Welfare Risk and Exposure Assessment for 

Ozone 

Second External Review Draft 

 
EPA-452/P-14-003a 

February 2014 

Welfare Risk and Exposure Assessment for Ozone 

Second External Review Draft

U.S. Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Risk and Benefits Group 

Research Triangle Park, North Carolina 27711 
DISCLAIMER

This draft document has been prepared by staff from the Risk and Benefits Group, Health and 

Environmental Impacts Division, Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency. Any findings and conclusions are those of the authors and do not necessarily reflect 

the views of the Agency. This draft document is being circulated to facilitate discussion with the Clean 

Air Scientific Advisory Committee to inform the EPA’s consideration of the ozone National Ambient 

Air Quality Standards. 

This information is distributed for the purposes of pre-dissemination peer review under 

applicable information quality guidelines. It has not been formally disseminated by EPA. It does not 

represent and should not be construed to represent any Agency determination or policy. 

Questions related to this preliminary draft document should be addressed to Travis Smith, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, C539-07, Research 

Triangle Park, North Carolina 27711 (email: smith.jtravis@epa.gov). 

 
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i 

TABLE OF CONTENTS 

1 INTRODUCTION.......................................................................................................... 1-1

1.1 HISTORY ........................................................................................................................ 1-3

1.2 CURRENT RISK AND EXPOSURE ASSESSMENTS: GOALS AND PLANNED 

APPROACH .................................................................................................................... 1-4

1.3 ORGANIZATION OF DOCUMENT ............................................................................. 1-5 

2 FRAMEWORK ............................................................................................................. 2-1

2.1 O3 CHEMISTRY ............................................................................................................. 2-2

2.2 SOURCES OF O3 AND O3 PRECURSORS ................................................................... 2-3

2.3 ECOLOGICAL EFFECTS .............................................................................................. 2-5

2.4 ECOSYSTEM SERVICES .............................................................................................. 2-7 

3 SCOPE ............................................................................................................................ 3-1

3.1 OVERVIEW OF RISK AND EXPOSURE ASSESSMENTS FROM PREVIOUS 

 REVIEW .......................................................................................................................... 3-2

3.1.1 Exposure Characterization ................................................................................... 3-3

3.1.2 Assessment of Risks to Vegetation ...................................................................... 3-4

3.2 OVERVIEW OF CURRENT ASSESSMENT PLAN .................................................... 3-5

3.2.1 Air Quality Considerations .................................................................................. 3-6

3.2.2 Relative Tree Biomass Loss and Crop Yield Loss .............................................. 3-8

3.2.3 Visible Foliar Injury ........................................................................................... 3-11

3.3 UNCERTAINTY AND VARIABILITY ....................................................................... 3-13 

4 AIR QUALITY CONSIDERATIONS ......................................................................... 4-1

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 OVERVIEW OF O3 MONITORING AND AIR QUALITY ......................................... 4-1

4.3 OVERVIEW OF AIR QUALITY INPUTS TO RISK AND EXPOSURE 

 ASSESSMENTS .............................................................................................................. 4-4

4.3.1 Air Quality Metrics .............................................................................................. 4-5

4.3.2 Ambient Air Quality Measurements .................................................................... 4-7

4.3.3 National-scale Air Quality Surfaces for Recent Air Quality ............................... 4-8

4.3.4 Air Quality Adjustments to Meet Existing Primary and Potential Alternative 

 Secondary O3 Standards .................................................................................... 4-10

4.4 QUALITATIVE ASSESSMENT OF UNCERTAINTY ............................................... 4-26

5 O3 RISK TO ECOSYSTEM SERVICES .................................................................... 5-1

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 SUPPORTING SERVICES ............................................................................................. 5-3

5.2.1 Net Primary Productivity ..................................................................................... 5-3
ii 

5.2.2 Community Composition and Habitat Provision ................................................. 5-4

5.3 REGULATING SERVICES ............................................................................................ 5-5

5.3.1 Hydrologic Cycle ................................................................................................. 5-5

5.3.2 Pollination ............................................................................................................ 5-6

5.3.3 Fire Regulation..................................................................................................... 5-6

5.4 PROVISIONING SERVICES ......................................................................................... 5-9

5.5 CULTURAL SERVICES .............................................................................................. 5-13

5.5.1 Non-Use Services............................................................................................... 5-14

5.6 QUALITATIVE ASSESSMENT OF UNCERTAINTY ............................................... 5-16

5.7 DISCUSSION ................................................................................................................ 5-20 

6 BIOMASS LOSS ............................................................................................................ 6-1

6.1 INTRODUCTION ........................................................................................................... 6-1

6.2 RELATIVE BIOMASS LOSS ........................................................................................ 6-2

6.2.1 Species-Level Analyses ....................................................................................... 6-6

6.3 COMMERCIAL TIMBER EFFECTS ........................................................................... 6-21

6.4 NON-TIMBER FOREST PRODUCTS ......................................................................... 6-31

6.4.1 Commercial Non-Timber Forest Products ......................................................... 6-34

6.4.2 Informal Economy or Subsistence Use of Non-Timber Forest Products .......... 6-36

6.5 AGRICULTURE ........................................................................................................... 6-38

6.5.1 Commercial Agriculture .................................................................................... 6-38

6.6 CLIMATE REGULATION ........................................................................................... 6-46

6.6.1 National Scale Forest Carbon Sequestration ...................................................... 6-46

6.6.2 Urban Case Study Carbon Storage .................................................................... 6-50

6.7 URBAN CASE STUDY AIR POLLUTION REMOVAL ............................................ 6-55

6.8 ECOSYSTEM-LEVEL EFFECTS ................................................................................ 6-59

6.8.1 Potential Biomass Loss in Federally Designated Areas .................................... 6-65

6.9 QUALITATIVE ASSESSMENT OF UNCERTAINTY ............................................... 6-66

6.10 DISCUSSION ................................................................................................................ 6-74 

7 VISIBLE FOLIAR INJURY ......................................................................................... 7-1

7.1 INTRODUCTION ........................................................................................................... 7-1

7.1.1 Ecosystem Services .............................................................................................. 7-4

7.2 NATIONAL-SCALE ANALYSIS OF FOLIAR INJURY ........................................... 7-10

7.2.1 Forest Health Monitoring Network .................................................................... 7-10

7.2.2 NOAA Palmer Z Drought Index ........................................................................ 7-12

7.2.3 Results ................................................................................................................ 7-13

7.3 SCREENING-LEVEL ASSESSMENT OF VISIBLE FOLIAR INJURY IN 
iii 

 NATIONAL PARKS ..................................................................................................... 7-20

7.3.1 Screening Assessment Methods ......................................................................... 7-21

7.3.2 Screening Assessment Results and Discussion .................................................. 7-28

7.3.3 Limitations and Uncertainty Characterization for Screening-Level 

 Assessment ......................................................................................................... 7-35

7.4 NATIONAL PARK CASE STUDY AREAS................................................................ 7-44

7.4.1 Great Smoky Mountains National Park ............................................................. 7-47

7.4.2 Rocky Mountain National Park ......................................................................... 7-55

7.4.3 Sequoia and Kings Canyon National Parks ....................................................... 7-62

7.5 QUALITATIVE ASSESSMENT OF UNCERTAINTY ............................................... 7-67

7.6 DISCUSSION ................................................................................................................ 7-72 

8 SYNTHESIS OF RESULTS .............................................................................................. 8-1 

8.1 INTRODUCTION ........................................................................................................... 8-1 

8.2 SUMMARY OF ANALYSES AND KEY RESULTS .................................................... 8-2 

8.2.1 National-Scale Analyses ........................................................................................ 8-3 

8.2.2 Case Study-Scale Analyses .................................................................................. 8-14 

8.3 PATTERNS OF RISK ................................................................................................... 8-16 

8.3.1 Risk Patterns Across or Between Geographic Areas ........................................... 8-17 

8.3.2 Risk Patterns Across Years .................................................................................. 8-21 

8.3.3 Risk Patterns Across Alternative W126 Standard Levels .................................... 8-23 

8.4 REPRESENTATIVENESS ........................................................................................... 8-27 

8.4.1 Species Representativeness .................................................................................. 8-27 

8.4.2 Geographic Representativeness ........................................................................... 8-28 

8.4.3 Temporal Representativeness .............................................................................. 8-29 

8.5 OVERALL CONFIDENCE IN WELFARE EXPOSURE AND RISK RESULTS ...... 8-30 

8.5.1 Uncertainties in Air Quality Analyses ................................................................. 8-30 

8.5.2 Uncertainties in Biomass Loss Analyses ............................................................. 8-31 

8.5.3 Uncertainties in Visible Foliar Injury Analyses ................................................... 8-32 

8.6 CONCLUSIONS............................................................................................................ 8-33 

9 REFERENCES ................................................................................................................... 9-1 
 iv 

LIST OF TABLES 

Table 4-1 Highest 2006-2008 Average W126 Concentrations in Observed and Existing 

Standard Air Quality Adjustment Scenarios; Highest 2006-2008 8-hour O3 Design 

Values in Observed and Potential Alternative Standard Air Quality Adjustment 

Scenarios ............................................................................................................ 4-14

Table 4-2 Summary of Qualitative Uncertainty Analysis of Key Air Quality Elements in the 

O3 NAAQS Risk Assessment ............................................................................ 4-27 

Table 5-1 Responses to NSRE Wildlife Value Questions ................................................... 5-4

Table 5-2 Area of Moderate to High-Fire Threat, Mixed Conifer Forest for Existing and 

Alternative Standard Levels (in km2) .................................................................. 5-8

Table 5-3 Area (km2) ‘At Risk’ of High Pine Beetle Loss at Various W126 

 Index Values ...................................................................................................... 5-12

Table 5-4 Tree Basal Area Considered ‘At Risk’ of High Pine Beetle Loss ByW126 Index 

Values after Just Meeting the Existing and Alternative Standard Levels (in 

millions of square feet) ...................................................................................... 5-13

Table 5-5 NSRE Responses to Non-Use Value Questions For Forests ............................. 5-15

Table 5-6 Value Components for WTP for Extensive Protection Program for Southern 

Appalachian Spruce-Fir Forests ......................................................................... 5-16

Table 5-7 Summary of Qualitative Uncertainty Analysis in Semi-Quantitative Ecosystem 

Services Assessments......................................................................................... 5-18 

Table 6-1 Relative Biomass Loss Functions for Tree Species ............................................. 6-4

Table 6-2 Relative Biomass Loss Functions for Crop Species ............................................ 6-4

Table 6-3 Comparison of Adult to Seedling Biomass Loss ................................................. 6-7

Table 6-4 Comparison of Seedling Biomass Loss to Adult Circumference ........................ 6-8

Table 6-5 Summary of Uncertainty in Seedling to Adult Tree Biomass Loss 

 Comparisons ........................................................................................................ 6-9

Table 6-6 Individual Species Relative Biomass Loss Values – Median, 75th Percentile, 

Maximum Percentages ....................................................................................... 6-16

Table 6-7 Mapping O3 Impacts to FASOMGHG Forest Types ......................................... 6-23

Table 6-8 Percent Relative Yield Loss for Forest Types by Region for 

 Modeled Scenarios ............................................................................................. 6-24

Table 6-9 Percent Relative Yield Gain for Forest Types by Region with Respect to the 

Existing Standard ............................................................................................... 6-25

Table 6-10 Percentage Changes in National Timber Prices ................................................. 6-29
 v 

Table 6-11 Consumer and Producer Surplus in Forestry, Million $2010 ............................ 6-31

Table 6-12 O3 Sensitive Trees and Their Uses ..................................................................... 6-32

Table 6-13 Quantity of NTFP Harvested on U.S. Forest Service and Bureau of Land 

Management Land ............................................................................................. 6-35

Table 6-14 Definition of FASOMGHG Production Regions and Market Regions ............. 6-39

Table 6-15 Mapping of O3 Impacts on Crops to FASOMGHG Crops ................................ 6-40

Table 6-16 Consumer and Producer Surplus in Agriculture (Million 2010$) ..................... 6-45

Table 6-17 Annualized Changes in Consumer and Producer Surplus in Agriculture and 

Forestry, 2010-2040, Million 2010$ (4% Discount Rate) ................................. 6-46

Table 6-18 Increase in Carbon Storage, MMtCO2e, Cumulative over 40 years .................. 6-48

Table 6-19 Tree Species with Available C-R Functions in Selected Urban Study Areas .... 6-52

Table 6-20 O3 Effects on Carbon Storage for Five Urban Areas over 25 Years 

 (in millions of metric tons) ................................................................................ 6-54

Table 6-21 Comparison of Pollutant Removal Between an Unadjusted Scenario and 

Alternative Simulations and Gains Between the Existing Standard and 

Alternatives (metric tons) .................................................................................. 6-58

Table 6-22 Percent of Total Basal Area Covered by 12 Assessed Tree Species ................. 6-60

Table 6-23 Grid Cells With No Data That Exceed W126 Index Values under Recent 

Conditions .......................................................................................................... 6-61

Table 6-24 Percent of Area Exceeding 2% Weighted Biomass Loss – Recent Conditions and 

Existing Standard ............................................................................................... 6-63

Table 6-25 Percent of Area Exceeding 2% Weighted Biomass Loss – Alternative W126 

Standard Levels .................................................................................................. 6-64

Table 6-26 Weighted RBL and Percent Cover in Class I Areas .......................................... 6-66

Table 6-27 Summary of Qualitative Uncertainty Analysis in Relative Biomass Loss 

Assessments ....................................................................................................... 6-68 

Table 7-1 Percent of Cover Category Exceeding W126 Index Values ................................ 7-4

Table 7-2 National Outdoor Activity Participation .............................................................. 7-8

Table 7-3 National Expenditures for Wildlife Watching, Trail, and Camp-Related 

Recreation (in billions of 2010$) ....................................................................... 7-10

Table 7-4 Summary of Biosite Index Values for 2006 to 2010 O3 Biomonitoring Sites. . 7-14

Table 7-5 Censored Regression Results ............................................................................. 7-16

Table 7-6 W126 Benchmark Criteria for O3 Exposure and Relative Soil Moisture in 6 

Scenarios used in Screening-Level Assessment of Parks .................................. 7-27

Table 7-7 Percent of 214 Parks that Exceed Benchmark Criteria in Each Year 

 (2006-2010) in 6 Scenarios ................................................................................ 7-29
 vi 

Table 7-8 Screening-level Foliar Injury Results in 42 Parks with an O3 Monitor using 3 

Methods for Assigning O3 Exposure to Each Park in Base Scenario ................ 7-34

Table 7-9 Foliar Injury Sensitivity Analyses for 214 Parks ............................................... 7-38

Table 7-10 Foliar Injury Sensitivity Analyses for Soil Moisture in 57 O3 Monitors 

 in Parks ........................................................................................................... 7-40

Table 7-11 Value of Most Frequent Visitor Activities at Great Smoky Mountains 

 National Park ..................................................................................................... 7-48

Table 7-12 Visitor Spending and Local Area Economic Impact of GRSM ........................ 7-48

Table 7-13 Median Travel Cost for GRSM Visitors ........................................................... 7-49

Table 7-14 Geographic Area of GRSM after Just Meeting Existing and 

 Alternative Standard Levels (km2

)..................................................................... 7-54

Table 7-15 Value of Most Frequent Visitor Activities at ROMO ........................................ 7-55

Table 7-16 Visitor Spending and Local Area Economic Impact of ROMO ......................... 7-56

Table 7-17 Median Travel Cost for ROMO Visitors ............................................................ 7-56

Table 7-18 Geographic Area of ROMO after Just Meeting Existing and 

 Alternative Standard Levels (km2)..................................................................... 7-58

Table 7-19 Value of Most Frequent Visitor Activities at Sequoia and Kings 

 Canyon National Parks ...................................................................................... 7-62

Table 7-20 Visitor Spending and Local Area Economic Impact of SEKI ............................ 7-63

Table 7-21 Median Travel Cost for SEKI Visitors ............................................................... 7-63

Table 7-22 Geographic Area of SEKI after Just Meeting Existing and 

 Alternative Standard Levels (km2).................................................................... 7-64

Table 7-23 Summary of Qualitative Uncertainty Analysis in Visible Foliar Injury 

Assessments. ...................................................................................................... 7-68

Table 8-1 Summary of O3-Exposure Risk Across Alternative W126 Standards Relative to 

Just Meeting Existing Standard – National-Scale Analyses .............................. 8-24

Table 8-2 Summary of O3-Exposure Risk Across Alternative Standards Relative to Just 

Meeting Existing Standard – Case Study-Scale Analyses ................................. 8-26
 vii 

LIST OF FIGURES 

Figure 2-1 Causal Determinations for O3 Welfare Effects .................................................... 2-6 

Figure 2-2 Linkages Between Ecosystem Services Categories and Components of 

 Human Well-Being .............................................................................................. 2-9 

Figure 4-1 Map of U.S. Ambient O3 Monitoring Sites in Operation During 2006-2010 ..... 4-3

Figure 4-2 Map of Monitored 8-hour O3 Design Values for the 2006-2008 Period ............. 4-4

Figure 4-3 Flowchart of Air Quality Data Processing for Different Parts of the Welfare Risk 

and Exposure Assessments. ................................................................................. 4-5

Figure 4-4 Monitored 2006-2008 Average W126 Concentrations in ppm-hrs ..................... 4-8

Figure 4-5 National Surface of Observed 2006-2008 Average W126 Concentrations, 

 in ppm-hrs ............................................................................................................ 4-9

Figure 4-6 Map of the 9 NOAA Climate Regions Used in National-scale Air Quality 

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

Figure 4-7 National Surface of 2006-2008 Average W126 Concentrations (in ppm-hrs) 

Adjusted to Just Meet the Existing O3 Standard of 75 ppb ............................... 4-16

Figure 4-8 Difference in ppm-hrs Between National Surface of Observed 2006-2008 

Average W126 Concentrations and National Surface of 2006-2008 Average 

W126 Concentrations Adjusted to Just Meet the Existing O3 Standard 

 of 75 ppb ............................................................................................................ 4-17

Figure 4-9 National Surface of 2006-2008 Average W126 concentrations (in ppm-hrs) 

Adjusted to Just Meet the Potential Alternative Standard of 15 ppm-hrs ......... 4-18

Figure 4-10 Difference in ppm-hrs Between National Surface of 2006-2008 Average W126 

Concentrations Adjusted to Just Meet Existing O3 Standard of 75 ppb and 

National Surface of 2006-2008 Average W126 Concentrations Adjusted to Just 

Meet Potential Alternative Standard of 15 ppm-hrs .......................................... 4-19

