
29 have noted that photochemical production via naturally occurring NOX-NMOC (non-methane
30 organic compounds) or carbon monoxide reactions in sunlight may be more important than
31 injection of O3 from the stratosphere. Natural emissions can influence O3 exposures
December 1993 4-19 DRAFT-DO NOT QUOTE OR CITE
-------
1 observed at remote sites (Chameides et al., 1988; Zimmerman, 1979; Trainer et al,, 1987).
2 Citing indirect evidence for the possible importance of natural emissions, Lindsay et al.
3 (1989) have emphasized that additional research is required to assess the role that natural
4 hydrocarbons might play in urban and regional O3 episodes.
5 Ozone concentrations at a specific location are influenced by local emissions and by
6 long-range transport from both natural and anthropogenic sources. In addition, levels are
7 also influenced by variables such as wind, solar insolation, vertical exchange rates, and the
8 nature of the surface. For a more complete discussion, see Chapter 3.
9 It is possible for urban emissions, as well as O3 produced from urban area emissions,
10 to be transported to more rural downwind locations. This can result in elevated
11 O3 concentrations at considerable distances from urban centers (Wolff et al., 1977; Husar
12 et al., 1977; Wight et al., 1978; Vukovich et al., 1977; Wolff and Lioy, 1980; Pratt et al.
13 1983; Logan, 1985; Altshuller, 1986; U.S. Environmental Protection Agency, 1986a; Kelly
14 et al., 1986; Pinkerton and Lefohn, 1986; Lefohn et al., 1987a; Logan, 1989; Lefohn and
15 Lucier, 1991; Taylor and Hanson, 1992). For example, on over 40% of the 98 days that the
16 maximum 1-h O3 concentrations exceeded 0.120 ppm, the highest value was measured
17 downwind of St. Louis at one of the rural sites, which was located approximately 50 km
18 from downtown St. Louis (Altshuller, 1986). Urban O3 concentration values are often
19 depressed because of titration by nitric oxide (Stasiuk and Coffey, 1974). Reagan (1984) and
20 Lefohn et al. (1987a) have observed this phenomenon where Oj exposures at center-city sites
21 were lower than some rural locations. Because of the absence of chemical scavenging,
22 O3 tends to persist longer in nonurban than in urban areas (U.S. Environmental Protection
23 Agency, 1986a; Coffey et al., 1977; Wolff et al., 1977; Isaksen et al., 1978).
24 The distribution of O3 or its precursors at a rural site near an urban source is affected
25 by wind direction (i.e., whether the rural site is located up- or down-wind from the source)
26 (Kelly et al., 1986; Lindsay and Chameides, 1988). Thus, it may be difficult to apply land-
27 use designations to the generalization of exposure regimes that may be experienced in urban
28 versus rural areas. Because of this, it is difficult to identify a set of unique Oj distribution
29 patterns that adequately describe exposures experienced at monitoring sites in rural locations
30 (Lefohn et al., 1990a).
31
December 1993 4-20 DRAFT-DO NOT QUOTE OR CITE
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1 4.3.2 Urban Area Concentrations
2 Figure 4-4 shows the highest second daily maximum 1-h average O3 concentrations in
3 1991 across the United States. The highest second daily maximum 1-h O3 concentrations by
4 Metropolitan Statistical Area (MSA) for the years 1989 to 1991 are summarized in
5 Table 4-3. The highest O3 concentrations are observed in southern California, but high
6 levels also persist in the Texas Gulf Coast, Northeast corridor and other heavily populated
7 regions in the United States.
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),
1 Lefohn (1992a) reported that for many urban sites that experience high second daily
2 maximum 1-h average values (i.e, > 0.125 ppm), most are associated with a few episodes.
3 Monitoring sites in polluted regions tend to experience frequent hourly average
4 O3 concentrations at or near minimum detectable levels and high O^ concentrations. The
5 percentile summary information for some of these sites shows that although some of the
6 highest hourly average concentrations occur at these locations, their occurrence is infrequent
7 (Table 4-4). For example, O3 monitoring sites at Delmar (CA), Stratford (CT), Madison
8 (CT), Baton Rouge (LA), Bayonne (NJ), New York (NY), Babylon (NY), Harris County
December 1993 4-21 DRAFT-DO NOT QUOTE OR CITE
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TABLE 4-3. THE HIGHEST SECOND DAILY MAXIMUM ONE-HOUR OZONE
CONCENTRATION BY METROPOLITAN STATISTICAL AREA (MSA) FOR
THE YEARS 1989 TO 1991
(Unite are ppm)
MSA
1989 1990 1991
MSA
1989 1990 1991
Akron, OH
Albany-Schenectady-Troy, NY
Albuquerque, NM
Alientown-Bethlehem, PA-NJ
Altoona,PA
Anaheim-Santa Ana, CA
Anderson, IN
Anderson, SC
AnnArhor, Ml
NeeiMh, WI
Asheville, NC
Atlanta, GA
Atlantic City, NJ
Augusta. GA-SC
Aurora-Elgin, IL
Austin, TX
Bakersfield, CA
Baltimore, MD
Baton Rouge, LA
Beaumont-Port Arthur, TX
Beaver County, PA
Bellingham, WA
Benton Harbor, MI
Bergen-Passaic, Nl
Billings, MT
Birmingham, AL
Boston, MA
Boulder-Longmont, CO
Bradenton, FL
Brazoria, TX
Bridgeport-Milford, CT
Brockton, MA
Buffalo, NY
Canton, OH
Cedar Rapids, LA
Champaign-Urbana-Rantoul, IL
Charleston, SC
Charleston, WV
Chartotte-Gattonia-RockHill, NC-SC
Chattanooga, TN-GA
Chicago, IL
Chico, CA
Cincinnati, OH-KY-IN
Cleveland, OH
Colorado Springs, CO
Columbia, SC
Columbus, GA-AL
Columbus, OH
Corpus Christi, TX
Cumberland, MD-WV
Dallas, TX
Danbury, CT
Davenport-Rock. Island-Moline, 1A-1L
Dayton-Springfield, OH
0.14 0.11 0.13 Decatur, IL 0.09 0.09 0.10
0.10 0.11 0.10 Denver, CO 0.11 0.11 0.11
0.10 0.10 0.09 DesMoines, IA 0,08 0.07 0.07
0.10 0.11 0.12 Detroit, MI 0.14 0.12 0.13
0.10 0.10 0.11 Duluth, MN-WI 0.06
0.24 0.21 0.20 Eau Claire, WI 0.06
0.10 El Paso, TX 0.14 0.14 0.13
0.09 Elmira, NY 0.09 0.10 0.10
0.10 0.09 0.11 Erie, PA 0.12 0.10 0.11
0.10 0.08 0.09 Eugene-Springfield, OR 0.08 0.09 0,09
0.08 0.09 0.08 Evansviile, IN-KY 0.12 0.11 0.12
0.12 0.15 0.13 Fayetteville, NC 0.11 0.10 0.10
0.12 0.16 0.14 Flint, MI 0.10 0.10 0.10
0.10 0.11 0.10 Fort Collins, CO 0.09 0.10 0.09
0.11 0.09 0.13 Ft. Lauderdale-Hollvwood-Pompano.FL 0.12 0.10 0.10
0.11 0.11 0.10 Fort Myers-Cape Coral, FL 0.10 0.08 0.08
0.16 0.16 0.16 Fort Wayne, IN 0.12 0.09 0.10
0.13 0.14 0.16 Fort Worth-Arlington, TX 0,13 0.14 0.15
0.16 0.18 0.14 Fresno, CA 0.15 0.15 0.16
0.15 0.15 0.13 Galveston-TexasCity, TX 0.14 0.15 0.15
0,10 0,10 0.11 Gary-Hammond, IN 0.11 0.12 0.12
0.05 0.08 0.07 Grand Rapids, MI 0.13 0.14 0.15
0.12 Greetey, CO 0.10 0.11 0.10
0,12 0.13 0.14 Green Bay, WI 0.09 0.09 0.10
0,08 Greensboro-WinstonSalem-High Point, NC 0.10 0.12 0.11
0.12 0.13 0.11 Greenville-Spartanburg, SC 0.10 0.11 0.11
0.12 0.11 0.13 Hamilton-Middletown, OH 0.11 0.13 0.12
0.11 0.10 0.10 Hanisburg-Lebanon-Catlisle.PA 0.11 0.12 0.11
0.10 0.10 0.10 Hartford, CT 0.14 0.15 0.15
0,15 0.13 Hickory, NC 0.09
0.18 0,16 0.15 Honolulu, HI 0.05 0.05 0.05
0.13 0.12 0.15 Houma-Thibodaux, LA 0.11 0.12 0,10
0.11 0.11 0.11 Houston, TX 0.23 0.22 0.20
0.12 0.11 0.12 Hu«ington-A»hUnd,WV-KY-OH 0.12 0.14 0.14
0,08 0.07 0.08 Himtsville, AL 0.09 0.09 0.11
0.09 0.09 0.08 Indianapolis, IN 0.12 0.11 0.11
0.09 0.10 0.09 Iowa City, IA 0.09 0.09 0.06
0.10 0.12 0.12 Jackson, MS 0.09 0.10 0.09
0.13 0.12 0.12 Jacksonville, FL 0.11 0.11 0.10
0.11 0.12 0.10 Jamestown-Dunkirk, NY 0.08 0.10
0.12 0.11 0.13 Janesville-Beloit, WI 0.12 0.09 0.11
0.10 0.12 0.09 Jersey City, NJ 0.12 0.18 0.14
0.12 0.15 0.14 Johnson City-Kingsport-Bristol, TN-WV 0.11 0.12 0.12
0,12 0.12 0.13 Johnstown, PA 0.10 0.10 0.11
0.09 0.09 0.09 Joliel, IL 0.10 0.09 0.12
0.10 0.11 0.11 Kalamazoo.MI 0.08
0.09 0.11 0,10 Kansas City MO-KS 0.11 0.11 0.12
0.11 0.11 0,12 Kenosha, WI 0.13 0.11 0.15
0.10 0.10 0.11 Knoxville,TN 0.10 0.12 0.11
0.09 0.10 Lafayette, LA 0.10 0.11 0.08
0.13 0.14 0,12 Lafayette, IN 0.09 0.10
0.13 0.15 0.14 Lake Charles, LA 0.13 0.13 0.12
0.11 0.10 0.10 Lake County, IL 0.13 0.10 0.12
0.15 0.12 0,12 Lancaster, PA 0.10 0.10 0.12
December 1993
4-22
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE 4-3 (cont'd). THE HIGHEST SECOND DAILY MAXIMUM ONE-HOUR
OZONE CONCENTRATION BY METROPOLITAN STATISTICAL
AREA (MSA) FOR THE YEARS 1989 TO 1991
(Units are ppra)
MSA
Lansing-East Lansing, Ml
La& Cruces, NM
Las Vegas, NV
Lawrence-Haverhm, MA-NH
Lexington-Fayette, KY
Lima, OH
Lincoln, NE
Little Rock-North Little Rock, AR
Longview-Marshall, TX
Lorain-Elyria, OH
Los Angeles-Long Beach, CA
Louisville, KY-1N
Lynchburg, VA
Madiion, WI
Manchester, NH
Medford, OR
Melbourne-Titusville-PalmBay, FL
Memphis, TN-AR-MS
Miami-Hialeah. FL
Middlesex-Somerset-Hunlerdon, NJ
Middletown, CT
Milwaukee, WT
Minneapolis-St. Paul, MN-WI
Mobue, AL
Modesto, CA
Monmouth-Ocean, NJ
Montgomery, AL
Muskegun, Ml
Nashua, NH
Nashville, TN
Nassau-Suffolk, NY
New Bedford, MA
New Haven-Meriden, CT
New London-Norwich, CT-RI
New Orleans, LA
New York, NY
Newark, NJ
Niagara Falls, NY
Norfolk- Virginia Beach-Newport News, VA
Oakland, CA
Oklahoma City, OK
Omaha, NE-1A
Orlando, FL
Owensboro, KY
Oxnard-Ventura, CA
Perkerburg-Marietta, WV-OH
Pascogoula, MS
Pensacola, FL
Peoria, IL
Philadelphia. PA NJ
Phoenix, AZ
Pittsburgh, PA
Pittsfield, MA
Portland, ME
1989 1990 1991
0,10 0.10 0.11
0.11 0.10 0.10
0.11 0.11 0.09
0.12 0.10 0.13
0.11 0.11 0.10
0.10 0.10 0.10
0.06 0,07 0.07
0.09 0,10 0.10
0.10 0.13 0.11
0.12 0.09 0.10
0.33 0.27 0.31
0.11 0.13 0.13
0.10 0.09
0.10 0.08 0.11
0.10 0.10 0.10
0.09 0.10 0.07
0.10 0.09 0.09
0.12 0.12 0.11
0.12 0.11 0.12
0.13 0.15 0.13
0.17 0.16 0.17
0.15 0.13 0.18
0.10 0.10 0.09
0.10 0.11 0.09
0.13 0.12 0.11
0.14 0.14 0.15
0.08 0.10 0.09
0,14 0.13 0.15
0.09 0.10 0.11
0.14 0.13 0.12
0.15 0.14 0.18
0.12 0,13 0.13
0.15 0.16 0.18
0.14 0.16 0.14
0.11 0.11 0.11
0.13 0.16 0.18
0.13 0.13 0.14
0.10 0.10 0.10
0.10 O.U 0.11
0.13 0.12 0.12
0.11 0.11 0.11
0.10 0.08 0.08
0.11 0.12 0.10
0.10 0.11 0.09
0.17 0.15 0.16
0.12 0.11 0.12
0.10 0.11 0.10
0.09 0.12 0.11
0.11 0.09 0.10
0.16 0.14 0.16
0.11 0.14 0.12
0.13 0.11 0.12
0.09 0.11 0.10
0.13 0,13 0.14
MSA
Portland, OR-WA
Portsmoulh-Dover-Rochester, NH-ME
Poughkeepsie, NY
Providence, RI
Provo-Orem, UT
Racine, WI
Raleigh-Duiham, NC
Reading, PA
Redding, CA
Reno, NV
Richmond-Petersburg, VA
Riverside-San Bernardino, CA
Roanoke, VA
Rochester, NY
Rockford, IL
Sacramento, CA
St. Louis, MO-EL
Salinas-Seaside-Monterey, CA
Salt Lake CHy-Ogden, UT
San Antonio, TX
San Diego, CA
Sao Francisco, CA
San Jose, CA
San Juan, PR
Santa Barbara Santa Maria-Lompoc, CA
Santa Cruz, CA
Santa Fe, NM
Santa Rosa-Petaluma, CA
Sarasota, FL
Scranton-Wilkeg-Barre, PA
Seattle, WA
Sharon, PA
Sheboygan, WI
Shreveport, LA
South Bend-Mishawak*, IN
Spokane, WA
Springfield, IL
Springfield, MO
Springfield, MA
Stamford, CT
SteubenviUe-Weirton, OH-WV
Stockton, CA
Syracuse, NY
Tacoma, WA
Tallahassee, FL
Tampa St. Peteraburg-Clearwater, FL
Terre Haute, IN
Toledo, OH
Trenton, NJ
Tucson, AZ
Tulsa, OK
Utica-Rome, NY
Vallejo-Fafafield-Napa, CA
Vancouver, WA
1989 1990 1991
0.09 0.15 0.11
0.11 0.10 0.13
0.08 O.U 0.13
0.13 0.14 0.16
0.11 0.09 0.08
0.14 0.11 0.14
0.11 0.12 0.11
0.11 0.11 0.12
0.09 0.09 0.08
0.10 0.14 0.09
0.11 0.12 0.12
0.28 0.30 0.25
0.10 0.09 0,10
0.11 0.11 0.11
0.10 0.09 0.09
0.14 0.16 0,16
0.13 0.13 0.12
0.11 0.09 0.09
0.15 0.12 0.11
0.11 0,10 0.11
0.19 0.17 0.18
0.09 0.06 0.07
0.13 0.12 0.12
0.06 0.07 0.08
0.16 0.13 0.10
0.08 0.08 0,10
0.05 0.08 0.08
0.10 0.08 0.10
0.10 0.10 0.10
0.11 0.11 0.13
0.09 0,13 0.11
0.11 0.10 0.11
0.11 0.11 0.16
0.12 0,12 0.11
0.10 0.10 0.11
0.07 0.08
0.11 0,10 0.10
0,09 0.08 0.08
0.13 0.12 0.13
0.16 0.14 0.15
0.11 0.09 0.12
0,11 0.12 0.11
0.10 0.11 0.11
0.09 0.13 0.09
0,07 0.05
0.10 0.11 0.11
0.11 0.11 0.10
0.11 0.10 0.12
0.14 0.14 0,15
0.10 0.10 0.09
0.12 0.12 0.12
0.09 0.10 0,10
0.11 0.10 0.11
0.09 0,11 0.10
December 1993
4-23
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE 4-3 (cont'd). THE HIGHEST SECOND DAILY MAXIMUM ONE-HOUR
OZONE CONCENTRATION BY METROPOLITAN STATISTICAL
AREA (MSA) FOR THE YEARS 1989 TO 1991
(Units are ppm)
MSA
Victoria, TX
Vinelund-Millvile-Bridgetori, NJ
Visdia-TuUre-Porterville, CA
Washington, DC-MD-VA
W. Palm Beach-Boca Raton-Delray, FL
Wheeling, WV-OH
Wichita, KS
Williamsport, PA
1989
0,10
0,13
0.15
0.13
0.11
0.11
0.09
0.08
1990
0.07
0.13
0.14
0.13
0.09
0.11
0.10
0.09
1991
0.10
0.12
0.12
0.14
0.09
0.11
0.10
0.10
MSA
Wilmington, DE-NJ-MD
Wilmington, NC
Worcester, MA
York, PA
Youngtfown-Warren, OH
Yuba City, CA
Yuma, AZ
1989
0.
0.
0.
0.
0
13
10
10
.11
.01
1990
0.14
0.09
0.12
0.12
0.10
0.09
0.09
1991
0.15
0.14
0.11
0.12
0.10
0.09
1 (TX), and Bayside (WI) exhibit maximum hourly average concentrations above 0.125 ppm;
2 however, only 1 % of the hourly average concentrations generally exceed 0.100 ppm.
3 Although for human health considerations, the occurrence of a second daily maximum hourly
4 average concentration is important, the table illustrates the point that, for the sites listed, the
5 occurrence of high hourly average concentrations is infrequent and that they are associated
6 with occasional episodes.
7 As indicated in the Introduction, interest has been expressed in characterizing
8 03 exposure regimes for sites experiencing daily maximum 8-h concentrations above specific
9 thresholds (e.g., 0.08 or 0.10 ppm). Table 4-5 summarizes the highest second daily
10 maximum 8-h average O3 concentrations by MSA for the years 1989 to 1991. The data have
11 been reported for the 03 season as summarized in Table 4-1. In some cases, high
12 concentrations occur in the fall and winter periods as well as the summertime. Analyses
13 reported by Rombout et al. (1986, 1989), Berglund et al. (1988), and Lioy and Dyba (1989)
14 documented the occurrence, at some sites, of multihour periods within a day of 63 at levels
15 of potential health effects. While most of these analyses were made using monitoring data
16 collected from sites in or near nonattainment areas, the analysis of Berglund et al. (1988)
17 showed that at five sites, two in New York state, two in rural California, and one in rural
18 Oklahoma, an alternative O3 standard of an 8-h average of 0.10 ppm would be exceeded
19 even though the existing 1-h standard would not be. Berglund et al. (1988) described the
20 occurrence at these five sites, none of which was in or near a nonattainment area, of
21 O3 concentrations showing only moderate peaks but showing multihour levels above
December 1993 4-24 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE 4-4. SUMMARY OF PERCENTILES OF HOURLY AVERAGE
CONCENTRATIONS FOR THE APRIL-TO-OCTOBER PERIOD
(Units are ppm)
1— »
^O AIRS Site Name
060370016 Glendora, CA
060595001 La Habra, CA
060710005 San Bernardino County, CA
060731001 Del Mar, CA
fa.
^ 090013007 Stratford, CT
Lf\
090093002 Madison, CT
j_j
w
EH 220330003 Baton Rouge, LA
H-j
0
o
2 340170006 Bayonne, NJ
^*n
s
O 360610063 New York, NY
c|
H
Year
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
Min.
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.001
0.000
0.000
0.002
10
0.000
0.000
0.000
0.000
0.000
0.000
0.020
0.020
0.020
0.020
0.020
0.020
0.008
0.010
0.007
0.008
0.008
0.007
0.001
0.000
0.002
0.001
0.001
0.002
0.015
0.014
0.015
30
0.020
0.010
0.010
0.010
0.010
0.010
0.050
0.040
0.040
0.040
0.030
0.040
0.024
0.023
0.019
0.022
0.023
0.023
0.009
0.011
0.010
0.008
0.009
0.011
0.028
0.029
0.029
50
0.030
0.030
0.020
0.030
0.020
0.020
0.060
0.060
0.060
0.040
0.040
0.050
0.036
0.033
0.030
0.033
0.033
0.034
0.021
0.023
0.020
0.021
0.022
0.024
0.040
0.039
0.041
70
0.060
0.050
0.050
0.040
0.040
0.040
0.090
0.080
0.080
0.050
0.050
0.050
0.046
0.044
0.042
0.043
0.043
0.045
0.034
0.038
0.031
0.036
0.036
0.038
0.051
0.051
0.056
90
0.120
0.110
0.100
0.070
0.070
0.070
0.140
0.120
0.120
0.070
0.060
0.060
0.064
0.059
0.060
0.059
0.063
0.065
0.059
0.063
0.054
0.059
0.058
0.065
0.073
0.074
0.082
95
0.150
0.140
0.140
0.090
0.090
0.090
0.160
0.150
0.140
0.080
0.070
0.070
0.077
0.068
0.074
0.070
0.075
0.082
0.069
0.079
0.067
0.074
0.073
0.082
0.086
0.090
0.096
99
0.220
0.200
0.200
0.140
0.140
0.130
0.200
0.180
0.190
0.120
0.100
0.100
0.115
0.100
0.110
0.103
0.107
0.123
0.094
0.109
0.092
0.099
0.106
0.110
0.110
0.116
0.123
Max.
0.340
0.290
0.320
0.260
0.210
0.210
0.270
0.330
0.270
0.250
0.170
0.150
0.202
0.176
0.157
0.149
0.197
0.193
0.168
0.187
0.134
0.147
0.185
0.167
0.134
0.175
0.177
Number of Observations
4,874
4,888
4,907
4,875
4,887
4,899
4,871
4,899
4,905
4,814
5,060
5,017
4,673
3,853
4,794
4,272
4,477
4,814
4,964
5,000
4,905
4,815
4,939
4,943
4,825
4,707
4,910
-------
I
TABLE 4-4 (cont'd). SUMMARY OF PERCENTILES OF HOURLY AVERAGE
CONCENTRATIONS FOR THE APRIL-TO-OCTOBER PERIOD
(units are ppm)
AIRS Site
Name
Year
Min.
10
30
SO
70
90
95
99
Max.
Number of Observations
U>
361030002 Babylon, NY 1989
1990
1991
0.001
0.000
0.001
0.004
0.006
0.005
0.015
0.017
0.018
0.027
0.027
0.030
0.039
0.040
0.044
0.060
0.060
0.067
0.073
0.075
0.081
0.101
0.105
0.111
0.156
0.146
0.217
4,407
4
4
,876
,873
482010024
550790085
Harris County, TX
Bayside, WI
1989
1990
1991
1989
1990
1991
0.000
0.000
0.000
0.002
0.002
0.002
0.000 0.010
0.000 0.010
0.000 0.000
0.006 0.024
0.009 0.025
0.008 0.025
0.020
0.020
0.020
0.035
0.034
0.035
0.030
0.040
0.030
0.046
0.044
0.047
0.060
0.070
0.060
0.066
0.061
0.070
0.070
0.090
0.080
0.077
0.071
0.081
0.110
0.130
0.110
0.101
0.094
0.113
0.230
0.220
0.170
0.151
0.130
0.189
4,728
4,274
4,322
4,376
4,395
4,303
I
8
O
-------
TABLE 4-5. THE HIGHEST SECOND DAILY MAXIMUM EIGHT-HOUR
AVERAGE OZONE CONCENTRATION BY METROPOLITAN STATISTICAL
AREA (MSA) FOR THE YEARS 1989 TO 1991
(Units are ppm)
MSA
1989 1990 1991 MSA
1989 1990 1991
Akron, OH
Albany-Schenectady-Troy, NY
Albuquerque, NM
Alexandria, LA
Allentown-Bethlehem, PA-NJ
Altoona, PA
Anaheim-Santa Ana, CA
Anderson, IN
Anderson, SC
Ann Arbor, MI
Appleton-Oshkosh-Neenah, WI
Asheville, NC
Atlanta, OA
Atlantic dry, NJ
Augusta, OA-SC
Aurora-Elgin, IL
Austin, TX
Bakersfield, CA
Baltimore, MD
Baton Rouge, LA
Beaumont-Port Arthur, TX
Beaver County, PA
BeUingham, WA
Benton Harbor, MI
Bergen Passaic, NJ
Billings, MT
Biloxi-GuHport, TX
Birmingham, AL
Bismark, ND
Bloomington-Normal, IL
Boston, MA
Boulder-Longmont, CO
Bradenton, FL
Brazor'ia, TX
Bridgeport-Milford, CT
Brockton, MA
Buffalo, NY
Canton, OH
Carson City, NV
Cedar Rapids, IA
Champaign-Urbana-Rantou], IL
Charleston, SC
Charleston, WV
Charlotte-Gastonia-Rock Hill, NC-SC
Charlottesville, VA
Chattanooga, TN-GA
Chicago, IL
Chieo, CA
Cincinnati, OH-KY-IN
Cleveland, OH
Colorado Springs, CO
Columbia, SC
Columbus, OA-AL
Columbus, OH
Corpus Christ!, TX
Cumberland, MD-WV
Dallas, TX
Danbury, CT
0.109 0.097 0.102
0.087 0.091 0.089
0.078 0.089 0.077
0.077 0.076 0.074
0.091 0.098 0,112
0.077 0.090 0.094
0.146 0,135 0,110
0,084
0.081
0,093 0,087 0.096
0.091 0,078 0,082
0.083 0.074 0.064
0.096 0.125 0.102
0,104 0.135 0.112
0.078 0.092 0.081
0,088 0,077 0.100
0.099 0,103 0.084
0.124 0.120 0.118
0.103 0.111 0.127
0.095 0.134 0.100
0.110 0.100 0.101
0.095 0.085 0.095
0.038 0.068 0.059
0.098
0.093 0.097 0.106
0,56
0.089
0.088 0.105 0.088
0.086 0.062 0.061
0.081 0.071 0.095
0.109 0.105 0.118
0.082 0.084 0.083
0.086 0.075 0.074
0.101 0.107
0.139 0.114 0.121
0.110 0.106 0.107
0,085 0.096 0.097
0.098 0.098 0.099
0.070
0.078 0.057 0.066
0.084 0.080 0,077
0.094 0,084 0.074
0.087 0,083 0.099
0,089 0,100 0.094
0.076 0.089 0.091
0.091 0.094 0.083
0.101 0.084 0.106
0.081 0.083 0.074
0.106 0.119 0.115
0.099 0,096 0.101
0.072 0.065 0,068
0.079 0.094 0.083
0.068 0.075 0,083
0.097 0.098 0.112
0.083 0.085 0.075
0.070 0,076
0.101 0.115 0.095
0.098 0.105 0.116
Davenport-Rock Island-Moline, IA-LL
Dayton-Springfield, OH
Decatur, E.