Figure 4-11 National Surface of 2006-2008 Average W126 Concentrations (in ppm-hrs) 

Adjusted to Just Meet Potential Alternative Standard of 11 ppm-hrs ............... 4-20

Figure 4-12 Difference in ppm-hrs Between National Surface of 2006-2008 Average W126 

Concentrations Adjusted to Just Meet the Existing O3 Standard of 75 ppb and 

National Surface of 2006-2008 Average W126 Concentrations Adjusted to Just 

Meet the Potential Alternative Standard of 11 ppm-hrs .................................... 4-21

Figure 4-13 National Surface of 2006-2008 Average W126 concentrations (in ppm-hrs) 

Adjusted to Just Meet the Potential Alternative Standard of 7 ppm-hrs ........... 4-22
 viii 

Figure 4-14 Difference in ppm-hrs Between National Surface of 2006-2008 Average W126 

Concentrations Adjusted to Just Meet the Existing O3 Standard of 75 ppb and 

National Surface of 2006-2008 Average W126 Concentrations Adjusted to Just 

Meet the Potential Alternative Standard of 7 ppm-hrs ...................................... 4-23

Figure 4-15 Empirical Probability Density and Cumulative Distribution Functions for 

Monitored 2006-2008 8-hour O3 Design Values, and the 2006-2008 8-hour O3

Design Values After Adjusting to Just Meet the Existing and Potential Alternative 

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

Figure 4-16 Empirical Probability Density and Cumulative Distribution Functions for 

Monitored 2006-2008 Average W126 Concentrations, and the 2006-2008 

Average W126 Concentrations After Adjusting to Just Meet the Existing and 

Potential Alternative Standards.......................................................................... 4-25 

Figure 5-1 Conceptual Diagram of the Major Pathway through which O3 Enters Plants 

 and the Major Endpoints that O3 May Affect in Plants and Ecosystems ............ 5-1

Figure 5-2 Relationship between Ecological Effects of O3 Exposure and 

 Ecosystem Services .............................................................................................. 5-2

Figure 5-3 Overlap of W126 Index Values for the Existing Standard and Alternative W126 

Standards, Fire Threat > 2, and Mixed Conifer Forest ........................................ 5-7

Figure 5-4 Location of Fires in 2010 in Mixed Conifer Forest Areas (under Recent O3 

Conditions) ........................................................................................................... 5-9

Figure 5-5 Southern Pine Beetle Damage ........................................................................... 5-10

Figure 5-6 Southern Pine Beetle Damage ........................................................................... 5-10

Figure 5-7 W126 Index Values for Just Meeting the Existing and Alternative Standards in 

Areas Considered ‘At Risk’ of High Basal Area Loss (>25% Loss) ................. 5-12

Figure 5-8 Percent of Forest Land in the US by Ownership Category, 2007 ...................... 5-14 

Figure 6-1 Conceptual Diagram of Relationship of Relative Biomass Loss to Ecosystem 

Services ............................................................................................................. 6-1

Figure 6-2 Relative Biomass Loss Functions for 12 Tree Species ........................................ 6-5

Figure 6-3 Relative Biomass Loss Functions for 10 Crop Species ....................................... 6-6

Figure 6-4 W126 Index Values for Alternative Percent Biomass Loss for Tree Species ... 6-11

Figure 6-5 W126 Index Values for Alternative Percent Biomass Loss for Crop Species ... 6-11

Figure 6-6 Relative Biomass Loss of Ponderosa Pine (Pinus ponderosa) Seedlings under 

Recent Ambient W126 Index Values (2006 – 2008) ......................................... 6-13

Figure 6-7 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After Simulating 

Meeting the Existing (8-hr) Primary Standard (75 ppb) .................................... 6-13
 ix 

Figure 6-8 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After Simulating 

Meeting an Alternative Secondary Standard of 15 ppm-hrs (after Meeting 

Existing O3 Standard) ........................................................................................ 6-14

Figure 6-9 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After Simulating 

Meeting an Alternative Secondary Standard of 11 ppm-hrs (after Meeting 

Existing O3 Standard) ........................................................................................ 6-14

Figure 6-10 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After Simulating 

Meeting an Alternative Secondary Standard of 7 ppm-hrs (after Meeting Existing 

O3 Standard) ....................................................................................................... 6-15

Figure 6-11 Relative Biomass Loss of Ponderosa Pine at the Existing Primary and Alternative 

Secondary Standards ......................................................................................... 6-18 

Figure 6-12 Proportion of Current Standard, Ponderosa Pine – Recent Conditions and 

Alternative Secondary Standards ....................................................................... 6-19 

Figure 6-13 Three-Year Compounded Relative Biomass Loss – Southeast and Southwest 

 Regions ........................................................................................................... 6-20 

Figure 6-14 Individual and 3-Year Average W126 Index Values – Southeast and Southwest 

Regions ........................................................................................................... 6-21 

Figure 6-15 RYG for Softwoods by Region .......................................................................... 6-27

Figure 6-16 RYG for Hardwoods by Region ........................................................................ 6-28

Figure 6-17 Percentage Changes in Corn RYG with Respect to 75 ppb ............................... 6-42

Figure 6-18 Percentage Changes in Corn RYG with Respect to 75 ppb .....................6-43 

Figure 7-1 Relationship between Visible Foliar Injury and Ecosystem Services ................. 7-2

Figure 7-2 Tree Species Sensitive to Foliar Injury ................................................................ 7-3

Figure 7-3 Examples of Foliar Injury from O3 Exposure ...................................................... 7-6

Figure 7-4 Examples of Southern Bark Beetle Damage ........................................................ 7-7

Figure 7-5 O3 Biomonitoring Sites ...................................................................................... 7-12

Figure 7-6 344 Climate Divisions with Palmer Z Soil Moisture Data ................................ 7-13

Figure 7-7 General Relationship of O3 (ppm-hrs) and Biosite Index .................................. 7-14

Figure 7-8 General Relationship of Average Palmer Z (April to August) and 

 Biosite Index ...................................................................................................... 7-15

Figure 7-9 Cumulative Proportion of Sites with Foliar Injury Present, by Year ................. 7-17

Figure 7-10 Cumulative Proportion of Sites with Elevated Foliar Injury, by Year .............. 7-18

Figure 7-11 Cumulative Proportion of Sites with Foliar Injury Present, 

 by Moisture Category ........................................................................................ 7-19

Figure 7-12 Cumulative Proportion of Sites with Elevated Foliar Injury, 

 by Moisture Category ........................................................................................ 7-19
 x 

Figure 7-13 Cumulative Proportion of Sites with Foliar Injury Present, 

 by Climate Region ............................................................................................. 7-20

Figure 7-14 Timeframe of W126 Estimates for 57 Monitors Located in Parks .................... 7-22

Figure 7-15 214 National Parks included in the Screening-Level Assessment ..................... 7-24

Figure 7-16 Distribution of O3 and Soil Moisture in 214 Parks by Year .............................. 7-25

Figure 7-17 Presence of O3-Sensitive Species in Parks ......................................................... 7-28

Figure 7-18 Screening-Level Results for Foliar Injury in 214 Parks in 6 Scenarios ............. 7-30

Figure 7-19 Parks Exceeding Benchmark Criteria for at least 3 years by Scenario 

 and Climate Region............................................................................................ 7-31

Figure 7-20 Foliar Injury Results Maps for 6 Scenarios for 214 Parks ................................. 7-32

Figure 7-21 Percent of 214 Parks at Different W126 Levels with Adjustments for 

 Existing and Alternative Standards (3-year average) ........................................ 7-43

Figure 7-22 O3 Exposure in Ten Most Visited National Parks after Just Meeting 

 Existing and Alternative Standards (3-year average) ........................................ 7-44

Figure 7-23 Identification of W126 Index Value where 10 Percent of Biosites Show 

 Any Foliar Injury ............................................................................................... 7-46

Figure 7-24 Cover of Sensitive Species in GRSM ................................................................ 7-51

Figure 7-25 Trail Cover of Sensitive Species in GRSM ........................................................ 7-52

Figure 7-26 GRSM Trail Kilometers by Species Cover Category ........................................ 7-53

Figure 7-27 Sensitive Vegetation Cover in GRSM Scenic Overlooks (3km) ....................... 7-54

Figure 7-28 Sensitive Species Cover in ROMO .................................................................... 7-59

Figure 7-29 ROMO Sensitive Species Trail Cover ............................................................... 7-60

Figure 7-30 ROMO Trail Cover by Sensitive Species Type ................................................. 7-61 

Figure 7-31 Sensitive Species Cover in SEKI ....................................................................... 7-65

Figure 7-32 Sensitive Species Trail Cover in SEKI .............................................................. 7-66

Figure 7-33 SEKI’s Sensitive Species Cover by Type .......................................................... 7-67

Figure 8-1 Map of 9 NOAA Climate Regions used in National-Scale Air 

 Quality Adjustments ........................................................................................... 8-5

Figure 8-2 Cumulative Proportion of Sites with Visible Foliar Injury Present, by Moisture 

Category ............................................................................................................. 8-12

Figure 8-3 National Surface of 2006-2008 Average W126 Index values Adjusted to Just 

Meet the Alternative Standard of 15 ppm-hrs.................................................... 8-19

Figure 8-4 National Surface of 2006-2008 Average W126 Index values Adjusted to Just 

Meet the Alternative Standard of 11 ppm-hrs.................................................... 8-20

Figure 8-5 Cumulative Proportion of Sites with Foliar Injury Present, by Year ................. 8-22
 xi 

APPENDICES 

4A: SPATIAL FIELDS FOR THE W126 METRIC 

5A: LARGER MAPS OF FIRE THREAT AND BASAL AREA LOSS 

6A: MAPS OF INDIVIDUAL TREE SPECIES 

6B: ASSESSMENT OF THE IMPACTS OF ALTERNATIVE OZONE 

 CONCENTRATIONS ON THE U.S. FOREST AND AGRICULTURE SECTORS 

6C: SUPPLY CURVE SHIFTS 

6D: iTREE MODEL 

6E: CLASS I AREAS AND WEIGHTED RBL AT CURRENT STANDARD 

 AND ALTERNATIVE W126 STANDARD LEVELS 

7A: ADDITIONAL INFORMATION FOR SCREENING-LEVEL ASSESSMENT OF 

 VISIBLE FOLIAR INJURY IN NATIONAL PARKS 

7B: NATIONAL PARKS CASE STUDY LARGE SCALE MAPS 
 xii 

LIST OF ACRONYMS/ABBREVIATIONS 

AGSIM Agriculture Simulation Model 

AQCD Air Quality Criteria Document 

AQS Air Quality System 

BLM Bureau of Land Management 

CAA Clean Air Act 

CAL FIRE California Department of Forestry and Fire Protection 

CASAC Clean Air Scientific Advisory Committee 

CASTNET Clean Air Status and Trends Network 

C.F.R. Code of Federal Regulations 

CH4 Methane 

CMAQ Community Multi-Scale Air Quality 

CO Carbon Monoxide 

C-R Concentration-Response 

CSTR Continuous Stirred Tank Reactors 

EGU Electric Generating Unit 

EPA Environmental Protection Agency 

FACE Free- Air Carbon Dioxide/Ozone Enrichment 

FASOMGHG Forest and Agriculture Sector Optimization Model with Greenhouse Gases 

FHM Forest Health Monitoring 

FHTET Forest Health Technology Enterprise Team 

FIA Forest Inventory and Analysis 

FR Federal Register 

GIS Geographic Information System 

GRSM Great Smoky Mountains National Park 

HDDM Higher-Order Decoupled Direct Method 

HNO3 Nitric Acid 

HO2 Hydro-Peroxy Radical 

IMPLAN® Impact Analysis for Planning Model 

IRP Integrated Review Plan 

ISA Integrated Science Assessment 

i-Tree Urban Forestry Analysis and Benefits Assessment Tool 

MEA Millennium Ecosystem Assessment 

MSA Metropolitan Statistical Area 

NAAQS National Ambient Air Quality Standards 

NCDC National Climatic Data Center 

NCLAN National Crop Loss Assessment Network 

NCore National Core 

NEI National Emissions Inventory 

FHWAR National Survey of Fishing, Hunting, and Wildlife Associated Recreation 

NHEERL-WED National Health and Environmental Effects Laboratory – Western Ecology 

Division 

NOAA National Oceanographic and Atmospheric Administration 

NOx Oxides of Nitrogen 

NPP Net Primary Productivity 
 xiii 

NPS National Park Service 

NSRE National Survey on Recreation and the Environment 

NTFP Non-Timber Forest Products 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

OBP Ozone Biomonitoring Program 

OH Hydroxl Radical 

OIF Outdoor Industry Foundation 

OTC Open-Top Camber 

PA Policy Assessment 

PAMS Photochemical Assessment Monitoring Stations 

ppb Parts per Billion 

ppm-hrs Parts per Million Hours 

POMS Portable O3 Monitoring System 

RBL Relative Biomass Loss 

REA Risk and Exposure Assessment 

ROMO Rocky Mountains National Park 

RYG Relative Yield Gain 

RYL Relative Yield Loss 

SEKI Sequoia/Kings Canyon National Parks 

SLAMS State and Local Monitoring Stations 

SOx Oxides of Sulfur 

SPMS Special Purpose Monitoring Stations 

STE Stratosphere-Troposphere Exchange 

TREGRO Tree Growth Model 

UNESCO United National Education, Scientific, and Cultural Organization 

U.S. United States 

USDA United States Department of Agriculture 

U.S. EPA United States Environmental Protection Agency 

USFS United States Forest Service 

USGS United States Geological Society 

VegBank Vegetation Plot Database 

VNA Voronoi Neighbor Averaging 

VOC Volatile Organic Compound 

WHO World Health Organization 

W126 Cumulative Integrated Exposure Index with a Sigmoidal Weighting 

Function 

WTP Willingness-to-Pay 

ZELIG A Forest Succession Simulation Model
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1-1 

1 1 INTRODUCTION 

2 The U.S. Environmental Protection Agency (EPA) is presently conducting a review of 

3 the national ambient air quality standard (NAAQS) for ozone (O3) and related photochemical 

4 oxidants. The NAAQS review process includes four key phases: planning, science assessment, 

risk/exposure assessment, and policy assessment/rulemaking.1 5 This process and the overall plan 

6 for this review of the O3 NAAQS are presented in the Integrated Review Plan for the Ozone 

7 National Ambient Air Quality Standards (IRP, US EPA, 2011a). The IRP additionally presents 

8 the schedule for the review; identifies key policy-relevant issues; and discusses the key scientific, 

9 technical, and policy documents. These documents include an Integrated Science Assessment 

10 (ISA), Risk and Exposure Assessments (REAs), and a Policy Assessment (PA). This draft 

11 Welfare REA is one of the two quantitative REAs developed for the review by EPA’s Office of 

12 Air Quality Planning and Standards (OAQPS); the second is a Health REA. This draft Welfare 

13 REA focuses on assessments to inform consideration of the review of the secondary (welfare14 based) NAAQS for O3.

15 The existing secondary standard for O3 is set identical to the primary standard at a level 

16 of 0.075 ppm, based on the annual fourth-highest daily maximum 8-hour average concentration, 

17 averaged over three years (73 FR 16436). The EPA initiated the current review of the O3

18 NAAQS on September 29, 2008 with an announcement of the development of an O3 ISA and a 

19 public workshop to discuss policy-relevant science to inform EPA’s integrated plan for the 

20 review of the O3 NAAQS (73 FR 56581). Discussions at the workshop, held on October 29-30, 

21 2008, informed identification of key policy issues and questions to frame the review of the O3

22 NAAQS. Drawing from the workshop discussions, EPA developed a draft and then final IRP 

(U.S. EPA, 2011a).2 23 In early 2013, EPA completed the Integrated Science Assessment for Ozone 

24 and Related Photochemical Oxidants (ISA, U.S. EPA, 2013). The ISA provides a concise 

 

1

 For more information on the NAAQS review process, see http://www.epa.gov/ttn/naaqs/review.html. 

2

 On March 30, 2009, EPA held a public consultation with the CASAC O3 Panel on the draft IRP. The final IRP 

took into consideration comments received from CASAC and the public on the draft plan, as well as input from 

senior Agency managers. 
1-2 

1 review, synthesis, and evaluation of the most policy-relevant science to serve as a scientific 

2 foundation for the review of the NAAQS. The scientific and technical information in the ISA, 

3 including that newly available since the previous review on the welfare effects of O3, includes 

4 information on exposure, physiological mechanisms by which O3 might adversely impact 

5 vegetation, and an evaluation of the ecological evidence, including information on reported 

6 concentration-response (C-R) relationships for O3-related changes in plant biomass. 

7 The REA is a concise presentation of the conceptual model, scope, methods, key results, 

8 observations, and related uncertainties associated with the quantitative analyses performed. This 

9 REA builds upon the welfare effects evidence presented and assessed in the ISA, as well as 

10 CASAC advice (Samet, 2011) and public comments on a scope and methods planning document 

11 for the REA (here after, “Scope and Methods Plan”, U.S. EPA, 2011b). Preparation of this 

12 second draft REA draws upon the final ISA and reflects consideration of CASAC and public 

13 comments on the first draft REA (Frey and Samet, 2012). This second draft welfare REA is 

14 being released, concurrently with the second draft health REA and second draft PA, for review 

15 by the CASAC O3 Panel at a public meeting scheduled for March 25-27, 2014, and for public 

16 comment. 

17 The second draft PA presents a staff evaluation and preliminary staff conclusions of the 

18 policy implications of the key scientific and technical information in the ISA and second draft 

19 REAs. When final, the PA is intended to help “bridge the gap” between the Agency’s scientific 

20 assessments presented in the ISA and REAs and the judgments required of the EPA 

21 Administrator in determining whether it is appropriate to retain or revise the NAAQS. The PA 

22 integrates and interprets the information from the ISA and REAs to frame policy options for 

23 consideration by the Administrator. In so doing, the PA recognizes that the selection of a 

24 specific approach to reaching final decisions on primary and secondary NAAQS will reflect the 

25 judgments of the Administrator. The development of the various scientific, technical and policy 

26 documents and their roles in informing this NAAQS review are described in more detail in the 

27 second draft PA. 
1-3 

1 1.1 HISTORY 

2 As part of the previous O3 NAAQS review completed in 2008, EPA’s OAQPS conducted 

3 quantitative risk and exposure assessments to estimate risks to human welfare based on 

4 ecological effects associated with exposure to ambient O3 (U.S. EPA 2007a, U.S. EPA 2007b). 

5 The assessment scope and methodology were developed with considerable input from CASAC 

6 and the public, with CASAC generally concluding that the exposure assessment reflected 

7 generally-accepted modeling approaches, and that the risk assessments were well done, balanced 

8 and reasonably communicated (Henderson, 2006a). The final quantitative risk and exposure 

9 assessments took into consideration CASAC advice (Henderson, 2006a; Henderson, 2006b) and 

10 public comments on two drafts of the risk and exposure assessments. 

11 The assessments conducted as part of the previous review focused on national-level O3-

12 related impacts to sensitive vegetation and their associated ecosystems. The vegetation exposure 

13 assessment was performed using an interpolation approach that included information from 

14 ambient monitoring networks and results from air quality modeling. The vegetation risk 

15 assessment included both tree and crop analyses. The tree risk analysis included three distinct 

16 lines of evidence: (1) observations of visible foliar injury in the field linked to monitored O3 air 

17 quality for the years 2001 – 2004; (2) estimates of seedling growth loss under then-current and 

18 alternative O3 exposure conditions; and (3) simulated mature tree growth reductions using the 

19 TREGRO model to simulate the effect of meeting alternative air quality standards on the 

20 predicted annual growth of mature trees from three different species. The crop risk analysis 

21 included estimates of crop yields under current and alternative O3 exposure conditions. The 

22 assessments also analyzed the associated changes in economic value upon meeting the levels of 

various alternative standards using an agricultural sector economic model.3 23 

24 Based on the 2006 Air Quality Criteria for Ozone (U.S. EPA, 2006), the 2007 Staff Paper 

25 (U.S. EPA, 2007) and related technical support documents (including the risk and exposure 

26 assessments), EPA published a proposed decision in the Federal Register on July 11, 2007 (72 

 

3

 We addressed key observations and insights from the O3 risk assessment, in addition to important caveats and 

limitations, in Section II.B of the Final Rule notice (73 FR 16440 to 16443, March 27, 2008). 
1-4 

1 FR 37818). The EPA proposed to revise the level of the primary standard to a level within the 

2 range of 0.075 to 0.070 ppm. Two options were proposed for the secondary standard: (1) 

3 replacing the current standard with a cumulative, seasonal standard, expressed as an index of the 

4 annual sum of weighted hourly concentrations cumulated over 12 daylight hours during the 

5 consecutive 3-month period within the O3 season with the maximum index value (W126), set at a 

6 level within the range of 7 to 21 ppm-hours, and (2) setting the secondary standard identical to 

7 the revised primary standard. EPA completed the review with publication of a final decision on 

8 March 27, 2008 (73 FR 16436), revising the level of the 8-hour primary O3 standard from 0.08 

9 ppm to 0.075 ppm, as the 3-year average of the fourth highest daily maximum 8-hour average 

10 concentration, and revising the secondary standard to be identical to the revised primary 

11 standard. 

12 In May 2008, state, public health, environmental, and industry petitioners filed suit 

13 against EPA regarding the 2008 decision. At EPA’s request, the consolidated cases were held in 

14 abeyance pending EPA’s reconsideration of the 2008 decision. The Administrator issued a 

15 notice of proposed rulemaking to reconsider the 2008 final decision on January 6, 2010. EPA 

16 held three public hearings. The Agency solicited CASAC review of the proposed rule on January 

17 25, 2010 and additional CASAC advice on January 26, 2011. On September 2, 2011, the Office 

18 of Management and Budget returned the draft final rule on reconsideration to EPA for further 

19 consideration. EPA decided to coordinate further proceedings on its voluntary rulemaking on 

20 reconsideration with the ongoing periodic review, by deferring the completion of its voluntary 

21 rulemaking on reconsideration until it completes its statutorily-required periodic review. In light 

22 of that, the litigation on the 2008 final decision proceeded. On July 23, 2013, the Court ruled on 

23 the litigation of the 2008 decision, denying the petitioners suit except with respect to the 

24 secondary standard, which was remanded to the Agency for reconsideration. The second draft 

25 PA provides additional description of the court ruling with regard to the secondary standard. 

26 1.2 CURRENT RISK AND EXPOSURE ASSESSMENTS: GOALS AND PLANNED 

27 APPROACH 

28 This second draft REA provides an assessment of exposure and risk associated with 

29 recent ambient concentrations of O3 and O3 air quality simulated to just meet the existing 
1-5 

1 secondary O3 standard and just meeting potential alternative O3 standards based on 

2 recommendations provided in the first draft of the PA. To inform the PA regarding the adequacy 

3 of existing standards and the potential for reductions in adverse effects associated with 

4 alternative standards that might be considered, the goals of the current quantitative welfare REA 

5 are to (1) provide estimates of the ecological effects of O3 exposure across a range of 

6 environments; (2) provide estimates of ecological effects within selected case study areas; (3) 

7 provide estimates of the effects of O3 exposure on specific urban and non-urban ecosystem 

8 services based on the causal ecological effects; and (4) develop a better understanding of the 

9 response of ecological systems and ecosystem services to changing O3 exposure. This current 

10 quantitative risk and exposure assessment builds on the approach used and lessons learned in the 

11 previous O3 risk assessment and focuses on improving the characterization of the overall 

12 confidence in the risk estimates, including related uncertainties, by improving the methods and 

13 data used in the analyses; this current risk and exposure assessment also incorporates the range 

14 of ecosystem effects and expands the characterization of adversity to include consideration of 

15 impacts to ecosystem services. This assessment considers a variety of welfare endpoints for 

16 which, in our judgment, there is adequate information to develop quantitative risk estimates that 

17 can meaningfully inform the review of the secondary O3 NAAQS. 

18 1.3 ORGANIZATION OF DOCUMENT 

19 The remainder of this document is organized into chapters. Chapter 2 provides a 

20 conceptual framework for the risk and exposure assessment, including discussions of O3

21 chemistry, sources of O3 precursors, ecological exposure pathways and uptake into plants, 

22 ecological effects, and ecosystem services endpoints associated with O3. This conceptual 

23 framework sets the stage for the scope of the risk and exposure assessments. Chapter 3 provides 

24 an overview of the scope of the quantitative risk and exposure assessments, including a summary 

25 of the previous risk and exposure assessments and an overview of the current risk and exposure 

26 assessments. Chapter 4 discusses air quality considerations relevant to the exposure and risk 

27 assessments, including available O3 monitoring data and important air quality inputs to the risk 

28 and exposure assessments. Chapter 5 describes the ecological effects of O3 exposure and the 

29 associated ecosystem services, including the ecosystem services for which data and methods for 

30 incremental analysis of direct O3 are not yet available. Chapter 6 provides quantitative analysis 
1-6 

1 of the biomass loss effects of O3 and the ecosystem services affected by this loss, such as 

2 provision of food and fiber, carbon sequestration and storage, and pollution removal. Chapter 7 

3 provides quantitative assessments of the effects of O3 on foliar injury and associated ecosystem 

4 services, particularly cultural services related to recreation and the three selected National Park 

5 case studies. Chapter 8 provides an integrated discussion of the risk estimates generated in these 

6 analyses, drawing on the results of the quantitative analyses and incorporating considerations 

7 from the qualitative discussion of ecosystem services. 
 2-1 

1 2 FRAMEWORK 

2 In this chapter, we summarize the conceptual framework for assessing exposures of 

3 ecosystems to O3 and the associated risks to public welfare. This conceptual framework includes 

4 elements related to characterizing: (1) O3 chemistry (Section 2.1); (2) important sources of O3

5 precursors, including oxides of nitrogen (NOx) and volatile organic compounds (VOC) (Section 

6 2.2); (3) O3-induced effects occurring on O3-sensitive species and in their associated ecosystems 

7 (Section 2.3); and (4) ecosystem services that are likely to be negatively impacted by changes in 

8 ecological functions resulting from O3 exposures (Section 2.4). We conclude the chapter with 

9 key observations relevant for developing the scope of the quantitative risk and exposure 

10 assessments. 

11 In the previous review of the secondary standards, we focused the ecological risk 

12 assessment on estimating changes in biomass loss in forest tree species and yield loss in 

13 agricultural crops, as well as qualitatively considering effects on ecosystem services. In this 

14 review, EPA expanded the analysis to consider the broader array of impacts on ecosystem 

15 services resulting from known effects of O3 exposure on ecosystem functions. This expanded 

16 scope is addressed in the risk assessment by quantifying the risks not just to ecosystems, but also 

17 to the aspects of public welfare dependent on those ecosystems, i.e., services. EPA has started 

18 using an ecosystem services framework to help inform determinations of the adversity to public 

19 welfare associated with changes in ecosystem functions (Rea et al, 2012). The Risk and 

20 Exposure Assessment conducted as part of the Review of the Secondary National Ambient Air 

21 Quality Standards for Oxides of Nitrogen and Oxides of Sulfur (U.S. EPA, 2009) presented 

22 detailed discussions of how ecosystem services and public welfare are related and how an 

23 ecosystem services framework may be employed to evaluate effects on welfare. In this risk 

24 assessment we will identify the ecosystem services associated with the ecological effects caused 

25 by O3 exposure for both the national scale assessment and the more refined case study areas. 

26 These services may be characterized as: supporting services that are necessary for all other 

27 services (e.g., primary production); cultural services including existence and bequest values, 

28 aesthetic values, and recreation values, among others; provisioning services (e.g., food and 

29 timber); and regulating services such as climate regulation or hydrologic cycle (Millenium 

30 Ecosystem Assessment, 2005). 
 2-2 

1 2.1 O3 CHEMISTRY 

2 O3 occurs naturally in the stratosphere where it provides protection against harmful solar 

3 ultraviolet radiation; O3 is also formed closer to the Earth’s surface in the troposphere by both 

4 natural and anthropogenic sources. O3 is not emitted directly into the air, but is created when its 

5 two primary precursors, VOC and NOx, combine in the presence of sunlight. VOC and NOx are, 

6 for the most part, emitted directly into the atmosphere. Carbon monoxide (CO) and methane 

7 (CH4) are also important for O3 formation (U.S. EPA, 2013, section 3.2.2). 

8 Rather than varying directly with emissions of its precursors, O3 changes in a nonlinear 

9 fashion with the concentrations of its precursors. Nitrogen oxide emissions lead to both the 

10 formation and destruction of O3, depending on the local quantities of NOx, VOC, and radicals 

11 such as the hydroxyl (OH) and hydro-peroxy (HO2) radicals. In areas dominated by fresh NOx 

12 emissions, these radicals are removed via the production of nitric acid (HNO3), which lowers the 

13 O3 formation rate. The reduction in, or scavenging of, O3 by this reaction is called “titration” 

14 and is often found in downtown metropolitan areas, especially near busy streets and roads, and in 

15 power plant plumes. Titration is usually short-lived and confined to areas close to strong NOx

16 sources; titration results in localized valleys in which O3 concentrations are low compared to 

17 surrounding areas. Consequently, O3 response to reductions in NOx emissions is complex and 

18 may include O3 decreases at some times and locations and O3 increases to fill in the local valleys 

19 of low O3. In contrast, in areas with low NOx concentrations, such as remote continental areas 

20 and rural and suburban areas downwind of urban centers, the net production of O3 varies directly 

21 with NOx concentrations and typically increases with increasing NOx emissions. 

22 In general, the rate of O3 production is limited by the concentration of VOC or NOx, and 

23 O3 formation based on these two precursors depends on the relative sources of OH and NOx. 

24 When OH radicals are abundant and are not depleted by reaction with NOx and/or other species, 

25 O3 production is “NOx-limited” (U.S. EPA, 2013, section 3.2.4). In this NOx-limited 

26 circumstance, O3 concentrations are most effectively reduced by lowering NOx emissions rather 

27 than by lowering VOC emissions. When OH and other radicals are not abundant, either through 

28 low production or reactions with NOx and other species, O3 production is referred to as “VOC29 limited”, “radical-limited”, or “NOx-saturated” (Jaegle et al., 2001), and O3 is most effectively 

30 reduced by lowering VOC emissions. However, even in NOx-saturated conditions, very large 

31 decreases in NOx emissions can cause the O3 formation regime to become NOx-limited. 
 2-3 

1 Consequently, large reductions in NOx emissions can make further emissions reductions more 

2 effective at reducing O3. Between the NOx-limited and NOx-saturated extremes there is a range 

3 where O3 is relatively insensitive to marginal changes in both NOx and VOC emissions. 

4 In rural areas and downwind of urban areas, O3 production is generally NOx-limited. 

5 This is particularly true in rural areas such as national parks, national forests, and state parks 

6 where VOC emissions from vegetation are high and anthropogenic NOx emissions are relatively 

7 low. Due to lower chemical scavenging in non-urban areas, O3 tends to persist longer in rural 

8 than in urban areas and tends to lead to higher cumulative exposures in rural areas than in urban 

9 areas (U.S. EPA, 2013, Section 3.6.2.2). 

10 We focused the analyses in the welfare risk and exposure assessments on the W126 O3

11 exposure metric. The W126 metric is a seasonal sum of hourly O3 concentrations, designed to 

12 measure the cumulative effects of O3 exposure on vulnerable plant and tree species. The W126 

13 metric uses a sigmoidal weighting function to place less emphasis on exposure to low 

14 concentrations and more emphasis on exposure to high concentrations. 

15 2.2 SOURCES OF O3 AND O3 PRECURSORS 

16 O3 precursor emissions can be divided into anthropogenic and natural source categories, 

17 with natural sources further divided into biogenic emissions (from vegetation, microbes, and 

18 animals) and abiotic emissions (from biomass burning, lightning, and geogenic sources). The 

19 anthropogenic precursors of O3 originate from a wide variety of stationary and mobile sources. 

20 In urban areas, both biogenic and anthropogenic VOC emissions are relevant to O3

21 formation. Hundreds of VOC are emitted by evaporation and combustion processes from a large 

22 number of anthropogenic sources. Based on the 2005 national emissions inventory (NEI), 

23 solvent use and highway vehicles are the two main sources of VOC emissions, with roughly 

24 equal contributions to total emissions (U.S. EPA, 2013, Figure 3-2). The emissions inventory 

25 categories of “miscellaneous” (which includes agriculture and forestry, wildfires, prescribed 

26 burns, and structural fires) and off-highway mobile sources are the next two largest contributing 

27 emissions categories, with a combined total of over 5.5 million metric tons a year (MT/year). 

28 In rural areas and at the global scale, VOC emissions from vegetation are much larger 

29 than those from anthropogenic sources. In the 2005 NEI, U.S. rural emissions from biogenic 
 2-4 

1 sources were 29 MT/year, and emissions of VOC from anthropogenic sources were 

2 approximately17 MT/year (wildfires constitute ~1/6 of that total). Vegetation emits substantial 

3 quantities of VOC, such as isoprene and other terpenoid and sesqui-terpenoid compounds. Most 

4 biogenic emissions occur during the summer because of they depend on temperature and incident 

5 sunlight. Biogenic emissions are also higher in southern and eastern states than in northern and 

6 western states for these reasons and because of species variations. 

7 Anthropogenic NOx emissions are associated with combustion processes. Based on the 

8 2005 NEI, the three largest sources of NOx emissions in the U.S. are on-road and off-road mobile 

9 sources (e.g., construction and agricultural equipment) and electric power generation plants 

10 (electric generating units, or EGUs) (U.S. EPA, 2013, Figure 3-2). Emissions of NOx are highest 

11 in areas with a high density of power plants and in urban regions with high traffic density. 

12 However, it is not possible to make an overall statement about their relative impacts on O3 in all 

13 local areas because there are fewer EGUs than mobile sources, particularly in the west and south, 

14 and because of the nonlinear chemistry discussed in Section 2.1. 

15 Major natural sources of NOx in the U.S. include lightning, soils, and wildfires. Biogenic 

16 NOx emissions are generally highest during the summer and occur across the entire country, 

17 including areas where anthropogenic emissions are low. It should be noted that uncertainties in 

18 estimating natural NOx emissions are much larger than uncertainties in estimating anthropogenic 

19 NOx emissions. 

20 O3 concentrations in a region are affected both by local formation and by transport from 

21 surrounding areas. O3 transport occurs on many spatial scales, including local transport between 

22 cities, regional transport over large regions of the U.S., and international/long-range transport. In 

23 addition, O3 is also transferred from the stratosphere into the troposphere, which is rich in O3,

24 through stratosphere-troposphere exchange (STE). These inversions or “foldings” usually occur 

25 behind cold fronts, bringing stratospheric air with them (U.S. EPA, 2013, section 3.4.1.1). 

26 Contribution to O3 concentrations in an area from STE are defined as being part of background 

27 O3 (U.S. EPA, 2013, section 3.4). 

28 Rural areas, such as national parks, national forests, and state parks, tend to be less 

29 directly affected by anthropogenic pollution sources than urban sites. However, they can be 

30 regularly affected by transport of O3 or O3 precursors from upwind urban areas. In addition, 
 2-5 

1 biogenic VOC emissions tend to be higher in rural areas, and major anthropogenic sources of O3

2 precursor emissions such as highways, power plants, biomass combustion, and oil and gas 

3 operations are commonly found in rural areas, adding to the O3 produced in these areas. Areas at 

4 higher elevations, such as many of the national parks in the western U.S., can also be affected 

5 more significantly by international transport of O3 or stratospheric intrusions that transport O3

6 into the area (U.S. EPA, 2013, section 3.7.3). 

7 2.3 ECOLOGICAL EFFECTS 

8 Recent studies reviewed in the ISA support and strengthen the findings reported in the 

9 2006 O3 Air Quality Criteria Document (AQCD) (U.S. EPA, 2006a). The most significant new 

10 body of evidence since the 2006 O3 AQCD comes from research on molecular mechanisms of 

11 the biochemical and physiological changes observed in many plant species in response to O3

12 exposure. These newer molecular studies not only provide very important information regarding 

13 the many mechanisms of plant responses to O3, they also allow for the analysis of interactions 

14 between various biochemical pathways that are induced in response to O3. However, many of 

15 these studies have been conducted in artificial conditions with model plants, which are typically 

16 exposed to very high, short doses of O3 and are not quantifiable as part of this risk assessment. 

17 Chapter 9 of the O3 ISA (U.S. EPA, 2013) provides a detailed review of the effects of O3

18 on vegetation including the major pathways of exposure and known ecological and ecosystem 

19 effects. In general, O3 is taken up through the stomata into the leaves. Once inside the leaves, O3

20 affects a number of biological and physiological processes, including photosynthesis. This leads, 

21 in some cases, to visible foliar injury as well as reduced plant growth, which are the main 

22 ecological effects assessed in this review. Visible foliar injury and reduced growth can lead to a 

23 reduction in ecosystem services, including crop and timber yield loss, decreased carbon 

24 sequestration, alteration in community composition, and loss of recreational or cultural value. 