Denver, CO
Des Moines, IA
Detroit, MI
Duluth, MN-WI
Eau Claire, WI
El Paso, TX
Elmira, NY
Erie, PA
Eugene-Springfield, OR
Evansville, IN-KY
Fayetteville, NC
Hint, MI
Fort Collins, CO
Ft. Lauderdale-Hollywood-Pompano.FL
Fort Myers-Cape Coral, FL
Fort Wayne, IN
Fort Worth-Arlington, TX
Fresno, CA
Gatveston-Texas City, TX
Gary-Hammond, IN
Grand Rapids, MI
Greeley, CO
Green Bay, WI
Greensboro-Winston Salem-High Point, NC
Greenville-Spartanburg, SC
Harnilton-Middletown, OH
Harrisburg-Lebanon-Cariule, PA
Hartford, CT
Hickory, NC
Honolulu, HI
Houma-Thibodaux, LA
Houston, TX
Huntuigton-Ashland, WV-KY-OH
Hunteville, AL
Indianapolis, IN
Iowa City, IA
Jackson, MS
Jacksonville, FL
Jamestown-Dunkirk, NY
Janesville-Beloil, WI
Jersey City, NJ
Johnson City Kingnport-Bristol, TN-WV
Johnstown, PA
Joliet, IL
Kalamazoo, MI
Kansas City MO-KS
Kenosha, WI
Knoxvilie, TN
Lafayette, LA
Lafayette, IN
Lake Charles, LA
Lake County, IL
Lancaster, PA
Lansing-East Lansing, MI
Las Cruces, NM
0.102 0.071 0.086
0.122 0.096 0.107
0.084 0.077 0.087
0,089 0,086 0,080
0.073 0,051 0.056
0.103 0.091 0.111
0.073 0.051
0,049
0.083 0.087 0.080
0.075 0.080 0.094
0.092 0.088 0.093
0.061 0.077 0.070
0,097 0.094 0.107
0.089 0.088 0,085
0.093 0.086 0.090
0.076 0.076 0.077
0.089 0.078 0.064
0.084 0,070 0.062
0.105 0.091 0.096
0.098 0.116 0.116
0,116 0.105 0.119
0,102 0.096 0.094
0.102 0,122 0.101
0.119 0.107 0,124
0.080 0.080 0.081
0.095 0.074 0.079
0.083 0.100 0.087
0.088 0.091 0.085
0.095 0.111 0.105
0.091 0.108 0.100
0.114 0.109 0.112
0.080
0.020 0.037 0.042
0.082 0,084 0.077
0.121 0.141 0,115
0.102 0.109 0.124
0,072 0,080 0.082
0.097 0.099 0.100
0.078 0.084 0.060
0.086 0,083 0.075
0,090 0.084 0.077
0.068 0.082
0.097 0,081 0,090
0.105 0.128 0.117
0.083 0,100 0.080
0.082 0.090 0,099
0.082 0.070 0.091
0.071
0.090 0.089 0.089
0.113 0.093 0.118
0,088 0.105 0,091
0.080 0.086 0,075
0.077 0,092 0.090
0.088 0.084 0.096
0.092 0.082 0.102
0 085 0.089 0.096
0.093 0.083 0.087
0.074 0.082 0.074
December 1993
4-27
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TABLE 4-5 (cont'd). THE HIGHEST SECOND DAILY MAXIMUM
EIGHT-HOUR AVERAGE OZONE CONCENTRATION BY METROPOLITAN
STATISTICAL AREA (MSA) FOR THE YEARS 1989 TO 1991
(Units are ppm)
MSA
1989 1990 1991 MSA
1989 1990 1991
Las Vegas, NV
Lawrence-Haverhill, MA-NH
Lewigton-Aubum, ME
Lexington-Fayette, KY
Lima, OH
Lincoln, NE
Little Rock-North Litlle Rock, AR
Longview-Marshall, TX
Lorain-EJyria, OH
Los Angeles Long Beach, CA
Louisville, KY-1N
Lynchburg, VA
Madison, Wl
Manchester, NH
Medford, OR
Melbourne-Titusviile-Pulm Bay, FL
Memphis, TN-AR-MS
Miami-Hialeah, FL
Middlesex-Somerset-Hunlerdon, NJ
Middletown. CT
Milwaukee, WI
Minneapolis-Si. Paul, MN-WI
Mobile, AL
Modesto, CA
Monmouth-Ocean, NJ
Montgomery, AL
Muskegon, Ml
Nashua, NH
Nashville, TO
Nassau-Suffolk, NY
New Bedford, MA
New Haven-Meriden, CT
New London-Norwich, CT-R1
New Orleans, LA
New York, NY
Newark, NJ
Niagara Falls, NY
Norfolk-Virginia Beach-Newport Newg, VA
Oakland, CA
Oklahoma City, OK
Omaha, NE-IA
Orlando, FL
Owensboro, KY
Oxnard-Ventura, CA
Parkersburg-Marietta, WV-OH
Pascagoula, MS
PensacoU, FL
Peoria, 1L
Philadelphia, PA-NJ
Phoenix, AZ
Pittsburgh, PA
PiiUfield, MA
Portland, ME
Portland, OR-WA
Portsmouth-Dover-Rochester, NH-ME
Poughkeepsie, NY
Providence, Rl
0.084
0.104
0.089
0.097
0.088
0.057
0.077
0.076
0.096
0.188
0.096
0.089
0.084
0.063
0.082
0.077
0.090
0.097
0.086
0.060
0.083
0.089
0.082
0.170
0.093
0.083
0.079
0.098
0.076
0.075
0.106
0.101
0.088
0.091
0.060
0.089
0086
0.091
0.178
0.119
0.079
0.089
0.087
0.055
0.082 0.082 0.069
0.099 0.100 0,093
0.087 0.076 0.072
0.108 0.111 0.111
O.U9 0.117 0.125
0.115 0.100 0.118
0.090 0.077 0.079
0.079 0.098 0.062
0.101 0.106 0.091
0.118 0.107 0.122
0.066 0.081 0.071
0.139 0.100 0.119
0.072 0.095 0.110
0.093 0.102 0.107
0.099 0.115 0.121
0.104 0.101 0.106
0.108 0.122 0.128
0.128 0.127 0.115
0.075 0.086 0.079
0.111 0.119 0.133
0.108 0.107 0.119
0.082 0.092 0.095
0,089 0.095 0.089
0.091 0,091 0.083
0.089 0.090 0.089
0.075 0.075 0.073
0.096 0.082 0.075
0.096 0.104 0.077
0.147 0.119 0.129
0.094 0.088 0,104
0.082 0,092 0.077
0.080 0.098 0.082
0.087 0.075 0.088
0.118 0.110 0.123
0.086 0.096 0.094
0.107 0.100 0.106
0,075 0.094 0.095
0.124 0.109 0.134
0.071 0.111 0.092
0.107 0.086 0.123
0,079 0.085 0.101
0.107 0.112 0.127
Provo-Orem, UT
Racine, WI
Raleigh-Durham, NC
Reading, PA
Redding, CA
Reno, NV
Richmond-Petersburg, VA
Riverside-San Bernardino, CA
Roanoke, VA
Rochester, NY
Rockford, &
Sacramento, CA
St. Louis, MO-IL
Salinas-Seaside-Monterey, CA
Salt Lake City-Ogden, UT
San Antonio, TX
San Diego, CA
San Francisco, CA
San Jose, CA
Sin Juan, PR
Santa Barbara-Santa Maria-Lompoc, CA
Santa Cruz, CA
Santa Fe, NM
Santa Rosa-Petaluma, CA
Sara sola, FL
Scranton-Wilkes-Barre, PA
Seattle, WA
Sharon, PA
Sheboygan,WI
Shreveport, LA
South Bend-Mishawafca, IN
Spokane, WA
Springfield, IL
Springfield, MO
Springfield, MA
Stamford, CT
Sleubenville-Weirton, OH-WV
Stockton, CA
Syracuse, NY
Tacoma, WA
Tallahassee, FL
Tampa-St. Petersburg-Clearwater, FL
Terre Haute, IN
Toledo, OH
Trenton, NJ
Tucson, AZ
Tulsa, OK
Utka4tame, NY
Vallejo-Fairfield-Napa, CA
Vancouver, WA
Victoria, TX
Vineland-Millvile-Bridgeton, NJ
Visalia-Tulare-Porterville. CA
Washington, DC MD-VA
W. Palm Beach-Boca Raton-Delray, FL
Wheeling, WV-OH
Wichita, KS
0.094 0.070 0.071
0.110 0.090 0.118
0.099 0.094 0.091
0.095 0.101 0.109
0.080 0.100 0.093
0.081 0.109 0.075
0.094 0.101 0.097
0.196 0.193 0.189
0.077 0.075 0.078
0.094 0.097 0.103
0.085 0.073 0.081
0.105 0.125 0.124
0.105 0.098 0.107
O.OB2 0.074 0.071
0.114 0.086 0.086
0.100 0.080 0.085
0.139 0.135 0.128
0.064 0.056 0.054
0.094 0.075 0,086
0.043 0.042 0.044
0.129 0.129 0.075
0.066 0.058 0.067
0.049 0,069 0.076
0.083 0.061 0.076
0.085 0.083 0.080
0.088 0.096 0.111
0.078 0.099 0.087
0.098 0.095 0.094
0.103 O.OS8 0.103
0.098 0.102 0,087
0.089 0.089 0.093
0.060 0.060
0.085 0.082 0.087
0,075 0.061 0.069
0.123 0.113 0,117
0.113 0.112 0.115
0.094 0.075 0.098
0.086 0.093 0.090
0.090 0.093 0.098
0.077 0.094 0.077
0.072
0.088 0.085 0.083
0.087 0.095 0.089
0.093 0.084 0.107
0.119 0.112 0.131
0.074 0.084 0.080
0.093 0.094 0.097
0.082 0.094 0.091
0.076 0.074 0.078
0.058 0.080 0.042
0.093 0.056 O.OS6
0.122 0.106 0.108
0.114 0.103 0.104
0.106 0.110 0.114
0.081 0.067 0.059
0.086 0.089 0.093
0.079 0.089 0.081
December 1993
4-28
DRAFT-DO NOT QUOTE OR CITE
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TABLE 4-5 (cont'd). THE HIGHEST SECOND DAILY MAXIMUM
EIGHT-HOUR AVERAGE OZONE CONCENTRATION BY METROPOLITAN
STATISTICAL AREA (MSA) FOR THE YEARS 1989 TO 1991
(Units are ppm)
MSA
Williamsport, PA
Wilmington, DE-NJ-MD
WUmingion, NC
Worcester, MA
1989 1990 1991
0,065 0.072 0,087
0.105 0.110 0.118
0.086
0.097 0.089 0.107
MSA
York, PA
Youngstown-Warren, OH
Yub* City, CA
Yuma, AZ
1989 1990 1991
0.091 0.108 0.103
0.088 0.085 0.101
0.084 0.076 0.084
0.080 0.075 0.070
1 0.10 ppm. Lefohn et al. (1993) have identified those areas in the United States for the
2 period 1987 to 1989 where more than one occurrence of an 8-h daily maximum average
3 concentration of 0.08 ppm was experienced, while an hourly average concentration equal to
4 or greater than 0.12 ppm never occurred.
5 A follow-up to the points made above is whether an improvement in O3 levels may
6 produce distributions of 1-h O3 that result in a broader diurnal profile than those seen in
7 high-oxidant urban areas where O3 regimes contain hourly average concentrations with
8 sharper peaks. The result would be an increase in the number of exceedances of daily
9 maximum 8-h average concentrations S0.08 ppm, when compared to those sites,
10 experiencing sharper peaks, Lefohn et al. (1993b), using aerometric data at specific sites,
11 observed how O3 concentrations change when the sites change compliance status. One of the
12 parameters examined was 4-h daily maxima. The number of exceedances for a specific daily
13 maximum average concentration tended to decrease as fewer exceedances of the current 1-h
14 standard were observed at a given site. The number of occurrences of the daily maximum
15 4-h average concentration >0.08 ppm and the number of exceedances of the current form of
16 the standard had a positive, weak correlation (r = 0.51). Lefohn et al. (1993a,b) reported
17 few changes in the shape of the average diurnal patterns as sites changed attainment status;
18 this may have explained why Lefohn et al. (1993b) could not find evidence that the number
19 of occurrences of the daily maximum 4-h average concentration ^0.08 ppm increased when
20 the sites experienced few high hourly average concentrations.
21 There has been considerable interest in possibly substituting one index for another when
22 attempting to relate O3 exposure with an effect. For example, using 03 ambient air quality
23 data, McCurdy (1988) compared the number of exceedances of 0.125 ppm and the number of
December 1993 4-29 DRAFT-DO NOT QUOTE OR CITE
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1 occurrences of the daily maximum 8-h average concentrations ^0.08 ppm and reported that
2 a positive correlation (r = 0.79) existed between the second-highest 1-h daily maximum in a
3 year and the expected number of days with an 8-h daily maximum average concentration
4 >0.08 ppm O3. In this case, the predictive strength of using one O3 exposure index to
5 predict another is not strong.
6 Similar to analysis performed by McCurdy (1988), all of the hourly averaged data from
7 rural agricultural and forested sites in the AIRS database were summarized into maximum
8 3-mo SUM06, second highest daily maximum hourly average concentration, and second
9 highest daily maximum 8-h average concentration exposure indices per year for the period
10 1980 to 1991. For the rural agricultural sites, the correlation coefficient between the 3-mo
11 SUM06 and (a) second highest daily maximum hourly average concentration and (b) second
12 highest daily maximum 8-h average concentration was 0.650 and 0.739, respectively
13 (Figure 4-5). For the rural forested sites, the correlation coefficient between the 3-mo
14 SUM06 and (a) second highest daily maximum hourly average concentration and (b) second
15 highest daily maximum 8-h average concentration was 0.585 and 0.683, respectively
16 (Figure 4-6).
17 One of the difficulties in attempting to use correlation analysis between indices for
18 rationalizing the substitution of one exposure index for another for predicting an effect (e.g.,
19 SUM06 versus the second highest daily maximum hourly average concentration) is the
20 introduction of the error associated with estimating levels of one index from another. Lefohn
21 et al. (1989) have recommended that if a different exposure index (e.g., second highest daily
22 maximum hourly average concentration) is to be compared to, for example, the SUM06 for
23 adequacy in predicting crop loss, then the focus should be on how well the two exposure
24 indices predict crop loss using the effects model that is a function of the most relevant index
25 and not on how well the indices predict one another. Using data from both urban and rural
26 O3 monitoring sites in the midwestern United States that were located near agricultural/
27 forested areas. Lefohn et al. (1989) reported a large amount of scatter between the second
28 highest daily maximum hourly average concentration and the SUM06 indices. This large
29 scatter indicated considerable uncertainty when attempting to predict a value for SUM06,
December 1993 4-30 DRAFT-DO NOT QUOTE OR CITE
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0.40i
a)
o^bj
o a o D D
V.VAJ ^
(
0.25-
I0-20-
of 0.15-
4»*
40.10-
* 0.05
S
0.001
) 50 100 1f
3-Month SUM06 (ppm-h)
b)
r-0.739
a
D °
^*D ocna D D
,
0 50 100 1
150
3-Month SUM06 (ppm-h)
Figure 4-5. The relationship between the (a) second highest daily maximum hourly
average O3 concentration and the maximum 3-mo SUM06 value and (b) the
second highest daily maximum 8-h average concentration and the maximum
3-mo SUM06 value for specific site years at rural agricultural sites for the
1980-to-1991 period.
1 given a specific second highest daily maximum hourly average concentration value. The
2 authors reported mat for a given second highest daily maximum hourly average
3 concentration, the SUM06 values varied over a large range. Lefohn et al. (1989) concluded
4 that such large uncertainty would introduce additional uncertainty when attempting to use the
5 predicted exposure index to estimate an effect. The authors concluded that less error would
December 1993
4-31
DRAFT-DO NOT QUOTE OR CITE
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0.20^
r-0.585
*
CM
3-Month SUM06 (ppm-h)
0.15
r-0.683
0
SO 100
3-Month SUM06 (ppm-h)
150
figure 4-6. The relationship between the (a) second highest daily maximum hourly
average O, concentration and the maximum 3-mo SUM06 value and (b) the
second highest daily maximum 8-h average concentration and the maximum
3-mo SUM06 value for specific site years at rural forested sites for the
1980-10-1991 period.
December 1993
4-32
DRAFT-DO NOT QUOTE OR OTE
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1 be introduced if either of the two indices were used directly in the development of an
2 exposure-response model.
3 As pointed out by the U.S. EPA (1986a), a familiar measure of 03 air quality is the
4 number or percentage of days on which some specific concentration is equalled or exceeded.
5 This measure, however, does not shed light on one of the more important questions
6 regarding the effects of O3 on both people and plants; that is, the possible significance of
7 high concentrations lasting 1 h or longer and then recurring on 2 or more successive days.
1 The recurrence of high Oj concentrations on consecutive days was examined for one
2 site in four cities by the U.S. EPA (1986a). The numbers of multiple-day events were tallied
3 by length of event (i.e., how many consecutive days) using data for the daylight hours (0600
4 to 2000 h) hi the second and third quarters of 1979 through 1981. These sites were selected
5 because they included areas known to experience high Oj concentrations (California), and
1 because they represent different geographic regions of the country (west, southwest, and
2 east).
3 Because of the importance of episodes and respites, EPA (1986a) commented on the
4 occurrences of the length of episodes and the time between episodes (respites). The agency
5 concluded that its analysis showed variations among sites in the lengths of episodes, as well
6 as the respite periods. In its discussion, the U.S. EPA (1986a) defined a day or series of
7 days on which the daily 1-h maximum reached or exceeded the specified level as an
8 "exposure"; the intervening day or days when that level was not reached was called a
9 "respite." Four O3 concentrations were selected: 0.06, 0.12, 0.18, and 0.24 ppm. At the
10 Dallas site, for example, the value equalled or exceeded 0.06 ppm for more than 7 days in a
11 row. The Pasadena site experienced 10 such exposures, but these 10 exposure events
12 spanned 443 days; in Dallas, the 11 exposures involved only 168 days. At the lowest
13 concentration (^0.06 ppm), the Dallas station recorded more short-term (^7 days)
14 exposures (45) involving more days (159) than the Pasadena station (14 exposures over
15 45 days) because the daily 1-h maximum statistic hi Pasadena remained above 0.06 ppm for
16 such protracted periods. At concentrations &0.12 ppm, the lengthy exposures at the
17 Pasadena site resolved into numerous shorter exposures, whereas in Dallas the exposures
18 markedly dwindled in number and duration.
19
December 1993 4.33 DRAFT-DO NOT QUOTE OR CITE
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1 4.3.3 Nonurban Area Concentrations
2 4.3.3.1 Pristine Areas
3 For those attempting to compare O3 exposures experienced under ambient or
4 experimental conditions with a reference point, it is important to identify the hourly average
5 concentration regimes that occur in pristine areas. The U.S. EPA (1989) has indicated that a
6 reasonable estimate of annual average natural Qj background concentration near sea-level in
7 the United States today is from 0.020 to 0.035 ppm. This estimate included a 0.010 to
8 0.015 ppm contribution from the stratosphere and a 0.01 ppm contribution from
9 photochemically-affected biogenic non-methane hydrocarbons. In addition, the U.S. EPA
10 (1989) estimated that an additional 0.010 ppm is possible from the photochemical reaction of
11 biogenic methane. The U.S. EPA concluded that a reasonable estimate of natural
12 O3 background concentration for a 1-h daily maximum at sea-level in the United States
13 during the summer is on the order of 0.03 to 0.05 ppm (U.S. Environmental Protection
14 Agency, 1989).
15 Using measurements at a remote site in South Dakota, Kelly et al. (1982) estimated the
16 background O3 in air masses entering the Midwest and eastern United States to be 0.020 to
17 0.050 ppm. Pratt et al. (1983), using data from low-elevation rural sites in Minnesota and
18 North Dakota, reported that annual average concentrations for an 63 monitoring site in
19 LaMoure County, ND, (400 m) for 1978 through 1981, ranged from 0.030 to 0.035 ppm,
20 while an O3 monitoring site in Traverse County, MN (311 m), had a range of 0.029 to
21 0.035 ppm. Bower et al. (1989) reported that the remote northern Scotland site, Strath
22 Vaich (270 m), had a 1987 to 1988 annual average O3 concentration of 0.031 ppm.
23 Lefohn and Jones (1986) have characterized several O3 monitoring sites, which
24 appeared to be isolated from major anthropogenic activities, independent of land use
25 designations (i.e., remote, rural, or urban), and reported that the data collected at these sites
26 show a tendency of few hourly mean O3 concentrations at or near the minimum detectable
27 level and few occurrences of hourly average concentrations above 0.08 ppm. At these sites,
28 more than 90% of the hourly average concentrations are greater than 0.015 ppm. The
29 infrequent minimally detectable hourly mean concentrations occur because of weak surface
30 sink effects. The authors referred to these sites as being located in clean areas.
December 1993 4.34 DRAFT-DO NOT QUOTE OR CITE
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1 Lefohn and Foley (1992) summarized O3 exposures experienced at several clean sites in
2 the United States (Table 4-6). Redwood NP (CA), Olympic NP (WA), Glacier NP (MT),
3 Sand Dunes NM (CO), Yellowstone NP (WY), Badlands NP (SD), and Theodore Roosevelt
4 NP (ND) experienced no hourly average concentration ^0.08 ppm for the period April to
5 October. Logan (1989) has noted that O3 hourly average concentrations above 0.08 ppm are
6 rarely exceeded at remote western sites. In almost all cases for the above sites, the
7 maximum hourly average concentration was ^0.07 ppm. In 1989, the maximum hourly
8 average concentration experienced at the Redwood NP (CA) site was 0.046 ppm.
9 Evans et al. (1983) summarized O3 hourly averaged data collected at eight stations
10 located in eight National Forests across the United States. The first three stations began
11 operations in 1976 (Green Mountain NF, Vermont; Kisatchie NF, Louisiana; and Custer NF,
12 Montana); the second three in 1978 (Chequamegon NF, Wisconsin; Mark Twain NF,
13 Missouri; and Croatan NF, North Carolina); and the last two in 1979 (Apache NF, Arizona;
14 and Ochoco NF, Oregon), For the period 1979 to 1983, hourly maximum average
15 concentrations occurring at the clean sites, Custer National Forest (MT), Ochoco National
16 Forest (OR), and Apache National Forest (AZ), were similar to the exposures determined for
17 6 of the 7 clean sites characterized by Lefohn and Foley (1992). In almost all cases,
18 (1) none of the sites experienced hourly average concentrations ^0.08 ppm and (2) the
19 maximum hourly average concentrations were in the range from 0.060 to 0.075 ppm.
20 Table 4-7 summarizes the percentUe distributions for the three national forest sites.
21 Several clean sites were characterized by Lefohn et al. (1990b), using various exposure
22 indices. One of the indices used was a sigmoidally weighted cumulative exposure index
23 (W126), which was described in Section 4.1. The sigmoidal exposure (W126) values,
24 calculated over an annual period, are provided in Table 4-8. The W126 values for Theodore
25 Roosevelt National Park, ND were in the range 6.48 to 8.03 ppm-h. The maximum hourly
26 average concentration reported at the site was 0.068 ppm. The W126 values at the Custer
27 National Forest, MT and Ochoco National Forest, OR sites ranged from 5.79 to
28 22.67 ppm-h. The maximum hourly average concentrations measured at each site were
29 0.075 and 0.080 ppm, respectively. The W126 values calculated for the Custer National
30 Forest and Ochoco National Forest sites showed greater variability from year-to-year than the
31 values calculated for the South Pole, Barrow, and Theodore Roosevelt National Park sites.
December 1993 4.35 DRAFT-DO NOT QUOTE OR CITE
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TABLE 4-6. SEASONAL (APRIL TO OCTOBER) PERCENHLE DISTRIBUTION
OF HOURLY OZONE CONCENTRATIONS, NUMBER OF HOURLY MEAN OZONE
OCCURRENCES >0.08 AND ^0.10, SEASONAL SEVEN-HOUR AVERAGE
CONCENTRATIONS, AND W126 VALUES FOR SITES IN
SELECTED CLASS I AREAS WITH DATA CAPTURE
(Afl concentrations are in ppm units)
*».
ON
M
^•^
s*
2
i^
O
o
^*.
s
^
o
$
CEJ
3
A*
n
Class I Area
Redwood, CA
Olympic, WA
Glacier, MT
Yellowstone, WY
Badlands, SD
Great Sand
Dunes, CO
Theodore
Roosevelt, ND
Norm Unit
Point Reyes, CA
Arches, UT
Rocky Mountains,
CO
Site/ AIRS ID
Redwood NP
06015002
Olympic NP
530090012
Glacier NP
300298001
Yellowstone NP
560391010
Badlands NP
460711001
Sand Dunes NM
08003002
Theodore
Roosevelt NP
380530002
Point Reyes, NP
060410002
Arches NP
490190101
Rocky Mountain
NP
080690007
Year
1989
1990
1991
1982
1984
1986
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
1989
1990
1991
1984
1985
1986
1989
1989
1990
1991
1989
1990
1989
1990
1991
Min.
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.000
0.004
0.006
0.006
0.005
0.011
0.010
0.008
0.000
0.000
0.004
0.004
0.006
0.006
0.006
0.000
0.000
0.003
0.006
0.004
10
0.010
0.011
0.012
0.000
0.000
0.000
0.003
0.005
0.006
0.003
0.001
0.001
0.018
0.015
0.020
0.020
0.019
0.020
0.031
0.030
0.029
0.017
0.019
0.017
0.023
0.020
0.017
0.019
0.031
0.020
0.025
0.022
0.026
30
0.017
0.018
0.019
0.010
0.010
0.010
0.010
0.012
0.014
0.015
0.014
0.014
0.027
0.023
0.030
0.027
0.027
0.028
0.037
0.037
0.037
0.025
0.026
0.027
0.032
0.025
0.022
0.025
0.040
0.025
0.034
0.029
0.037
Percentiles (ppm)
50 70 90
0.022
0.023
0.025
0.010
0.010
0.020
0.015
0.018
0.019
0.026
0.026
0.027
0.036
0.029
0.037
0.034
0.032
0.034
0.041
0.041
0.043
0.032
0.032
0.033
0.039
0.031
0.025
0.030
0.045
0.027
0.039
0.034
0.043
0.027
0.027
0.031
0.020
0.020
0.020
0.022
0.023
0.024
0.036
0.035
0.036
0.044
0.036
0.042
0.041
0.037
0.040
0.045
0.045
0.048
0.039
0.038
0.039
0.045
0.036
0.029
0.034
0.050
0.031
0.043
0.038
0.048
0.034
0.035
0.038
0.030
0.020
0.040
0.030
0.030
0.033
0.046
0.044
0.046
0.051
0.043
0.048
0.049
0.044
0.047
0.050
0.051
0.055
0.047
0.046
0.047
0.054
0.041
0.036
0.040
0.056
0.036
0.051
0.046
0.055
95
0.038
0.038
0.041
0.030
0.020
0.040
0.035
0.034
0.036
0.050
0.047
0.049
0.056
0.046
0.051
0.053
0.048
0.050
0.051
0.055
0.058
0.050
0.049
0.050
0.058
0.045
0.040
0.043
0.059
0.039
0.055
0.049
0.059
99
0.041
0.043
0.045
0.040
0.030
0.040
0.046
0.043
0.044
0.058
0.052
0.056
0.063
0.053
0.057
0.060
0.054
0.056
0.056
0.061
0.065
0.059
0.054
0.056
0.065
0.058
0.046
0.048
0.065
0.045
0.070
0.058
0.074
Max.