25 Overall causal determinations are made based on the full range of evidence including 

26 controlled exposure studies and ecological studies. Figure 2-1 shows the O3 welfare effects that 

27 have been categorized by strength of evidence for causality in the O3 ISA (U.S. EPA, 2013, 

28 Chapter 2). These determinations support causal or likely causal relationships between exposure 

29 to O3 and ecological and ecosystem-level effects. 

30 
 2-6 

1 

2 Figure 2-1 Causal Determinations for O3 Welfare Effects 

3 

4 The adequate characterization of the effects of O3 on plants for the purpose of setting air 

5 quality standards depends not only on the choice of the index used (i.e., W126) to summarize O3

6 concentrations (Section 9.5 of the O3 ISA), but also on quantifying the response of the plant 

7 variables of interest at specific values of the selected index. The factors that determine the 

8 response of plants to O3 exposure include species, genotype and other genetic characteristics, 

9 biochemical and physiological status, previous and current exposure to other stressors, and 

10 characteristics of the exposure. 

11 Quantitative characterization of exposure-response in the 2006 O3 AQCD was based on 

12 experimental data generated for projects conducted by the National Crop Loss Assessment 

13 Network (NCLAN) and EPA’s National Health and Environmental Effects Research Laboratory, 

14 Western Ecology Division (NHEERL-WED) that used open-top chambers (OTCs) to expose 

15 crops and trees seedling to O3. In recent years, additional yield and growth results for soybean 

16 and aspen, respectively, (two of the species that provided extensive exposure-response 

17 information in those projects) have become available from studies that used free-air carbon 

18 dioxide/ozone enrichment (FACE) technology, which is intended to provide conditions much 

19 closer to natural environments (Pregitzer et al., 2008; Morgan et al., 2006; Morgan et al., 2004; 

20 Dickson et al., 2000). The results of these FACE studies provided support for the earlier 

21 findings reported in the OTC studies. 

Notlikely Inadequate

to infer Suggestive Likely Causal

Alteration of

terrestrial ecosystem

water cycling

Reduced carbon

sequestration in

terrestrial ecosystems

Visible foliar injury

effects

Reduced vegetation growth

Reduced productivity in

terrestrial ecosystems

Reduced yield and

quality of agricultural

crops

Alteration of below

ground biogeochemical

cycles

Alteration of

terrestrial community

composition
 2-7 

1 The quantitative exposure-response relationships described in the 2006 O3 AQCD have 

2 not changed in the current ISA, with the exception of the addition of one new species. The 

3 exposure-response models are summarized in the final ISA (U.S. EPA, 2013) and are computed 

4 using the W126 metric, cumulated over 90 days. These response functions provide an adequate 

5 basis for quantifying biomass loss damages. 

6 Visible foliar injury resulting from exposure to O3 has also been well characterized and 

7 documented over several decades of research on many tree, shrub, herbaceous, and crop species 

8 (U.S. EPA, 2006, 1996a, 1984, 1978). O3-induced visible foliar injury symptoms on certain 

9 bioindicator plant species are considered diagnostic as they have been verified experimentally in 

10 exposure-response studies, using exposure methodologies such as continuous stirred tank 

11 reactors (CSTRs), OTCs, and free-air fumigation. Experimental evidence has clearly established 

12 a consistent association of visible injury with O3 exposure, with greater exposure often resulting 

13 in greater and more prevalent injury. This REA assesses the risk of visible foliar injury at 

14 differing concentrations of O3 using U.S. Forest Service biomonitoring data along with soil 

15 moisture information to establish certain risk benchmarks. However, without robust 

16 concentration-response functions, a detailed quantitative assessment that can be applied across a 

17 range of ecosystems for foliar injury is not currently possible. 

18 2.4 ECOSYSTEM SERVICES 

19 The Risk and Exposure Assessment conducted as part of the Review of the Secondary 

20 National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur evaluates 

21 the benefits received from the resources and processes that are supplied by ecosystems. 

22 Collectively, these benefits are known as ecosystem services and include products or provisions, 

23 such as food and fiber; processes that regulate ecosystems, such as carbon sequestration; cultural 

24 enrichment; and supportive processes for services, such as nutrient cycling. Ecosystem services 

25 are distinct from other ecosystem products and functions because there is human demand for 

26 these services. In the Millennium Ecosystem Assessment (MEA), ecosystem services are 

27 classified into four main categories: 

28  Provisioning -- includes products obtained from ecosystems, such as the production 

29 of food and water. 
 2-8 

1  Regulating -- includes benefits obtained from the regulation of ecosystem processes, 

2 such as the control of climate and disease. 

3  Cultural -- includes the nonmaterial benefits that people obtain from ecosystems 

4 through spiritual enrichment, cognitive development, reflection, recreation, and 

5 aesthetic experiences. 

6  Supporting -- includes those services necessary for the production of all other 

7 ecosystem services, such as nutrient cycles and crop pollination (MEA, 2005). 

8 The concept of ecosystem services can be used to help define adverse effects as they 

9 pertain to NAAQS reviews. The most recent secondary NAAQS reviews have characterized 

10 known or anticipated adverse effects to public welfare by assessing changes in ecosystem 

11 structure or processes using a weight-of-evidence approach that includes both quantitative and 

12 qualitative data. For example, the previous O3 NAAQS review evaluated changes in foliar 

13 injury, growth loss, and biomass reduction on trees beyond the seedling stage using the 

14 TREGRO model. The presence or absence of foliar damage in counties meeting the existing 

15 standard has been used as a way to evaluate the adequacy of the secondary NAAQS. 

16 Characterizing a known or anticipated adverse effect to public welfare is an important 

17 component of developing any secondary NAAQS. According to the Clean Air Act (CAA), 

18 welfare effects include the following: 

19 “Effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, 

20 weather, visibility, and climate, damage to and deterioration of property, and hazards to 

21 transportation, as well as effect on economic values and on personal comfort and well-being, 

22 whether caused by transformation, conversion, or combination with other air pollutants.” 

23 (Section 302(h)) 

24 In other words, welfare effects are those effects that are important to individuals and/or 

25 society in general. Ecosystem services can be generally defined as the benefits that individuals 

26 and organizations obtain from ecosystems. EPA has defined ecological goods and services as 

27 the “outputs of ecological functions or processes that directly or indirectly contribute to social 

28 welfare or have the potential to do so in the future. Some outputs may be bought and sold, but 

29 most are not marketed” (U.S. EPA, 2006). Conceptually, changes in ecosystem services may be 
 2-9 

1 used to aid in characterizing a known or anticipated adverse effect to public welfare. In the 

2 context of this review, ecosystem services may also aid in assessing the magnitude and 

3 significance of a resource and in assessing how O3 concentrations may impact that resource. 

4 Figure 2-2 provides the World Resources Institute’s schematic demonstrating the 

5 connections between the categories of ecosystem services and human well-being (MEA, 2005). 

6 The interrelatedness of these categories means that any one ecosystem may provide multiple 

7 services. Changes in these services can impact human well-being by affecting security, health, 

8 social relationships, and access to basic material goods (MEA, 2005). The strength of the 

9 linkages, as indicated by arrow width, and the potential for mediation, as indicated by arrow 

10 color, differ in different ecosystems and regions. 

11 

12 

13 Figure 2-2 Linkages Between Ecosystem Services Categories and Components of Human 

14 Well-Being 

15 

16 The ecosystems of interest in this welfare risk and exposure assessment are impacted by 

17 the effects of anthropogenic air pollution, which may alter the services provided by the 

18 ecosystems in question. For example, changes in forest conditions as a result of O3 exposure 
 2-10 

1 may affect supporting services such as net primary productivity; provisioning services such as 

2 timber production; regulating services such as climate regulation; provisioning services such as 

3 food; and cultural services such as recreation and ecotourism. 

4 Where possible, we developed linkages to ecosystem services from indicators of each 

5 effect identified in the ISA (U.S. EPA, 2013). These linkages were based on existing literature 

6 and models, focus on the services identified in the peer-reviewed literature, and are essential to 

7 any attempt to evaluate O3-induced changes on the quantity and/or quality of ecosystem services 

8 provided. According to EPA’s Science Advisory Board Committee on Valuing the Protection of 

9 Ecological Systems and Services, these linkages are critical elements for determining the 

10 valuation of benefits of EPA-regulated air pollutants (SAB CVPESS, 2009). 

11 We have identified the primary ecosystem service(s) potentially impacted by O3 for 

12 major ecosystem types and components (i.e., terrestrial ecosystems, productivity) under 

13 consideration in this risk and exposure assessment. The impacts associated with various 

14 ecosystem services for each targeted effect are assessed in Chapters 5, 6, and 7 of this document 

15 at a national scale and in the more refined case studies. 
3-1 

1 3 SCOPE 

2 This chapter provides an overview of the scope and key design elements of the welfare 

3 risk and exposure assessment. The design of this assessment began with a review of the risk and 

4 exposure assessments completed during the previous review of the National Ambient Air Quality 

5 Standard for Ozone (O3 NAAQS) (U.S. EPA, 2007), with an emphasis on considering key 

6 limitations and sources of uncertainty recognized in that analysis. 

7 In October 2008, as an initial step in the current O3 NAAQS review, the Environmental 

8 Protection Agency (EPA) invited outside experts, representing a broad range of expertise, to 

9 participate in a workshop with EPA staff to help inform EPA’s plan for the review. The 

10 participants discussed key policy-relevant issues that would frame the review, as well as the most 

11 relevant new science that would be available to inform our understanding of these issues. One 

12 workshop session focused on planning for quantitative risk and exposure assessments, taking 

13 into consideration what new research and/or improved methodologies would be available to 

14 inform the design of a quantitative welfare risk and exposure risk assessment. Based in part on 

15 the workshop discussions, EPA developed a draft Integrated Review Plan for the Ozone National 

16 Ambient Air Quality Standards (IRP) (U.S. EPA, 2009) outlining the schedule, process, and key 

17 policy-relevant questions that would frame this review. On November 13, 2009, EPA held a 

18 consultation with the Clean Air Scientific Advisory Committee (CASAC) on the draft IRP (74 

19 FR 54562, October 22, 2009), which included opportunity for public comment. The final IRP 

20 incorporated comments from CASAC (Samet, 2009) and the public on the draft plan, as well as 

21 input from senior Agency managers. The final IRP included initial plans for the quantitative risk 

22 and exposure assessments for both human health and welfare (U.S. EPA, 2011a, chapters 5 and 

23 6). 

24 As a next step in the design of these quantitative assessments, the Office of Air Quality 

25 Planning and Standards (OAQPS) staff developed more detailed planning documents, including 

26 the following: O3 National Ambient Air Quality Standards: Scope and Methods Plan for Health 

27 Risk and Exposure Assessment (Health Scope and Methods Plan; U.S. EPA, 2011b) and O3

28 National Ambient Air Quality Standards: Scope and Methods Plan for Welfare Risk and 

29 Exposure Assessment (Welfare Scope and Methods Plan, U.S. EPA, 2011c). These plans were 

30 the subject of a May 19-20, 2011, consultation with CASAC (76 FR 23809, April 28, 2011). 
3-2 

1 Based on consideration of CASAC (Samet, 2011) and public comments on these plans and 

2 information in the second draft Integrated Science Assessment (ISA), we modified the scope and 

3 design of the risk and exposure assessment and drafted a memo with updates to the information 

4 presented in these plans (Wegman, 2012). We further modified the scope in response to 

5 comments from CASAC on the first draft assessment (Frey and Samet, 2012a). These plans, 

6 together with the update memo and comments from CASAC and the public, provide the basis for 

7 the discussion of the scope of the risk and exposure assessment provided in this chapter. 

8 Section 3.1 of this chapter provides a brief overview of the risk and exposure assessment 

9 completed for the previous O3 NAAQS review, including key limitations and uncertainties 

10 associated with that analysis. Section 3.2 provides a summary of the design of the current 

11 exposure assessment, including the ecosystem services framework, assessments for biomass loss 

12 and visible foliar injury. Section 3.3 provides an overview of the uncertainty and variability 

13 assessments. 

14 3.1 OVERVIEW OF RISK AND EXPOSURE ASSESSMENTS FROM PREVIOUS 

15 REVIEW 

16 The assessments conducted as part of the previous review focused on national-level O3-

17 related impacts to sensitive vegetation and their associated ecosystems. The vegetation exposure 

18 assessment was performed using an interpolation approach that included information from 

19 ambient monitoring networks and results from air quality modeling. The vegetation risk 

20 assessment included both tree and crop analyses. The tree risk analysis included three distinct 

21 lines of evidence: (1) observations of visible foliar injury in the field linked to monitored O3 air 

22 quality for the years 2001 – 2004; (2) estimates of tree seedling growth loss under then current 

23 and alternative O3 exposure conditions; and (3) simulated mature tree growth reductions of 

24 meeting alternative air quality standards on the predicted annual growth of mature trees from 

25 three different species. The crop risk analysis included estimates of crop yields under current 

26 and alternative O3 exposure conditions. EPA analyzed the associated changes in economic value 

27 upon meeting the levels of various alternative standards using an agricultural sector economic 

28 model. Key elements and observations from these risk and exposure assessments are outlined in 

29 the following sections. 
3-3 

1 3.1.1 Exposure Characterization 

2 In many rural and remote areas where sensitive species of vegetation can occur, 

3 monitoring coverage is limited. Thus, the 2007 Staff Paper (U.S. EPA, 2007) concluded that it 

4 was necessary to use an interpolation method to better characterize O3 concentrations over broad 

5 geographic areas and at the national scale. Based on the significant difference in monitoring 

6 network density between the eastern and western U.S., the 2007 Staff Paper further concluded 

7 that it was appropriate to use separate interpolation techniques in these two regions. EPA used 

8 monitoring data for the eastern interpolation, and in the western U.S., where rural monitoring is 

9 sparser, EPA used the Community Multi-scale Air Quality (CMAQ) model 

10 (http://www.epa.gov/asmdnerl/CMAQ, Byun and Ching, 1999; Byun and Schere, 2006) to 

11 develop scaling factors to augment the monitor interpolation. 

12 To evaluate changing vegetation exposures under selected air quality scenarios, EPA 

13 conducted a number of analyses. One analysis adjusted 2001 base year O3 concentration 

14 distributions using a rollback method (Rizzo, 2005, 2006) to reflect meeting the current and 

15 alternative secondary standard options. For the “just meet” and alternative 8-hour average 

standard scenarios, EPA generated the associated maps of estimated 12-hour, W126 exposures.1 16 

17 A second analysis in the 2007 Staff Paper identified the overlap between different forms 

18 of the secondary standard. The analysis was designed to evaluate the extent to which county19 level O3 concentrations measured in terms of various concentrations of the then current 8-hour 

20 average form overlapped with concentrations measured in terms of various concentrations of the 

21 12-hour W126 cumulative, seasonal form. This analysis found that the number of counties 

22 meeting either one or both of the standard forms depended greatly on the level of the forms 

23 selected as well as the air quality pattern that exists in a particular year or set of years. Thus, the 

24 2007 Staff Paper indicated that it remained uncertain as to the extent to which air quality 

25 improvements designed to reduce 8-hour average O3 concentrations would also reduce O3

26 exposures measured by a seasonal, cumulative W126 index. The 2007 Staff Paper stated this 

27 was an important consideration because: (1) the biological database stresses the importance of 

28 cumulative, seasonal exposures in determining plant response; (2) plants have not been 

29 specifically tested for the importance of daily maximum 8-hour O3 concentrations in relation to 

 1

 See Section 4.3.1 for more information regarding the W126 O3 exposure metric. 
3-4 

1 plant response; and (3) the effects of attainment of an 8-hour standard in upwind urban areas on 

2 rural air quality distributions cannot be characterized with confidence because of the lack of 

3 monitoring data in rural and remote areas. 

4 3.1.2 Assessment of Risks to Vegetation 

5 The risk assessments in the previous review reflected the availability of several lines of 

6 evidence that provided a picture of the scope of O3-related vegetation risks for seedling, sapling 

7 and mature tree species growing in field settings and, indirectly, for forested ecosystems. To 

8 assess visible foliar injury, the 2007 Staff Paper presented an assessment that combined USFS 

9 Forest Inventory and Analysis (FIA) biomonitoring site data with the county-level air quality 

10 data for those counties containing the FIA biomonitoring sites. 

11 EPA conducted separate assessments for seedlings and mature trees. To estimate growth 

12 reductions in seedlings, EPA used concentration-response (C-R) functions developed from open13 top chamber (OTC) studies for biomass loss for available seedling tree species and from 

14 information on tree growing regions derived from the U.S. Department of Agriculture's (USDA) 

15 Atlas of United States Trees. The C-R functions were then combined with projections of air 

16 quality based on 2001 interpolated exposures. To estimate growth reductions in mature trees, 

17 EPA used a tree growth model (TREGRO) to evaluate the effect of changing O3 concentration 

18 scenarios from just meeting alternative O3 standards on the growth of mature trees. TREGRO is 

19 a process-based, individual tree growth simulation model (Weinstein et al, 1991) that is linked 

20 with concurrent climate data to account for O3 and climate/meteorology interactions on tree 

21 growth. The model was run for a single western species (ponderosa pine) and two eastern 

22 species (red maple and tulip poplar). These three species were chosen based on the availability 

23 of species-specific parameterization in the model, their relative abundance in their respective 

24 regions, and the importance of their associated ecosystem services. 

25 To estimate yield loss in agricultural commodity, fruit and vegetable crops, EPA applied 

26 information from the National Crop Loss Assessment Network (NCLAN) program and a 1996 

27 California fruit and vegetable analysis to develop C-R functions. The crop risk assessment, like 

28 the tree seedling assessment, combined C-R information on nine commodity crops and six fruit 

29 and vegetable species with crop growing regions, and interpolated exposures during each crop 

30 growing season. 
3-5 

1 The 2007 Staff Paper also presented estimates of economic valuation for crops associated 

2 with the then current and alternative standards. The Agriculture Simulation Model (AGSIM) 

3 (Taylor, 1993) was used to calculate annual average changes in total undiscounted economic 

4 surplus for commodity crops and fruits and vegetables when then current and alternative 

5 standard levels were met. The 2007 Staff Paper recognized that the modeled economic impacts 

6 from AGSIM had many associated uncertainties, which limited the usefulness of these estimates. 

7 3.2 OVERVIEW OF CURRENT ASSESSMENT PLAN 

8 Since the 2008 O3 NAAQS review, new scientific information on the direct and indirect 

9 effects of O3 on vegetation and ecosystems, respectively, has become available. With respect to 

10 mature trees and forests, the information regarding O3 impacts to forest ecosystems has 

11 continued to expand, including limited new evidence that implicates O3 as an indirect contributor 

12 to decreases in stream flow resulting from direct impacts on whole tree-level water use. 

13 Recently published results from the long-term FACE studies provide additional evidence 

14 regarding chronic O3 exposures in forests, including decreased tree heights, stem volumes 

15 (Kubiske et al., 2006), seed weight and seed germination (Darbah et al., 2008, 2007); and 

16 changes in tree community structure (Kubiske et al., 2007). In addition, a comparison, presented 

17 in the ISA (Section 9.6.3), using recent data from Aspen FACE found that O3 effects on biomass 

18 accumulation in aspen during the first seven years of the experiment closely agreed with the 

19 exposure-response function based on data from earlier OTC experiments. In addition, recent 

20 available data from annual field surveys conducted by the USFS to assess visible foliar injury to 

21 selected tree species is available. In light of this more recent information, we are updating the 

22 analysis that combines the USFS data with recent air quality data to determine the incidence of 

23 visible foliar injury occurring across the U.S. at recent air quality concentrations and have 

24 included new assessments that combine foliar injury information with soil moisture data. 

25 One of the objectives of the risk assessment for a secondary NAAQS is to quantify the 

26 risks to public welfare, including ecosystem services. For example, the Risk and Exposure 

27 Assessment for Review of the Secondary National Ambient Air Quality Standards for Oxides of 

28 Nitrogen and Oxides of Sulfur (U.S. EPA, 2009) includes detailed discussions of how ecosystem 

29 services and public welfare are related and how an ecosystem services framework may be 

30 employed to evaluate effects on welfare. To the extent applicable, we provide qualitative and/or 
3-6 

1 quantitative assessments of ecosystem services impacted by O3 to inform the current review. In 

2 Chapter 5 of this assessment, we identify and describe the ecosystem services associated with the 

3 ecological effects for which data and methods for incremental analysis of direct O3 are not yet 

4 available. For example, we overlay data on fire incidence, risk, and expenditures related to fires 

5 in California (CAL-FIRE with O3 data to better characterize areas where O3 may result in 

6 increased risks of fires. Similarly, we also overlay data on bark beetle infestation with O3 data. 

7 In chapters 6 and 7, we identify and describe the ecosystem services associated with the 

8 ecological effects for biomass loss and foliar injury, respectively, including national scale 

9 assessments and more refined case study areas. 

10 3.2.1 Air Quality Considerations 

11 Air quality information and analyses are used to inform and support welfare-related 

12 assessments. The air quality information and analyses for this review build upon those in the 

13 ISA and include: (1) summaries of recent ambient air quality data; (2) application of a 

14 methodology to extrapolate measured O3 concentrations to areas without monitors, including 

15 natural areas important to a welfare effects assessment such as national parks; and (3) adjustment 

16 of air quality to simulate the distributions of O3 when just meeting existing or potential 

17 alternative W126 secondary standards. In this assessment, we use W126 as a shorthand for the 

18 maximum consecutive 3-month, 12-hour daylight W126 index value. Consistent with the 2007 

19 Staff Paper (U.S. EPA, 2007) and CASAC recommendation (Henderson et al., 2007), the air 

20 quality analyses in this assessment focus on the W126 metric. We provide more information 

21 regarding the air quality analyses in Chapter 4. 

22 3.2.1.1 Recent Ambient Data 

23 In addition to updating air quality summaries from the previous review, these air quality 

24 analyses include summaries of the recent ambient measurements for 2006 to 2010 for the 

25 existing form of the standard and potential alternative form of secondary standard. The ambient 

26 measurements are from monitor data from EPA’s Air Quality System (AQS) database (which 

27 includes National Park Service monitors) and the EPA’s Clean Air Status and Trends Network 

28 (CASTNET) network. We provide more information regarding the air quality analyses in 

29 section 4.3.2. 
3-7 

1 3.2.1.2 National O3 Exposure Surfaces 

2 Since the previous review, the extent of monitoring coverage in non-urban areas has not 

3 significantly changed. The vegetation exposure assessments rely on recent O3 concentrations 

4 adjusted to simulate just meeting the existing standard and of potential alternative W126 

5 secondary standards. National-scale O3 surfaces are used as input to the national foliar injury 

6 assessments described in subsequent sections. To estimate O3 exposure in areas without 

7 monitors, particularly those gaps left by a sparse rural monitoring network in the western United 

8 States, we used a spatial interpolation technique, called Voronoi Neighbor Averaging (VNA), 

9 (Gold, 1997; Chen et al., 2004) to create an air quality surface for the contiguous United States. 

10 We created annual W126 surfaces for each year between 2006 and 2010 and for a three year 

11 average for 2006-2008 at a 12km grid resolution. We provide more information regarding these 

12 data in section 4.3.1. 

13 3.2.1.3 Simulation of Existing and Alternative Standards 

14 To generate a national-scale spatial surface that simulates just attaining the existing 

15 standard, a spatial surface of O3 for 2006-2008 was created using VNA and monitor 

16 concentrations adjusted to reflect just meeting the existing standard. For potential alternative 

17 secondary standards, we simulated just meeting W126 standard levels of 15 ppm-hrs, 11 ppm18 hrs, and 7 ppm-hrs at O3 monitor locations, assuming the monitors already met the existing 

19 standard. We selected these standard levels for analysis in this REA because CASAC 

20 recommended and supported a range of alternative W126 standard levels from 15 to 7 ppm-hrs 

21 during the previous review. These adjusted monitor values were then used to create a spatial 

22 surface that provided W126 index values to areas without monitors. The adjusted surfaces are 

23 used in several vegetation assessments, including the geographic analysis for fire risk and bark 

24 beetle, the national and case study biomass loss assessments, and the park case studies for foliar 

25 injury. Each of these surfaces represents the 3-year average W126 index values. We provide 

26 more information regarding these data in section 4.3.2. 
3-8 

1 3.2.2 Relative Tree Biomass Loss and Crop Yield Loss 

2 3.2.2.1 National-Scale Assessment: Concentration-Response Functions 

3 for Tree Seedlings and Crops 

4 In the 2007 Staff Paper, the EPA derived information on tree species growing regions 

5 from the USDA Atlas of United States Trees (Little, 1971). In this assessment, we use more 

6 recent information (2006-2008) from the USFS Forest Health Technology Enterprise Team 

7 (FHTET) to update growing ranges for the 12 tree species studied by National Health and 

8 Environmental Effects Research Laboratory, Western Ecology Division (NHEERL-WED). We 

9 combine the national O3 surface with seedling C-R functions for each of the tree species and 

10 information on each tree species growing region to produce estimates of O3-induced seedling 

11 biomass loss for each of the 12 tree species. From this information, we generate GIS maps 

12 depicting seedling biomass loss for each species for each air quality scenario. For crops, we 

13 estimate yield loss for each of the 10 crop species from NCLAN. This analysis enabled direct 

14 evaluation of estimated seedling biomass loss for trees and yield loss for crops expected to occur 

15 under air quality exposure scenarios expressed in terms of recent air quality and, after simulation, 

16 of just meeting the existing standard and potential alternative secondary standards. In addition, 

17 this assessment can be used to determine the W126 benchmark values associated with 1 to 2 

18 percent seedling biomass loss for trees and 5 percent yield loss for crops. For biomass loss, 

19 CASAC recommended that EPA should consider options for W126 standard levels based on 

20 factors including a predicted 1 to 2 percent biomass loss for trees and a predicted 5 percent loss 

21 of crop yield. Small losses for trees on a yearly basis compound over time and can result in 

22 substantial biomass losses over the decades-long lifespan of a tree (Frey and Samet, 2012b). 

23 3.2.2.2 National Scale Assessment: National weighted RBL and Class I 

24 Areas 

25 To assess overall ecosystem-level effects from biomass loss, we used FHTET data for 

26 modeled predictions of stand density and basal area. The resolution of the FHTET data is 1,000 

27 square meter grids, and we summed these data into the larger CMAQ grid cells (12 km x 12 km). 

28 For the individual species analyses, these data were used only as a predictor of presence or 

29 absence. In the ecosystem-level analysis, these data were used to scale the biomass loss by the 

30 proportion of total basal area for each species. We combined the RBL values for 12 tree species 
3-9 

1 into a weighted RBL rate and considered the weighted value in relation to proportion of basal 

2 area covered (as measured by proportion of geographic area with available data on species). A 

3 weighted RBL value is a relatively straightforward metric to attempt to understand the potential 

4 ecological effect on some ecosystem services. We provide more information regarding the 

5 individual species analysis in section 6.2.1.3 and the combined analysis in 6.2.1.4. 

6 We also calculated an average weighted biomass loss for 12 tree species occurring in 

7 federally designated Class I areas using USFS estimates of the proportion of total basal area from 

8 FHTET. Out of 156 Class I areas nation-wide, 119 Class I areas had tree data available for this 

9 analysis. This analysis was conducted for air quality exposure scenarios expressed in terms of 

10 recent air quality (2006-2008) and after simulation of just meeting the existing standard and 

11 potential alternative secondary standards. We provide more information regarding this analysis 

12 in section 6.8.1.1. 

13 3.2.2.3 National-Scale Assessment: Ecosystem Services 

14 The national-level ecosystem services quantified in this review associated with biomass 

15 and yield loss include provisioning services (e.g., timber and crops) and regulating services (e.g., 

16 carbon sequestration). Where information is available, we describe the impacts on other 

17 ecosystem services such as impacts on biodiversity, biological community composition, health of 

18 forest ecosystems, aesthetic values of trees and plants, and the nutritive quality of forage crops. 

19 We also describe the cultural ecosystem services associated with non-timber forest products. In 

20 addition, there is new preliminary evidence that O3 adversely affects the ability of pollinators to 

21 find their targets, which could have broad implications for agriculture, horticulture, and forestry. 

22 We use the Forest and Agricultural Sector Optimization Model Greenhouse Gas version 

23 (FASOMGHG) model (Adams et al., 2005) to estimate O3 impacts on the agriculture and 

24 forestry sectors and quantify how O3 exposure to vegetation affects the provision of timber and 

25 crops and carbon sequestration. FASOM has been used recently in many evaluations of effects of 

26 climate change on the timber and agriculture market sectors, in part because it accounts for the 

27 tradeoffs between land use for forestry and agriculture. Specifically, FASOM is a dynamic, non28 linear programming model designed for use by the EPA to evaluate welfare benefits and market 

29 effects of O3-induced biomass loss in trees and of carbon sequestration in trees, understory, 

30 forest floor, wood products and landfills that would occur under different agricultural and 
3-10 

1 forestry scenarios. Using this model, we calculate the economic impacts of yield changes 

2 between recent ambient O3 conditions and after simulating just meeting the existing 75 ppb 

3 standard and alternative W126 standards. 

4 3.2.2.4 Case Study Areas: Five Urban Areas 

5 In selecting urban case study areas for more in-depth analysis of the ecosystem services 

6 associated with urban tree biomass loss, EPA relied on several criteria: 

7  Areas expected to have elevated W126 index values where ecological effects might 

8 be expected to occur. 

9  Occurrence of O3 sensitive tree species and/or species for which O3 concentration10 response curves have been generated. 

11  Availability of vegetation information in the case study area. 

12  Geographic coverage representing a cross section of the nation, including urban and 

13 natural settings. 

14 We use the i-Tree model to assess effects on regulating ecosystem services provided by 

15 urban forests, including pollution removal and carbon storage and sequestration for the case 

16 study areas. The i-Tree model is a publicly available, peer-reviewed software suite developed by 

17 the USFS and its partners to assess the ecosystem service impacts of urban forestry (available 

18 here: http://www.itreetools.org/). We collaborated with the USFS to vary the tree growth metric 

19 in the model, which allows us to assess the effects of O3 exposure on the ability of the forests in 

20 the selected case study area to provide the services enumerated by the model. Specifically, we 

21 estimate impacts on vegetation in Atlanta, Baltimore, Syracuse, the Chicago region, and the 

22 urban areas of Tennessee. We present results for model runs representing recent ambient O3

23 conditions, just meeting the existing 75 ppb standard, and just meeting alternative W126 

24 standards. 
3-11 

1 3.2.3 Visible Foliar Injury 

2 3.2.3.1 National Analysis of Visible Foliar Injury 

3 To assess visible foliar injury (hereafter referred to as foliar injury) at a national scale, we 

4 compared data from the USFS Forest Health Monitoring Network (USFS, 2011) with O3

5 exposure estimates and soil moisture data for 2006-2010. For estimates of short-term soil 

6 moisture in the contiguous U.S., we use NOAA’s Palmer Z drought index (NCDC, 2012b). 

7 Foliar injury sampling data were not available for several western states (Montana, Idaho, 

8 Wyoming, Nevada, Utah, Colorado, Arizona, New Mexico, Oklahoma, and portions of Texas). 

9 This analysis provides estimates of the presence and absence of foliar injury for each of the 5 

10 years by soil moisture category, which provides insight into whether drought provides protection 

11 from foliar injury. In addition, we estimated foliar injury by soil moisture category for elevated 

12 foliar injury. Using this analysis, we derived multiple W126 benchmark s for evaluating foliar 

13 injury at national parks in a screening-level assessment and three case studies. 

14 3.2.3.2 National Scale Screening-level Assessment of Visible Foliar Injury 

15 in 214 National Parks 

16 A study by Kohut (2007) assessed the risk of O3-induced visible foliar injury on O3-

17 sensitive vegetation in 244 parks managed by the National Park Service (NPS). We modified this 

18 screening-level assessment to use more recent O3 exposure and soil moisture data and to 

19 incorporate benchmarks derived from the national-scale foliar injury analysis (described above in 

20 section 3.2.3.1). Specifically, we use O3 monitoring data to create spatial surfaces of O3 exposure 

21 and short-term soil moisture data (Palmer Z) (NCDC, 2012b) for 2006 to 2010. These data 

22 reflect the contiguous U.S. only, which is a key reason why this assessment includes fewer parks 

23 than Kohut (2007). Overall, the screening-level assessment includes 42 parks with O3 monitors 

24 and 214 parks with O3 exposure estimated from the interpolated O3 surface. We combine these 

25 data with lists from the NPS of the parks containing O3-sensitive vegetation species (NPS, 2003, 

26 2006). Consistent with Kohut (2007), we consider the results for these parks without identified 

27 species as potential until sensitive species are identified in field surveys at these parks. 

28 Using the results of the national-scale foliar injury analysis, we derived six W126 

29 benchmark scenarios for evaluating foliar injury risk at parks in this screening-level assessment. 

30 One scenario reflects O3 exposure only, four scenarios reflect O3 exposure and soil moisture 
3-12 

1 jointly for different percentages of biosites with injury, and one scenario reflects O3 exposure 

2 and soil moisture jointly for elevated injury. For each of these scenarios, we identify the number 

3 of parks that exceed the benchmark criteria in each year. 

4 3.2.3.3 National Scale Assessment: Ecosystem Services 

5 We use GIS mapping developed for the ecological effects analysis to illustrate where 

6 foliar injury may be occurring, and we cross reference those areas to national statistics for 

7 recreational use available through the National Survey of Fishing, Hunting, and Wildlife8 Associated Recreation (U.S. DOI, 2011) and the National Survey on Recreation and the 

9 Environment (USDA, 2002). We also scale the resulting estimates of cultural service provision 

10 to the current population and values assigned using existing meta-data on willingness-to-pay 

11 from the Recreation Values Database.2 We understand that these estimates are limited to current 

12 levels of service provision and provide a snapshot of the overall magnitude of services 

13 potentially affected by O3 exposure. Currently, estimates of service loss from recent O3 

14 exposure is beyond the available data and resources, as is the calculation of changes in 

15 ecosystem services that might result from meeting existing and alternative O3 standards. 

16 However, the current losses in service from O3 exposure are embedded in estimates of the 

17 current level of services. 

18 3.2.3.4 Case Study Analysis: Three National Parks 

19 In selecting case study areas for more in-depth analysis of the ecosystem services 

20 associated with visible foliar injury, EPA relied on several criteria: 

21  Areas expected to have elevated W126 index values where ecological effects might 

22 be expected to occur. 

23  Availability of vegetation mapping, including estimates of species cover. 

24  Geographic coverage representing a cross section of the nation, including urban and 

25 natural settings. 

26  Occurrence of O3 sensitive species and/or species for which O3 concentration27 response curves have been generated. 

 

2

 Available at: http://recvaluation.forestry.oregonstate.edu/. 
3-13 

1 We selected Great Smoky Mountains National Park, Rocky Mountain National Park, 

2 and Sequoia/Kings National Park. All three of these park units are in areas with elevated 

3 ambient W126 index values, have vegetation maps, and have species that are considered O3

4 sensitive. We considered including Acadia National Park, but we determined it did not fit our 

5 selection criteria for O3 exposure. Using GIS, we compare the NPS vegetation maps to the 

6 national O3 surface to illustrate where foliar injury may be occurring, particularly with respect to 

7 park amenities such as trails. Ecological metrics quantified for each park include: 

8  Percent of vegetation cover affected by foliar injury. 

9  Percent of trails affected by foliar injury. 

10 In national parks, foliar injury affects primarily cultural values that include existence, 

11 bequest and recreational values. In addition, we describe the other nonuse values associated with 

12 national parks including existence and bequest values. We also provide park-specific statistics 

13 for recreational use available and estimates of service provision values using existing meta-data 

14 on willingness-to-pay from Kaval and Loomis (2003). We understand that these estimates are 

15 limited to current levels of service provision. Estimates of service loss due to O3 exposure are 

16 beyond the available data and/or resources for many if not all ecosystem services listed above. 

17 3.3 UNCERTAINTY AND VARIABILITY 

18 An important issue associated with any ecological risk assessment is the characterization 

19 of uncertainty and variability. Variability refers to the heterogeneity in a variable of interest that 

20 is inherent and cannot be reduced through further research. For example, there may be 

21 variability among C-R functions describing the relationship between O3 and vegetation injury 

22 across selected study areas. This variability may be due to differences in ecosystems (e.g., 

23 species diversity, habitat heterogeneity, and rainfall), concentrations and distributions of O3

24 and/or co-pollutants, and/or other factors that vary either within or across ecosystems. 