0.046
0.053
0.054
0.060
0.050
0.060
0.065
0.064
0.056
0.067
0.066
0.062
0.071
0.060
0.064
0.071
0.063
0.066
0.063
0.070
0.077
0.068
0.061
0.062
0.073
0.080
0.063
0.072
0.083
0.055
0.098
0.070
0.095
Hours
No. ofObs. 2:0.08 2=0.10
4,624
4,742
4,666
4,704
4,872
4,776
4,220
4,584
4,677
4,770
5,092
5,060
4,079
4,663
4,453
4,840
4,783
4,584
4,436
4,624
4,130
4,923
4,211
4,332
4,206
4,577
4,856
4,588
4,260
4,639
4,366
4,091
4,730
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
2
0
9
0
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Seasonal W126
7-h (ppm) (ppm-h)
0.024
0.025
0.027
0.020
0.015
0.025
0.021
0.022
0.025
0.036
0.036
0.036
0.042
0.034
0.042
0.040
0.037
0.038
0.044
0.044
0.046
0.038
0.038
0.039
0.046
0.033
0.028
0.031
0.047
0.030
0.043
0.038
0.048
1.0
1.1
1.7
7.4
1.6
13.7
0.7
0.8
0.9
5.9
4.1
5.3
10.7
3.7
7.7
9.0
4.7
6.2
10.5
13.3
17.0
7.0
5.0
5.5
14.2
4.7
1.8
3.0
20.6
1.7
13.6
5.5
22.3
-------
I
OJ
.0
•J
o
o
TABLE 4-7. SEASONAL (APRIL TO OCTOBER) PERCENTILE DISTRIBUTION OF HOURLY OZONE
CONCENTRATIONS, NUMBER OF HOURLY MEAN OZONE OCCURRENCES ^0.08 AND ^0.10,
SEASONAL SEVEN-HOUR AVERAGE CONCENTRATIONS, AND W126 VALUES FOR THREE "CLEAN"
NATIONAL FOREST SITES WITH DATA CAPTURE ^75%
(All concentrations are in ppm units)
Site
AIRS ID
Percentiles (ppm)
Year Min.
10
30
50
70
90
95
99
Max. No. of Obs.
Hours Seasonal W126
7-h
2: .08 i.10 (ppm) (ppm-h)
Custer NF, MT 300870101
Ochoco NF, OR 4101301 1 1
Apache NF, AZ 040110110
1978 0.000
1979 0.010
1980 0.010
1983 0.010
1980 0.010
1981 0.010
1982 0.010
1983 0.010
1981 0.010
1982 0.015
1983 0.004
0.010
0.025
0.025
0.025
0.030
0.025
0.025
0.025
0.025
0.030
0.025
0.020
0.035
0.035
0.035
0.035
0.030
0.030
0.035
0.030
0.040
0.035
0.035
0.040
0.040
0.040
0.040
0.035
0.035
0.035
0.035
0.045
0.040
0.040
0.045
0.050
0.045
0.045
0.040
0.040
0.040
0.040
0.050
0.045
0.050
0.050
0.055
0.050
0.055
0.045
0.045
0.045
0.045
0.055
0.055
0.055
0.055
0.060
0.055
0.055
0.045
0.050
0.050
0.050
0.060
0.055
0.060
0.060
0.065
0.060
0.065
0.055
0.055
0.055
0.055
0.065
0.065
0.070
0.075
0.070
0.065
0.080
0.075
0.065
0.060
0.065
0.075
0.070
4,759
5,014
4,574
4,835
4,759
4,459
4,697
4,423
4,806
4,714
4,788
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.033 8.3
0.043 13.2
0.043 19.7
0.042 10.7
0.044 16.5
0.035 4.7
0.038 7.6
0.039 6.8
0.039 7.6
0.047 21.9
0.042 14.6
i
-------
f'
u>
oo
1
TABLE 4-8. THE VALUE OF THE W126 SIGMOIDAL EXPOSURE
PARAMETER CALCULATED OVER THE ANNUAL PERIOD
(Units in ppm-h)
Elevation (m) 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
South Pole, Antarctica
Bitumount, Alberta, Canada
Barrow, AK
Theodore Roosevelt NP, ND
Custer National Forest, MT
Ochoco National Forest, OR
Birch Mountain, Alberta, Canada
2,835
350
11
727
1,006
1,364
850
2.65
2.99
2.60
14.08 22.67
19.54
19.73
3.72 3.01 2.41 3.54 2.76 4.09
2.60 3.15 2.36 2.79 2.03 2.46 3.69
8.03 6.69 6.48*
12.18
5.79 9.10 8.02
White River Oil Shale Project, UT,
U-4 1600
Fortress Mountain, Alberta, Canada
Apache National Forest, AZ
Mauna Loa, HI
Whiteface Mountain, NY
Hohenpeissenberg, FRG
American Samoa
19.98 32.10
2,103 25.04 83.89
2,424 81.39 10.24 27.18 17.48
3,397 27.48 45.68 33.68 48.90 19.18 32.66 24.48
1,483 86.50 68.30 33.75 32.03 37.82 42.94 41.36 32.07 58.33
975 61.28 25.04 35.64 21.76 18.53 29.53 49.00 19.85 40.43
82 0.28 0.24 0.25 0.28 0.30 0.26 0.30 0.32
Collection did not occur during the months of October, November, and December.
Source: Lefohn et al. (1990b).
-------
1 As the W126 values increased, the magnitude of the year-to-year variability also
2 increased. For 2 years of data, the W126 values calculated for the White River U-4 Oil
3 Shale (UT) site were 19.98 and 32.10 ppm-h. The maximum hourly concentration recorded
4 was 0.079 ppm. The W126 values calculated for the Apache National Forest, AZ site
5 ranged from 10.24 to 81.39 ppm-h. The highest hourly average concentration was
6 0.090 ppm.
7 The 7-h (0900 to 1559 h) average concentration has been used by vegetation researchers
8 to characterize O3 exposures experienced in plant chamber experiments (see Chapter 5).
9 Because Oj concentrations are highest during the warm-season months and, at many low-
10 elevation sites, during daylight hours, the 7-mo seasonal, 7-h (0900 to 1559 h) average
11 concentration is higher than annual average values. Most remote sites outside North America
12 experience seasonal 7-h averages of 0.025 ppm (Table 4-9) (Lefohn et aL, 1990b). The
13 seasonal average of the daily 7-h average values for the South Pole, Antarctica, range from
14 0.024 to 0.027 ppm. The values range from 0.022 to 0.026 ppm at Barrow, Alaska. In the
15 continental United States and southern Canada, values range from approximately 0.028 to
16 0.050 ppm (Lefohn, 1990b). At an O3 monitoring site at the Theodore Roosevelt National
17 Park in North Dakota, a 7-mo (April to October) average of the 7-h daily average ;
18 concentrations of 0.038, 0.039, and 0.039 ppm, respectively, was experienced in 1984,
19 1985, and 1986. These 7-mo seasonal averages (i.e., 0.038 and 0.039 ppm) appear to be
20 representative of values that may occur at other fairly clean sites located in the United States
21 and other locations in the northern hemisphere. In earlier investigations, Lefohn (1984)
22 reported 3-mo (June to August), 7-h averages of 0.048, 0.044, and 0.059 ppm at remote
23 national forest sites at Custer, MT, Ochoco, OR, and Apache, AZ, respectively.
24
25 4.3.3.2 Urban-Influenced Nonurban Areas
26 It is difficult to identify a set of unique O3 distribution patterns that adequately
27 describes exposures experienced at monitoring sites in nonurban locations because, as
28 indicated earlier, many nonurban sites in the United States are influenced by local sources of
29 pollution or long-range transport of 03 or its precursors. Unlike the clean sites characterized
30 by Lefohn and Jones (1986), Lefohn et al. (1990b), and Lefohn and Foley (1992),
December 1993 4.39 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE 4-9. THE VALUE OF THE OZONE SEASON (SEVEN-MONTH) AVERAGE OF
THE DAILY SEVEN-HOUR (0900 TO 1559 HOURS) CONCENTRATION
(Units in ppm)
Site
Elevation (m)
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
South Pole, Antarctica
Bitumount, Alberta, Canada
Barrow, AK
Theodore Roosevelt NP, ND
Custer National Forest, MT
Ochoco National Forest, OR
Birch Mountain, Alberta, Canada
White River Oil Shale Project, UT,
U-4 1600
Fortress Mountain, Alberta, Canada
Apache National Forest, AZ
MaunaLoa, HI
Whiteface Mountain, NY
Hohenpeissenberg, FRG
American Samoa
2,835
350
11
727
1,006
1,364
850
2,103
2,424
3,397
1,483
975
82
0.025 0.027 0.026 0.027 0.024 0.025
0.028
0.022 0.025 0.024 0.024 0.022 0.026 0.022 0.026
0.038 0.039 0.039*
0.043 0.044 0.042
0.043 0.035 0.038 0.038
0.036
0.045 0.045
0.054 0;©39 0.047 0.040
0.035 0.039 0.034 0.038
0.049 0.046 0.040 0.034 0.041
0.047 0.040 0.044 0.040 0.037 0.043 0.047 0.040
0.010 0.010 0.011 0.009
0.041 0.050
0.035 0.035 0.034
0.044 0.043 0.043 0.045
0.043
0.012 0.010 0.011
M Collection did not occur during the months of October, November, and December.
S Source: Lefohn et al. (199®)),
-------
urban-influenced nonurban sites sometimes show frequent hourly average concentrations near
1 the minimum detectable level, but almost always show occurrences of hourly average
2 concentrations above 0.10 ppm. The frequent occurrence of hourly average concentrations
3 near the minimum detectable level is indicative of scavenging processes (i.e., NOX); the
4 presence of high hourly average concentrations can be attributable to the influence of either
5 local generation or the long-range transport of O3. For example, Evans et al. (1983)
6 reported that the Green Mt. (VT) and Mark Twain (MO) national forest sites were influenced
7 by long-range transport of O3. Environmental Protection Agency (1986a) reported that the
8 maximum hourly average concentrations at Green Mt. (for the period 1977 to 1981) and
9 Mark Twain (for the period 1979 to 1983) were 0.145 and 0.155 ppm, respectively. Using
10 hourly averaged data from the AIRS database for a select number of rural monitoring sites,
11 Table 4-10 summarizes the percentiles of the hourly average O3 concentrations, the number
12 of occurrences of the hourly average concentration ^0.10 ppm, and the 3-mo sum of all
13 hourly average concentrations ^0.06 ppm.
14 As part of a comprehensive air monitoring project sponsored by the Electric Power
15 Research Institute (EPRI), O3 measurements were made by the chemiluminescence method
16 from 1977 through 1979 at nine "nonurban" Sulfate Regional Experiment Sites (SURE) and
17 Eastern Regional Air Quality Study (ERAQS) sites in the eastern United States. On the basis
18 of diurnal NOX patterns that indicated the influence of traffic emissions, five of the sites were
19 classed as "suburban;" the other four were classed as "rural." The 03 data from these nine
20 stations are summarized in Table 4-11. The sites are either influenced by local sources or
21 transport of O3 or its precursors. The maximum hourly average concentrations are generally
22 higher than 0.125 ppm and the occurrence of hourly average concentrations near minimum
23 detectable levels indicates NOX scavenging processes.
24 As part of its effort to provide long-term estimates of dry acidic deposition across the
25 United States, the National Dry Deposition Network (NDDN) operated more than 50 sites,
26 which include 41 in the eastern United States and 9 in the western United States, that
27 routinely recorded hourly average 03 concentrations. Figure 4-7 shows the locations of the
28 NDDN sites. Edgerton and Lavery (1992) have summarized the O3 exposures at some of the
29 sites for the period 1988 to 1990. Table 4-12 summarizes the 7-h (0900 to 1559 h) growing
30 season average concentration (May to September) for selected sites in the Midwest and East.
December 1993 4-41 DRAFT-DO NOT QUOTE OR CITE
-------
g
t
§
TABLE 4-10, SUMMARY OF PERCENTILES, NUMBER OF HOURLY OCCURRENCES ^0.10 ppm,
AND THREE-MONTH SUM06 VALUES FOR SELECTED RURAL OZONE MONITORING SITES
IN 1989 (APRIL TO OCTOBER)
(Concentration values in ppm)
AIRS Site Name
RURAL AGRICULTURAL
170491001 Effingham County, IL
180970042 Indianapolis, IN
240030014 Anne Arundel, MD
310550032 Omaha, NE
420070003 New Brighton, PA
510610002 Fauquier County, VA
RURAL FOREST
060430004 Yosemite NP, CA
360310002 Eaaex County, NY
470090101 Smoky Mountain NP, TN
511870002 ShenNP(DkyRdg),VA
RURAL OTHER
040132004 Scottsdale, AZ
350431001 Sandoval County, NM
370810011 OuUford County, NC
371470099 Fannville, NC
550270001 Horicon, WI
551390007 Oshkosh, WI
Min.
0.000
0,001
0.000
0.002
0.000
0.000
0.000
0.016
0.000
0.004
0.000
0.000
0.004
0.000
0.002
0.002
10
0.009
0.006
0.006
0.021
0.008
0.009
0.008
0.031
0.025
0.027
0.006
0.010
0.010
0.010
0.019
0.016
30
0.023
0.021
0.021
0.030
0.021
0.021
0.022
0.040
0.036
0.037
0.018
0.020
0.023
0.023
0.029
0.028
50
0.036
0.034
0.032
0.037
0.032
0.033
0.035
0.049
0.044
0.045
0.031
0.030
0.034
0.034
0.037
0.038
Percentiles (ppm)
70 90
0.046
0.046
0.045
0.047
0.043
0.045
0.049
0.056
0.053
0.054
0.045
0.040
0.046
0.044
0.047
0.048
0.063
0.063
0.064
0.062
0.062
0.061
0.065
0.066
0.065
0.065
0.062
0.060
0.063
0.062
0.062
0.063
95
0.070
0.072
0.073
0.067.
0.070
0.069
0.072
0.072
0.070
0.071
0.071
0.060
0.070
0.070
0.070
0.070
99
0.081
0.085
0.090
0.075
0.087
0.084
0.083
0.086
0.081
0.082
0.084
0.070
0.083
0.083
0.088
0.084
Max.
0.104
0.103
0.120
0.098
0.102
0.122
0.111
0.106
0.098
0.100
0.107
0.090
0.113
0.100
0.111
0.121
Number of
Hourly
Value*
4,600
4,592
4,360
4,160
5,055
5,050
4,853
4,792
4,764
4,454
5,070
5,059
4,853
4,833
4,142
4,206
Number of
Occurrence
i 20.10
1
3
10
0
4
5
3
4
0
1
4
0
2
2
11
3
Max. Uncorrected
3-moSUM06 Value
(ppm-h)
25.3
25.4
25.5
24.9
29.4
24.6
37.6
45.6
35.9
33.5
31.7
25.1
27.7
26.4
24.6
27.9
-------
TABLE 4-11. SUMMARY OF PERCENTILES OF HOURLY AVERAGE CONCENTRATIONS FOR ELECTRIC POWER
8 RESEARCH INSTITUTE SULFATE REGIONAL EXPERIMENT SITES (SURE)/ERAQS OZONE MONITORING SITES
I- (Units are ppm)
VO
VO
W SURE/ERAQS Name
Montague, MA
Scranton, PA
Indian River, DE
Duncan Falls, OH
f* Rockport, IN
Giles County, TN
2 Roanoke, IN
V Research Triangle Park, NC
8
3 Lewisburg, WV
H
O
0
O
Year
1978
1979
1978
1979
1978
1979
1978
1979
1978
1979
1978
1979
1978
1979
1978
1979
1978
1979
Min.
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.000
10
0.002
0.000
0.015
0.011
0.010
0.008
0.005
0.010
0.008
0.008
0.000
0.000
0.004
0.004
0.001
0.001
0.020
0.013
30
0.018
0.013
0.031
0.022
0.024
0.020
0.022
0.021
0.021
0.019
0.018
0.014
0.019
0.017
0.017
0.012
0.034
0.022
Percentiles
50 70
0.032
0.025
0.040
0.030
0.035
0.031
0.034
0.029
0.032
0.028
0.032
0.024
0.032
0.026
0.032
0.024
0.045
0.029
0.043
0.035
0.048
0.040
0.049
0.042
0.049
0.042
0.044
0.038
0.046
0.036
0.044
0.038
0.049
0.037
0.056
0.039
90
0.061
0.056
0.062
0.061
0.072
0.063
0.071
0.060
0.066
0.055
0.066
0.055
0.067
0.061
0.076
0.058
0.072
0.056
95
0.075
0.070
0.073
0.074
0.085
0.073
0.081
0.069
0.078
0.064
0.075
0.065
0.079
0.074
0.087
0.068
0.079
0.065
99
0.119
0.103
0.094
0.097
0.103
0.092
0.110
0.086
0.101
0.083
0.087
0.081
0.106
0.098
0.108
0.084
0.091
0.080
Max.
0.202
0.149
0.126
0.132
0.134
0.138
0.144
0.110
0.145
0.104
0.110
0.130
0.160
0.133
0.142
0.131
0.115
0.099
Number of
Observations
7,138
8,485
5,461
8,313
6,874
8,527
5,125
7,595
6,849
8,391
6,034
8,439
5,874
8,001
7,081
8,652
7,019
7,849
-------
Upper
Northeast
Northeast
South
Central
figure 4-7. The location of National Dry Deposition Network monitoring sites as of
December 1990.
Source: Edgerton and Laveiy (1992).
1 Fifty-nine percent of the monitoring sites listed in the table have been classified as
2 agricultural and 36% as forested. One site was classified as commercial. As noted by the
3 U.S. EPA (1992a), 1988 was an exceptionally high 63 concentration year, when compared
4 with 1989 and 1990. The number of hourly 0^ concentrations &0.08 ppm is presented in
5 the table. Edgerton and Lavery (1992) have summarized O3 hourly average concentration
6 data for several sites using the cumulative integrated exposure index, W126, as proposed by
7 Lefohn and Runeckles (1987). Based on evidence presented in the literature relating
8 O3 exposure with agricultural yield reduction, the index was proposed as a way to weight the
9 higher hourly average concentrations greater than the lower values. The data in the table
10 illustrate the large differences in cumulative exposure between those that occurred in 1988
December 1993
4-44 DRAFT-DO NOT QWXl OK CITE
-------
TABLE 4-12.
SEVEN-HOUR GROWING SEASON MEAN, W126 VALUES, AND NUMBER ^80 ppb FOR
SELECTED EASTERN NATIONAL DRY DEPOSITION NETWORK SITES
Cp 7-h Mean (ppb)
£
U>
**>
•k
0
e
2|
bL
o
o
o
»
Subregion
NUKTHKASST
Connecticut Hill
Washington's Crossing
Pennsylvania State University
Laurel Hill State Park
Beltsville
Cedar Creek State Park
UPPER NORTHEAST
Whiteface Mountain
Ashland
MIDWEST
Argonne National Lab
Vincennes
Oxford
UPPER MIDWEST
Unionville
Perkinstown
SOUTH CENTRAL
Sand Mountain
Georgia Station
Penyville
Research Triangle Park
Coweeta
Edgar Evins State Park
Horton Station
State
NY
NJ
PA
PA
MD
WV
NY
ME
n.
IN
OH
MI
WI
AL
OA
KY
NC
NC
TN
VA
Site
110
144
106
117
116
119
105
135
146
140
122.
124
134
152
153
129
101
137
127
120
Land Class
RF
RA
RA
RF
RA
RA
RF
RA
RA
RA
RA
RA
RA
RA
RA
RA
RC
RF
RF
RF
1988
55.0
—
59.0
62.7
—
59.8
43.5
—
61.1
62.0
65.3
—
—
65.2
62.3
55.6
—
62.3
1989
48.3
52.8
46.0
48.4
54.6
44.9
45.7
37.9
51.4
51.1
53.5
51.5
44.2
52.6
48.1
50.8
50.8
41.0
47.2
51.4
1990
45.3
52.4
51.0
48.6
55.5
48.2
42.3
35.3
46.3
50.9
51.7
47.4
38.8
63.6
62.6
—
—
47.9
56.1
54.4
1988
75.5
—
63.5
68.8
—
50.4
37.8
—
59.1
68.1
91.8
—
—
103.6
62.3
44.3
—
127.6
W126 (ppm-h)
1989
40.3
46.0
25.4
29.1
45.4
19.6
25.3
9.1
29.6
36.4
48.4
35.4
19.0
40.6
28.1
39.7
31.7
16.1
26.9
61.2
1990
36.8
43.7
42.7
31.0
45.7
24.3
31.2
8.7
21.6
35.8
46.4
30.7
11.6
68.7
69.7
—
—
21.3
44.5
70.6
1988
86.8
—
65.6
75.5
—
56.3
40.9
—
69.4
78.5
103.2
—
—
99.6
71.0
—
150.7
SUM06 (ppm-h)
1989
47.2
52.1
28.1
30.8
48.6
19.0
25.2
5.4
35.0
40.3
55.3
41.6
18.3
33.2
21.8
38.9
35.5
24.4
64.0
1990
35.8
48.4
45.0
32.1
49.4
23.2
29.0
5.8
25.7
41.2
51.7
31.6
7.6
83.4
77.7
—
—
49.2
82.8
SUM08 (ppm-h)
1988
44.3
—
32.3
41.6
—
27.0
17.0
—
32.3
36.7
56.8
—
—
39.7
20.5
—
60.2
1989
5.5
21.2
5.0
7.0
22.9
4.1
2.6
0.6
10.4
8.7
15.8
9.0
0.2
3.4
4.6
5.2
6.6
1.5
8.5
1990
3.3
21.3
11.6
8.0
21.9
6.5
8.4
0.8
3.6
11.8
17.2
7.1
0.0
24.0
28.4
—
—
8.2
9.2
-------
TABLE 4-12 (cont'd). SEVEN-HOUR GROWING SEASON MEAN, W126 VALUES, AND NUMBER £80 ppb FOR
SELECTED EASTERN NATIONAL DRY DEPOSITION NETWORK SITES
7-h Mean (ppb) W126 (ppm-h) SUM06 (ppm-h) SUM08 (ppm-h)
Subregion State Site Land CUss* 1988 1989 1990 1988 1989 1990 1988 1989 1990 1988 1989 1990
SL»i)i.miKi>i taLkkiUlbk'i
Caddo Valley
Sumatra
If
AR
FL
150
156
RF
RF
- 46.2
- 39.8
49.5 -
46.3 —
18.5
17.8
21.0 —
20.0 -
15.6
16.5
25.2 -
17.4 -
0.2
1.0
2.3
0.9
t— = No data or insufficient data.
RA - Rural agricultural.
RF = Rural forest.
RC = Rural commercial.
Source: Edgerton and Lavery (1992).
-------
1 and those that were experienced in 1989 and 1990. The percentile of the hourly average
2 concentrations is summarized in Table 4-13. Although several of the monitoring sites are
3 located in fairly remote locations in the eastern United States (based on land use
4 characterization) the maximum hourly average concentrations reflect the transport of Oj or
5 its precursors into the area.
6 Taylor et al. (1992) have summarized the O3 exposures that were experienced at
7 10 EPRI Integrated Forest Study sites in North America. The authors reported that in 1988
8 all sites experienced maximum hourly average concentrations SO. 08 ppm. In almost all
9 cases, Hie sites experienced multiple occurrences above 0.08 ppm. This implies that although
10 the sites were located in remote forested areas, the sites experienced elevated O3 exposures
11 that were more than likely due to long-range transport of O^ or its precursors.
12 Ozone concentrations on a seasonal basis in the Shenandoah National Park exhibit some
13 features in common with both urban and rural areas. During some years, maximum hourly
14 average concentrations exceed 0.12 ppm, although some sites in the Park exhibit a lack of
15 hourly average concentrations near minimum detectable level. Taylor and Norby (1985)
16 have characterized Oj episodes, which they defined as any day in which a 1-h mean
17 O3 concentration was >0.08 ppm. Based on a 4-year monitoring period in the Park, the
18 probability was 80% that any given episode during the growing season would last 2 or more
19 days, while the probabilities of episodes lasting for periods > 3, 4, and 5 days were 30, 10,
20 and 2%, respectively. Single-day O3 episodes were infrequent. Taylor and Norby (1985)
21 noted that, given the frequency of respites, there was a 50% probability that a second
22 episode would occur within 2 weeks.
23 Because of a lack of air quality data collected at rural and remote locations, it has been
24 necessary to use interpolation techniques to estimate O3 exposures in nonurban areas. In the
25 absence of actual O3 data, interpolation techniques have been applied to the estimation of
26 O3 exposures across the United States (Reagan, 1984; Lefohn et al., 1987a; Knudsen and
27 Lefohn, 1988). Kriging, a mathematical interpolation technique, has been used to provide
28 estimates of seasonal O3 values for the National Crop Loss Assessment Network (NCLAN)
29 for 1978 through 1982 (May to September for each year) (Reagan, 1984). These values,
30 along with updated values, coupled with exposure-response models, were used to predict
31
December 1993 4.47 DRAFT-DO NOT QUOTE OR CITE
-------
? TABLE 4-13. SUMMARY OF PERCENTILES FOR NATIONAL DRY DEPOSITION NETWORK MONITORING SITES
5 (Units are ppm)
3 Site No. Name
Year
Min.
10
30
Percentiles
50 70
90
95
99
Max. Number of Observations
W RURAL AGRICULTURAL SITES
106
116
119
122
k.
^
o
124
3 129
O
>
ri
-3 134
3
2 135
i
D 140
H
3
1
u
5 144
0
Pennsylvania State University, PA
Beltsville, MD
Cedar Creek, WV
Oxford, OH
Unionville, MI
PerryviUe, KY
Perkinstown, WI
Loring AFB/ Ashland, ME
Vincennes, IN
Washington Crossing, NJ
1988
1989
1990
1989
1990
1988
1989
1990
1988
1989
1990
1989
1990
1988
1989
1989
1990
1989
1990
1988
1989
1990
1989
1990
0.000
0.000
0.000
0.002
0.000
0.000
0.001
0.001
0.001
0.001
0.000
0.003
0.004
0.002
0.001
0.007
0.006
0.002
0.002
0.000
0.000
0.000
0.000
0.001
0.013
0.010
0.015
0.003
0.001
0.008
0.006
0.007
0.019
0.017
0.015
0.021
0.020
0.024
0.020
0.023
0.020
0.017
0.014
0.007
0.009
0.009
0.006
0.008
0.026
0.022
0.027
0.014
0.015
0.017
0.013
0.014
0.032
0.029
0.028
0.031
0.029
0.038
0.033
0.032
0.028
0.026
0.023
0.024
0.025
0.025
0.021
0.021
0.036
0.033
0.038
0.029
0.027
0.029
0.024
0.024
0.044
0.039
0.037
0.038
0.036
0.049
0.043
0.038
0.035
0.032
0.029
0.036
0.036
0.035
0.033
0.032
0.049
0.043
0.048
0.044
0.041
0.044
0.037
0.038
0.058
0.050
0.048
0.047
0.044
0.062
0.052
0.046
0.041
0.039
0.036
0.052
0.047
0.045
0.046
0.043
0.073
0.059
0.065
0.068
0.067
0.069
0.056
0.057
0.083
0.069
0.067
0.063
0.061
0.080
0.066
0.057
0.050
0.049
0.046
0.076
0.064
0.062
0.067
0.065
0.086
0.066
0.074
0.081
0.080
0.082
0.065
0.067
0.0%
0.077
0.077
0.071
0.069
0.094
0.072
0.062
0.056
0.055
0.051
0.089
0.072
0.073
0.078
0.079
0.114
0.082
0.090
0.096
0.103
0.108
0.082
0.085
0.117
0.092
0.092
0.086
0.084
0.110
0.086
0.071
0.065
0.063
0.068
0.104
0.085
0.089
0.100
0.104
0.143
0.104
0.120
0.131
0.137
0.134
0.172
0.116
0.221
0.109
0.116
0.113
0.105
0.143
0.102
0.085
0.074
0.103
0.088
0.120
0.112
0.110
0.159
0.148
4,716
5,089
5,056
5,062
4,597
4,938
5,044
5,025
4,746
5,073
5,077
5,041
5,065
4,061
4,787
5,029
5,063
5,067
5,080
4,908
5,065
5,084
5,053
5,058
-------
TABLE 4-13 (cont'd). SUMMARY OF PERCENTILES FOR NATIONAL DRY DEPOSITION NETWORK
MONITORING SITES
0*
8
h— i
i§
(Units are ppm)
Site No. Name
Year
Min.
10
30
Percentiles
50 70
90
95
99
Max. Number of Observations
RURAL AGRICULTURAL SITES (cont'd)
1
0
%
1
i
146 Argonne National Laboratory, IL
152 Sand Mountain, AL
153 Georgia Station, GA
RURAL FOREST SITES
105 Whiteface Movmtain, NY
110 Ithaca, NY
117 Laurel Hill, PA
120 Horton Station, VA
127 Edgar Evins State Park, TN
1988
1989
1990
1989
1990
1989
1990
1988
1989
1990
1988
1989
1990
1988
1989
1990
1988
1989
1990
1989
1990
0.000
0.000
0.000
0.000
0.000
0.002
0.002
0.000
0.003
0.005
0.005
0.002
0.001
0.001
0.000
0.001
0.010
0.002
0.004
0.000
0.001
0.004
0.005
0.004
0.020
0.021
0.014
0.021
0.016
0.022
0.018
0.025
0.025
0.022
0.012
0.009
0.009
0.031
0.032
0.032
0.017
0.019
0.019
0.019
0.017
0.031
0.035
0.025
0.034
0.026
0.030
0.028
0.034
0.036
0.033
0.025
0.020
0.020
0.045
0.043
0.044
0.028
0.032
0.032
0.029
0.028
0.041
0.045
0.034
0.044
0.034
0.038
0.036
0.043
0.044
0.041
0.036
0.031
0.030
0.057
0.050
0.052
0.037
0.041
0.046
0.041
0.039
0.051
0.057
0.045
0.056
0.044
0.047
0.046
0.055
0.052
0.049
0.050
0.043
0.042
0.067
0.059
0.059
0.047
0.052
0.073
0.061
0.057
0.065
0.074
0.062
0.073
0.062
0.059
0.060
0.080
0.065
0.063
0.076
0.061
0.060
0.084
0.070
0.071
0.062
0.067
0.085
0.070
0.065
0.072
0.080
0.069
0.084
0.074
0.066
0.069
0.090
0.071
0.069
0.092
0.069
0.071
0.096
0.076
0.075
0.067
0.073
0.103
0.088
0.077
0.082
0.093
0.082
0.102
0.098
0.078
0.086
0.103
0.081
0.081
0.119
0.087
0.086
0.114
0.085
0.084
0.077
0.085
0.146
0.126
0.097
0.097
0.117
0.118
0.144
0.129
0.093
0.115
0.126
0.101
0.093
0.156
0.110
0.109
0.145
0.103
0.097
0.090
0.109
5,037
5,055
5,033
4,509
5,068
3,540
4,814
5,051
4,698
5,016
4,827
5,064
5,075
5,007
4,697
5,032
5,012
4,976
5,066
5,060
5,027
-------
TABLE 4-13 (cont'd).
SUMMARY OF PERCENTTLES FOR NATIONAL DRY DEPOSITION NETWORK
MONITORING SITES
(Units are ppm)
£0 Site No.
RURAL
137
150
156
RURAL
101
Name
FOREST SITES (cont'd)
Coweeta, NC
Caddo Valley, AR
Sumatra, FL
COMMERCIAL SITE
Research Triangle Park, NC
Year
1988
1989
1990
1989
1990
1989
1990
1988
1989
Min.
0.001
0.001
0.000
0.002
0.002
0.001
0.000
0.000
0.000
10
0.010
0.007
0.008
0.005
0.004
0.012
0.011
0.004
0.004
30
0.022
0.016
0.018
0.016
0.015
0.022
0.023
0.020
0.019
Percentiles
50 70
0.034
0.025
0.029
0.028
0.029
0.030
0.033
0.035
0.030
0.047
0.037
0.043
0.041
0.041
0.040
0.043
0.050
0.042
90
0.065
0.055
0.059
0.057
0.057
0.057
0.057
0.072
0.063
95
0.072
0.061
0.064
0.063
0.065
0.065
0.063
0.084
0.071
99
0.094
0.071
0.072
0.075
0.077
0.075
0.072
0.111
0.083
Max. Number of Observations
0.145
0.094
0.085
0.102
0.094
0.098
0.118
0.137
0.121
4,182
4,275
5,046
5,046
5,078
4,700
4,444
5,030
4,893
^•4
3
«
-------
1 agriculturally related economic benefits anticipated by lower O3 levels in the United States
2 (Adams et al., 1985; Adams et al., 1989).
3 Kriging is a statistical tool developed by Matheron (1963) and named in honor of D.G,
4 Krige. Although originally developed specifically for ore reserve estimation, kriging has
5 been used for other spatial estimation applications, such as analyzing and modeling air
6 quality data (Grivet, 1980; Faith and SheshensM, 1979). At its simplest, kriging can be
7 thought of as a way to interpolate spatial data much as an automatic contouring program
8 would. In a more precise manner, kriging can be defined as a best linear unbiased estimator
9 of a spatial variable at a particular site or geographic area. Kriging assigns low weights to
10 distant samples and vice versa, but also takes into account the relative position of the samples
11 to each other and the site or area being estimated.
12 Figure 4-8 shows the average for the 1985 through 1987 period for the seasonal (April
13 to October) average of the daily maximum 7- and 12-h values across the United States, The
14 estimates made for the Rocky Mountain region bad large uncertainties associated with them
15 because of a lack of monitoring sites.
16 Because of the importance of the higher hourly average concentrations in eliciting
17 injury and yield reduction for agricultural crops (U.S. Environmental Protection Agency,
18 1986b; 1992b), kriging was used to predict 03 exposures in the eastern United States, using
19 the sigmoidally weighted W126 exposure index as described earlier hi this section. Lefohn
20 et al. (1992b) used the W126 index in its kriging to characterize the 63 exposures that
21 occurred during the period 1985-1989. Figure 4-9 illustrates the integrated 03 exposure for
22 the 1988 and 1989 periods (data derived from work described in Lefohn et al., 1992b),
23 Using the kriged data in the East, the 1988 exposures were the highest for the 5-year period,
24 while 1989 exhibited the lowest exposures. The Oj gradient pattern analyses described by
25 Lefohn et al. (1992b) identified contiguous areas of persistent relatively high seasonal
26 O3 values. The largest area extended from New Jersey south to northern Georgia and South
27 Carolina. This area was roughly bounded on the west by the Appalachian Mountains.
28 A second area, which exhibited persistent relatively high seasonal Oj exposures, was
29 centered over the Ohio River Valley in the region near the Kentucky-Indiana-Ohio borders.
30 Relatively low O3 exposures were found in Minnesota, Iowa, Wisconsin, Maine, Vermont,
31
December 1993 4-51 DRAFT-DO NOT QUOTE OR CITE
-------
(a)
(b)
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. (1990a).
1
2
3
4
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.
December 1993
4-52
HOT
errs
-------
(a)
(b)
figure 4-9. Hie kriged estimates of the W126 integrated ozone exposure index for the
eastern United States for (a) 1988 and (b) 1989.
Source: Lefohn et al.
December 1993
4-53 DRAFT-DO NOT QUOTE OR CITE
-------
1 4.4 DIURNAL VARIATIONS IN OZONE CONCENTRATIONS
2 4.4.1 Introduction
3 By definition, diurnal variations are those that occur during a 24-h period. Diurnal
4 patterns of O3 may be expected to vary with location,, depending on the balance among the
5 many factors affecting O3 formation, transport, and destruction. Although they vary with
6 locality, diurnal patterns for 63 typically show a rise in concentration from tow or levels
7 near minimum detectable amounts to an early afternoon peak. The 1978 criteria document
8 (U.S. Environmental Protection Agency, 1978) ascribed the diurnal pattern of concentrations
9 to three simultaneous processes: (1) downward transport of O3 from layers aloft;
10 (2) destruction of O3 through contact with surfaces and through reaction with nitric oxide at
11 ground level; and (3) in situ photochemical production of Oj (U.S. Environmental Protection
12 Agency, 1978; Coffey et al., 1977; Mohnen et al., 1977; Reiter, 1977a).
13 The form of an average diurnal pattern may provide information on sources, transport,
14 ami chemical formation/destruction effects at various sites (Lefohn, 1992b). Non transport
15 conditions will produce early afternoon peaks. However, long-range transport processes will
16 influence the actual timing of a peak from afternoon to evening or early morning hours.
17 Investigators have utilized diagrams mat illustrate composite diurnal patterns as a means to
18 describe qualitatively the differences in O3 exposures between sites (Lefohn and Jones, 1986;
19 Bohm et al., 1991). Although it might appear mat composite diurnal pattern diagrams could
20 be used to quantify the differences in O3 exposures between sites, Lefohn et al. (1990a)
21 cautioned their use for this purpose. The average diurnal patterns are derived from long-
22 term calculations of the hourly average concentrations, and the resulting diagram cannot
23 adequately identify, at most sites, the presence of high hourly average concentrations and
24 thus may not adequately be able to distinguish 03 exposure differences among sites. Logan
25 (1989) noted that diurnal variation of O3 did not reflect the presence of high hourly average
26 concentrations.
27 Unique families of diurnal average profiles exist and it is possible to distinguish
28 between two types of O3 monitoring sites. A seasonal diurnal diagram provides the
29 investigator with the opportunity to identify whether a specific O3 monitoring site has more
30 scavenging than any other site. Ozone is rapidly depleted near the surface below the
31 nocturnal inversion layer (Berry, 1964), Mountainous sites, which are above the nocturnal
December 1993 4-54 DRAFT-DO NOT QUOTE OR CITE
-------
1 inversion layer, do not necessarily experience this depletion (Stasiuk and Coffey, 1974).
2 Taylor and Hanson (1992) reported similar findings using data from the Integrated Forest
3 Study. For the low-elevation sites, the authors reported that intra-day variability was most
4 significant due to the pronounced daily amplitude in O3 concentration between the pre-dawn
5 minimum and mid-afternoon-to-early evening maximum. The authors reported that the inter-
6 day variation was more significant in the high-elevation sites. Ozone trapped below the
7 inversion layer is depleted by dry deposition and chemical reactions if other reactants are
8 present in sufficient quantities (Kelly et al., 1984), Above the nocturnal inversion layer, dry
9 deposition generally does not occur and the concentration of O3 scavengers is generally lower
10 so that 63 concentration remains fairly constant (Wolff et al., 1987). A flat diurnal pattern
11 is usually interpreted as indicating a lack of efficient scavenging of Qj and/or a lack of
12 photochemical precursors, whereas a varying diurnal pattern is taken to indicate the opposite.
13 With the composite diagrams alone, it is difficult to quantify the daily or long-term exposures
14 of 63. For example, the diurnal patterns for two such sites are illustrated in Figure 4-10.
15 The Jefferson County (KY) site is urban-influenced and experiences elevated levels of O3 and
16 NOX. The Oliver County (ND) site is fairly isolated from urban-influenced sources and
17 hourly average O3 concentrations are mostly below 0.09 ppm. The flat diurnal pattern
18 observed for the Oliver County site is usually interpreted as indicating a lack of efficient
19 scavenging of 03 and/or a lack of photochemical precursors, whereas the varying diurnal
20 pattern observed at the Jefferson County site may be interpreted to indicate the opposite.
21 Logan (1989) has described the diurnal pattern for several rural sites in the United States
22 (Figure 4-11) and noted that average daily profiles showed a broad maximum from about
23 noon until about 1800 LT at all the eastern sites, except for the peak of Whiteface Mountain.
24 Logan (1989) noted that the maximum concentrations were higher at the SURE sites than at
25 the NAPBN sites in the east because the latter were situated in more remote or coastal
26 locations.
27
28 4.4.2 Urban Area Diurnal Patterns
29 The U.S. EPA (1986a) has discussed diurnal patterns for urban sites. Figure 4-12,
30 reproduced from the previous document, shows the diurnal pattern of 03 concentrations on
31 July 13, 1979, in Philadelphia, Pennsylvania. On this day a peak 1-h average concentration
December 1993 4.55 DRAFT-DO NOT QUOTE OR CITE
-------
0.08-
0.06-
Jefferson Co., KYI
Oliver Co., ND
1 3 5 7 9 11 13 15 17 19 21 23
Hour
Figure 4-10. Hie comparison of the seasonal diurnal patterns using 1988 data for
Jefferson County, KY and Oliver County, ND.
1 of 0.20 ppm, the highest for the month, was reached at 2:00 p.m., presumably as the result
2 of meteorological factors, such as atmospheric mixing and local photochemical processes.
3 The severe depression of concentrations to below detection limits (less than 0.005 ppm)
4 between 3:00 and 6:00 a.m. is usually explained as resulting from the scavenging of O3 by
5 local nitric oxide emissions. In this regard, this station is typical of most urban locations.
6 Diurnal profiles of O3 concentrations can vary from day to day at a specific site,
7 however, because of changes in the various factors that influence concentrations. Composite
8 diurnal data (that is, concentrations for each hour of the day averaged over multiple days or
9 months) often differ markedly from the diurnal cycle shown by concentrations for a specific
10 day. In Figures 4-13 through 4-15, reproduced from the previous document, diurnal data for
11 2 consecutive days are compared with composite diurnal data (1-mo averages of hour-by-
12 hour measurements) at three different kinds of sites: center city-commercial
December 1993
4-56
DRAFT-DO NOT QUOTE OR CITE
-------
50
40
30
20
(a)
- AZ
m ^ /- ^
— • '~Ili;—*'x .
I l
1 '
--- — \ ^*"""*
,--' OR
i i
' MT
"*•••-.
'* **• ••» — _
i
_
—
4 8 12 16 20
Time of Day (h)
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).
December 1993
DRAFT-DO NOT OTTOTR OP PTTP.
-------
OL
a.
a
03
8
8
O
I
12 1 23456789 10 11 fl 23 456789
Noon
a.m. Hour of Day p.m-
1011
figure 4-12. Diurnal pattern of 1-h ozone concentrations On July 13,1979,
Philadelphia, PA.
Source: U.S. Environmental Protection Agency (1986a).
1 (Washington, DC), rural-near urban (St. Louis, MO), and suburban-residential (Alton, EL).
2 Several obvious points of interest present themselves in these figures: (1) at some sites, at
3 least, peaks can occur at virtually any hour of the day or night but these peaks may not show
4 up strongly in the longer-term average data; (2) some sites may be exposed to multiple peaks
5 during a 24-h period; and (3) disparities, some of them large, can exist between peaks (the
6 diurnal date) and the 1-mo average (the composite diurnal data) of hourly O3 concentrations.
7 When diurnal or short-term composite diurnal O3 concentrations are compared with
8 longer-term composite diurnal 03 concentrations, the peaks are smoothed as the averaging
9 period is lengthened. Figure 4-16 demonstrates the effects of lengthening the period of tune
December 1993
4-58
DtAFT-DO NOT QUOffi OR CltE
-------
0.12
0.10
&0.08
.o
£ 0.06
-------
o.ta
0.10
0.08-
0.06-
8
i
o
0.04 —
0.02-
Rural - Near Urban
I I I I I
- Sept. 29-30
- 1-month
TT
I I I I I I I
24 4 8 12 16 20 24 4 8 12 16 20 24
|< - a.m. — Noon- — p.m.
a.m. — Noon — — p.m. — •>]
Figure 4-14. Diurnal an«l 1-mo composite diurnal variations in ozone concentrations,
St. Lends County, MO, September 1981.
Source: U.S. Environmental Protection Agency (1986a).
1 4.4.3 Nonurban Area Diurnal Patterns
2 Non-urban areas only marginally affected by transported O3 usually have a flatter
3 diurnal profile than sites located in urban areas. Nonurban O3 monitoring sites experience
4 differing types of diurnal patterns (Bohm et at., 1991; Lefohn, 1992b). As indicated earlier,
5 O3 concentrations at a specific location are influenced by local emissions and by long-range
6 transport from both natural and anthropogenic sources. Thus, considerable variation of
7 O3 exposures among sites characterized as agricultural or forested is found and there is no
8 preference for maximum diurnal patterns to occur in either the second or third quarter.
December 1993
4-60
DRAFT-ISO N€*t QUOTE OH CITE
-------
0.12
0.10-
0.08
8
o
o
I
0.06
0.04
0.02
I I I I I I I I I I
Suburban - Residential
! ! t
I I I I
Oct. 11-12
1-month
24 4 8 12 16 20 24 4
8 12 16
|g - a.m -- Noon — p.m.
a.m. — Noon — p.m. — \
Hour of Day
Figure 4-15. Diurnal and 1-mo composite diurnal variations in ozone concentrations,
Alton, IL, October 1981 (fourth quarter).
Source: U.S. Environmental Protection Agency (1986a).
1 The diurnal patterns for several agricultural sites have been characterized (U.S.
2 Environmental Protection Agency, 1986a). Figures 4-17 and 4-18 show some typical
3 patterns of exposure. As discussed by U.S. EPA (1986a), the six sites, whose diurnal
4 patterns are illustrated in Figure 4-16, represent counties with high soybean, wheat, or hay
5 production. The figures show a distinct afternoon maximum with the lowest concentrations
6 occurring in the early morning and evening hours. Quarterly composite diurnal patterns
7 clearly show the division of the afternoon 03 concentrations into two seasonal patterns, the
8 low "winter" levels in the first and fourth quarters and the high "summer8 levels in the
9 second and third quarters of the year.
December 1993
4-61
DRAFT-DO NOT QUOTE OR CTTE
-------
_<
D.
C
0
"05
C.
"c
8
c
o
O
0)
c
o
N
U.1U
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
I M M MM M M M Ml MM
— Alton, IL -
— —
i — —
— —
_ „-- \-,^ 3rd Q
-^
/ \
— / s'' 2nd Q"--.> —
/ '"' \\
/•'' \\
~~ /"' \"i
/Xs
..-"" 4th Q"- . x^..._..-^
« ^ f/ ^.. ^^
Iimuij_ •-?T^-' ^***
M i I M I M M M M M M M M
24
10 12 14 16 18 20 22 24
a.m.
Noon
p.m.
Hour of Day
Figure 4-16. Composite diurnal patterns of ozone concentrations by quarter, Alton, IL,
1981.
1 Remote forested sites experience unique patterns of 63 exposures (Evans et al., 1983;
2 Lefohn, 1984). ITiese sites tend to experience a weak diurnal pattern, with hourly average
3 03 concentrations that occur frequently in the range of 0.04 to 0.05 ppm. Figure 4-19 shows
4 diurnal patterns for several sites in the NDDN network that are located in forested areas.
5 Several of the NDDN sites analyzed by Edgerton and Lavery (1992) exhibit fairly flat
6 average diurnal patterns. Such a pattern is based on average concentrations calculated over
7 an extended period. On a daily basis, some variation in O3 concentration does occur from
8 hour to hour and, in some cases, high hourly average concentrations are experienced either
9 during daytime or nighttime periods (Lefohn and Mohnen, 1986; Lefohn and Jones, 1986;
10 Logan, 1989; Lefohn et al., 1990c; Taylor et aL, 1992).
December 1993
4-62
DRAFT-DO NOT QUOTE OR CITE
-------
[ 0.09
5 0.08
i 0-07
f 0.08
^ 0.06
j 0.04
: o-03
5 OjOZ
3 0.01
0,
i
1 1 1 1 1 1 1 1
~ N. Little Roc»
-(a)
ifrtOi
_______ 4.U4 f^
- . 3rdO
....... JJh ft
*F*»..rr^%~H4sx'
i i i j i 1 i I
w 2 * e t
i i i i i i i i i i i i i i
c,AR
janer
barter
uarter
jartw _•*—»*.
/ \_
ijllllllilljlj
10 12 14 16 IB 20 22
^y- n.m.
•^
2}
-3)1
a.m.
p.m.
\j
J
0.10
0.09
0.08
0.07
0.08
0.05
0.04
0.03
0.02
0.01
0
i i i i i i i i i iiiii jiITiiiri
~ Sacramento, CA
(c)
0.10
0.09
0.08
0.07
OM
0.05
0.04
0.03
0.02
0.01
K-
a.m.
riTiiT i i i i i rrri iiii i i i
" Alton, IL
s,»--»..;fc,«^i,,«'
i i i i i'i"i i i M i i i i i i i i i i
W4-
11 j 111 j_t n
14 18 18 20 22 24
24
K-
a.m, 9^-
p.m.
^> °'10
1 0.09
& 0.08
J °-07
g 0.08
g 0.05
g 0.04
O OJ»
®
§ OJ02
o °-01
o
1 1 1 1 1 I 1 1
CIaikCO.,OH
(*)
1 f 1 1 1 1 1 1 1 1
1 i ' 1 i L' 1' J.1 JJJJ JJ LUJ JJ
-_g ^ ^ g 10 12 14 18 18 »} 22
8 10 12 14 18 18
a.m, ^ p.m~
Hour of Day, LSI
0.10
0.09
aoe
0.07
0.06
0.06
0.04
0.09
0.02
0.01
I I I I I I I I I I I I I II I I I I I I ! I
Edmond.OK
(0
24
I I I I I I I I M I I I I I I I I I I I I
"242 4 § 8 10 12 14 18 18 20 22 2
.. . ;>!<; p.m....
Hour of Day, LSI
Figure 4-17. Quarterly composite diurnal patterns of ozone concentrations at selected
sites representing potential for exposure of major crops, 1981.
Source: U.S. Environmental Protection Agency (1986a).
December 1993
4-63
DRAFT-DO NOT QUOTE OR CITE
-------
Figure 4-18. 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 (1986a).
1 Lefohn et al. (1990c) characterized O3 exposures at high-elevation monitoring sites,
2 The authors reported that a fairly flat diurnal pattern for the Whiteface Mountain summit site
3 (WF1) was observed (Figure 4-20a), with the maximum hourly average concentrations
4 occurring in the late evening or early morning hours. A similar pattern was observed for the
5 mid-elevation site at Whiteface Mountain (WF3). The site at the base of Whiteface
6 Mountain (WF4) showed the typical diurnal pattern expected from sites that experience some
7 degree of Qj scavenging. More variation in the diurnal pattern for the highest Shenandoah
8 National Park sites occurred than for the higher elevation Whiteface Mountain sites, with the
9 typical variation for urban-influenced sites in diurnal pattern at the lower elevation
10 Shenandoah National Park site (Figure 4.20b). Aheja and Li (1992), in their analysis of the
11 5 high-elevation Mountain Cloud Chemistry Program sites (see Section 4.6.2 for site
12 descriptions), noted the flat diurnal pattern typical of high-elevation sites that has been
13 described previously in the literature. Aneja and Li (1992) noted that the peak of the diurnal
December 1993
4^64 DRAFT-GO NOT QUOTE Q& CITE
-------
60
50
-40
CL
o.
^ 30
8 20
10
••• 108 -Prince Edward, VA
ooo 119-Cedar Creek, WV
.8
O
•
8 o
* •
o *•
o
o o
o o
o
2 4 ' 6 ' 6 ' 10 ' 1'2 ' 1'4 ' 1'8 ' 18 20 22 24
Hour
60-
50
V 30
c
o
fi 20
10-
••• 168-Glacier NP.MT
ooo 174 -Grand Canyon, AZ
o o o o o o o
ooooooooo
o o o
• • •
2 4 6 8 10 12 14 16 18 20 22 24
Hour
Figure 4-19. Composite diurnal ozone pattern at selected National Dry Deposition
Network sites.
Source: Edgertan and Laveiy (1992).
December 1993
4-65 DRAFT-DO NOT QUOTE OR CITE
-------
0 24 6 8 10 12 14 16 18 20 22 24
0.00
0.08-1
0.06
^ 0.04-
« 0.03-J
0.02-
0.01-
0.00
(b)
-SHI
-SH2
-SH3
0 24 6 8 10 12 14 16 18 20 22 24
Hour
Figure 4-20. Composite diurnal pattern at (a) Whiteface Mountain, NY, and
(b) Mountain Cloud Chemistry Program's Shenandoah National Park site
for May to September 1987.
Source: Lefohn et al. (1990c).
1993
4-66 DRAFT-DO NOT QUOTS OR OTB
-------
1 patterns over the period May to October (1986 to 1988) for the 5 sites occurred between
2 1800 and 2400 h, while the minimum was observed between 0900 and 1200h. However, it
3 is important to note that as indicated by Lefohn et al. (1990), the flat diurnal pattern is not
4 observed for all high-elevation sites.
5
6
7 4.5 SEASONAL PATTERNS IN OZONE CONCENTRATIONS
8 4.5.1 Urban Area Seasonal Patterns
9 Seasonal variations in 63 concentrations in 1981 were described by the U.S. EPA
10 (1986a). Figure 4-21 shows the 1-mo averages and the single 1-h maximum concentrations
11 within the month for eight sites across the nation. The data from most of these sites exhibit
12 the expected pattern of high O3 in late spring or in summer and low levels in the winter.
13 Data from Pomona (Figure 4-21c) and Denver (Figure 4-21d) show summer maxima.
14 Tampa shows a late spring maximum but with concentrations in the fall (i.e., October)
15 approaching those of spring (June) (Figure 4-2If). Dallas data also tend to be skewed toward
16 higher spring concentrations; but note that November concentrations are also relatively high
17 (Figure 4-2lh). Because of seasonal changes in temperature, relative humidity, and storm
18 tracks from year to year, the general weather conditions in a given year may be more
19 favorable for the formation of 03 and other oxidants than during the prior or following year.
20 For example, 1988 was a hot and dry year during which some of the highest
21 O3 concentrations of the last decade occurred, while 1989 was a cold and wet year in which
22 some of the lowest concentrations occurred (U.S. Environmental Protection Agency, 1992a).
23
24 4.5.2 Nonurban Area Seasonal Patterns
25 In the literature, several investigators have reported on the tendency for average
26 63 concentrations to be higher in the second versus the third quarter of the year for many
27 isolated rural sites (Evans et al., 1983; Singh et al., 1978). This observation has been
28 attributed to either stratospheric intrusions or an increasing frequency of slow-moving, high-
. 29 pressure systems that promote the formation of 03. Lefohn et al. (1990b) reported that for
30 several clean sites, the highest exposures occurred in the third quarter rather than in the
31 second. The results of this analysis will be discussed in the Section 4.5.3. Taylor et al.
December 1993 4-6? DRAFT-DO NOT QUOTE OR CITE
-------
OJE
QMr
JFMAU4JA
Month of Yi
Figure 4-21. Seasonal variations in ozone concentrations as indicated by monthly
averages and the 1-h maximum in each month at selected sites, 1981.
Scarce: U.S. Environmental Protection Agency (1986a).
4-68
DRAFT-DO NOT QUOT1 CJR OTB
-------
I (1992) reported that for 10 forest sites in North America, the temporal patterns of Q, on
2 quarterly or annual periods exhibited less definitive patterns. Based on the exposure index
3 selected, different patterns were reported. The different patterns may be associated with the
4 observations by Logan (1989) that rural O3 in the eastern United States in the spring and
5 summer is severely impacted by anthropogenic and possibly natural emissions of NOX and
6 hydrocarbons and that O3 episodes occur when the weather is particularly conducive to
7 photochemical formation of O3. Meagher et al. (1987) reported for rural O3 sites in the
8 southeastern United States that the daily maximum 1-h average concentration was found to
9 peak during the summer months. Taylor and Norby (1985) reported that in the Shenandoah
10 National Park, the probability of a day occurring in which a 1-h mean O3 concentration was
11 >0.08 ppm was the same during the months of May, June, and July, while the probability
12 was nearly 40% less in August. The probability of an episode during each of the remaining
13 months of the growing season was < 5 %. The month of July experienced both the highest
14 frequency of episodes and the highest mean duration of exposure events.
IS Aneja and LI (1992) reported that the maximum monthly ozone levels occurred in either
16 the spring or the summer (May to August), and the minimum occurred in the fall (September
17 and October). The timing of the maximum monthly values differed across sites and years.
18 However, in 1988, an exceptionally high concentration O3 year, for almost all of the 5 sites,
19 June was the month in which the highest monthly average concentration occurred. This was
20 the month in which the greatest number of O3 episodes occurred in the eastern United States.
21
22 4.5.3 Seasonal Pattern Comparisons with "Pristine" Sites
23 Lefohn et al. (1990b) have characterized the 63 concentrations that occurred at several
24 clean sites in the United States. The Theodore Roosevelt National Park, ND site experienced
25 its maximum in July for 1984 and 1985 and in May for 1986. Of the three western national
26 forest sites evaluated by Lefohn et al. (1990b), only Apache National Forest experienced its
27 maximum monthly mean concentration in the Spring. The Apache National Forest site was
28 above mean nocturnal inversion height and no decrease of concentrations occurred during the
29 evening hours. This site also experienced the highest hourly maximum concentration, as
30 well as the highest W126 03 exposures. The Custer and Ochoco National Forest sites
31 experienced most of their maximum monthly mean concentrations in the summer. The White
December 1993 4.69 DRAFT-DO NOT QUOTE OR fTTR
-------
1 River Oil Shale site in Colorado experienced its maximum monthly mean during the spring
2 and summer months.