25 Uncertainty refers to the lack of knowledge regarding both the actual values of model 

26 input variables (parameter uncertainty) and the physical systems or relationships (model 

27 uncertainty – e.g., the shapes of concentration-response functions). In any risk assessment, 

28 uncertainty is, ideally, reduced to the maximum extent possible, through improved measurement 

29 of key parameters and ongoing model refinement. However, significant uncertainty often 
3-14 

1 remains, and emphasis is then placed on characterizing the nature of that uncertainty and its 

2 impact on risk estimates. The characterization of uncertainty can include both qualitative and 

3 quantitative analyses, the latter requiring more detailed information and, often, the application of 

4 sophisticated analytical techniques. Sources of variability that are not fully reflected in the risk 

5 assessment can consequently introduce uncertainty into the analysis. 

6 The goal in designing a quantitative risk assessment is to reduce uncertainty to the extent 

7 possible and to incorporate the sources of variability into the analysis approach to insure that the 

8 risk estimates are representative of the actual response of an ecosystem (including the 

9 distribution of that adverse response across the ecosystem). An additional aspect of variability 

10 that is pertinent to this risk assessment is the degree to which the set of selected case study areas 

11 provide coverage for the range of O3-related ecological risk across the U.S. 

12 Recent guidance from the World Health Organization (WHO, 2008) presents a four13 tiered approach for characterizing uncertainty. With this four-tiered approach, the WHO 

14 framework provides a means for systematically linking the characterization of uncertainty to the 

15 sophistication of the underlying risk assessment, where the decision to proceed to the next tier is 

16 based on the outcome of the previous tier’s assessment. Ultimately, the decision as to which tier 

17 of uncertainty characterization to include in a risk assessment will depend both on the overall 

18 sophistication of the risk assessment and the availability of information for characterizing the 

19 various sources of uncertainty. We used the WHO guidance as a framework for developing the 

20 approach used for characterizing uncertainty in this assessment. The four tiers described in the 

21 WHO guidance include: 

22  Tier 0: recommended for routine screening assessments, uses default uncertainty 

23 factors (rather than developing site-specific uncertainty characterizations); 

24  Tier 1: the lowest level of site-specific uncertainty characterization, involves 

25 qualitative characterization of sources of uncertainty (e.g., a qualitative assessment of 

26 the general magnitude and direction of the effect on risk results); 

27  Tier 2: site-specific deterministic quantitative analysis involving sensitivity analysis, 

28 interval-based assessment, and possibly probability bounded (high-and low-end) 

29 assessment; and 
3-15 

1  Tier 3: uses probabilistic methods to characterize the effects on risk estimates of 

2 sources of uncertainty, individually and combined. 

3 In this assessment, we applied a variety of quantitative (WHO Tier 2) and qualitative 

4 (WHO Tier1) analyses to address uncertainty and variability in this assessment of O3-related 

5 ecological risks. In general, we attempted to quantify uncertainty and variability where we had 

6 sufficient data to do so and addressed these aspects qualitatively where we did not have data. 

7 Two analyses include quantitative assessments of uncertainty and variability. For the analysis of 

8 the alternative percentages of biomass and yield loss, we plotted the C-R relationship for 54 crop 

9 studies and 52 tree seedling studies to estimate the differences in within-species variability. We 

10 also qualitatively compared the uncertainty in the relationship between C-R functions for tree 

11 seedlings and the effects on adult trees. For the screening-level assessment of foliar injury, we 

12 conducted several quantitative sensitivity analyses, including six scenarios reflecting different 

13 degrees of injury and consideration of soil moisture, three approaches for estimating O3 exposure 

14 at monitored parks, three durations for soil moisture data, and two time periods evaluating 

15 different years of analysis. We provide detailed tables characterizing the uncertainty inherent in 

16 the risk and exposure analyses at the end of Chapters 4, 5, 6, and 7. 
4-1 

1 4 AIR QUALITY CONSIDERATIONS 

2 4.1 INTRODUCTION 

3 Air quality information is used to assess exposures and ecological risks for national-scale 

air quality surfaces generated to estimate 2006-20081 4 average concentrations based on the W126 

5 exposure metric, which is defined later in this chapter. These national-scale air quality surfaces 

6 are generated for five air quality scenarios by the methodology summarized in Section 4.3.1 and 

7 4.3.4 below. The five scenarios are for recent air quality, air quality adjusted to just meet the 

8 current standard, and air quality further adjusted to just meet three different W126 index values: 

9 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. Additional national-scale air quality surfaces are 

10 generated using observed W126 concentrations for individual years from 2006-2010. This 

11 chapter describes the air quality information used in these analyses, providing an overview of 

12 monitoring data and air quality (section 4.2), and an overview of air quality inputs to the welfare 

13 risk and exposure assessments (section 4.3).

14 4.2 OVERVIEW OF O3 MONITORING AND AIR QUALITY 

15 To monitor compliance with the NAAQS, state and local environmental agencies operate 

16 O3 monitoring sites at various locations, depending on the population of the area and typical peak 

17 O3 concentrations (US EPA, 2013, sections 3.5.6.1, 3.7.4). In 2010, there were over 1,300 state, 

18 local, and tribal O3 monitors reporting concentrations to EPA (US EPA, 2012a, Figures 3-21 and 

19 3-22). The minimum number of O3 monitors required in a Metropolitan Statistical Area (MSA) 

20 ranges from zero, for areas with a population under 350,000 and with no recent history of an O3

21 design value greater than 85% of the NAAQS, to four, for areas with a population greater than 

10 million and an O3 design value greater than 85% of the NAAQS.2 22 In areas for which O3

23 monitors are required, at least one site must be designed to record the maximum concentration 

24 for that particular metropolitan area. Since O3 concentrations are usually significantly lower in 

 

1

 The focus was placed on the years of 2006-2008 based on availability of data during that time period. 

2

The existing monitoring network requirements (40 CFR Part 58, Appendix D) have an urban focus and do not 

address siting in non-urban (rural) areas. States may operate ozone monitors in non-urban (rural) areas to meet other 

objectives (e.g., support for research studies of atmospheric chemistry or ecosystem impacts). 
4-2 

1 the colder months of the year, O3 is required to be monitored only during the required O3

monitoring season, which varies by state (US EPA, 2012a, section 3.5.6 and Figure 3-20).3 2 

3 While the existing U.S. O3 monitoring network has a largely urban focus, to address 

4 ecosystem impacts of O3 such as biomass loss and foliar injury, it is equally important to focus 

5 on O3 monitoring in rural areas. Figure 4-1 shows the location of all U.S. O3 monitors operating 

6 during the 2006-2010 period. The gray dots which make up over 80% of the O3 monitoring 

7 network are “State and Local Monitoring Stations” (SLAMS) monitors which are largely 

8 operated by state and local governments to meet regulatory requirements and provide air quality 

9 information to public health agencies, and thus are largely focused on urban areas. The blue dots 

10 highlight two important subsets of the SLAMS network: “National Core” (NCore) multipollutant 

11 monitoring sites, and the “Photochemical Assessment Monitoring Stations” (PAMS) network. 

12 The green dots represent the Clean Air Status and Trends Network (CASTNET) monitors 

13 which are focused on rural areas. There were about 80 CASTNET sites operating in 2010, with 

14 sites in the Eastern U.S. being operated by EPA and sites in the Western U.S. being operated by 

15 the National Park Service (NPS). Finally, the black dots represent “Special Purpose Monitoring 

16 Stations” (SPMS), which include about 20 rural monitors as part of the “Portable O3 Monitoring 

17 System” (POMS) network operated by the NPS. Between the CASTNET, NCore, and POMS 

18 networks, there were about 120 rural O3 monitoring sites in the U.S. in 2010. 

19 

 

3

Some States and Territories are required to operate ozone monitors year-round, including Arizona, California, 

Hawaii, Louisiana, Nevada, New Mexico, Puerto Rico, Texas, American Samoa, Guam and the Virgin Islands.
4-3 

1 

2 Figure 4-1 Map of U.S. ambient O3 monitoring sites in operation during the 2006-2010 

3 

4 To determine whether or not the NAAQS have been met at an ambient O3 monitoring 

5 site, a statistic commonly referred to as a “design value” must be calculated based on 3 

6 consecutive years of data collected from that site. The form of the existing O3 NAAQS design 

value statistic is the 3-year average of the annual 4th 7 highest daily maximum 8-hour O3

8 concentration in parts per billion (ppb), with decimal digits truncated. The existing primary and 

9 secondary O3 NAAQS are met at an ambient monitoring site when the design value is less than 

or equal to 75 ppb.4 10 Figure 4-2 shows the design values for the existing 8-hour O3 NAAQS for 

11 all regulatory monitoring sites in the U.S. for the 2006-2008 period. Monitors shown as red dots 

12 had design values above the existing O3 NAAQS of 75 ppb in 2006-2008. 

13 

 

4

For more details on the data handling procedures used to calculate design values for the existing O3 NAAQS, see 

40 CFR Part 50, Appendix P.
4-4 

1 

2 Figure 4-2 Map of monitored 8-hour O3 design values for the 2006-2008 period 

3 

4 4.3 OVERVIEW OF AIR QUALITY INPUTS TO RISK AND EXPOSURE 

5 ASSESSMENTS 

6 In this section, we summarize the air quality inputs for the welfare risk and exposure 

7 assessments, and discuss the methodology used to adjust air quality to meet the existing standard 

8 and potential alternative standards. These steps are summarized in the flowchart in Figure 4-3 

9 and discussed in more detail in this section. 

10 Section 4.3.1 describes the W126 metric upon which the potential alternative standards 

11 are based. Section 4.3.2 describes the ambient air quality monitoring data used in the welfare 

12 risk and exposure assessments. Section 4.3.3 describes the procedure used to generate the 
4-5 

1 national-scale air quality surfaces upon which several of the welfare risk and exposure analyses 

2 are based, with further details in Appendix 4a. Finally, section 4.4.4 summarizes the method 

3 used to adjust observed air quality concentrations to just meet the existing standard and potential 

4 alternative standards, and discusses the resulting distributions of adjusted W126 concentrations. 

VNA

Recent

monitoredW126O3

Recent national ambient  

O3 W126 Fields

CMAQ

HDDM modeling

Relationships between

O3 sensitivities and

hourly O3

Recent

monitored  hourly O3

Hourly rolled backO3

Values at all monitors

In region

Translate modeled

Sensitivitiesto

“observed” sensitivities

based on monitored O3

Rolled‐back gridded

O3 W126 Fields

VNA

Check if all

Monitorsmeet

targeted

W126 alternative

standard

Hourly rolled backO3

Values at all monitors

In region

No

yes

Roll back to 75 ppb

Primary standard

Roll back to targeted

W126 alternative

standard

Aggregate hourlyO3

to W126 at each

monitor

Aggregate hourlyO3

to W126 at each

monitor

5 6 

7 Figure 4-3 Flowchart of air quality data processing for different parts of the welfare 

8 risk and exposure assessments. 

9 

10 4.3.1 Air Quality Metrics 

11 EPA focused the analyses in the welfare risk and exposure assessments on the W126 O3

12 exposure metric. The W126 metric is a seasonal aggregate of hourly O3 concentrations, designed 

13 to measure the cumulative effects of O3 exposure on vulnerable plant and tree species, with units 

14 in parts per million-hours (ppm-hrs). The metric uses a logistic weighting function to place less 

15 emphasis on exposure to low hourly O3 concentrations and more emphasis on exposure to high 

16 hourly O3 concentrations (Lefohn et al, 1988). 
4-6 

1 The first step in calculating W126 concentrations was to sum the weighted hourly O3

2 concentrations within each month, resulting in monthly index values. Since most plant and tree 

3 species are not photochemically active during nighttime hours, only O3 concentrations observed 

4 during daytime hours (defined as 8:00 AM to 8:00 PM local time) were included in the 

5 summations. The monthly W126 index values were calculated from the hourly O3 concentration 

6 data as follows: 

ࢎࢊ࡯ ෍ ෍ ൌ૚૛૟ ࢃ ࢟࢒ࢎ࢚࢔࢕ࡹ

ሻࢎࢊ࡯ כ ૟૛૚ሺെ ܘܠ܍ כ ૜૙૝૝ ൅ ૚

૚ૢ

ୀૡࢎ

ࡺ

૚ୀࢊ

7 where N is the number of days in the month, 

8 d is the day of the month (d = 1, 2, ..., N), 

9 h is the hour of the day (h = 0, 1, ..., 23), 

10 Cdh is the hourly O3 concentration observed on day d, hour h, in parts per million. 

11 Next, the monthly W126 index values were adjusted for missing data. If Nm is defined as 

12 the number of daytime O3 concentrations observed during month m (i.e. the number of terms in 

13 the monthly index summation), then the monthly data completeness rate is Vm = Nm / 12 * N. 

14 The monthly index values were adjusted by dividing them by their respective Vm. Monthly index 

15 values were not computed if the monthly data completeness rate was less than 75% (Vm < 0.75). 

16 Finally, the annual W126 index values were computed as the maximum sum of their 

17 respective adjusted monthly index values occurring in three consecutive months (i.e., January–

18 March, February–April, etc.). Three-month periods spanning across two years (i.e., November–

19 January, December–February) were not considered, because the seasonal nature of O3 makes it 

20 unlikely for the maximum values to occur at that time of year. The annual W126 concentrations 

21 were considered valid if the data met the annual data completeness requirements for the existing 

22 standard. Three-year W126 index values are calculated by taking the average of annual W126 

index values in the same three-month period in three consecutive years.5 23 

 

5

 W126 calculations are slightly modified in the case of the model adjustment scenarios described in Section 4.3.4. 

When calculating W126 for the model adjustment cases, we first found the three-year average of each three-month 

period, and then selected the three-month period with the highest three-year average using the same three-month 

period for each of the three years. 
4-7 

1 4.3.2 Ambient Air Quality Measurements 

2 Air quality monitoring data from 1,468 U.S. ambient O3 monitoring sites were retrieved 

3 for use in the risk and exposure assessments. The initial dataset was the same as the one used for 

4 the Health REA, which consisted of hourly O3 concentrations in ppb collected between 1/1/2006 

5 and 12/31/2010 from these monitors. Data for nearly 1,400 of these monitors were extracted 

from EPA’s Air Quality System (AQS) database6 6 , while the remaining data came from EPA’s 

7 Clean Air Status and Trends Network (CASTNET) database which consists of primarily rural 

8 monitoring sites. While the CASTNET monitors did not begin reporting regulatory data to AQS 

9 until 2011, it is generally agreed that data collected from these monitors prior to 2011 is of 

10 comparable quality to the data reported to AQS. 

11 Observations flagged in AQS as having been affected by exceptional events were 

12 included the initial dataset, but were not used in design value calculations in accordance with 

13 EPA’s exceptional events policy. Missing data intervals of 1 or 2 hours in the initial dataset 

14 were filled in using linear interpolation. These short gaps often occur at regular intervals in the 

15 ambient data due to an EPA requirement for monitoring agencies to perform routine quality 

16 control checks on their O3 monitors. Quality control checks are typically performed between 

17 midnight and 6:00 AM when O3 concentrations are low. Missing data intervals of 3 hours or 

18 more were not replaced, and interpolated data values were not used in design values calculations. 

19 Annual W126 concentrations were calculated from the ambient data for each year in the 

20 2006-2010 period, as well as 3-year averages of the 2006-2008 annual W126 concentrations. 

21 Figure 4-4 shows the 2006-2008 average W126 concentrations in ppm-hrs at all monitoring sites 

22 in the contiguous U.S. Monitors outside of the contiguous U.S. were not included in the welfare 

23 analyses since they fell outside of the CMAQ 12 km modeling domain, and were already well 

24 below the existing and potential alternative standards. 

25 

 

6

 EPA’s Air Quality System (AQS) database is a national repository for many types of air quality and related 

monitoring data. AQS contains monitoring data for the six criteria pollutants dating back to the 1970’s, as well as 

more recent additions such as PM2.5 speciation, air toxics, and meteorology data. At present, AQS receives hourly 

O3 monitoring data collected from nearly 1,400 monitors operated by over 100 state, local, and tribal air quality 

monitoring agencies.
4-8 

1 

2 Figure 4-4 Monitored 2006-2008 average W126 concentrations in ppm-hrs 

3 

4 4.3.3 National-scale Air Quality Surfaces for Recent Air Quality 

5 In addition to ambient monitoring data, the welfare risk and exposure assessments 

6 analyzed national-scale air quality surfaces. For the biomass loss analyses presented in Chapter 

7 6, a national-scale surface was generated from the monitored 2006-2008 average W126 

8 concentrations using the Voronoi Neighbor Averaging (VNA) technique (Gold, 1997; Chen et al, 

9 2004) (Figure 4-5). For the foliar injury analysis presented in Chapter 7, national-scale surfaces 

10 were generated from the monitored annual W126 concentrations for individual years 2006-2010, 

11 also using VNA. Maps of the annual W126 air quality surfaces for 2006-2010 are included in 

12 Appendix 4a. 

13 
4-9 

1 

2 Figure 4-5 National surface of observed 2006-2008 average W126 concentrations, in 

3 ppm-hrs 

4 

In the 1st 5 draft of the REA, the national-scale air quality surfaces were created by 

6 “fusing” monitored 2006-2008 average W126 concentrations with annual W126 concentrations 

7 from a 2007 CMAQ model simulation, using the enhanced Voronoi Neighbor Averaging 

8 (eVNA) technique (Timin et al., 2010). The resulting surfaces contained estimates of the 2006-

9 2008 average annual W126 concentrations at a 12km grid cell resolution in the contiguous U.S. 

10 modeling domain. In this draft, the air quality surfaces of the 2006-2008 average W126 

11 concentrations are based solely on monitored W126 concentrations and do not include CMAQ 

12 model predictions. The reason for this change from the first draft REA is discussed below. 

13 In addition to the VNA methodology, two alternative methods for creating the national14 scale air quality surfaces were also considered: eVNA and Downscaler (Berrocal et al, 2012; 

15 used in the health REA). Both the eVNA and Downscaler methods were tested using updated 
4-10 

2007 12km CMAQ modeling7 1 that is described in detail in Appendix 4b of the Health REA. 

2 While each of the three methods had its own advantages and disadvantages, the VNA method 

3 was ultimately selected because large differences between the modeled W126 surface and the 

monitored W126 concentrations8 4 made the two “data fusion” methods more uncertain in some 

5 instances, whereas VNA did not suffer from this problem since it is based solely on monitored 

6 values. Technical justification for the change from eVNA to VNA, including a cross-validation 

7 analysis, and comparisons between the resulting air quality surfaces for these three methods, can 

8 be found in Appendix 4a. 