3 The W126 sigmoidal weighting function index was also used to identify the month of
4 highest O3 exposure. A somewhat more variable pattern was observed than when the
5 maximum monthly average concentration was used. For some sites, the winter/spring
6 pattern was represented; for others, it was not. In some cases, the highest W126 exposures
7 occurred earlier in the year than was indicated by the maximum monthly concentration. For
8 example, in 1979, the Custer National Forest site experienced its highest W126 exposure in
9 April, although the maximum monthly mean occurred in August. In 1980, the reverse
10 occurred.
11 There was no consistent pattern for those sites located in the continental United States.
12 The Theodore Roosevelt National Park, Custer National Forest, Ochoco National Park, and
13 White River Oil Shale sites experienced their maximum Oj exposures during the spring and
14 summer months. The sites experiencing their highest O3 exposures in the fall to spring
15 period did not necessarily experience the lowest 03 exposures.
16
17
18 4.6 SPATIAL VARIATIONS IN OZONE CONCENTRATIONS
19 4.6.1 Urban-Nonurban Area Concentration Differences
20 Diurnal concentration data presented earlier indicate that peak O3 concentrations can
21 occur later in the day in rural areas than in urban, with the distances downwind from urban
22 centers generally determining how much later the peaks occur. Meagher et al. (1987)
23 reported that for five rural sites in the Tennessee Valley region of the southeastern United
24 States, O3 levels were found to equal or exceed urban values for the same region. Data
25 presented in the 1978 criteria document demonstrated that peak concentrations of Oj in rural
26 areas are generally lower than those in urban areas, but mat average concentrations in rural
27 areas are comparable to or even higher than those in urban areas (U.S. Environmental
28 Protection Agency, 1978). Reagan (1984) noted that Oj concentrations measured near
29 population-oriented areas were depressed in comparison with data collected in more isolated
30 areas. As noted earlier, urban O3 values are often depressed because of titration by nitric
31 oxide (Stasiuk and Coffey, 1974). In reviewing the National Crop Loss Assessment
December 1993 4^70 DRAFT-DO NOT QtJQTfi OR CTTB
-------
1 Network's use of kriging to estimate the 7-h seasonal average O3 levels, Lefohn et ai.
2 (1987a) found that the 7-h values derived from kriging for sites located in rural areas tended
3 to be lower than the actual values because of the effect of using data from urban areas to
4 estimate rural values. In addition to the occurrence of higher average concentrations and
5 occasionally higher peak concentrations of O3 in nonurban areas than in urban, it is well
6 documented that O3 persists longer in nonurban than in urban areas (Coffey et al., 1977;
7 Wolff et al., 1977; Isaksen et al., 1978). The absence of chemical scavengers appears to be
8 the main reason.
9
10 4.6.2 Concentrations Experienced at High-Elevation Sites
11 The distributions of hourly average concentrations experienced at high-elevation urban
12 sites are similar to those experienced at low-elevation areas. For example, the distribution of
13 hourly average concentrations for several Oj sites located in Denver were similar to
14 distributions observed at many low-elevation sites in the United States. However, as will be
IS discussed in Section 4.6.3, for assessing the possible impacts of Oj at high-elevation sites,
16 the use of absolute concentration (e.g., in units of micrograms per cubic meter) instead of
17 mixing ratios (e.g., ppm) may be an important consideration.
18 Lefohn et al. (1990c) have summarized the characterization of gaseous exposures at
19 rural sites in 1986 and 1987 at several Mountain Cloud Chemistry Program (MGCP) high-
20 elevation sites. Aneja and Li (1992) have reported the ozone exposures for 1986 to 1988.
21 Table 4-14 summarizes the sites characterized by Lefohn et al. (1990c). Table 4-15
22 summarizes the exposures that occurred at several of the sites for the period 1987 to 1988.
23 In 1987, the 7- and 12-h seasonal means were similar at the Whiteface Mountain WF1 and
24 WF3 sites (Figure 4-22a). The 7-h mean values were 0.0449 and 0.0444 ppm, respectively;
25 the 12-h mean values were 0.0454 and 0.0444 ppm, respectively. Note that, in some cases,
26 the 12-h mean was slightly higher than the 7-h mean value. This resulted when the 7-h mean
27 period (0900 to 1559 h) did not capture the period of the day when the highest hourly mean
28 O3 concentrations were experienced. A similar observation was made, using the 1987 data,
29 for the MGCP Shenandoah National Park sites. The 7- and 12-h seasonal means were
30 similar for the SHI and SH2 sites (Figure 4-22b). Based on cumulative indices, the
31 Whiteface Mountain summit she (WF1) experienced a slightly higher exposure than the WF3
December 1993 4-71 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE 4-14. DESCRIPTION OF MOUNTAIN CLOUD CHEMISTRY
PROGRAM SITES
Site Elevation (m)
Howland Forest (HF1), ME
Mt. Moosilauke (MSI), NH
Whiteface Mountain (WFI), NY
Shenandoah Park (SHI), VA
Shenandoah Park (SH2), VA
Shenandoah Park (SH3), VA
Whitetop Mountain (WT1), VA
Mt, Mitchell (MMl), NC
Mt. Mitchell (MMl), NC
65
1,000
1,483
1,015
716
524
1,689
2,006
1,760
Latitude
45°
43°
44°
38°
38°
38°
36°
35°
35°
11'
59'
23'
37'
37'
37'
38'
44'
45'
18"
26"
12"
30"
45"
20"
15"
68°
71°
73°
78°
78°
78"
81°
82°
82°
Longitude
46'
48'
51'
20'
21'
21'
36'
17'
15'
28"
34"
48"
13"
28"
21"
15"
1 site (Figure 4-22c). Both the sum of the concentrations ^0.07 ppm (SUM07) and the
2 number of hourly concentrations ^0.07 ppm were higher at the WFI site than at the WF3
3 site. The site at the base of the mountain (WF4) experienced the lowest exposure of the
4 three O3 sites. Among the MGCP Shenandoah National Park sites, the SH2 site experienced
5 marginally higher 03 exposures, based on the index that sums all of the hourly average
6 concentrations (i.e., referred to as total dose in the figure) and sigmoidal values, than the
7 high-elevation site (SHI; Figure 4~22d); the reverse was true for the sums of the
8 concentrations S0.07 ppm and number of hourly concentrations S0.07 ppm.
9 When the Big Meadows, Dickey Ridge, and Sawmill Run Shenandoah National Park
10 data for 1983 to 1987 were compared, it was again found that the 7- and 12-h seasonal
11 means were insensitive to the different O3 exposure patterns. A better resolution of the
12 differences was observed when the cumulative indices were used (Figure 4-23). There was
13 no evidence that the higher elevation, Big Meadows, site had consistently experienced higher
14 03 exposures lhan the lower elevation sites. In 2 of the 5 years, the higher elevation site
15 experienced lower exposures than the Dickey Ridge and Sawmill Run sites, based on "total
16 dose" or sigmoidal indices. For 4 of the 5 years, the SUM07 index yielded the same result.
17 Taylor et al. (1992) indicate that forests experienced marked quantitative and qualitative
18 differences in Og exposure. The principal spatial factors underlying this variation were
December 1993
4-72
DRAFT-DO NOT QUOTE OR CITE
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TABLE 4-15. SEASONAL (APRIL TO OCTOBER) PERCENTILES, SUM06, SUM08, AND W126
VALUES FOR THE MOUNTAIN CLOUD CHEMISTRY PROGRAM SITES
2 Site Year
Co Howland Forest, ME 1987
g (HF1) 1988
Mt. Moosilauke, NH 1987
(MSI) 1988
Whiteface Mountain, NY 1987
(WF1) (36-031-0002) 1988
Whiteface Mountain, NY 1987
(WF3)
Whiteface Mountain, NY 1987
(WF4)
Mt. Mitchell, NC 1987
(MM1) 1988
1989
f- 1992
-4
•** Mt. Mitchell, NC 1987
(MM2) 1988
Shenandoah Park, VA 1987
M (SHI) 1988
5 Shenandoah Park, VA a!987
C (SH2)
1
o
X Shenandoah Park, VA 1987
^ (SH3) 1988
Q Whitetop Mountain, VA 1987
^ (WT1) 1988
Min.
0.000
0.000
0.006
0.010
0.011
0.014
0.010
0.000
0.008
0.011
0.010
0.005
0.017
0.009
0.000
0.006
0.003
0.000
0.006
0.011
0.000
10
0.013
0.012
0.027
0.026
0.029
0.025
0.025
0.011
0.034
0.038
0.038
0.036
0.032
0.029
0.023
0.024
0.027
0.018
0.020
0.038
0.030
30
0.021
0.021
0.036
0.033
0.037
0.033
0.033
0.023
0.044
0.054
0.047
0.043
0.042
0.041
0.036
0.036
0.040
0.029
0.031
0.051
0.046
50
0.028
0.028
0.045
0.043
0.046
0.043
0.039
0.031
0.051
0.065
0.054
0.048
0.049
0.050
0.044
0.047
0.049
0.037
0.040
0.059
0.058
70
0.035
0.036
0.053
0.055
0.053
0.056
0.047
0.041
0.058
0.075
0.059
0.053
0.056
0.060
0.054
0.058
0.059
0.047
0.051
0.066
0.068
90
0.046
0.047
0.065
0.076
0.067
0.078
0.064
0.056
0.067
0.095
0.068
0.063
0.067
0.080
0.069
0.077
0.071
0.061
0.067
0.078
0.084
95
0.052
0.054
0.074
0.087
0.074
0.089
0.075
0.065
0.074
0.106
0.072
0.069
0.073
0.092
0.076
0.087
0.077
0.068
0.076
0.085
0.094
99
0.065
0.076
0.086
0.113
0.087
0.110
0.091
0.081
0.085
0.126
0.081
0.081
0.083
0.110
0.085
0.103
0.086
0.080
0.097
0.096
0.119
Max.
0.076
0.106
0.102
0.127
0.104
0.135
0.117
0.117
0.105
0.145
0.147
0.096
0.096
0.162
0.135
0.140
0.145
0.108
0.135
0.111
0.163
No. Obs.
4,766
4,786
4,077
2,835
4,704
4,673
4,755
4,463
3,539
2,989
2,788
3,971
3,118
2,992
3,636
3,959
2,908
3,030
4,278
4,326
3,788
SUM06
5.9
10.9
45.0
51.9
62.0
65.8
45.4
23.8
59.4
145.1
54.8
37.8
47.0
68.7
54.2
80.9
55.7
23.1
52.3
147.7
133.8
SUM08
0.0
2.9
9.5
21.2
12.2
40.8
14.4
5.1
7.8
69.7
3.5
4.4
5.1
28.1
8.5
29.6
7.8
55.8
2.6
15.6
32.4
51.0
W126
7.7
11.6
40.1
43.4
49.5
56.5
40.3
21.3
46.5
116.6
40.7
36.7
37.4
57.7
42.0
67.2
41.8
19.2
44.2
105.7
102.8
§ .
Q Calculations based on a May to September season.
o
73
-------
0,00
I 7h
H2h
WF1
WF3
WF4
i7h
[12h
SHI
SHZ
SH3
200-
Total Dosa
Sigmoldal
Sum * 0.07
> 100
200-1 (d)
0)
c
'100-
WF1
WF3
WF4
SH1
SHZ
SH3
Figure 4-22. 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. (1990c).
1 elevation, proximity to anthropogenic sources of oxidant precursors, regional-scale
2 meteorological conditions, and airshed dynamics between the lower free troposphere and the
3 surface boundary layer. Table 4-16 summarizes the exposure values for the ten EPRI
4 Integrated Forest Study Sites located in North America.
5
6 4.6.3 Other Spatial Variations in Ozone Concentrations
7 Despite relative intraregional homogeneity, evidence exists for intracity variations in
8 concentrations that are pertinent to potential exposures of human populations and to the
9 assessment of actual exposures sustained in epidemiologic studies. Two illustrative pieces of
December 1993
4-74
DRAFT-DO NOT QUOTE OR CITE
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May - September 1983
• ToM Doss
• SJgmokJal
G Sum 2 0.07
300
zoo-
100
June - September 1985
Big Meadows Dickey RMg« Sawmill Run
(0)
• Total DOM
• StgmohU
D Sum » 0,07
Big Meadows Dtefcey RUge Sawml Run
May-September 1984
Total DOM
SlamokJal
Sim * 0.07
300 n
200-
May-September
(d)
• Slgmoldal
Q Sun 2 0.07
Run
ol •"! Jh=L_Jlb_
Big Meadow* DtekeyRWga Sawmill Run
300 n
May - September 1987
(e)
• Total DOM
• SlgmoMal
Q Sum 2 0.07
figure 4-23. Integrated exposures for three non-Mountain Cloud Chemistry Program's
Shenandoah National Park sites, 1983 to 1987.
Source: Lefohn et al. (199(te).
December 1993
4-75 DRAFT-DO NOT OUOTB OR CITE
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TABLE 4-16. SUMMARY STATISTICS FOR 11 INTEGRATED FOREST STUDY SITES
(All units are in ppb)
ff
H- '
vo Site
HIGH ELEVATION SITES
Whiteface Mountain, NY
Great Smoky Mountain NP
Coweeta Hydrologic Lab, NC
•^
ON
LOW ELEVATION SITES
W Huntington Forest, NY
£!
3
6
0
2 Howland, MA
9
H
8
Year
1987
1987
1988
1988
1987
1987
1988
1988
1987
1987
1988
1988
1987
1987
1988
1988
1987
1987
1988
1988
Quarter
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
24-h
(Ppb)
42
45
49
44
54
53
71
59
50
47
61
57
36
24
40
37
34
26
36
24
12-h
(Ppb)
43
44
50
43
52
51
70
57
48
44
59
54
42
32
46
46
39
32
41
30
7-h
(Ppb)
42
43
49
43
49
49
68
55
47
42
59
51
42
33
46
48
39
31
41
30
1-h Max.
(Ppb)
104
114
131
119
99
95
119
120
85
95
104
100
88
76
106
91
69
76
90
71
SUM06
(ppm-h)
13.2
30.1
33.5
22.6
57.1
34.3
126.3
74.7
32.4
24.1
81.6
63.6
9.8
5.4
19.2
18.6
1.9
3.8
8.1
1.7
SUM08
(ppm-h)
2.5
11.8
13.9
10.4
10.9
8.8
61.2
22.2
2.6
2.4
18.5
19.8
0.9
0.2
6.1
2.7
0.0
0.0
2.9
0.0
-------
TABLE 4-16 (cont'd). SUMMARY STATISTICS FOR 11 INTEGRATED FOREST STUDY SITES
1
H- Site
5 LOW ELEVATION SITES (cont'd)
Oak Ridge, TN
Thompson Forest, WA
B.F. Grant Forest, GA
^
^
Gainseville, FL
o
H Duke Forest, NC
I
0
o
g
^ Nordmoen, Norway
§
~H
S
o
* aData were insufficient to calculate statistic.
n
Year
1987
1987
1988
1988
1987
1987
1988
1988
1987
1987
1988
1988
1987
1987
1988
1988
1987
1987
1988
1988
1987
1987
1988
1988
Quarter
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
2
3
24-h
42
29
40
32
36
30
32
32
32
33
47
32
42
29
35
20
38
52
54
38
32
14
22
11
12-h
53
44
57
47
43
36
39
39
46
52
63
47
53
44
48
29
48
59
69
51
40
18
28
15
7-h
50
41
58
51
41
34
37
36
48
54
64
48
50
41
51
30
52
50
75
54
41
20
29
16
1-h Max.
112
105
104
122
103
94
103
140
99
102
127
116
a
a
84
70
100
124
115
141
75
32
53
30
SUM06
(ppm-h)
39.5
24.3
26.4
19.7
10.7
10.3
8.1
13.5
26.1
31.3
53.1
24.1
a
a
23.4
1.9
29.2
a
a
52.9
2.4
0.0
0.0
0.0
SUM08
(ppm-h)
13.5
9.0
9.8
7.7
3.6
2.1
2.3
6.7
5.1
10.3
21.9
7.4
a
a
0.5
0.1
7.8
a
a
23.4
0.0
0.0
0.0
0.0
Source: Adapted from Taylor et al. (1992).
-------
1 data are presented in this section, one a case of relative homogeneity in a city with a
2 population under 500,000 (New Haven, Connecticut) and one a case of relative in
3 homogeneity of concentrations in a city of greater than 9 million population (New York
4 City).
5 As described in the previous version of the criteria document (U.S. Environmental
6 Protection Agency, 1986a), the general similarity of the percentiles of the hourly average
7 concentrations for a New Haven site, and two other monitoring stations in the county that
8 were operating at the time, one in Derby, Connecticut, 9 miles west of New Haven, and one
9 in Hamden, Connecticut, 6 miles north of New Haven, is evident. Table 4-17 shows that the
10 data and time of the maximum hourly concentrations by quarter at these three sites are
11 similar.
12
TABLE 4-17. QUARTERLY MAXIMUM 1-H OZONE VALUES AT SITES IN AND
AROUND NEW HAVEN, CONNECTICUT, 1976
(Chemiluminescence method, hourly values in ppm)
7 -* *-r
Quarter of Year
New Haven, CT
No. measurements
Max 1-h, ppm
Hour of day
Date
Derby, CT
No. measurements
Max 1-h, ppm
Hour of day
Date
Hamden, CT
No. measurements
Max 1-h, ppm
Hour of day
Date
1
10
0.045
11. -00 a.m.
3/29
11
0.015
11:00 p.m.
3/31
56
0.050
12:00 p.m.
3/29
2
1,964
0.274
2:00 p.m.
6/24
2,140
0.280
2:00 p.m.
6/24
2,065
0.240
3:00 p.m.
6/24
3
2,079
0.235
2:00 p.m.
8/12
2,187
0.290
2:00 p.m.
8/12
1,446
0.240
1:00 p.m.
7/20
4
66
0.066
10:00 p.m.
10/3
1,360
0.060
7:00 p.m.
12/20
286
0.065
3:00 p.m.
10/7
Source: U.S. Environmental Protection Agency (1986a).
December 1993
4-78
DRAFT-DO NOT QUOTE OR CTTB
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1 The source of much of the O3 experienced in the New Haven, Connecticut, area is the
2 greater New York area (e.g., Wolff et al., 1975; Cleveland et al., 1976a,b) and an urban
3 plume transported over the distance from New York City to New Haven would tend to be
4 relatively well-mixed and uniform, such that intracity variations in New Haven would
5 probably be minimal.
6 As indicated in the previous version of the Criteria Document (U.S. Environmental
7 Protection Agency, 1986a), intracity differences in 03 concentrations have also been reported
8 by Kelly et al. (1986) for a 1981 study in Detroit, Michigan. Ozone concentrations were
9 measured for about 3 mo at 16 sites in the metropolitan Detroit area and in nearby Ontario,
10 Canada, Values at 15 sites were correlated with those at a site adjacent to the Detroit
11 Science Center, about 3 km north of the central business district in Detroit. In general, the
12 correlation decreased as distance from the Science Center site increased; and, in general, the
13 actual concentrations increased with distance from that site toward the north-northeast. The
14 highest O3 concentrations were recorded at sites about 10 to 70 km north-northeast of the
15 urban core. At greater distances or in other directions, 03 maxima decreased.
16 Concentrations of O3 vary with altitude and with latitude. While a number of reports
17 contain data on O3 concentrations at high altitudes (e.g., Coffey et al., 1977; Reiter, 1977b;
18 Singh et al., 1977; Evans et al., 1985; Lefohn and Jones, 1986), fewer reports are available
19 that present data for different elevations at the same locality. There appears to be no
20 consistent conclusion concerning the relationship between O3 exposure and elevation.
21 Wolff et al. (1987) have reported, for a short-term study at High Point Mountain in
22 northwestern New Jersey, that both the daily maximum and mid-day O3 concentrations were
23 similar at different altitudes, but that the O3 exposures increased with elevation. Wolff et al.
24 (1987) conducted a study of the effects of altitude on 03 concentrations at three sites located
25 at three separate elevations on High Point Mountain in northwestern New Jersey. Data for
26 several days indicate that in mid-May, when atmospheric mixing was good, vertical profiles
27 were nearly constant, with concentrations increasing only slightly with elevation. Likewise,
28 the daily O3 maxima were similar at different elevations. At night, however,
29 O3 concentrations were nearly zero in the valley (i.e., the lowest-elevation site) and increased
30 with elevation. Comparison of the O3 exposures at the three sites (number of hours
31 > 0.08 ppm) showed that greater cumulative exposures were sustained at the higher
December 1993 4.79 DRAFT-DO NOT QUOTE OR CITE
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1 elevations. Comparable data from an urban area (Bayonne, NJ) about 80 km southeast of
2 High Point Mountain showed that the cumulative exposures were higher at all three of the
3 mountain sites than in the urban area (Wolff et al., 1987). The investigators concluded from
4 their concentration and meteorological data that elevated, mountainous sites in the eastern
5 United States may be expected to be exposed to higher O3 exposures than valley sites
6 throughout the year.
7 Winner et al. (1989) have reported that for three Shenandoah National Park sites (i.e.,
8 Big Meadows, Dickey Ridge, and Sawmill Run), the 24-h monthly mean O3 concentrations
9 tended to increase with elevation, but that the number of elevated hourly occurrences equal to
10 or above selected thresholds did not. The authors reported that the highest elevation site (Big
11 Meadows) experienced a smaller number of concentrations at or below the minimum
12 detectable level than did the other two sites. The larger number of hourly average
13 concentrations that occurred at or below the minimum detectable level at both Dickey Ridge
14 and Sawmill Run resulted in lower 24-h averages at these sites.
15 Lefohn et al. (1990c), characterizing the O3 exposures at several high-elevation sites,
16 reported that based on cumulative indices, the Whiteface Mountain summit site (WF1)
17 experienced a slightly higher exposure than the lower elevation Whiteface Mountain (WF3)
18 site. The site at the base of Whiteface Mountain (WF4) experienced the lowest exposure of
19 the three O3 sites at Whiteface Mountain. Among the MCCP Shenandoah National Park
20 sites, the SH2 site experienced higher O3 exposures than the high-elevation site (SHI). The
21 "total dose" (the sum of all hourly average concentrations) and sigmoidal (W126) indices
22 were slightly higher at the SH2 than the SHI site. The data capture at the two sites for the
23 5-mo period was similar. However, the sum of the concentrations SO. 07 ppm and number
24 of hourly concentrations SO. 07 ppm were slightly higher at the SHI than at the SH2 site.
25 For the Whiteface Mountain sites, both the sum of the concentrations SO.07 ppm (SUM07)
26 and the number of hourly concentrations S0.07 ppm were higher at the WF1 site than at the
27 WF3 site.
28 When the Big Meadows, Dickey Ridge, and Sawmill Run Shenandoah National Park
29 data for 1983 to 1987 were compared, a higher resolution of the differences among the
30 regimes was observed when the cumulative indices were used. No specific trend could be
31 identified that showed the higher elevation, Big Meadows, site had consistently experienced
December 1993 4-80 DRAFT-DO NOT QUOTE OR CITE
-------
1 higher 03 exposures than the lower elevation sites. In 2 of the 5 years, the higher elevation
2 site experienced lower exposures than the Dickey Ridge and Sawmill Run sites, based on
3 "total dose" or sigmoidal indices. For 4 of the 5 years, the SUM07 index yielded the same
4 result.
5 An important issue for assessing possible impacts of 03 at high-elevation sites that
6 requires further attention is the use of mixing ratios (e.g., ppm) instead of absolute
7 concentration (e.g., in units of micrograms per cubic meter) to describe 63 concentration.
8 In most cases, mixing ratios (e.g., ppm) or mole fractions are used to describe
9 63 concentrations. Lefohn et al. (1990c) have pointed out that the manner in which
10 concentration is reported may be important when assessing the potential impacts of air
1 1 pollution on high-elevation forests. Concentration (in units of micrograms per cubic meter)
12 varies as a function of altitude. Although the change in concentration is small when the
13 elevational difference between sea level and the monitoring site is small, it becomes
14 substantial at high-elevation sites. Given the same part-per-million value experienced at both
15 a high- and low-elevation site, the absolute concentrations (i.e., micrograms per cubic meter)
16 at the two elevations will be different. Since both 03 and ambient air are gases, changes in
17 pressure directly affect their volume. According to Boyle's law, if the temperature of a gas
18 is held constant, the volume occupied by the gas varies inversely with the pressure (i.e., as
19 pressure decreases, volume increases). This pressure effect must be considered when
20 measuring absolute pollutant concentrations. At any given sampling location, normal
21 atmospheric pressure variations have very little effect on air pollutant measurements.
22 However, when mass/volume units of concentration are used and pollutant concentrations
23 measured at significantly different altitudes are compared, pressure (and hence volume)
24 adjustments are necessary.
25 These exposure considerations are trivial at low-elevation sites. However, when one
26 compares exposure-effects results obtained at high-elevation sites with those from low-
27 elevation sites, the differences may become significant (Lefohn et al., 1990c). In particular,
28 assuming that the sensitivity of the biological target is identical at both low and high
29 elevations, some adjustment will be necessary when attempting to link experimental data
30 obtained at low-elevation sites with air quality data monitored at the high-elevation stations.
31
December 1993 4_ai nuAFT-nn MOT nTTnrn rat
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1 4.7 INDOOR OZONE CONCENTRATIONS
2 Most people in the United States spend a large proportion of their time indoors,
3 A knowledge of actual exposures of populations to indoor levels of 63 is essential for the
4 interpretation and use of results associated with epidemiological studies. However,
5 essentially all routine air pollution monitoring is done on outdoor air. Until the early 1970s,
6 very little was known about the O3 concentrations experienced inside buildings. The ratio of
7 the indoor/outdoor O3 concentrations (I/O) is a parameter that has been widely used for
8 studying the indoor and outdoor relationships, sources, and exposure patterns of O3.
9 However, the data base on this subject is not large and a wide range of I/O Oj concentration
10 relationships can be found in the literature. The only significant source of 03 in indoor
11 residential air is infiltration of outdoor 03, with ventilation rates affecting the flow of air
12 between indoor and outdoor (Zhang and Lioy, in press).
13 Reported I/O values for 03 are highly variable (U.S. Environmental Protection Agency,
14 1986a) and range from <0.1 to 0.80±0.10 for various indoor environments and ventilation
15 rates (Weschler et al., 1989). Unfortunately,, the number of experiments and kinds of
16 structures examined to date provide only limited data for use in modeling indoor exposures.
17 Data were summarized by Yocom (1982) describing studies of indoor-outdoor gradients in
18 buildings and residences for either O3 or photochemical oxidant. This information was
19 presented in the previous document (U.S. Environmental Protection Agency, 1986a). The
20 results were highly variable. A relatively large number of factors can affect the difference in
21 O3 concentrations between the inside of a structure and the outside air. In general, outside
22 air infiltration or exchange rates, interior air circulation rates, and interior surface
23 composition (e.g., rugs, draperies, furniture, walls) affect the balance between replenishment
24 and decomposition of O3 within buildings (U.S. Environmental Protection Agency, 1986a).
25 Although indoor concentrations of O3 will almost invariably be less than outdoors, the fact
26 that people spend more time indoors than outdoors may result in greater overall indoor
27 exposures.
28 Cass et al. (1991) have discussed the importance of protecting works of art from
29 damage due to O3. Based on experiments that show that the fading of artists' pigments in
30 the presence of 03 is directly related to the product of concentration x duration of exposure,
31 it appears that museum personnel face unusual challenges because indoor 03 exposure must
December 1993 4-82 DRAFT-DO NOT QUOTE OR CITE
-------
1 be reduced to very low levels in order to protect the collections from accumulated damage
2 over periods of 100 years or more. Druzik et al. (1990) reported that in a survey of
3 11 museums, galleries, historical houses and libraries in southern California, facilities with a
4 high air exchange with the outdoors and no pollutant removal system have indoor
5 O3 concentrations greater than two-thirds as high as outdoor concentrations. The author
6 reported mat museums with conventional air conditioning systems showed indoor
7 O3 concentrations about 30 to 40% of those outside, while museums with no forced
8 ventilation system, where slow air infiltration provides the only means of air exchange, have
9 indoor Qj levels typically 10 to 20% of those outdoors. Several other studies have been
10 reported in the literature and Table 4-18 lists the I/O ratios reported from these efforts as
11 well as those from earlier years.
12 Automobiles and other vehicles constitute another indoor environment in which people
13 may spend appreciable amounts of time. As with buildings, the mode of ventilation and
14 cooling helps determine the inside concentrations. The U.S. EPA (1986a) describes studies
15 for the I/O ratios. In one study reported by Contant et al. (1985), the I/O ratios from
16 49 measurements inside vehicles were 0.44 for the mean, 0.33 for the median, and 0.56 for
17 maximum concentrations measured. Chan et al. (1991) reported an I/O ratio of 0.20 for
18 median in-vehicle concentrations (0.011 ppm) and time-matched fixed-site measurements
19 (0.051 ppm).
20 At present, there are no long-term monitoring data on indoor air pollutant
21 concentrations comparable to the concentration data available for outdoor locations. Thus,
22 for estimates of the exposure of building or vehicle occupants to O3 and other photochemical
23 oxidants, it is necessary to rely on extrapolations of very limited I/O data.
24
25
26 4.8 ESTIMATING EXPOSURE TO OZONE
27 4.8.1 Introduction
28 It is important that accurate estimates of both human and vegetation exposure to 63 are
29 available for assessing the risks posed by the pollutant. In the Introduction of this chapter,
30 the differences between concentration, exposure, and dose were discussed. In this section,
31 examples are provided on how both fixed-site monitoring information, as well as human
December 1993 4-83 DRAFT-DO NOT QUOTE OR CITE
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TABLE 4-18. SUMMARY OF REPORTED INDOOR-OUTDOOR OZONE RATIOS
Structure
Indoor-Outdoor Ratio
Reference
Hospital
Residence
(with evaporative cooler)
Office
(air conditioned; 100% outside air
intake)
(air conditioned; 70% outside air
intake)
0.67s
0,60a
0.80±0.10
0.65 ±0.10
Thompson et al. (1971)
Thompson et al. (1973)
Sabersky et al. (1973)
Sabersky et al. (1973)
Office
Office/Lab
Residence
Residence
Two offices
Residence
(gas stoves)
(all electric)
Office
School Room
Residence
Residences (1 each)
(air conditioned)
(100% outside air; no air
conditioning)
Residences (12)
(air conditioned)
Residences (41)
Residences (6)
(window open)
(window closed)
(air conditioning)
Art Gallery
0.66
0.54
0.62
0.70
0.50-O.70
0.3
0.19
0.20
0.29
0. 19 (max. cone.)
0.10-0.25
0.00-0.09
1.0
0.21 (mean cone.)
0,12(med. cone.)
0.59 (max. cone.)
0.3
0.59±0.16
0.26 ±0.12
0.28 ±0.12
0.5
Shair and Heitner (1974)
Shair and Heitner (1974)
Hales et al. (1974)
Sabersky et al. (1973)
Sabersky et al. (1973)
Moschandreas et al. (1978)
Moschandreas et al. (1981)
Moschandreas et al. (1978)
Berk et al. (1980)
Berk et al. (1981)
Stock et al. (1983)
Contant et al. (1985)
Lebowitz et al. (1984)
Zhang and Lioy (In press)
Shaver et al. (1983)
December 1993
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TABLE 4-18 (cont'd). SUMMARY OF REPORTED INDOOR-OUTDOOR
OZONE RATIOS
Structure
Indoor-Outdoor Ratio
Reference
Ait Gallery
(three modes of ventilation in each
24-h period: recirculation,
mixture of recirculated and
outside air, and 100% outside air)
Museums
Museum
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
nitration system)
0.70±0.10
(mean cone.)
Davies et al. (1984)
0.45
0.69-0,84 (1-h)
0.50-0.87 (8-h)
0.10-0,59(14i)
0.10-0.58
Shaver et al. (1983)
Nazaroff and Cass (1986)
Dnuak et al. (1990)
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)
Measured as total oxidants.
1 exposure models, are used to estimate risks associated with Oj exposure. A short discussion
2 is provided on the importance of hourly average concentrations, used in the human health
3 and vegetation experiments, mimicking as closely as possible "real world" exposures.
4 Human exposure represents the joint occurrence of an individual being located at point
5 (x,y,z) during time t, with the simultaneous presence of an air pollutant at concentration
6 C*,y,z (t) (U.S. Environmental Protection Agency, 1991). Consequently, an individual's
7 exposure to an air pollutant is a function of location as well as time. If a volume at a
8 location can be defined such that air pollutant concentrations within it are homogeneous yet
9 potentially different from other locations, the volume may be considered a
10 "microenvironment" (Duan, 1982). Microenvironmente may be aggregated by location (i.e.,
December 1993
DRAFT-DO NOT OTTOTR ntt rnrr
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1 indoor or outdoor) or activity performed at a location (i.e., residential, commercial) to form
2 microenvironment types.
3 The Air Quality Criteria for Carbon Monoxide (U.S. Environmental Protection Agency,
4 1991) discusses the difference between individual and population exposures. The report
5 notes that Sexton and Ryan (1988) define the pollutant concentrations experienced by a
6 specific individual during normal daily activities as "personal" or "individual" exposures,
7 A personal exposure depends on the air pollutant concentrations that are present in the
8 location through which the person moves, as well as on the time spent at each location.
9 Because time-activity patterns can vary substantially from person to person, individual
10 exposures exhibit wide variability (U.S. Environmental Protection Agency, 1991). Thus,
11 although it is a relatively straightforward procedure to measure any one person's exposure,
12 many such measurements may be needed to quantify exposures for a defined group. The
13 daily activities of a person in time and space define his or her activity pattern. Accurate
14 estimates of air pollution exposure generally require that an exposure model account for the
IS activity patterns of the population of interest.
16 From a public health perspective, it is important to determine the "population
17 exposure," which is the aggregate exposure for a specified group of people (e.g., a
18 community or an identified occupational cohort). Because exposures are likely to vary
19 substantially between individuals, specification of the distribution of personal exposures
20 within a population, including the average value and the associated variance, is often the
21 focus of exposure assessment studies.
22 In many cases, the upper tail of the distribution, which represents those individuals
23 exposed to the highest concentrations, is frequently of special interest because the
24 determination of the number of individuals who experience elevated pollutant levels can be
25 critical for health risk assessments. This is especially true for pollutants for which the
26 relationship between dose and response is highly nonlinear. Runeckles and Bates (1991)
27 have pointed out the importance of peak concentrations in eliciting adverse human effects.
28 As indicated in the Introduction, results using controlled human exposures have shown the
29 possible importance of concentration in relation to duration of exposure and inhalation rate.
30 The implication of the importance of concentration can be translated into the conclusion that
December 1993 4-86 DRAFT-DO NOT QUOTE OR CITE
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1 the simple definition of exposure as equal to concentration multiplied by time may be too
2 simplified.
3 Because, for most cases, it is not possible to estimate population exposure solely from
4 fixed-station data, several human exposure models have been developed. Some of these
5 models include information on human activity patterns (i.e., the microenvironttients people
6 visit and the times they spend there). These models also contain submodels depicting the
7 sources and concentrations likely to be found in each microenvironment, including indoor,
8 outdoor, and in-transit settings.
9
10 4.8.2 Fixed-Site Monitoring Information Used To Estimate Population
11 and Vegetation Exposure
12 For most cases, from the information provided in earlier sections in this chapter, fixed-
13 site monitors alone cannot accurately depict population exposures because (1) indoor and
14 in-transit concentrations of Gj may be significantly different from ambient Qj concentrations,
15 and (2) ambient outdoor concentrations of O3 that people come in contact with may vary
16 significantly from O3 concentrations measured at fixed-site monitors. Fixed-site monitors
17 measure concentrations of pollutants in ambient air. Ambient air as noted by the EPA (1991)
18 is defined in the Code of Federal Regulations (1991) as air that is "external to buildings, to
19 which the general public has access." But the nature of modem urban lifestyles in many
20 countries, including the United States, is that people spend an average of over 20 h per day
21 indoors (Meyer, 1983). Reviews of studies summarized in Section 4.7 show that indoor
22 O3 concentration measurements vary significantly from simultaneous measurements in
23 ambient air. The difference between indoor and outdoor air quality and the amount of time
24 people spend indoors reinforces the conclusion that, for most cases, using ambient air quality
25 measurements alone do not provide accurate estimates of population exposure.
26 For vegetation, in most cases, it is assumed that exposure is the same as the
27 concentration information provided at fixed monitors in the field (see Sections 5.5 and 5.6).
28 In some cases, because of (a) foliar scavenging and (b) height differences between the
29 vegetation canopy and pollutant monitor, the measured concentration is not equivalent to the
30 vegetation exposure.
December 1993 4-87 DRAFT-DO NOT QUOTE OR CITE
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1 A subgroup that has been studied by several investigators to assess the influence of
2 ambient air pollution on their respiratory health and function is children attending summer
3 camp. Because children are predominantly outdoors and relatively active while at camp,
4 they provide a unique opportunity to assess the relationships between respiratory health and
5 function and concurrent air pollution levels. Children may be at potentially increased risk
6 from air pollution by virtue of their lifestyle patterns, which often involve several hours of
7 outdoor exercise, regardless of air quality, during daylight hours.
8 For campers, attempts have been made to estimate human exposure to O3 using types
9 of activity patterns (Mage et al., 1985); Paul et al., 1987). Mage et al. (1985) developed an
10 objective approach to estimate the dose delivered to the lung of a 12-year-old camper by
11 using: pulmonary minute volume associated with a specific activity, the fractional
12 penetration beyond the trachea, and infiltration of ozone indoors. Lioy and Dyba (1989)
13 have applied the parameters used by Mage et al. (1985) to predict the delivered O3 dose over
14 a four-day episode period. The schedule of a hypothetical camper was matched to the actual
15 03 concentrations, and the predicted doses were estimated.
16 Several studies involving children attending summer camp have been summarized hi
17 Chapter 7. In one study, Avol et al. (1990) reported that 03 levels at a southern California
18 summer camp, located 190 km southeast of Los Angeles, rose gradually throughout each
19 day, displaying a "broad peak* between 1000 and 2000 h each day. Daily maxima typically
20 occurred in late afternoon (1500 to 1700 h); subsequently, concentrations gradually declined
21 to non-zero overnight O3 levels of 0.025 to 0.050 ppm. Spektor et al. (1991) investigated
22 the pulmonary function of 46 healthy children on at least 7 days for each child during a
23 4-week period at a northwestern New Jersey residential summer camp in 1988. The daily
24 levels of 1-h peak O3 and the 12-h average H+ concentrations are shown in Figure 4-24.
25 On 5 of these days, the current NAAQS of 0.12 ppm was exceeded. The maximum hourly
26 concentration attained during the study was 0.150 ppm. The year 1984 was a milder
27 O3 exposure year and Figure 4-25 summarizes the maximal 1-h O3 concentrations at
28 Fairview Lake during a 1984 study period (Spektor et al., 1988).
29
30
December 1993 4-88 DRAFT-DO NOT QUOTE OR CTTB
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National Ambient
0 5 10 15 20
Day Number
Figure 4-24. Maximum 1-h ozone concentrations (in parts per billion) and average
8:00 a.m. through 8:00 p.m. strong acid concentrations (expressed as
micrograms per cubic meter of sulfuric acid) for each day that pulmonary
function data were collected at Fairview Lake camp in 1988. The
correlation coefficient (r) between O3 and H+ was 0.56.
Source: Spektor et al. (1991).
National Ambient Air Quality Standard
Dates -1984
August
Figure 4-25. Maximal 1-h ozone concentrations at Fairview Lake during the study
period.
Source: Spektor et al. (1988).
December 1993
4-8Q
DRAFT-DO NOT QUOTE OR CITE
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1 4.8.3 Personal Monitors
2 A personal exposure profile can be identified by using a personal exposure monitor,
3 McCurdy (1994) has described the development of personal exposure monitors by several
4 companies. However, little data are available describing personal exposures for individuals
5 using these monitors. An example of a pilot study using a personal exposure monitor was
6 described for assessing O3 exposure to 23 children by Liu et al. (1993). The authors
7 collected indoor, outdoor, and personal 03 concentration data as well as time-activity data in
8 State College, Pennsylvania. Results from the pilot study demonstrated that fixed-site
9 ambient measurements may not adequately represent individual exposures. Outdoor
10 O3 concentrations showed substantial spatial variation between rural and residential regions.
11 The authors reported that the use of fixed-site measurements could result in an error as high
12 as 127%. In addition, Liu et al. (1993) reported that models based on time-weighted indoor
13 and outdoor concentrations explained only 40% of the variability in personal exposures.
14 When the model used included observations for only those participants who spent the
15 majority of their day in or near their homes, an R of 0.76 resulted when estimates were
16 regressed on measured personal exposures. The authors concluded mat contributions from
17 diverse indoor and outdoor microenvironraents should be considered to estimate personal
18 O3 exposure accurately. From these results, it is clear that additional data are needed to
19 better quantify the O3 exposures to which populations are exposed.
20
21 4.8.4 Population Exposure Models
22 The availability of personal exposure monitors has facilitated the use of the direct and
23 indirect approaches to assessing personal exposure. Whether the direct or indirect approach
24 is followed, the estimation of population exposure requires a model. Sexton and Ryan (1988)
25 suggest that most exposure models can be classified as one of three types: statistical,
26 physical, or physical-stochastic.
27 In the U.S. EPA (1991), all three types are discussed. The statistical approach requires
28 the collection of data on human exposures and the factors thought to be determinants of
29 exposure. These data are combined in a statistical model, normally a regression equation or
30 an analysis of variance, to investigate the relationship between air pollution exposure
31 (dependent variable) and the factors contributing to the measured exposure (independent
December 1993 4-90 DRAFT-DO NOT QUOTE OR CITE
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1 variables). If the study group constitutes a representative sample, the derived statistical
2 model may be extrapolated to the population defined by the sampling frame. In the physical
3 modeling approach, the investigator makes an a priori assumption about the underlying
4 physical processes that determine air pollution exposure and then attempts to approximate
5 these processes through a mathematical formulation. Because the model is chosen by the
6 investigator, it may produce biased results because of the inadvertent inclusion of
7 inappropriate parameters or the improper exclusion of critical components. Hie physical-
8 stochastic approach combined elements of both the physical and statistical modeling
9 approaches. The investigator begins by constructing a mathematical model that describes the
10 physical basis for air pollution exposure. Then a random or stochastic component (that takes
11 into account the imperfect knowledge of the physical parameters that determine exposure) is
12 introduced into the model. The physical-stochastic approach limits the effect of
13 investigator-induced bias by the inclusion of the random component, and allows for estimates
14 of population distributions for air pollution exposure. Misleading results still may be
15 produced, however, because of poor selection of model parameters. In addition, the required
16 knowledge about distributional characteristics may be difficult to determine.
17 McCurdy (1994) has reviewed the current status of human exposure modeling. The
18 author describes two distinct types of O3 exposure models: those that focus narrowly on
19 predicting indoor O3 levels and those that focus on predicting 03 exposures on a community-
20 wide basis. The models that predict indoor O3 levels have been described by Sabersky et al.
21 (1973), Shair and Heitner (1974), Nazaroff and Cass (1986), and Hayes (1989, 1991).
22 McCurdy (1994) discusses four distinct models that predict 03 exposure on a community-
23 wide basis. These models are:
24
25 1. pNEM/O3 based on the NEM series of models (Paul et al., 1986; Johnson
26 et al., 1990; McCurdy et al., 1991).
27
28 2. SAI/NEM (Hayes et al., 1984; Hayes and Lundberg, 1985; Austin et al.,
29 1986; Hayes et al., 1988; Hayes and Rosenhaum, 1988).
30
31 3. REHEX (Lurmann and Colome, 1991; Winer et al., 1989; Lurmann et al.,
32 1989; Lurmann et al., 1990).
33
34 4. Event probability exposure model (EPEM) (Johnson et al., 1992).
35
December 1993 4-Q1 DRAFT-DO NOT QUOTE OR CTTE
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1 McCurdy (1994) points out that ail four models are related to the NBM (National
2 Ambient Air Quality Standards Exposure Model approach). The NEM is an EPA exposure
3 model developed in the 1980s (Bifler et at, 1981). Outdoor air quality data are obtained
4 from monitoring or modeling data. In most applications of NEM, fixed-site monitoring data
5 are used. The hourly average values are transformed by a suitable relationship so that they
6 better represent air quality outside of the various microenvironments of interest. McCurdy
7 (1994) points out that the important point of the NEM spatial dimension is that people can be
8 assigned to a monitor using United States Census data. In addition, community trips can be
9 assigned among the districts, grid cells, or neighborhood types using Census data. Thus, the
10 NEM model simulates the movement of people through space for work-trip purposes.
11 Interested readers are referred to McCurdy (1994) for further discussion of the pNEM
12 model.
13 The SAI/NEM is based on an earlier version of NEM. The SAI version has more
14 districts, more monitoring input data, and a more detailed mass-balance model to predict
15 indoor O3 concentrations that pNEM/Oj or earlier versions. The REHEX model adopted
16 some of the NEM computer code but uses a more detailed geographic resolution. Similar to
17 the NEM models and SAI/NEM, REHEX explicitly uses home/work trip data to "move"
18 people through the region during their day. The REHEX calculates O3 exposure and dose
19 using discrete distributions of hourly averaged air quality. The model contains an exposure-
20 response relationship that allows analysts to directly estimate discrete, hourly averaged
21 O3 dose levels in exposed individuals (McCurdy, 1994). The author points out that the
22 Event Probability Model (EPEM) does not provide distributions of Oj exposure for any
23 specified population group. The model estimates the probability that a person selected at
24 random will experience a particular exposure situation. The estimate is based on an
25 individual being outdoors for an entire hour. McCurdy (1994) notes that if a person were
26 outdoors for a shorter period, he or she would not be counted. Vostal and Johnson (1993)
27 have described the use of the EPEM for the Houston, Texas area for the 1982 O3 season.
28
29 4.8.5 Concentration and Exposures Used in Research Experiments
30 It is important to adequately characterize the exposure patterns that result in vegetation
31 and human health effects. In Chapter 5 (see Section 5.5), it has been pointed out that the
December 1993 4-92 DRAFT-DO NOT QUOTE OR CITE
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1 hourly average concentrations used in many of the high treatment experimental studies did
2 not necessarily mimic those concentrations observed under ambient conditions. Although the
3 ramifications of this observation on the effects observed is not clear, it was pointed out that
4 the highest treatments used in many of the open-top chamber experiments were bimodal in
5 the distribution of the hourly average concentrations. In other experiments designed to assess
6 the effects of O3 on vegetation, constant concentration (i.e., square wave) exposures were
7 implemented. As has been discussed in earlier sections of this Chapter, hourly average
8 concentrations change by the hour and "square wave" exposure regimes do not normally
9 occur under ambient conditions. In addition to the exposures used at the highest treatment
10 levels, there is concern that the hourly average concentrations used in the control treatments
11 may be lower than those experienced at isolated sites in the United States or in other parts of
12 the world. Although the ramifications of using such exposure regimes is unclear, there is
13 some concern that the use of such levels may result in an overestimation of vegetation yield
14 losses when compared to treatments greater than the control treatment (see Section 5.5 and
15 Lefohn and Foley, 1992).
16 For assessing the human health effects of Og exposure, a series of studies has explored
17 prolonged 6.6 h O3 exposures at low levels (i.e., 0.08 to 0.12 ppm) (Horstman, 1990).
18 McDonnell et al. (1991), using similar hourly average concentration regimes, have confirmed
19 the findings reported by Horstman et al. (1990). All the research investigations using 6.6-h
20 durations have applied constant concentrations during the exposure period. If, as indicated in
21 the Introduction of this chapter, concentration is more important than duration and ventilation
22 rate, different human health effects may occur as a result of different exposure regimes that
23 have identical 6.6-h average concentrations. Because of this, it is important to explore the
24 different types of exposure regimes that occur under ambient conditions during an 8-h
25 episode.
26 Lefohn and Foley (1993) reported on an analysis of hourly average data for
27 O3 monitoring sites that (1) never experienced an exceedance of an hourly average
28 concentration equal to or greater than 0.12 ppm and (2) experienced 8-h daily maximum
29 average concentrations greater than 0.08 ppm. For those monitoring sites that met the above
30 two criteria, they identified the number of times the 8-h daily maximum average
31 concentration exceeded 0,08 ppm during the monitoring year. For the period 1987 to 1989,
December 1993 4-
-------
1 there were 925 exposure regimes identified from 166 site-years of data that met the above
2 criteria. The data were then organized into the following seven categories:
3
4 I. The occurrence of 8-h daily maximum averages greater than 0.08 ppm
5 and less than 0.09 ppm;
6
7 n. The occurrence of 8-h daily maximum averages greater than
8 0.08 ppm but less than or equal to 0.082 ppm, which contained
9 only hourly average concentrations greater than 0.08 ppm but less
10 than or equal to 0.082 ppm;
11
12 m. 8-h daily maximum averages greater than 0.08 ppm, which
13 contained hourly average concentrations less than 0.09 ppm;
14
15 IV. 8-h daily maximum averages greater than 0.08 ppm and less than
16 0.09 ppm, which contained at least 1 hourly average concentration
17 greater man or equal to 0.09 ppm but less than 0.10 ppm;
18
19 V. 8-h daily maximum averages greater than 0.08 ppm and less than
20 0.09 ppm, which contained at least 1 hourly average concentration
21 greater than or equal to 0.10 ppm;
22
23 VI. 8-h daily maximum averages less than 0.08 ppm, which contained
24 at least 1 hourly average concentration greater than or equal to
25 0.09 ppm but less than 0.10 ppm; and
26
27 VII. 8-h daily maximum averages less than 0.08 ppm, which contained
28 at least 1 hourly average concentration greater man or equal to
29 0.10 ppm,
30
31 Figure 4-26 summarizes the results of the analysis. The results indicated that there was a
32 poor relationship between the value of the 8-h daily maximum average concentration and the
33 frequency of occurrence of hourly average concentrations within specific ranges (e.g.,
34 between 0.09 and 0.10 ppm). In no case could the authors identify a monitoring site that
35 experienced the "square-wave" type of exposure that was described in Category n (i.e., the
36 occurrence of 8-h daily maximum averages greater than 0.08 ppm but less than or equal to
37 0.082 ppm, which contained only hourly average concentrations greater than 0.08 ppm but
38 less than or equal to 0.082 ppm). Lefohn and Foley (1993) concluded that the "square
39 wave" exposures used in the 6.6-h duration human health effects experiments were not found
40 under ambient conditions. The authors identified 453 additional exposure regimes, where the
December 1993 4-94 DRAFT-DO NOT QUOTE OR COTE
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Categories
Figure 4-26. The number of occurrences for each of the seven categories described in
text.
Source: Lefohn and Foley (1993).
1
2
3
4
5
6
7
8
9
10
11
12
13
8-h daily maximum average was less than 0.08 ppm but experienced maximum hourly
average concentrations greater than or equal to 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, lexicological studies of animals,
and plant response and yield have been considered previously (U.S. Environmental
Protection Agency, 1986). Controlled human exposure studies involving Oj and O3 + PAN
December 1993 4.0* DRAFT-DO NOT OTTOTP rat rrm
-------
1 are discussed elsewhere in this document (Section 7.2.6.3). Some effects on respiratory
2 parameters have been reported in one study, but not in others. However, the PAN
3 concentrations used in these studies have been well above the maximum ambient
4 concentrations usually experienced within die Los Angeles Basin many years ago (U.S.
5 Environmental Protection Agency, 1986) and, most especially, above the maximum ambient
6 concentrations in the more recent measurements considered in this section.
7 The PANs are of importance as reservoirs for NC^ as NOX is depleted relative to
§ VOCs in plumes moving downwind into less polluted areas (Section 3.2.4). In performance
9 evaluation of ozone air quality models, measured concentrations of PANs are useful in model
10 evaluation (Section 3.6.4.2).
11 In the previous air quality criteria for O3 and other photochemical oxidants (U.S.
12 Environmental Protection Agency, 1986), extensive tabulations of PAN and peroxypropionyl
13 nitrate (PPN), CH3CH2C(O)OONO2, concentrations were given based on measurements
14 made between 1965 and 1981 based on references up to 1983. In the present work,
15 references starting in 1983 up to the present are used for measurements of PANs in urban
16 and rural locations. The urban area measurements are from the United States, Canada,
17 France, Greece, and Brazil. The use of measurements from aboard serve to illustrate or
IS' support certain U.S. results as well as to demonstrate the widespread presence of PANs in
19 the atmosphere.
m
21 4.9.2 Urban Area Peroxyacetyl Nitrate Concentrations
22 The prior criteria document for ozone and other photochemical oxidants contains for
23 urban sites a number of tables tabulating measurements of PAN, peroxypropionyl nitrate
24 (PPN), the PPN to PAN ratios, and the PAN to 03 ratios (Altshuller, 1983; U.S.
25 Environmental Protection Agency, 1986). Based on comparisons of PAN measurements in
26 Eos Angeles in 1980 with those made in the 1960s, it was uncertain whether PAN
27 concentrations had decreased. In tile Los Angeles area, the average and maximum PAN
28 concentrations reported ranged from 1.6 to 31 ppb and from 6 to 214 ppb. The wide
29 variations at least in part was associated with the range of years, different seasons, and
30 differing average times among studies. The PPN to PAN ratios in Los Angeles on average
31 ranged among studies from 0.15 to 0.2, whereas the PAN to O3 ratios on average ranged
1993 4193 DRAFT-DO NOT QUOTfi
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1 among studies from 0.04 to 0.2. In the earlier PAN measurement results, studies conducted
2 in die South Coast Air Basin of California predominated.
3 The average PAN concentrations measured in other cities usually were lower than in
4 the Los Angeles area, whereas the maximum PAN concentrations overlapped with the lower
5 end of range in Los Angeles. The PPN and PAN ratios in other cities ranged from 0.1 to
6 0.4, while the PAN to Oj ratios were in the 0.01 to 0.05 range.
7 Seasonally, PAN to O3 ratios tended to be somewhat higher in the winter. The diurnal
8 characteristics of O3 and of PAN were similar, but not identical.
9 The recent urban area measurement results are tabulated in Table 4-19. The earlier
10 maximum PAN concentrations reported usually were substantially higher than those given hi
11 Table 4-19. A possible exception occurs for the Claremont, CA, results. Measurements of
12 PAN and PPN were made in 1989 and 1990 at sites downwind of Los Angeles: Perrin,
13 90 km east-southeast, and Palm Springs, 120 km east of Los Angeles (Grosjean and
14 Williams, 1992). The concentrations of PAN and PPN were high, and the concentration
IS maxima occurred during the evening hours consistent with downwind transport from the
16 Los Angeles area rather man local sources.
17 In Southern California, the maximum PAN concentrations appear to be more evenly
18 distributed spatially during the fall than during the summer (Williams and Grosjean, 1990).
19 At coastal and central locations, the PAN maxima during the fall were comparable to those
20 observed at inland locations during the summer.
21 As observed previously, PAN concentrations in other U.S. cities as well as cities in
22 other countries tend to be substantially lower than in Los Angeles and its surrounding urban
23 areas (Table 4-19). An exception occurs for the measurements from Paris (Tsalkoni et al.,
24 1991). Maximum PAN concentrations in the 20 to 35 ppb range were observed.
25 In recent measurements in Atlanta, GA, at the Georgia Institute of Technology (GIT)
26 campus site made in 1992, not only were PAN and PPN measured, but very occasionally
27 peroxymethacryloyl nitrate (MPAN) CH.2=C(CH$ C(O)OONO2 was observed (Williams
28 et al., 1993). Maximum diurnal concentrations of PANs and 63 occur in late afternoon and
29 early evening. The average MPAN concentration was 0.3 ppb, and the maximum value was
30 0.5 ppb and constituted about 15 % of the concurrent PAN concentrations. MPAN is a
31 product of the atmospheric photooxidation of local biogemc sources of isoprene.