9 4.3.4 Air Quality Adjustments to Meet Existing Primary and Potential Alternative 

10 Secondary O3 Standards 

11 In addition to observed W126 levels, the risk and exposure assessments also consider the 

12 relative change in risk and exposure after adjusting air quality to just meet the existing O3

13 standard of 75 ppb, and further adjusting air quality to just meet possible alternative standards 

14 with forms based on the W126 metric and levels of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. The 

15 sections below summarize the methodology used to adjust observed air quality concentrations to 

16 just meet the existing standard and potential alternative standards, and discuss the resulting 

17 adjusted distributions of W126 concentrations. More details on these inputs are provided in 

18 Appendix 4A. 

19 4.3.4.1 Adjustment Methods 

20 The model-based HDDM O3 adjustment approach used for this analysis is the same 

21 general methodology developed for evaluating air quality distributions that could occur if 

22 meeting various alternate levels of the primary standard. This methodology is described in detail 

23 in Chapter 4 and Appendix 4d of the health REA. There are a few key differences between the 

24 adjustments made in the health REA and those performed here. First, the adjustments in health 

25 REA focused on 15 urban case study areas while those used in the welfare REA cover all 

26 monitoring sites across the US. In the health REA, a uniform reduction of U.S. anthropogenic 

 

7

 The updated CMAQ modeling used wildfire emissions based on a multi-year average instead of 2007-specific 

wildfires. 

8

 The 2007 CMAQ simulation over-predicted W126 values by an average of 4 ppm-hrs in monitored locations. A 

more in depth model evaluation of CMAQ W126 values is provided in Appendix 4b.
4-11 

1 emissions was applied to all sites within an urban area. By applying equal proportional 

2 decreases in emissions throughout the contiguous U.S., we were able to estimate how hourly O3

3 concentrations would respond to changes in ambient NOx and VOC concentrations without 

4 simulating a specific control strategy. Note that the HDDM-adjustment approach was not 

5 designed to produce an optimal control scenario but instead aimed to characterize a potential 

6 distribution of air quality across a region when all monitors are meeting the existing standard and 

7 potential alternative standards. In this analysis, we recognize the regional nature of W126 values, 

8 thus we determined the requisite level of U.S. emissions reduction independently for nine 

9 distinct regions of the contiguous U.S. (Figure 4-6) based on the National Oceanic and 

10 Atmospheric Administration (NOAA) climate regions (Karl and Koss, 1984). NOAA 

11 characterizes each region as being “climatically consistent” and routinely uses these regions to 

12 describe regional climate trends. These regions were deemed an appropriate delineation for this 

13 analysis since geographic patterns of both O3 and plant species are driven by climatic features 

14 such as temperature and precipitation. Analogous to the procedure used in the health REA for 

15 the urban case study areas, a single NOx emissions perturbation was used to adjust ambient air 

16 quality data at all O3 monitoring sites for each region and standard. The magnitude of this 

17 emissions perturbation was determined independently for each region and standard by 

18 determining the smallest perturbation necessary to bring all sites into attainment of the existing 

19 standard or the potential alternative standards. By evaluating the effect of U.S. anthropogenic 

20 emissions reductions on all monitoring sites within a region, our analysis incorporates the effects 

21 of emissions reductions on both local O3 production and regional transport. Since each region is 

22 treated independently, the effects of the emissions reductions required to bring a particular region 

23 down to the targeted standard levels do not affect other regions which require less drastic 

24 emissions reductions. In portions of the country with lower W126 values than nearby locations, 

25 the emissions perturbation determined by the “controlling” monitor in the region may be larger 

26 than the emissions reductions that would be required if the nine climate regions were replaced by 

27 many smaller localized areas. However, by considering larger regions, we are able to account 

28 for the fact that nearby emissions reductions will affect O3 monitors already meeting the targeted 

standard level.9 29 

 

9

 Another proponent for the use of large regions is that the air quality adjustments are computationally intensive, and 
4-12 

1 A second distinction between the welfare air quality adjustments and those in the health 

2 REA is that only U.S. anthropogenic NOx emissions reductions were applied in the HDDM 

3 adjustment methodology for the welfare assessment (i.e. changes in U.S. anthropogenic VOC 

4 emissions changes were not considered). NOx emissions reductions are believed to be the most 

5 effective method for reducing O3 regionally, since most areas outside of urban population centers 

6 tend to be NOx limited in terms of O3 formation. 

7 Finally, it should be noted that this analysis includes adjustment to four standard levels: 

1) the existing standard of 75 ppb based on the 3-year average of the 4th 8 highest 8-hour daily 

9 maximum O3 concentration, 2) a W126-based standard with a level of 15 ppm-hrs, 3) a W126-

10 based standard with a level of 11 ppm-hrs, and 4) a W126-based standard with a level of 7 ppmhrs. The 2006-2008 average W126 concentrations and 4th 11 highest 8-hour daily maximum O3

12 concentrations were calculated for every monitor in each adjusted air quality scenario. For the 

13 analysis of each of the W126 standards, we started with W126 air quality values resulting from 

14 emission reductions required to just meet the existing standard at all monitors in the region, and 

15 only applied the HDDM adjustments to those regions where all sites were not already below the 

16 targeted W126 standard. In some cases, the emissions reductions necessary to meet the existing 

17 standard resulted in W126 values below the level of one or more potential alternative standards 

18 at all monitors within the region. In those cases, there is no change in air quality between the 

19 scenario meeting the existing standard and the scenario meeting the potential alternative 

20 standard. 

21 National-scale spatial surfaces that represent 2006-2008 W126 concentrations when just 

22 meeting the existing standard and the potential alternate standards (at the highest monitor in the 

23 region) were then created using the monitor values from the appropriate adjustment scenario and 

24 the Voronoi Neighbor Averaging (VNA) spatial interpolation technique. Additional details on 

25 the VNA technique can be found in Appendix 4A. Note that since each region was adjusted 

26 independently, in some cases distinct boundaries may be visible in the adjusted surfaces. These 

27 boundaries may be obscured to some degree due to the VNA interpolation procedure. 

28 

 

focusing on a small number of large regions, rather than many localized areas, greatly reduces the problem size. 
4-13 

1 

2 Figure 4-6 Map of the 9 NOAA climate regions (Karl and Koss, 1984) used in the 

3 national-scale air quality adjustments 

4 

5 4.3.4.2 Results 

6 Table 4-1 shows the highest monitored 2006-2008 average W126 concentration in each 

7 region for observed air quality and air quality adjusted to meet the existing O3 standard of 75 

8 ppb, and the highest monitored 2006-2008 8-hour O3 design value in each region for observed air 

9 quality and air quality adjusted to meet alternative standards based on the W126 metric with 

10 levels of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. Recall that the adjusted air quality surfaces 

11 used in the welfare risk and exposure analyses adjusted each region down to the existing O3

12 standard before applying additional reductions to meet the alternative standards. So effectively, 

13 Table 4-1 shows which standard was the “controlling” standard in each region. For example, 
4-14 

1 when all monitors in the Central region were adjusted to meet the existing standard, the highest 

2 resulting W126 value was 14 ppm-hrs. Thus, in the Central region, no further adjustments were 

3 necessary to meet the alternative standard of 15 ppm-hrs, but further adjustments were necessary 

4 to meet the alternative standards of 11 ppm-hrs and 7-ppm-hrs. 

5 Table 4-1 Highest 2006-2008 average W126 concentrations in the observed and existing 

6 standard air quality adjustment scenarios; highest 2006-2008 8-hour O3

7 design values in the observed and potential alternative standard air quality 

8 adjustment scenarios 

Region 

Highest W126 value (ppmhrs) Highest 8-hour maximum-based design value (ppb) 

Observed 75 ppb 

adjustment Observed 15 ppm-hr adjustment 

11 ppm-hr 

adjustment 

7 ppm-hr 

adjustment 

Central 18.3 14.0 88 83 72 66 

East North Central 13.8 6.4 86 86 83 76 

Northeast 17.9 2.6 92 94 89 76 

Northwest 6.6 3.8 76 76 76 76 

Southeast 22.2 11.9 95 81 74 67 

South 18.1 6.4 91 89 91 79 

Southwest 24.3 17.7 86 71 65 62 

West 48.6 18.9 119 71 66 61 

West North Central 12.2 9.3 80 80 79 72 

9 

10 From Table 4-1, it can be inferred that while each of the 9 regions had at least one 

11 monitor with 2006-2008 air quality data not meeting the existing O3 standard, there were 3 

12 regions (East North Central, Northwest, West North Central) with all monitors meeting the 

13 potential alternative standard with a W126 level of 15 ppm-hrs based on 2006-2008 air quality 

14 data. Furthermore, all monitors in the Northwest region met the alternative standards of 11 ppm15 hrs and 7-ppm-hrs based on 2006-2008 ambient data. When the air quality was adjusted to meet 

16 the existing standard, only two regions (West and Southwest) had monitors with W126 

17 concentrations remaining above 15 ppm-hrs. In addition, there were 4 regions (East North 

18 Central, Northeast, Northwest, and South) that already met 7 ppm-hrs when air quality was 

19 adjusted to meet the existing standard. 
4-15 

1 Figure 4-7 shows the national-scale 2006-2008 average W126 surface adjusted to just 

2 meet the existing O3 standard of 75 ppb using the HDDM adjustment procedure described in 

3 Section 4.3.2.1, and Figure 4-8 shows the difference between the recent air quality surface 

4 (Figure 4-5) and Figure 4-7. Figure 4-9, Figure 4-11, and Figure 4-13 show the 2006-2008 

5 average W126 surfaces further adjusted to just meet 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs, 

6 respectively, while Figure 4-10, Figure 4-12, and Figure 4-14 show the differences between the 

7 surface adjusted to just meet the existing O3 standard of 75 ppb, and the surfaces further adjusted 

8 to just meet the potential alternative standards based on the W126 metric with levels of 15 ppm9 hrs, 11 ppm-hrs, and 7 ppm-hrs. It is immediately apparent from these figures that the reductions 

10 in W126 between recent air quality and air quality just meeting the existing standard (Figure 4-8) 

11 are much larger than the additional reductions in W126 between air quality just meeting the 

12 existing standard and air quality meeting the alternative standards (Figure 4-10, Figure 4-12, 

13 Figure 4-14). 

14 This is further exemplified in Figure 4-15 and Figure 4-16, which show empirical 

15 probability density and cumulative distribution functions based on the monitored 8-hour O3

16 design values (Figure 4-15) and W126 concentrations (Figure 4-16) for each of the air quality 

17 scenarios. Both sets of density functions show a large shift leftward going from observed air 

18 quality to just meeting the existing standard, followed by much smaller leftward shifts from air 

19 quality just meeting the existing standard to air quality just meeting the potential alternative 

20 standards. The shift between air quality just meeting the existing standard and air quality just 

21 meeting the potential alternative standard based on the W126 metric with a level of 15 ppm-hrs 

22 is especially small, since only a few monitors in the Southwest and West regions did not meet a 

23 W126 level of 15 ppm-hrs when air quality was adjusted to meet the existing standard. 
4-16 

1 

2 Figure 4-7 National surface of 2006-2008 average W126 concentrations (in ppm-hrs) 

3 adjusted to just meet the existing O3 standard of 75 ppb 

4 
4-17 

1 

2 

3 Figure 4-8 Difference in ppm-hrs between the national surface of observed 2006-2008 

4 average W126 concentrations and the national surface of 2006-2008 average 

5 W126 concentrations adjusted to just meet the existing O3 standard of 75 

6 ppb 
4-18 

1 

2 Figure 4-9 National surface of 2006-2008 average W126 concentrations (in ppm-hrs) 

3 adjusted to just meet the potential alternative standard of 15 ppm-hrs 

4 
4-19 

1 

2 Figure 4-10 Difference in ppm-hrs between the national surface of 2006-2008 average 

3 W126 concentrations adjusted to just meet the existing O3 standard of 75 

4 ppb and the national surface of 2006-2008 average W126 concentrations 

5 adjusted to just meet the potential alternative standard of 15 ppm-hrs 

6 
4-20 

1 

2 Figure 4-11 National surface of 2006-2008 average W126 concentrations (in ppm-hrs) 

3 adjusted to just meet the potential alternative standard of 11 ppm-hrs 

4 

5 

6 
4-21 

1 

2 Figure 4-12 Difference in ppm-hrs between the national surface of 2006-2008 average 

3 W126 concentrations adjusted to just meet the existing O3 standard of 75 

4 ppb and the national surface of 2006-2008 average W126 concentrations 

5 adjusted to just meet the potential alternative standard of 11 ppm-hrs 

6 
4-22 

1 

2 Figure 4-13 National surface of 2006-2008 average W126 concentrations (in ppm-hrs) 

3 adjusted to just meet the potential alternative standard of 7 ppm-hrs 

4 

5 

6 
4-23 

1 

2 Figure 4-14 Difference in ppm-hrs between the national surface of 2006-2008 average 

3 W126 concentrations adjusted to just meet the existing O3 standard of 75 

4 ppb and the national surface of 2006-2008 average W126 concentrations 

5 adjusted to just meet the potential alternative standard of 7 ppm-hrs 

6 
4-24 

1 

2 Figure 4-15 Empirical probability density and cumulative distribution functions for the 

3 monitored 2006-2008 8-hour O3 design values, and the 2006-2008 8-hour O3

4 design values after adjusting to just meet the existing and potential 

5 alternative standards 

6 
4-25 

1 

2 Figure 4-16 Empirical probability density and cumulative distribution functions for the 

3 monitored 2006-2008 average W126 concentrations, and the 2006-2008 

4 average W126 concentrations after adjusting to just meet the existing and 

5 potential alternative standards. Note W126 concentrations are displayed using 

6 a square root scale. 

7 
4-26 

1 4.4 QUALITATIVE ASSESSMENT OF UNCERTAINTY 

2 As noted in Chapter 3, we have based the design of the uncertainty analysis for this 

3 assessment on the framework outlined in the WHO guidance (WHO, 2008). For this qualitative 

4 uncertainty analysis, we have described each key source of uncertainty and qualitatively assessed 

5 its potential impact (including both the magnitude and direction of the impact) on risk results, as 

6 specified in the WHO guidance. In general, this assessment includes qualitative discussions of 

7 the potential impact of uncertainty on the results (WHO Tier1) and quantitative sensitivity 

8 analyses where we have sufficient data (WHO Tier 2). 

9 Table 4-2 includes the key sources of uncertainty identified for the O3 REA. For each 

10 source of uncertainty, we have (a) provided a description, (b) estimated the direction of influence 

11 (over, under, both, or unknown) and magnitude (low, medium, high) of the potential impact of 

12 each source of uncertainty on the risk estimates, (c) assessed the degree of uncertainty (low, 

13 medium, or high) associated with the knowledge-base (i.e., assessed how well we understand 

14 each source of uncertainty), and (d) provided comments further clarifying the qualitative 

15 assessment presented. The categories used in describing the potential magnitude of impact for 

16 specific sources of uncertainty on risk estimates (i.e., low, medium, or high) reflect our 

17 consensus on the degree to which a particular source could produce a sufficient impact on risk 

18 estimates to influence the interpretation of those estimates in the context of the secondary O3

19 NAAQS review. Where appropriate, we have included references to specific sources of 

20 information considered in arriving at a ranking and classification for a particular source of 

21 uncertainty.
4-27 

1 Table 4-2 Summary of Qualitative Uncertainty Analysis of Key Air Quality Elements in the O3 NAAQS R

Source Description 

Potential influence of 

uncertainty on risk estimates Knowledge- Base 

uncertainty* 

Com

(KB: knowledge base, INF: in

Direction Magnitude estim

A. Ambient air quality measurement data 

O3 concentrations measured by 

ambient monitoring instruments have inherent uncertainties associated with them. Additional uncertainties due to other factors may include: - monitoring network design - required O3 monitoring seasons 

- monitor malfunctions - wildfire and smoke impacts - interpolation of missing data 

Both Low Low 

KB: O3 measurements are assumed 

instrument’s Method Detection Limmost instruments. EPA requires thare performed on all regulatory insto AQS are certified by both the mcorresponding EPA regional officefor details. The CASTNET monitoquality assurance requirements, anto be of comparable quality to the KB: Monitor malfunctions sometimmissing data or poor data quality. malfunctions are usually flagged bremoved from AQS. In addition, tseveral routines to identify suspiciKB: There is a known tendency focause interference in O3 instrumen

analyzers were reported to be biaseμg/m3 of PM2.5 from wildfire smo

smoke concentrations high enough are infrequent and the overall impaKB: Missing intervals of 1 or 2 hointerpolated, which may cause somHowever, due to the short length otendency for these periods to occur are low, the overall impact is belieINF: EPA’s current O3 monitoring 

Part 58, Appendix D) are primarily areas where O3 concentrations are 

by the current monitoring networkstate that at least one monitor withcapture the highest O3 concentratio

some bias toward higher measured INF: Each state has a required O3 m

length from May – September to yO3 monitors off during months out
4-28 

Source Description 

Potential influence of 

uncertainty on risk estimates Knowledge- Base 

uncertainty* 

Com

(KB: knowledge base, INF: in

Direction Magnitude estim

others leave them on. This can caudata available throughout the year outside of the required O3 monitor

B. Veronoi Neighbor Averaging (VNA) spatial fields 

VNA is a spatial interpolation technique used to estimate W126 concentrations in unmonitored areas, which has inherent uncertainty 

Both Low- Medium Low-Medium 

KB: VNA interpolates 2006-2008 from hourly ambient air quality mecell in each of the 9 NOAA climat

weighted based on distance from nthe uncertainty tends to increase wsites becomes greater. As a result, VNA estimates near urban areas w

dense, and more uncertainty in spamonitors are further apart, particul

C.CMAQ modeling 

Model predictions from CMAQ, like all deterministic photochemical models, have both parametric and structural uncertainty associated with them 

Both Low- Medium Low-Medium 

KB: Structural uncertainties are unphysical and chemical processes in of chemical mechanism used to chatmosphere, choice of land surface boundary layer model. KB: Parametric uncertainties inclu(hourly meteorological fields, hourconditions, and boundary conditionKB: Uncertainties due to initial co10 day ramp-up period from which KB: Evaluations of models against build confidence that the model pe

despite the uncertainties listed aboevaluation provided in Appendix 4acceptable model performance whtypical state-of-the science regionasummarized in Simon et al (2012). and over-estimations do occur at sothe largest mean biases occur on loseason. In addition, the model did high ozone events occurring in the of biases are not likely to substantisince low ozone days are not heavicalculation and since the highest 3
4-29 

Source Description 

Potential influence of 

uncertainty on risk estimates Knowledge- Base 

uncertainty* 

Com

(KB: knowledge base, INF: in

Direction Magnitude estim

calculated for April-October in thi

D. Higher Order Decoupled Direct Method (HDDM) 

HDDM allows for the approximation of ozone concentrations under alternate emissions scenarios without rerunning the model simulation multiple times using different emissions inputs. This approximation becomes less accurate for larger emissions perturbations especially under nonlinear chemistry conditions. 