December 1993 4.97 DRAFT-DO NOT QUOTE OR CITE
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TABLE 4-19. SUMMARY OF MEASUREMENTS OF PEROXYACETYL NITRATE
AND PEROXYPROPIONYL NITRATE IN URBAN AREAS
I
t>
oi
g
g
H
§'
55
9
O
c
2
r>
Site
Long Beach, CA
Anaheim, CA
Los Angeles, CA
Burbank, CA
Azusa, CA
Claremont, CA
Perrin, CA
Palm Springs, CA
Downey, CA
Boulder, CO
Denver, CO
Houston, TX
Philadelphia, PA
Staten Island, NY
Atlanta, GA
Edmonton, Alberta,
Canada
Calgary, Alberta,
Canada
University of
Calgary, Alberta,
Canada
Simcoe, Ontario,
Canada
Months/
Year
6-12/1987
6-12/1987
6-12/1987
6-12/1987
6-9/1987
6-9/1987
6/1989 to 6/1990
6/1989 to 6/1990
2/1984
5-6 and
8-9/1987
3/1984
3/1984
4/1983
4/1983
7-8/1992
12/1983 to 4/1984
7/1981 to 2/1982
10/1980 to 8/1981
6/1980 to 3/1981
Number
of Days
Sampled
16
14
16
16
11
10
NAa
NAa
10
12
45
9
9
19
7
36
66
213
175
191
PAN
Concentration
(ppb)
Average/Mean
NAa
NAa
NAa
NAa
NAa
NAa
1.6
1.6
1.2
0.63
0.59
0.64
0.75
1.1
1.6
0.71
0.14
0.22
1.3
Maximum
16
19
13
19
13
30
9.1
7.6
6.7
2.0
3.8
2.0
7.9
3.7
5.5
2.9
7.5
6.6
2.4
5.6
PPN
Concentration
(ppb)
Average/Mean
NAa
NAa
NAa
NAa
NAa
NAa
NAa
NAa
0.06
0.08
0.07
0.02
0.045
0.14
0.21
0.14
NAa
NAa
NAa
NAa
Maximum
NAa
NAa
NAa
NAa
NAa
NAa
0.73
0.42
0.40
0.3
0.6
0.09
0.54
0.50
0.90
0.37
NAa
NAa
NAa
NAa
Reference
Williams and Grosjean (1990)
Williams and Grosjean (1990)
Williams and Grosjean (1990)
Williams and Grosjean (1990)
Williams and Grosjean (1990)
Williams and Grosjean (1990)
Grosjean and Williams (1992)
Grosjean and Williams (1992)
Singh and Salas (1989)
Ridley et al. (1990)
Singh and Salas (1989)
Singh and Salas (1989)
Singh and Salas (1989)
Singh and Salas (1989)
Williams et al. (1993)
Peake et al. (1988)
Peake and Sandhu (1983)
Peake and Sandhu (1983)
Corkum et al. (1986)
-------
S
>
TABLE 4-19 (cont'd). SUMMARY OF MEASUREMENTS OF PEROXYACETYL NITRATE
AND PEROXYPROPIONYL NITRATE IN URBAN AREAS
1
s
u>
Site
Rio de Janeiro
Vila Isabel
PUC/RJ
Athens, Greece
Paris, France
Months/
Year
7/1985
7/1985
2-11/85
11/85-11/86
Number
of Days
Sampled
8
4
113
NAa
PAN
Concentration
(ppb)
Average/Mean
NAa
NAa
NAa
1.1
Maximum
5.4
3.3
3.7
20.5
PPN
Concentration
(ppb)
Average/Mean
NAa
NAa
NAa
NAa
Maximum
1.0
0.6
NAa
NAa
Reference
Tanner et al. (1988)
Tanner et al. (1988)
Tsani-Bazaca et al. (1988)
Tsalkani et al. (1991)
*NA = Not available.
i
-------
1 In a study in Rio de Janeiro made to investigate the effects of the use of ethanol or
2 ethanol-containing fuel on PAN concentrations, the maximum PAN concentration reached
3 5.4 ppb (Tanner et al., 1988). However, this maximum concentration is well below the
4 maximum concentrations reported in and around Los Angeles, and it falls within the
5 maximum PAN values reported for a number of other cities hi Table 4-19.
6
1 4.9.3 Concentration of Perox) acetyl Nitrate and Peroxypropionyl Nitrate
2 in Rural Areas
3 Prior measurements of nonurban PAN and PPN concentrations and PAN to 03 ratios
4 are available (Altshuller, 1983; U.S. Environmental Protection Agency, 1986), At nonurban
5 sites, not impacted by urban plumes, PAN and PPN concentrations are much lower than in
6 urban areas. Average PAN concentrations ranged between 0.1 and 1 ppb, while the PAN to
7 03 ratios were at or below 1 %.
8 Concentrations of PAN, PPN, and other PANs have been reported (Table 4-20) at
9 Tanbark Flat, CA, 35 km northeast of Los Angeles, during 1989, 1990, and 1991 and at
10 Franklin Canyon, CA, 25 km west of Los Angeles, during 1991 (Grosjean and Williams,
11 1992; Grosjean et al., 1993). As indicated by the results tabulated in Table 4-20, the
12 concentrations were high at these mountain sites, the PPN to PAN ratios were relatively
13 high, and the concentration maxima occurred during the afternoon hours. These
14 concentration levels of PAN and PPN are attributed to downwind transport from the
15 Los Angeles urban area. The MPAN, CH2 = C(CH3)C(0)OONO2, was very occasionally
16 detected with average concentrations of 1.2 ppb at Tanbark Flat and 1.0 ppb at Franklin
17 Canyon in 1991.
18 At Tanbark Flat, the O3 and PAN diurnal concentration patterns were similar to those
19 in upwind urban areas. The PAN to O3 ratios at the Qj maximum were as follows: 1989,
20 0.05; 1990, 0.08; 1991, 0.05—all the ratios are within the same range as at sites in urban
21 areas in and around Los Angeles.
22 Other measurements of PAN and PPN or PAN are available over a period of years at
23 Niwot Ridge, CO, just west of the Denver-Boulder area, at Point Arena, CA, and at a forest
24 site, Scotia, PA (Ridley et al., 1990). The concentrations reported at all of these sites are
25 much lower than the mountain sites in California. The Niwot Ridge site, which does show
December 1993 4-100 DRAFT-DO NOT QUOTE OR CTTE
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TABLE 4-20. SUMMARY OF MEASUREMENTS OF PEROXYACETYL NITRATE
AND PEROXYPROPIONYL NITRATE IN RURAL AREAS
I
So
U)
^,
=5
—
o
W
^
|
g
s
O
cj
Site
Tanbark Flat, CA
Tanbark Flat, CA
Tanbark Flat, CA
Franklin Canyon, CA
Niwot Ridge, CO
Niwot Ridge, CO
Niwot Ridge, CO
Niwot Ridge, CO
Point Arena, CA
Point Arena, CA
Scotia, PA
Scotia, PA
Kananaskis Valley, Alberta,
Canada
Frijoles Mesa, NM
*NA = Not available.
Months/
Year
8-10/1989
8,9/1990
8/1991
9/1991
7/1984
6-7/1984
8-9/1984
6-7/1987
1/1984
Spring/1985
Summer/1986
6-8/1988
9/1979,
4/1982,
6-8/1982
10/1987-
1/1989
Number
of Days
Sampled
69
34
22
9
16
23
21
46
14
NAa
NAa
47
NAa
NAa
PAN
Concentration
(Ppb)
Average/Mean
2.9
4.8
2.8
1.6
0.28
»0.25
»0.25
0.81 (E)
0.21 (W)
0.12
0.05
»0.6
1.0
»0.5
0.26
Maximum
>16.1
22.0
12.8
7.0
2.3
NAa
NAa
3.2
1.1
NAa
NAa
NAa
2.3
1.9
PPN
Concentration
(ppb)
Average/Mean
0.75
0.76
0.43
0.18
0.016
NAa
NAa
0.08 (E)
0.01 (W)
0.005
NAa
NAa
NAa
NAa
NAa
Maximum
5.1
4.3
2.66
1.15
0.17
NAa
NAa
0.45
0.07
NAa
NAa
NAa
NAa
NAa
Reference
Williams and Grosjean
(1991)
Grosjean et al. (1993)
Grosjean et al. (1993)
Grosjean et al. (1993)
Singh and Salas (1989)
Fahey et al. (1986)
Fahey et al. (1986)
Ridley et al. (1990)
Singh and Salas (1989)
Ridley (1991)
Ridley (1991)
Buhr et al. (1990)
Paske et al. (1983)
Gafmey et al. (1993)
-------
1 the effects of easterly upslope flow of air parcels from Denver-Boulder, are still low
2 compared to the sites downwind of the urban Los Angeles area (Table 4-20).
3 The PAN concentrations at the Scotia, PA, rural site in the eastern United States tend
4 to be somewhat higher than the Niwot Ridge or Point Arena sites (Table 4-20). This
5 difference may relate to higher regional precursor concentration levels.
6
7
8 4.10 CONCENTRATION AND PATTERNS OF HYDROGEN PEROXIDE
9 IN THE AMBIENT ATMOSPHERE
10 Efforts to measure hydrogen peroxide (H2O2> began in the 1970s, but the early reports
11 of H2O2 concentrations above 10 ppb and even 100 ppb appear to be in error because of the
12 artifact H2O2 generated within the presence of O3 (Section 3.5.1.3). Subsequent
13 measurements of H2O2 in the 1980s resulted in maximum H2O2 concentrations at or below
14 5 ppb and mean concentrations at or below 1 ppb (Sakugawa et al, 1990).
15 Studies comparing more recent methods for measuring H2O2, which were conducted in
16 North Carolina, indicated differences among measurement methods in synthetic mixtures of
17 H202, including possible interferences, and in the ambient atmosphere of up to about ±25%
18 (Kleindienst et al., 1988). However, results from the same study from mixtures irradiated in
19 a smog chamber produced larger differences among methods, especially with the luminol
20 technique compared with the fluorescence techniques and with tunable-diode laser absorption
21 spectroscopy. Another comparison study was conducted in California, resulting in
22 differences in methods for measuring H^ varying by a factor or two (Lawson ^ al,, 1988).
23 In the measurements of H2O2 discussed below, the cryogenic fluorescence method or the
24 scrubber-coil fluorescence methods were generally used.
25 Based on interpretation of a compilation of H2O2 measurements made between
26 1984 and 1988 at a number of urban locations, at rural/remote locations, and on aircraft
27 flights, it was concluded that the higher H202 concentrations were associated with the
28 following measurement conditions: (1) in the afternoon hours, (2) during summer months,
29 (3) at rural locations, and (4) at lower latitudes (Sakugawa et al., 1990; Van Valin et al.,
30 1987). The H202 concentrations increase from the surface to the top of the boundary layer
31 (Daum et al., 1990). Available values for mean H2O2 concentrations at U.S. locations were
December 1993 4-102 DRAFT-DO NOT QUOTE OR CITE
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1 (1) summit of Whitetop Mountain, VA: summer, 0.80 ppb; winter, 0.15 ppb (Olszyna
2 et al., 1988); (2) summit of Whiteface Mountain, NY: 1986, 0.6 ppb; 1987; 0.8 ppb
3 (Mohnen and Kadlecek, 1989); and (3) Westwood, CA: summer, »1.0 ppb; winter,
4 0.2 ppb (Sakugawa and Kaplan, 1989). At Westwood, the highest correlation with various
5 parameters was found for solar radiation consistent with the higher H2O2 concentrations
6 being observed in the afternoon during the late spring and early summer months (Sakugawa
7 and Kaplan, 1989). In the same study, the average H2O2 concentrations were observed to
8 increase from Westwood, near the coast in the Los Angeles Basin, to Duarte (inland) and at
9 Daggett in the Mohave Desert and at Sky Mountain and Lake Gregory in the San Bernadino
10 Mountains. The ratios of Oj to H2O2 concentrations at the these sites were & 100,
11 In subsequent measurements, the same relationship in H2O2 concentrations between
12 Westwood and the other California sites listed above was observed (Sakugawa and Kaplan,
13 1993). Unlike the results at several urban sites and other mountain sites, it was reported that
14 the highest diurnal H2O2 concentrations at Lake Gregory in the San Bernadino Mountains
15 were observed during the nighttime hours (Sakaugawa and Kaplan, 1993).
16
17
18 4.11 CO-OCCURRENCE OF OZONE
19 4.11.1 Introduction
20 There have been several attempts to characterize air pollutant mixtures (Lefohn and
21 Tingey, 1984; Lefohn et al., 1987b). Pollutant combinations can occur at or above a
22 threshold concentration either together or temporally separated from one another. For
23 example, for characterizing the different types of cooccurrence patterns, Lefohn et al.
24 (I987b) grouped air quality data within a 24-h period starting at 0000 h and ending at
25 2359 h. Patterns that showed air pollutant pairs appearing at the same hour of the day at
26 concentrations equal to or greater than a minimum hourly mean value were defined as
27 simultaneous-only daily cooccurrences. When pollutant pairs occurred at or above a
28 minimum concentration during the 24-h period, without occurring during the same hour, a
29 sequential-only cooccurrence was defined. During a 24-h period, if the pollutant pair
30 occurred at or above the minimum level at the same hour of the day and at different hours
31 during the period, the cooccurrence pattern was defined as complex-sequential.
December 1993 4_103 DRAFT-DO NOT QUOTE OR GTTB
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1 A cooccurrence was not indicated if one pollutant exceeded the minimum concentration just
2 before midnight and the other pollutant exceeded the minimum concentration just after
3 midnight. As will be discussed below, studies of the joint occurrence of gaseous NCyC^
4 and SO2/O3 have concluded mat (a) the cooccurrence of two-pollutant mixtures lasted only a
5 few hours per episode, where an episode was defined by the threshold concentration used,
6 and (b) the time between episodes is generally long (i.e., weeks, sometimes months) (Lefohn
7 and Tingey, 1984; Lefohn et aL, 1987b).
8 For exploring the cooccurrence of 03 and other pollutants (e.g., acid precipitation,
9 acidic cloudwater, and acidic sulfate aerosols), there are limited data available. In most
10 cases, routine monitoring data are not available from which to draw general conclusions.
11 However, published results are reviewed and summarized for the purpose of assessing an
12 estimate of the possible importance of cooccurrence patterns of exposure.
13
14 4.11.2 Nitrogen Oxides
15 Ozone occurs frequently at concentrations equal to or greater than 0.03 ppm at many
16 rural and remote monitoring sites in the United States (Evans et al., 1983; Lefohn, 1984;
17 Lefohn and Jones, 1986). Therefore, for many rural locations in the United States, the
18 cooccurrence patterns observed by Lefohn and Tingey (1984) for 63 and NC^ were defined
19 by the presence or absence of N02. As anticipated, Lefohn and Tingey (1984) reported that
20 most of the sites analyzed experienced fewer than 10 cooccurrences (when both pollutants
21 were present at an hourly average concentration &O.Q5 ppm). However, the authors did
22 note that several urban monitoring sites in the southern California South Coast Air Basin
23 experienced more than 450 cooccurrences. The rural sites of Riverside, Fontana, and
24 Rubidoux, California had more man 100 cooccurrences. Denver, Colorado and San Jose,
25 California, also experienced more than 100 cooccurrences of O^NC^. Lefohn and Tingey
26 (1984) reported that for Rubidoux, because NO2 concentration maxima tended to peak in the
27 evenings or early morning, the cooccurrences were present at these times. For more
28 moderate areas of the country, Lefohn et al. (1987b) reported that even with a threshold of
29 0.03 ppm O3, the number of cooccurrences with NQ^ was small.
30
31
December 1993 4-104 DRAFT-DO NOT QUOTE OR CITE
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1 4.11.3 Sulfur Dioxide
2 Because elevated SO2 concentrations are mostly associated with industrial activities
3 (U.S. Environmental Protection Agency, 1992a), cooccurrence observations are usually
4 associated with monitors located near these types of sources. Lefohn and Tingey (1984)
5 reported that for the rural and nonrural monitoring sites investigated, most sites experienced
6 fewer than 10 cooccurrences of SO2 and O3. Only Rockport, Indiana and Paradise No, 21
7 (Kentucky) had more than 40 cooccurrences during the monitoring period (48 and 45,
8 respectively). The monitors at these two sites were influenced by the local sources. The
9 authors noted that at Fontana, California, there were numerous 63 episodes above 0.05 ppm
10 and there was a high probability mat when the SO2 hourly average concentrations rose above
11 0.05 ppm, both pollutants would be present at levels equal to or greater than 0.05 ppm.
12 Meagher et al. (1987) reported that several documented O3 episodes at specific rural
13 locations appeared to be associated with elevated SO2 levels. The investigators defined the
14 cooccurrence of O3 and SO2 to be when hourly mean concentrations were equal to or greater
15 than 0.10 ppm and 0.01 ppm, respectively. Upon reviewing the hourly mean O3 and SO2
16 data used by Lefohn et al. (19S7b), in 1980 (using a threshold of 0.05 ppm for both
17 pollutants) the Paradise No. 23 (KY), Giles County (TN), Murphy Hill (reported as Marshall
18 Co. by Meagher el al., 1987) (AL), and Saltillo (reported as Hardin Co. by Meagher et al.,
19 1987) (TN) sites experienced fewer than 7 days over a 153-day period for a cooccurrence of
20 any form (i.e., simultaneous-only, sequential, and complex-cooccurrence). Thus, as reported
21 by Lefohn et al. (1987b), the cooccurrence pattern of 63 and SO2 was infrequent.
22 The above discussion was based on the cooccurrence patterns associated with the
23 presence or absence of hourly average concentrations of pollutant pairs. Taylor el al. (1992)
24 have discussed the joint occurrence of O3, nitrogen, and sulfur in forested areas using
25 cumulative exposures of O3 with data on dry deposition of sulfur and nitrogen. The authors
26 concluded in their study that the forest landscapes with the highest loadings of sulfur and
27 nitrogen via dry deposition tended to be the same forests with the highest average
28 O3 concentrations and largest cumulative exposure. Although the authors concluded that the
29 joint occurrences of multiple pollutants in forest landscapes were important, nothing was
30 mentioned about the hourly cooccurrences of Og and SO2 or Oj and NO2.
31
December 1993 4-105 DRAFT-DO NOT QUOTE OR fTTR
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1 4.11.4 Acidic Sulfate Aerosols
2 Acid sulfates, which are usually composed of sulfuric acid, ammonium bisulfate, and
3 ammonium sulfate, have been measured at a number of locations in North America. Acidic
4 sulfate and neutralized species can accumulate and range in concentration from 0 to 50 /*g/m3
5 at a specific location or a number of locations simultaneously (lioy, 1989). For many
6 summertime studies, peaks of H2SO4 and/or H+ appear to be associated with the presence of
7 a slow-moving high pressure system (Lioy and Waldman (1989). Acid sulfates are found
8 primarily in the fine particle size range (<2.5 /im in diameter). Lioy (1989) reports that the
9 acidic sulfate concentrations measured in the summertime can be found at 20 /ig/m for over
10 an hour and can be found at high concentrations of 10 to 20 pg/m3 for 6 to 24 h at one or
11 more sites (lioy, 1989). Acidic sulfate aerosol concentrations can occur at concentrations in
12 the summertime above 10 /*g/m for periods greater than 5 h (Lioy, 1989), As has been
13 discussed earlier in this chapter, the highest O3 exposures for sites affected by
14 anthropogenically derived photoxidant precursors are expected to occur during the late spring
15 and summer months. Thus, the potential for O3 and acidic sulfate aerosols to cooccur at
16 some locations in some form (i.e., simultaneously, sequentially, or complex-sequentially) is
17 real. Our knowledge of the potential exposure of the cooccurrence of acidic sulfate aerosols
18 and O3 is limited because routine monitoring data for acidic aerosols are not available.
19 Information on the cooccurrence patterns is limited to research studies and some of the
20 results of these studies is provided in this section.
21 Spektor et al. (1991) investigated the effects of single- and multiday 03 exposures on
22 respiratory function in active normal children aged 8 to 14 years at a northwestern New
23 Jersey residential summer camp in 1988. During the investigation, the authors measured
24 daily levels of 1-h peak Oj and the 12-h average H+ concentrations. On 7 days the acid
25 aerosol concentrations (reported as H2S04) were higher than 10 /ig/m , reaching a 12-h
26 maximum of 18.6 /*g/m3. Figure 4-24 shows the relationship between daily maximum
27 O3 and daily 12-h average H+ concentrations. Thurston et al. (1992) have reported
28 occurrences in 1988 of maximum 24-h average concentrations of H+ as high as 18.7 ^g/m3
29 (Buffalo, New York) and a maximum daily hourly average concentration of 0.164 ppm.
30 Although lower than Buffalo, high O3 or H+ values were reported by the investigators for
31 Albany and White Plains, New York. It is unclear whether the O3 or H+ maximum
December 1993 4406 DRAFT-DO NOT QUOTE OR CTTE
-------
1 concentrations occurred simultaneously; however, it is clear that high concentrations could
2 occur either sequentially, complex-sequentially, or simultaneously. Evidence exists in the
3 literature indicating that hourly cooccurrences are experienced. Raizenne and Spengler
4 (1989) have described an episodic cooccurrence pattern in 1986 of high hourly averaged
5 concentrations of O3 and H2SO4 that occurred at a residential summer camp located on the
6 north shore of Lake Erie, Ontario, Canada (Figure 4-27). Thurston et al. (1994) have
7 conducted a study of ambient acidic aerosols in the Toronto, Ontario metropolitan area in
8 July and August of 1986, 1987, and 1988, and have reported on the fine particle
9 (da<2.5 ^m) samples collected twice per day. The authors reported that their results
10 indicated that acidic aerosol episodes (i.e., H+ & 100 nmol/m ) occurred routinely during
11 the summer months and that H peaks were correlated with sulfate episodes. Figure 4-28
f\ i
12 illustrates the relationship among SO4 , H , and O3.
13
300
290
280
260
«£ 270
oJ
*c 250
O
t5 240
1 23°
C 220
o
O 210
I 200
6 190
180
170
50
40
30
o
20
10
8
o
O
7 8 9 10 11 12 13 14 15 16 17 18 1
Time (h)
Figure 4-27. The co-occurrence pattern of O3 and H2SO4 for July 25, 1986.
Source: Raizenne and Spengler (1989).
December 1993
im
MAT rmrvrc rm nrm
-------
1986
1887
1888
August
.My
AngiHt
JJy
August
Figure 4-28. Sulfate, hydrogen ion, and ozone measured at Breadalbane St. (Site 3)
during July and August, 1986,1987, and 1988.
Source: Thurston et al. (1994),
1 4.11.5 Acid Precipitation
2 Concern has been expressed about the possible effects on vegetation from cooccurring
3 exposures of O3 and acid precipitation (Prinz et al., 1985; National Acid Precipitation
4 Assessment Program, 1987; Prinz and Krause, 1988). Little information has been published
5 concerning the cooccurrence patterns associated with the joint distribution of 03 and acidic
6 deposition (i.e., H+). In a nonpeer-reviewed paper, Lefohn and Benedict (1983) reviewed
7 the EPA's SAROAD monitoring data for 1977 through 1980 and, using National
8 Atmospheric Deposition Program (NADP) and Electric Power Research Institute (EPRI) wet
9 deposition data, evaluated the frequency distribution of pH events for 34 NADP and 8 EPRI
10 chemistry monitoring sites located across the United States. Unfortunately, there were few
11 sites where O3 and acidic deposition were comonitored.
12 As a result, Lefohn and Benedict (1983) focused their attention on O3 and acidic
13 deposition monitoring sites that were closest to one another. In some cases, the sites were as
14 far apart as 144 km. Using hourly O3 monitoring data, and weekly and event acidic
December 1993
4-108
DRAFT-DO NOT QUOTE OR CTTE
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1 deposition data from the NADP and EPRI databases, the authors identified specific locations
2 where the hourly mean Oj exposures were SO. 10 ppra and 20% of the wetfall daily or
3 weekly samples were below pH 4.0. Elevated levels of 03 were defined as hourly mean
4 concentrations equal to or greater than 0.10 ppm. Although for many cases, experimental
5 research results of acidic deposition on agricultural crops show few effects at pH levels
6 above 3.5 (NAPAP, 1987), it was decided to use a pH threshold of 4.0 to take into
7 consideration the possibility of synergistic effects of Oj and acidic deposition.
8 Based on their analysis, Lefohn and Benedict (1983) reported five sites where there
9 may be the potential for agricultural crops to experience additive, less than additive, or
10 synergistic (i.e., greater than additive) effects from elevated O3 and hydrogen ion exposures.
11 The authors stated that they believed, based on the available data, the greatest potential for
12 interaction between acid rain and O3 exposures in the United States, with possible effects on
13 crop yields, may be in the most industrial areas (e.g., Ohio and Pennsylvania). However,
14 they cautioned that, because no documented evidence existed to show that pollutant
15 interaction had occurred under field growth conditions and ambient exposures, their
16 conclusions should only be used as a guide for further research.
17 In their analysis, Lefohn and Benedict (1983) found no colocated sites. The authors
18 rationalized that data from non-co-monitoring sites (i.e., O3 and acidic deposition) could be
19 used because O3 exposures are regional in nature. However, work by Lefohn et al. (1988a)
20 has shown that hourly mean 03 exposures vary from location to location within a region and
21 that cumulative indices, such as the percent of hourly mean concentrations equal to or greater
22 than 0.07 ppm, do not form a uniform pattern over a region. Thus, extrapolating hourly
23 mean O3 concentrations from known locations to other areas within a region may provide
24 only qualitative indications of actual 63 exposure patterns.
25 In the late 1970s and the 1980s, both the private sector and the government funded
26 research efforts to better characterize gaseous air pollutant concentrations and wet deposition.
27 The event-oriented wet deposition network, EPRI/Utility Acid Precipitation Study Program,
28 and the weekly oriented sampling network (NADP) provided information that can be
29 compared with hourly mean concentrations of O3 collected at several comonitored locations.
30 No attempt was made to include hydrogen ion cloud deposition information. In some cases,
31 for mountaintop locations (e.g., Clingman's Peak, Shenandoah, Whiteface Mountain, and
December 1993 4.1 no DRAFT-DO NOT OITOTR cm
-------
1 Whitetop), the hydrogen ion cloud water deposition is greater than the hydrogen ion
2 deposition in precipitation (Mohnen, 1988) and the cooccurrence patterns associated with
3 O3 and: cloud deposition will be different than those patterns associated with Oj and
4 deposition in precipitation.
5 Smith and Lefohn (1991) explored the relationship between O3 and hydrogen ion in
6 precipitation, using data from sites which monitored both Qj and wet deposition
7 simultaneously and within one minute latitude and longitude of each other. The authors
8 reported that individual sites experienced years hi which both hydrogen ion deposition and
9 total O3 exposure were at least moderately high (i.e., annual H+ deposition 5:0.5 kg ha"1
10 and an annual O3 cumulative sigmoidally-weighted exposure (W126) value S50 ppm-h).
11 With data compiled from all sites, it was found that relatively acidic precipitation (pH £4.31
12 on a weekly basis or pH £4.23 on a daily basis) occurred together with relatively high
13 O3 levels (i.e., W126 values SO.66 ppm-h for die same week or W126 values S0.18 ppm-h
14 immediately before or after a rainfall event) approximately 20% of the time, and highly
15 acidic precipitation (i.e, pH £4.10 on a weekly basis or pH £4.01 on a daily basis)
16 occurred together with a high 03 level (i.e., W126 values ^1.46 ppm-h for the same week
17 or W126 values £0.90 ppm-h immediately before or after the rainfall event) approximately
18 6% of the time. Whether during the same week or before, during, or after a precipitation
19 event, correlations between O3 level and pH (or H+ deposition) were weak to nonexistent.
20 Sites most subject to relatively high levels of both hydrogen ion and O3 were located in the
21 eastern portion of the United States, often in mountainous areas.
22
23 4.11.6 Acid Cloudwater
24 In addition to the cooccurrence of O3 and acid precipitation, results have been reported
25 on the cooccurrence of 03 and acidic cloudwater in high-elevation forests. Vong and
26 Guttorp (1991) characterized the frequent O3-only and pH-only single-pollutant episodes, as
27 well as the simultaneous and sequential cooccurrences of O3 and acidic cloudwater. The
28 authors reported that both simultaneous and sequential cooccurrences were observed a few
29 times each month above cloud base. Episodes were classified by considering hourly
30 O3 average concentrations SO.07 ppm ami cloudwater events with pH £3.2. The authors
31 reported that simultaneous occurrences of 0$ and pH episodes occurred 2-3 times per month
Bteembar 1993 4-110 DRAFT-DO NOT QUOTE OR CITE
-------
1 at two southern sites (Mitchell, NC and Whitetop, VA) and the two northern sites (Whiteface
2 Mountain, NY and Moosilauke, NH) averaged 1 episode/month. No cooccurrences were
3 observed at the central Appalachian site (Shenandoah, VA), due to a much lower cloud
4 frequency. Vong and Guttorp (1991) reported that the simultaneous occurrences were
5 usually of short duration (mean 1.5 h/episode) and were followed by an O3-only episode.
6 As would be expected, O3-only episodes were longer than cooccurrences and pH episodes,
7 averaging an 8-h duration.
8
9
10 4.12 SUMMARY
11 Ozone is an omnipresent compound that is measured at levels above the minimum
12 detectable level at all monitoring locations in the world. Although all 03 and other
13 photochemical oxidant-induced effects on vegetation and ecosystems, as well as human
14 health, rely on an accurate determination of exposure through knowledge of O3 and other
15 photochemical oxidant concentrations, most of the human and welfare effects research is
16 focused on O3 exposures (e.g., hourly average concentration and duration of exposure).
17 To obtain a better understanding of the potential for ambient O3 exposures affecting human
18 health and vegetation, hourly average concentration information was summarized for urban,
19 rural forested, and rural agricultural areas in the United States.
20 The distribution of Oj or its precursors at a rural site near an urban source is affected
21 by wind direction (i.e., whether the rural site is located up- or down-wind from the source).
22 It is difficult to apply land-use designations to the generalization of exposure regimes that
23 may be experienced in urban versus rural areas, because the land use characterization of
24 "rural" does not imply that a specific location is isolated from anthropogenic influences.
25 Rather, the characterization only implies the existing use of the land. Because it is possible
26 for urban emissions, as well as O3 produced from urban area emissions, to be transported to
27 more rural downwind locations, elevated O3 concentrations can occur at considerable
28 distances from urban centers. Urban O3 concentration values are often depressed because of
29 titration by nitric oxide. Because of the absence of chemical scavenging, O3 tends to persist
30 longer in nonurban than in urban areas and exposures may be higher than in urban locations.
December 1993 4-111 DRAFT-DO NOT QUOTE OR CITE
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1 For purposes of using air quality data for assessing human health and vegetation effects,
2 it is important to distinguish among concentration, exposure, and dose. For human health
3 considerations, the following definitions are used:
4
5 1. The "concentration" of a specific air pollutant is the amount of mat
6 material per unit volume of air. Air pollution monitors measure pollutant
7 concentrations, which may or may not provide accurate exposure estimates.
8
9 2. The term "exposure" is defined as any contact between an air contaminant
10 of a specific concentration and the outer (e.g., skin) or inner (e.g.,
11 respiratory tract epithelium) surface of the human body. Exposure implies
12 the simultaneous occurrence of the two events.
13
14 Similar to human health considerations for vegetation, concentrations of airborne
15 contaminants are considered to be exposure when they are experienced by a plant. For the
16 purposes of vegetation, this chapter has adopted the concept mat dose is the amount of
17 pollutant absorbed by the plant. Because most of the data presented in this chapter are from
18 fixed monitors, dose is not addressed.
19 For vegetation, as indicated in Chapter 5 (Section 5.5), extensive research has focused
20 on identifying exposure indices with a firm foundation on biological principles. Many of
21 these exposure indices have been based on research results indicating that the magnitude of
22 vegetation responses to air pollution is more an effect of the magnitude of the concentration
23 than the length of the exposure. For O3, the short-term, high concentration exposures have
24 been identified by many researchers as being more important than long-term, low
25 concentration exposures (see Chapter 5 for further discussion). Similarly, for human health
26 considerations, results using controlled human exposures have shown the possible importance
27 of concentration in relation to duration of exposure and inhalation rate.
28 In summarizing the hourly average concentrations in this chapter, specific attention is
29 given to the relevance of the exposure indices used. For example, for human health
30 considerations, concentration (or exposure) indices such as the daily maximum 1-h average
31 concentrations, as well as the number of daily maximum 4-h or 8-h average concentrations
32 above a specified threshold, are used to characterize information in the population-oriented
33 locations. For vegetation, several different types of exposure indices are used. Because
34 much of the NCLAN exposure information is summarized in terms of 7-h average
December 1993 4-112 DRAFT-DO NOT QUOTE OR CITE
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1 concentrations, this exposure index is used. However, because peak-weighted, cumulative
2 indices (i.e., exposure parameters that sum the products of hourly average concentrations
3 multiplied by time over an exposure period have shown considerable promise in relating
4 exposure and vegetation response (see Section 5.5), several exposure indices that use either a
5 threshold or a sigmoidally weighted scheme are used in this chapter to provide insight
6 concerning the 03 exposures that are experienced at a select number of rural monitoring sites
7 in the United States. The peak-weighted cumulative exposure indices such as the SUM06
8 (the sum of all hourly average concentrations equal to or greater than 0.06 ppm), SUM08
9 (the sum of all hourly average concentrations equal to or greater than 0.08 ppm), and W126
10 (the sum of the hourly average concentrations that have been weighted according to a
11 sigmoid function that is theoretically based on a hypothetical vegetation response) are used.
12 Ozone hourly average concentrations have been recorded for many years by the State
13 and local air pollution agencies who report their data to the U.S. Environmental Protection
14 Agency. The 10-year (1983 to 1992) composite average trend for the second highest daily
15 maximum hourly average concentration during the 03 season for 509 trend sites and a subset
16 of 196 NAMS sites, shows that the 1992 composite average for the trend sites is 21 % lower
17 than the 1983 average and 20% lower for the subset of NAMS sites. The 1992 value is the
18 lowest composite average of the past ten years. The 1992 composite average is significantly
19 less than all the previous nine years, 1983 to 1991. The relatively high Oj concentrations in
20 1983 and 1988 were likely attributable in part to hot, dry stagnant conditions in some areas
21 of the country that were especially conducive to O3 formation.
22 Between 1991 and 1992, the composite mean of the second highest daily maximum 1-h
23 03 concentrations decreased 1% at the 672 sites and 6% at the subset of 222 NAMS sites.
24 Between 1991 and 1992, the composite average of the number of estimated exceedances of
25 the O3 standard decreased by 23% at the 672 sites, and 19% at the 222 NAMS sites.
26 Nationwide VOC emissions decreased 3% between 1991 and 1992 (U.S. Environmental
27 Protection Agency, 1993). The composite average of the second daily maximum
28 concentrations decreased in eight of the ten EPA Regions between 1991 and 1992, and
29 remained unchanged in Region YE, Except for Region YE, the 1992 regional composite
30 means are lower than the corresponding 1990 levels.
December 1993 4413 DRAFT-DO NOT QUOTE OR CITE
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1 Information is provided in the Chapter on methods used for investigating techniques for
2 adjusting O3 trends for meteorological influences. Historically, the long-term Oj trends in
3 the United States characterized by the U.S. Environmental Protection Agency have
4 emphasized air quality statistics that are closely related to the NAAQS. Information is
5 provided on the use of alternative indices. Besides the U.S. Environmental Protection
6 Agency, additional investigators have assessed trends at several locations in the United States
7 and information is provided for both urban and rural areas.
8 Interest has been expressed in characterizing O3 exposure regimes for sites experiencing
9 daily maximum 8-h concentrations above specific thresholds (e.g., 0.08 or 0.10 ppm).
10 Documented evidence has been published showing the occurrence, at some sites, of
11 multihour periods within a day of G, at levels of potential health effects. While most of
12 these analyses were made using monitoring data collected from sites in or near nonattainment
13 areas, one analysis showed that at five sites, two in New York state, two in rural California,
14 and one in rural Oklahoma, an alternative O3 standard of an 8-h average of 0.10 ppm would
15 be exceeded even though the existing I-h standard would not be. The study indicated the
16 occurrence at these five sites, none of which was in or near a nonattainment area, of
17 O3 concentrations showing only moderate peaks but showing multihour levels above
18 0.10 ppm.
19 An important question is whether an improvement in O^ levels would produce
20 distributions of 1-h O3 that result in a broader diurnal profile than those seen in high-oxidant
21 urban areas where O3 regimes contain hourly average concentrations with sharper peaks.
22 The result would be an increase in the number of exceedances of daily maximum 8-h average
23 concentrations ^0.08 ppm, when compared to those sites experiencing sharper peaks. One
24 research effort observed, using aerometric data at specific sites, how 03 concentrations
25 change when the sites change compliance status. One of the parameters examined was 4-h
26 daily maxima. The number of exceedances for a specific dairy maximum average
27 concentration tended to decrease as fewer exceedances of the current 1-h standard were
28 observed at a given site. The number of occurrences of the daily maximum 4-h average
29 concentration 2:0.08 ppm and the number of exceedances of the current form of the standard
30 had a positive, weak correlation (r = 0.51). The investigators reported few changes in the
31 shape of the average diurnal patterns as sites changed attainment status. The lack of a
December 1993 4-114 DRAFT-DO NOT QUOTE OR CITE
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1 change in shape may have explained why the investigators could not find evidence that the
2 number of occurrences of the daily maximum 4-h average concentration 3:0.08 ppm
3 increased when the sites experienced few high hourly average concentrations.
4 There has been considerable interest in possibly substituting one index for another when
5 attempting to relate O3 exposure with an effect. For example, using O3 ambient air quality
6 data, the number of exceedances of 0.125 ppm and the number of occurrences of the daily
7 maximum 8-h average concentrations ^0.08 ppm have been compared with the result that a
8 positive correlation (r = 0.79) existed between the second-highest 1-h daily maximum in a
9 year and the expected number of days with an 8-h daily maximum average concentration
10 >0.08 ppm O3. However, there was not much predictive strength in using one 63 exposure
11 index to predict another was not strong. Similarly, the maximum 3-mo SUM06, second
12 highest daily maximum hourly average concentration, and second highest daily maximum 8-h
13 average concentration exposure indices were compared. For the rural agricultural and forest
14 sites, the relationships among the indices were not strong.
15 One of the difficulties in attempting to use correlation analysis between indices for
16 rationalizing the substitution of one exposure index for another for predicting an effect (e.g.,
17 SUM06 versus the second highest daily maximum hourly average concentration) is the
18 introduction of the error associated with estimating levels of one index from another.
19 Evidence has been presented in the literature for recommending that if a different exposure
20 index (e.g., second highest daily maximum hourly average concentration) is to be compared
21 to, for example, the SUM06 for adequacy in predicting crop loss, then the focus should be
22 on how well the two exposure indices predict crop loss using the effects model that is a
23 function of the most relevant index and not on how well the indices predict one another.
24 Less error would be introduced if either of the two indices were used directly in the
25 development of an exposure-response model.
26 The U.S. EPA has indicated that a reasonable estimate of natural O3 background
27 concentration near sea-level in the United States today, for an annual average, is from
28 0.020 to 0.035 ppm. This estimate included a 0.010 to 0.015 ppm contribution from the
29 stratosphere and a 0.01 ppm contribution from photochemically-affected biogenic non-
30 methane hydrocarbons. In addition, the U.S. EPA estimated that an additional 0.010 ppm is
31 possible from the photochemical reaction of biogenic methane. The U.S. EPA concluded
December 1993 /i-i 11 nuAFT-nn NOT nunTB rvo rrn?
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1 that a reasonable estimate of natural O3 background concentration for a 1-h daily maximum
2 at sea-level in the United States during the summer is on the order of 0.03 to 0.05 ppm.
3 Reviewing data from sites that appear to be isolated from anthropogenic sources, it has been
4 reported that in almost all cases, (1) none of the sites experienced hourly average
5 concentrations SO.08 ppm and (2) the maximum hourly average concentrations were in the
6 range from 0.060 to 0.075 ppm. Using data from these sites, in the continental United States
7 and southern Canada, the 7-mo (April to October) average of the 7-h daily average
8 concentrations range from approximately 0.028 to 0.050 ppm. At an 03 monitoring site at
9 the Theodore Roosevelt National Park in North Dakota, 7-mo (April to October) averages of
10 the 7-h daily average concentrations of 0.038, 0.039, and 0.039 ppm, respectively, were
11 experienced in 1984, 1985, and 1986. These 7-mo seasonal averages (i.e., 0.038 and
12 0.039 ppm) appear to be representative of values that may occur at other fairly clean sites in
13 the United States and other locations in the Northern Hemisphere.
14 Diurnal variations are those that occur during a 24-h period. Diurnal patterns of
15 O3 may be expected to vary with location, depending on the balance among the many factors
16 affecting O3 formation, transport, and destruction. Although they vary with locality, diurnal
17 patterns for 03 typically show a rise in concentration from low or levels near minimum
18 detectable amounts to an early afternoon peak. The diurnal pattern of concentrations can be
19 ascribed to three simultaneous processes: (1) downward transport of Oj from layers aloft;
20 (2) destruction of O3 through contact with surfaces and through reaction with nitric oxide
21 (NO) at ground level; and (3) in situ photochemical production of 03.
22 Although it might appear that composite diurnal pattern diagrams could be used to
23 quantify the differences of O3 exposures between sites, caution has been expressed in their
24 use for this purpose. The average diurnal patterns are derived from long-term calculations of
25 the hourly average concentrations, and the resulting diagram cannot adequately identify, at
26 most sites, the presence of high hourly average concentrations and thus may not adequately
27 be able to distinguish O3 exposure differences among sites. Unique families of diurnal
28 average profiles exist and it is possible to distinguish between two types of 03 monitoring
29 sites. A seasonal diurnal diagram provides the investigator with the opportunity to identify
30 whether a specific O3 monitoring site has more scavenging than any other site. For low-
31 elevation sites, intra-day variability is most significant due to the pronounced daily amplitude
December 1993 4-116 DRAFT-DO NOT QUOTE OR CITE
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1 in O3 concentration between the pre-dawn minimum and mid-afteraoon-to-early-evening
2 maximum, while inter-day variation is more significant in the high-elevation sites.
3 Seasonal variations in Qj concentrations in urban areas usually show the pattern of high
4 O3 in late spring or in summer and low levels in the winter. Because of temperature,
5 relative humidity, and seasonal changes in storm tracks from year to year, the general
6 weather conditions in a given year may be more favorable for the formation of O3 and other
7 oxidants than during the prior or following year. For example, 1988 was a hot and dry year
8 in which some of the highest Oj concentrations of the last decade occurred, while 1989 was
9 a cold and wet year in which some of the lowest concentrations occurred.
10 Several investigators have reported on the tendency for average Oj concentrations to be
11 higher in the second versus the third quarter of the year for many isolated rural sites. This
12 observation has been attributed to either stratospheric intrusions or an increasing frequency of
13 slow-moving, high-pressure systems that promote the formation of Oj. However, for several
14 clean rural sites, the highest exposures have occurred in the third quarter rather than in the
15 second. For rural O3 sites in the southeastern United States, the daily maximum 1-h average
16 concentration was found to peak during the summer months. For sites located in rural areas,
17 but not isolated from anthropogenic sources of pollution, the different patterns may be
18 associated with anthropogenic emissions of NOX and hydrocarbons.
19 Concentrations of O3 vary with altitude and with latitude. There appears to be no
20 consistent conclusion concerning the relationship between O3 exposure and elevation.
21 An important issue for assessing possible impacts of O3 at high-elevation sites that requires
22 further attention is the use of mixing ratios (e.g., ppm) instead of absolute concentration
23 (e.g., in units of micrograms per cubic meter) to describe O3 concentration. In most cases,
24 mixing ratios (e.g., ppm) or mole fractions are used to describe O3 concentrations. The
25 manner in which concentration is reported may be important when assessing the potential
26 impacts of air pollution on high-elevation forests. Concentration (in units of micrograms per
27 cubic meter) varies as a function of altitude. Although the change in concentration is small
28 when the elevational difference between sea level and the monitoring site is small, it becomes
29 substantial at high-elevation sites. Given the same part-per-million value experienced at both
30 a high- and low-elevation site, the absolute concentrations (i.e., micrograms per cubic meter)
31 at the two elevations will be different. Since both pollutants and ambient air are gases,
December 1993 4-117 DRAFT-DO NOT QUOTE OR CITE
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1 changes in pressure directly affect their volume. This pressure effect must be considered
2 when measuring absolute pollutant concentrations. Although these exposure considerations
3 are trivial at low-elevation sites, when one compares exposure-effects results obtained at
4 high-elevation sites with those from low-elevation sites, the differences may become
5 significant.
6 Most people in the United States spend a large proportion of their time indoors. Until
7 the early 1970s, very little was known about the O3 concentrations experienced inside
8 buildings. Even to date, the data base on this subject is not large and a wide range of
9 indoor/outdoor O3 concentration relationships can be found in the literature. Reported I/O
10 values for O3 are highly variable. A relatively large number of factors can affect the
11 difference in O3 concentrations between the inside of a structure and the outside air.
12 In general, outside air infiltration or exchange rates, interior air circulation rates, and interior
13 surface composition (e.g., rugs, draperies, furniture, walls) affect the balance between
14 replenishment and decomposition of O3 within buildings. Indoor/outdoor O3 concentration
15 ratios generally fall in the range from 0.1 to 0.7 and indoor concentrations of O3 will almost
16 invariably be less than outdoors.
17 It is important that accurate estimates of both human and vegetation exposure to 03 are
18 available for assessing the risks posed by the pollutant. Examples are provided on how both
19 fixed-site monitoring information and human exposure models are used to estimate risks
20 associated with O3 exposure. A short discussion is provided on the importance of hourly
21 average concentrations, used in the human health and vegetation experiments, mimicking as
22 closely as possible the "real world" exposures.
23 In many cases, the upper tail of the distribution, which represents those individuals
24 exposed to the highest concentrations, is frequently of special interest because the
25 determination of the number of individuals who experience elevated pollutant levels can be
26 critical for health risk assessments. This is especially true for pollutants for which the
27 relationship between dose and response is highly nonlinear.
28 Because, for most cases, it is not possible to estimate population exposure solely from
29 fixed-station data, several human exposure models have been developed. Some of these
30 models include information on human activity patterns (i.e., the microenvironments people
31 visit and the times they spend there). These models also contain submodels depicting the
December 1993 4-118 DRAFT-DO NOT QUOTE OR CITE
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1 sources and concentrations likely to be found in each microenvironment, including indoor,
2 outdoor, and in-transit settings.
3 A subgroup that has been studied by several investigators to assess the influence of
4 ambient air pollution on their respiratory health and function is children attending summer
5 camp. Because children are predominantly outdoors and relatively active while at camp,
6 they provide a unique opportunity to assess the relationships between respiratory health and
7 function and concurrent air pollution levels. Examples are provided on the type of exposure
8 patterns that children experience.
9 A personal exposure profile can be identified by using a personal exposure monitor.
10 Little data are available for individuals using personal exposure monitors. Results from a
11 pilot study demonstrated that fixed-site ambient measurements may not adequately represent
12 individual exposures. Outdoor Qj concentrations showed substantial spatial variation
13 between rural and residential regions. The study showed that the use of fixed-site
14 measurements could result in an error as high as 127%. In addition, the study showed that
15 models based on time-weighted indoor and outdoor concentrations explained only 40% of the
16 variability in personal exposures. The investigators concluded that contributions from
17 diverse indoor and outdoor microenvironments could estimate personal O3 exposure
18 accurately.
19 The field of human exposure modeling is relatively young, with the first rigorous
20 exposure modeling analyses appearing in the mid-1970s and the theoretical constructs
21 regarding human exposure to environmental pollution being published in the early 1980s.
22 Two distinct types of O3 exposure models exist: those that focus narrowly on predicting
23 indoor Qj levels and those that focus on predicting 03 exposures on a community-wide basis.
24 The following four distinct models address the prediction of 03 exposures on a community-
25 wide basis:
26 1. pNEM/C^ (bared on the NEM series of models)
27
28 2. SAI/NEM
29
30 3. REHBX
31
32 4. Event probability exposure model (EPEM)
33
December 1993 4.11Q DRAFT-DO NOT OTTOTP OP rrrp
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1 It is important to adequately characterize the exposure patterns that result in vegetation
2 and human health effects. In Chapter 5 (see Section 5.5), it has been pointed out that the
3 hourly average concentrations used in many of the high treatment experimental studies did
4 not necessarily mimic those concentrations observed under ambient conditions. Although the
5 ramifications of this observation on the effects observed is not clear, it was pointed out that
6 the highest treatments used in many of the vegetation open-top chamber experiments were
7 bimodal in the distribution of the hourly average concentrations. In other experiments
8 designed to assess the effects of C^ on vegetation, constant concentration (i.e., square wave)
9 exposures were implemented. As has been discussed in earlier sections of this Chapter,
10 "square wave" exposure regimes do not normally occur under ambient conditions. Similar
11 "square wave" exposures have been used in human health effects studies. In addition to the
12 exposures used at the highest treatment levels for vegetation experiments, there is concern
13 that the hourly average concentrations used in the charcoal-filtered control treatments may be
14 lower than those experienced at isolated sites in the United States or in other parts of the
15 world. Although the ramifications of using such exposure regimes is unclear, there is some
16 concern that the use of such levels may result in an overestimation of vegetation yield losses
17 when compared to treatments greater than the control treatment.
18 Published data on the concentrations of photochemical oxidants other than O3 in
19 ambient air are neither comprehensive nor abundant. A review of the data shows that PAN
20 and peroxypropionyl nitrate (PPN) are the most abundant of the non-Qj oxidants in ambient
21 air in the United States, other than the inorganic nitrogenous oxidants such as nitrogen
22 dioxide (NC^), and possibly nitric acid (HNO3). At least one study has reported that a
23 higher homologue of the series, peroxybenzoyl nitrate (PBzN), like PAN, is a kchrymator.
24 No unambiguous identification of PBzN in the ambient air of the United States has been
25 made.
26 Given the information available on PAN, the concentrations of PAN that are of most
27 concern are those to which vegetation could potentially be exposed, especially during
28 daylight hours in agricultural areas. These are followed hi importance by concentrations
29 both indoors and outdoors, in urban and nonurban areas, to which human populations could
30 potentially be exposed. Most of the available data on concentrations of PAN and PPN in
31 ambient air are from urban areas. The levels to be found in nonurban areas will be highly
December 1993 4-120 DRAFT-DO NOT QUOTE OR Cm
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1 dependent upon the transport of PAN and PPN or their precursors from urban areas, since
2 the concentrations of the NOX precursors to these compounds are considerably lower in
3 nonurban than in urban areas.
4 There nave been several attempts to characterize air pollutant mixtures. Pollutant
5 combinations can occur at or above a threshold concentration either together or temporally
6 separated from one another. Studies of the joint occurrence of gaseous NO2/O^ and SO2/O3
7 have concluded that (a) the cooccurrence of two-pollutant mixtures lasted only a few hours
8 per episode, and (b) the time between episodes is generally long (i.e, weeks, sometimes
9 months). Using hourly averaged data collected at rural sites for vegetation considerations,
10 the periods of cooccurrence represent a small portion of the potential plant growing period.
11 For human ambient exposure considerations, in most cases, the simultaneous cooccurrence of
12 NO2/O3 was infrequent. However, for several sites located in the southern California South
13 Coast Air Basin, more than 450 simultaneous cooccurrences of each pollutant, at hourly
14 average concentrations equal to or greater than 0.05 ppm, were present. Although the focus
15 of cooccurrence research has been on patterns associated with the presence or absence of
16 hourly average concentrations of pollutant pairs, some researchers have discussed the joint
17 occurrence of O3, nitrogen, and sulfur in forested areas, combining cumulative exposures of
18 O3 with data on dry deposition of sulfur and nitrogen. One study reported that several forest
19 landscapes with the highest dry deposition loadings of sulfur and nitrogen tended to
20 experience the highest average 03 concentrations and largest cumulative exposure. Although
21 the investigators concluded that the joint occurrences of multiple pollutants in forest
22 landscapes were important, nothing was mentioned about hourly cooccurrences of O3 and
23 SOj or Oj and NC^.
24 Our knowledge of the potential exposure of the cooccurrence of acidic sulfate aerosols
25 and O3 is limited because routine monitoring data for acidic aerosols are not available.
26 Information on the cooccurrence patterns is limited to research studies and some of the
27 results are provided in tins chapter. Acid sulfates, which are usually composed of sulfuric
28 acid, ammonium bisulfate, and ammonium sulfate, have been measured at a number of
29 locations in North America. Acidic sulfate and neutralized species can accumulate and range
30 in concentration from 0 to 50 pg/m3 at a specific location or a number of locations
•f i
31 simultaneously. For many summertime studies, peaks of H2SO4 and/or H appear to be
December 1993 4-171 DRAFT-DO NOT OTTOTE OP rrrp
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1 associated with the presence of a slow-moving high pressure system. Acid sulfates are found
2 primarily in the fine particle size range (< 2.5 /*m in diameter). The acidic sulfate
3 concentrations measured in the summertime can be found at 20 ^g/m for over an hour and
4 can be found at high concentrations of 10 to 20 ^ig/m for 6 to 24 h at one or more sites,
5 Acidic sulfate aerosol concentrations can occur at concentrations in the summertime above
6 10 /*g/m for periods greater than 5 h. The highest 63 exposures for sites affected by
7 anthropogenically derived photoxidant precursors are expected to occur during the late spring
8 and summer months. Thus, the potential for 03 and acidic sulfate aerosols to cooccur at
9 some locations in some form (i.e., simultaneously, sequentially, or complex-sequentially) is
10 real and requires further characterization.
1 1 Concern has been expressed about the possible effects on vegetation from cooccurring
12 exposures of 03 and acid precipitation. One study explored the relationship between O3 and
13 hydrogen ion in precipitation, using data from sites which monitored both O3 and wet
14 deposition simultaneously and within one minute latitude and longitude of each other. The
15 investigators reported that individual sites experienced years in which both hydrogen ion
16 deposition and total 63 exposure were at least moderately high (i.e., annual H deposition
17 ^0.5 kg ha*1 and an annual 03 cumulative sigmoidally-weighted exposure (W126) value
18 ^50 ppm-h). With data compiled from all sites, it was found that relatively acidic
19 precipitation (pH £4.31 on a weekly basis or pH £4.23 on a daily basis) occurred together
20 with relatively high 63 levels (i.e., W126 values ^0.66 ppm-h for the same week or W126
21 values 5: 0. 18 ppm-h immediately before or after a rainfall event) approximately 20% of the
22 time, and highly acidic precipitation (i.e, pH £4,10 on a weekly basis or pH £4.01 on a
23 daily basis) occurred together with a high 03 level (i.e. , W126 values ^ 1.46 ppm-h for the
24 same week or W126 values ^ 0.90 ppm-h immediately before or after the rainfall event)
25 approximately 6 % of the time. Whether during the same week or before, during, or after a
26 precipitation event, correlations between C^ level and pH (or H+ deposition) were weak to
27 nonexistent. Sites most subject to relatively high levels of both hydrogen ion and 63 were
28 located in the eastern portion of the United States, often in mountainous areas.
29 The cooccurrence of Qj and acidic cloudwater in high-elevation forests has been
30 characterized. The frequent O3-only and pH-only single-pollutant episodes, as well as the
December 1993 4-122 DRAFT-DO NOT QUOTE OR CITE
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1 simultaneous and sequential cooccurrences of 03 and acidic cloudwater, have been reported.
2 Both simultaneous and sequential cooccurrences were observed a few times each month
3 above cloud base.
4
5
December 1993 d-m DRAFT-DO NOT OTTOTR np rrro
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