Both Low- Medium Low-Medium 

KB: To accommodate increasing uperturbations, the HDDM modelinemissions levels to allow for a bett

response over the entire range of ebrute force10 hourly ozone concent

approximation was quantified for 5for each urban case study areas (as hREA). At 50% NOx cut conditiothese multiple simulations predicte

a mean bias and a mean error less tstudy areas compared to brute forccut conditions, HDDM using inforsimulations predicted hourly ozone 

less than +/- 3ppb and a mean errocase study areas. 

E. Application of HDDM sensitivities to ambient data 

In order to apply modeled sensitivities to ambient measurements, regressions were developed which relate ozone response to emissions perturbations with ambient ozone concentrations for every season, hour-of-the-day and monitor location. Applying ozone responses based on this relationship adds uncertainty. 

Both Medium Medium 

KB: Preliminary work showed thathese regressions were generally stseason, hour-of-the-day, and monimodeling in Detroit and Charlotte. evaluated for each regression in th280,000 regressions created (1300 coefficients × 3 emissions levels × regressions). Statistics can quantifmodeled relationships and can quaany given ozone concentration basthat portion of the distribution for possible to quantify the applicabilithe actual atmosphere. KB: The regression model providestandard error value for ozone respozone concentration. The base ana

which will inherently dampen som

10 Brute force model concentrations refer to model results obtained by changing the emissions inputs and re-running the CMAQ model. 

estimates are an approximation of the model results that would be obtained by re-running the simulation with different inputs. 
4-30 

Source Description 

Potential influence of 

uncertainty on risk estimates Knowledge- Base 

uncertainty* 

Com

(KB: knowledge base, INF: in

Direction Magnitude estim

response. The standard error of eapropagated through the calculation 

at various standard levels. These sof variability that is lost due to the emissions reductions increased for errors were larger for adjustments 

percentile) standard errors of hourlcase ranged from 0.13 (0.29) to 1.0regions. Mean (95th percentile) sta

the 7 ppmh adjustment case ranged ppb. The largest standard errors ocregions. INF: The NOx emissions reductiondecreases in ozone depending on thwhere the use of the central tendenestimated emissions reductions reqlevel, we expect that the benefits oconcentrations and the disbenefits generally underestimated. Since thcalculate W126 amplifies the impoabove 50-60 ppb and dampens the concentrations below 50 ppb, this bunderestimation of the W126 metruse of the central tendency of respemissions reductions required to acthat the W126 metric would be ove

F. Applying modeled sensitivities to unmodeled time periods 

Relationships between ozone response and hourly ozone concentration were developed based on 7 months of modeling: AprilOctober 2007. These relationships were applied to ambient data from 2006-2008. 

Both Low- Medium Low-Medium 

KB: The seven months that were mmeteorological conditions. In case

frequent occurrences of certain typ

should be able to account for this. 

2-3 high ozone days associated witin the 2007 modeling but had 30-4regression may be more uncertain should still be able to capture the capplied to the more frequent occurother hand, the meteorology/ozone completely outside the range of cothen the regression based on mode
4-31 

Source Description 

Potential influence of 

uncertainty on risk estimates Knowledge- Base 

uncertainty* 

Com

(KB: knowledge base, INF: in

Direction Magnitude estim

capture those conditions. KB: If emissions change drasticallthe time of the ambient data measurelationship between ozone responregressions derived from the 2007 to measurements made within one Although some emissions changes believe it is still reasonable to applsmall window of measurements whmodeling. 

G. Assumptions of across-the-board emissions reductions 

Ozone response is modeled for across-the-board reductions11 in 

U.S. anthropogenic NOx. These across-the-board cuts do not reflect actual emissions control strategies. 

Both Medium Medium 

KB: The form, locations, and timinwould be undertaken to meet variounknown. The across-the-board em

down uniformly across time and sprespond to changes in ambient levereflect spatial and temporal heterogregional emissions reductions. 

1 * Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the context of assessing and characterizi2 classified as having a “low” impact would not be expected to impact the interpretation of risk estimates in the context of the O3 NAAQ3 classified as having a “medium” impact have the potential to change the interpretation; and sources classified as “high” are likely to inf4 of risk in the context of the O3 NAAQS review. 

11 “Across the board” emission reductions refer to equal percentage NOx emissions cuts in all source categories and all locations at all t
5-1 

1 5 O3 RISK TO ECOSYSTEM SERVICES 

2 5.1 INTRODUCTION 

3 The EPA is using an ecosystem services framework as described in Chapter 2 to help 

4 define how the damage to ecosystems informs determinations of the adversity to public welfare 

5 associated with changes in ecosystem functions. Figure 9-1 of the ISA (U.S. EPA, 2013) is 

6 reproduced below (Figure 5-1) as a summary of exposure and effects that lead to potential loss of 

7 ecosystem services. Figure numbers in this figure refer to Chapter 9 of the ISA.

8 

9 

10 Figure 5-1 Conceptual Diagram of the Major Pathway through which O3 Enters Plants and the Major 

11 Endpoints that O3 May Affect in Plants and Ecosystems 

12 

13 

O3 exposure

O3 uptake & physiology (Fig 9-2)

•Antioxidant metabolism up-regulated

•Decreased photosynthesis

•Decreased stomatal conductance 

or sluggish stomatal response

Effects on leaves

•Visible leaf injury

•Altered leaf production 

•Altered leaf chemical composition

Plant growth (Fig 9.8)

•Decreased biomass accumulation

•Altered reproduction

•Altered carbon allocation

•Altered crop quality

Belowground processes (Fig 9.8)

•Altered litter production and decomposition

•Altered soil carbon and nutrient cycling

•Altered soil fauna and microbial communities

Differential O3 sensitivity

Affected ecosystem services

•Decreased productivity 

•Decreased C sequestration

•Altered water cycling (Fig 9-7)

•Altered community composition 

(i.e., plant, insect & microbe)
5-2 

1 This chapter focuses primarily on those ecosystem services potentially at risk from O3

2 exposure that we were only able to assess qualitatively, due to a lack of sufficient data, methods, 

3 or resources to allow quantification of the incremental effects of O3. It also includes semi4 qualitative GIS driven assessments of the potential impacts of O3 on risks of fire and bark beetle 

5 damage and identifies additional adverse effects associated with O3 exposure that we are not able 

6 to assess, even qualitatively. In contrast, Chapters 6 and 7 provide quantitative assessments for 

7 risks related to tree biomass loss, timber and crop yield loss and visible foliar injury. Figure 5-2 

8 illustrates the relationships between the ecological effects of O3 and the anticipated ecosystem 

9 services impacts that will be discussed in the following sections. 

10 

11 Figure 5-2 Relationship between Ecological Effects of O3 Exposure and Ecosystem Services 

12 

13 While most of the impacts of O3 on these services cannot be specifically quantified, it is 

14 important to provide an understanding of the magnitude and significance of the services that may 

15 be negatively impacted by O3 exposures. For many services, we can estimate the current total 
5-3 

1 magnitude and, for some, we can estimate the current value of the services in question. The 

2 estimates of current service provision will reflect the loss of services occurring from historical 

3 and current O3 exposure and provide context for the importance of any potential impacts of O3

4 on those services, e.g., if the total value of a service is small, the likely impact of O3 exposure 

5 will also be small. Likewise, if the total value is large, there is a higher potential for significant 

6 damage, even if the relative contribution of O3 as a stressor is small. Also, in some cases we can 

7 provide information on locations where high O3 exposures occur in conjunction with significant 

8 ecosystem service impairment. Specifically, we can provide information on areas where high 

9 W126 index values may have the greatest contribution to the service impairment caused by fires 

10 in California and bark beetle damage in forests. This assessment will address O3 impacts on 

11 ecosystem services following the framework of the Millennium Ecosystem Assessment (MEA, 

12 2005). In line with the framework, the subsequent sections are divided into supporting, 

13 regulating, provisioning, and cultural ecosystem services. 

14 5.2 SUPPORTING SERVICES 

15 Supporting services are the services needed by all of the other ecosystem services. Other 

16 categories of services have relatively direct or short-term impacts on humans, while the impacts 

17 on public welfare from supporting services are generally either indirect or occur over a long 

18 time. The next sections describe potential impacts of O3 on some of these supporting services. 

19 5.2.1 Net Primary Productivity 

20 Primary productivity underlies the provision of many subsequent ecosystem services that 

21 are highly valued by the public, including provision of food and timber. The ISA determined 

22 that biomass loss due to O3 exposure may reduce net primary productivity (NPP). According to 

23 the ISA (U.S. EPA, 2013), when compared to 1860’s era preindustrial conditions, NPP in U.S. 

24 Mid-Atlantic temperate forests decreased 7-8 percent per year from 1991-2000 due to O3

25 exposure, even with growth stimulation provided by elevated carbon dioxide and nitrogen 

26 deposition. Also, compared to a presumed pristine condition in 1860, NPP for the conterminous 

27 U.S from 1950-1995 decreased as much as 13 percent per year in some areas in the agricultural 

28 region of the Midwest during the mid-summer. While there are models available to help 

29 quantify changes in NPP and in the hydrologic cycle discussed in Section 5.3.1 we were not able 
5-4 

1 to attempt quantification of NPP or hydrology due to resource limitations. Additionally these 

2 services are more difficult to interpret in ways that are meaningful to people. 

3 5.2.2 Community Composition and Habitat Provision

4 Community composition or structure is also affected by O3 exposure. Since species vary 

5 in their response to O3, those species that are more resistant to the negative effects of O3 are able 

6 to out-compete more susceptible species. For example, according to studies cited in the ISA 

7 (U.S. EPA, 2013), the San Bernardino area community composition in high- O3 sites has shifted 

8 toward O3- tolerant species such as white fir, sugar pine, and incense cedar at the expense of 

9 ponderosa and Jeffrey pine. Changes in community composition underlie possible changes in 

10 associated services such as herbivore grazing, production of preferred species of timber, and 

11 preservation of unique or endangered communities or species, among others. 

12 The National Survey on Recreation and the Environment (NSRE) is an ongoing survey 

13 of a random sample of adults over the age of 16 on their interactions with the environment that 

14 provides data on the values survey respondents place on the provision of habitat for wild plants 

15 and animals. Table 5-1 summarizes the responses to survey questions regarding the value of 

16 wildlife habitat and preservation of unique or endangered species. 

17 Table 5-1 Responses to NSRE Wildlife Value Questions 

Service 

Percent of Respondents Considering the Service 

Important 

Extremely 

Important 

Very 

Important 

Moderately 

Important 

Total 

Wildlife Habitat 51 36 9 96 

Preserving Unique Wild Plants and 

Animals 

44 36 13 93 

Protecting Rare or Endangered Species 50 33 11 94 

18 *The remaining respondents felt these services were not important. 

19 There exist meta-analyses on the monetary values Americans place on threatened and 

20 endangered species. One such study (Richardson and Loomis, 2009) estimates the average 

21 annual willingness to pay (WTP) for a number of species. The authors report a wide range of 

22 values dependent on the change in the size of the species population, type of species, and 
5-5 

1 whether visitors or households are valuing the species. The average annual WTP for surveyed 

2 species ranged from $9/year for striped shiner for Wisconsin households to $261/year for 

3 Washington state households value for anadromous fish, such as salmon, in constant 2010$. 

4 5.3 REGULATING SERVICES 

5 Regulating services as defined by the MEA (2005) are those services that regulate 

6 ecosystem processes. Services such as air quality, water, climate, erosion, and pollination 

7 regulation fit within this category. The next sections describe potential impacts of O3 on some of 

8 these services. 

9 5.3.1 Hydrologic Cycle

10 Regulation of the water cycle is another ecosystem service that can be adversely affected 

11 by the effects of O3 on plants. Studies of O3-impacted forests in eastern Tennessee in or near the 

12 Great Smoky Mountains has shown that ambient O3 exposures resulted in increased water use in 

13 O3-sensitive species which led to decreased modeled late-season stream flow in those 

14 watersheds. The increased water use resulted from a sluggish stomatal response that increases 

15 water loss, which in turn increases water requirements (U.S. EPA, 2013). Ecosystem services 

16 potentially affected by such a loss in stream flow could include habitat for species (e.g., trout) 

17 that depend on an optimum stream flow or temperature. Additional downstream effects could 

18 potentially include a reduction in the quantity and/or quality of water available for irrigation or 

19 drinking and for recreational use. Conversely, one model study reported in the ISA (U.S. EPA, 

20 2013) associate reduced stomatal aperture from O3 exposure combined with nitrogen limitation 

21 with decreased water loss, which in turn increased runoff; increased runoff could lead to more 

22 soil erosion. Regardless of the response, water cycling in forests is affected by O3 exposure and 

23 has impacts on ecosystem services associated with both water quality and quantity. As part of 

24 the NSRE, the United States Forest Service (USFS) and the National Oceanographic and 

25 Atmospheric Administration (NOAA) jointly surveyed Americans, age 16 and over, for their 

26 report on Uses and Values of Wildlife and Wilderness in the United States. The NSRE 

27 specifically asked respondents to rank the importance of water quality as a benefit of wilderness. 

28 Ninety one percent of respondents ranked water quality protection as either extremely or very 

29 important; less than 1 percent of respondents ranked this service as not important at all. 
5-6 

1 5.3.2 Pollination 

2 The ISA (U.S. EPA, 2013) identifies O3 as a possible agent affecting the travel distance 

3 of and the loss of specificity of volatile organic compounds emitted by plants, some of which act 

4 as scent cues for pollinators. While it is not possible to explicitly calculate the loss of pollination 

5 services resulting from this negative effect on scent cues, the loss is reflected in the current 

6 estimated value of $18.3 billion (2010$) for all pollination services, managed and wild, in North 

7 America (U.S., Canada, and Bermuda) (Gallai et al., 2009). 

8 5.3.3 Fire Regulation

9 Fire regime regulation is also negatively affected by O3 exposure. Grulke et al. (2009) 

10 reported various lines of evidence indicating that O3 exposure may contribute to southern 

11 California forest susceptibility to wildfires by increasing leaf turnover rates and litter. This, in 

12 turn, creates increased fuel loads on the forest floor, O3-increased drought stress, and increased 

13 susceptibility to bark beetle attacks. According to the National Interagency Fire Center 

14 (http://www.nifc.gov/fireInfo/fireInfo_statistics.html), in 2010 in the United States over 3 

15 million acres burned in wildland fires and an additional 2 million acres were burned in 

16 prescribed fires. Over the 5-year period from 2004 to 2008, Southern California alone 

17 experienced, on average, over 4,000 fires per year burning, on average, over 400,000 acres per 

18 fire (National Association of State Foresters [NASF], 2009). 

19 The short-term benefits of reducing the O3-related fire risks include the value of avoided 

20 residential property damages; avoided damages to timber, rangeland, and wildlife resources; 

21 avoided losses from fire-related air quality impairments; avoided deaths and injury due to fire; 

22 improved outdoor recreation opportunities; and savings in costs associated with fighting the fires 

23 and protecting lives and property. For example, the California Department of Forestry and Fire 

24 Protection (CAL FIRE) estimated that average annual losses to homes due to wildfire from 1984 

25 to 1994 were $226 million (CAL FIRE, 1996) and were over $263 million in 2007 (CAL FIRE, 

26 2008) in inflation adjusted 2010$. In fiscal year 2008, CAL FIRE’s budgeted costs for fire 

27 suppression activities were nearly $304 million 2010 dollars (CAL FIRE, 2008). CAL FIRE also 

28 estimates fire risk in the state on a -1 to 5 scale, with 2 being moderate risk. Using GIS, we 

29 developed maps that overlay the area of California with mixed conifer forest and the fire risk 

30 area calculated by CAL FIRE. We then generated maps overlaying the current ambient O3

31 conditions and the modeled alternative scenarios with the areas of mixed conifer forest that have 
5-7 

1 a fire risk in the moderate and higher range. These maps allow us to calculate the area of mixed 

2 conifer forests with moderate to high fire risk and high W126 index values under various 

3 scenarios. Figure 5-3 shows W126 index values after just meeting the existing and alternative 

4 standard levels in areas in California with fire risk greater than 2 on CAL FIRE’s scale. 

5 

6 Figure 5-3 Overlap of W126 Index Values for the Existing Standard and Alternative W126 Standards, 

7 Fire Threat > 2, and Mixed Conifer Forest 

8 

9 The highest fire risk and highest W126 index values overlap with each other, as well as 

10 with significant portions of mixed conifer forest. Under recent conditions, over 97 percent of 

11 mixed conifer forests (21,800 square kilometers) have W126 index values over 7 ppm-hrs and a 

12 moderate to severe fire risk, and 74 percent (16,500 square kilometers) have W126 index values 

13 over 15 ppm-hrs with moderate to severe fire risk. When we simulate just meeting the existing 

14 standard almost all of the area of mixed conifer forest where there is a moderate to high fire 

15 threat sees a reduction in O3 to below a W126 index value of 7 ppm-hrs. At the adjusted 

alternative W126 standard level of 15 ppm-hrs all but 40 km2 16 are under a W126 index value of 7 

17 ppm-hrs and at 11or 7 ppm-hrs all of the moderate to high fire threat area is under 7 ppm-hrs. 

18 Table 5-2 summarizes the reductions in areas of moderate to high-fire threat, mixed conifer 

19 forests at the existing and alternative standard levels. 

20 
5-8 

1 Table 5-2 Area of Moderate to High-Fire Threat, Mixed Conifer Forest for Existing and Alternative 

Standard Levels (in km2 2 ) 

<7ppm-hrs 7-11ppm-hrs 11-15 ppm-hrs >15 ppm-hrs

Recent Conditions 482 2,542 5,271 16,544

Existing Standard 

(75 ppb) 22,180 117 0 0

15 ppm-hrs 22,257 40 0 0

11ppm-hrs 22,297 0 0 0

7 ppm-hrs 22,297 0 0 0 

3 

4 In the long term, decreased frequency of fires could result in an increase in property 

5 values in fire-prone areas. Mueller et al. (2007) conducted a hedonic pricing study to determine 

6 whether increasing numbers of wildfires affect house prices in southern California. They 

7 estimated that house prices would decrease 9.7 percent after one fire and 22.7 percent after a 

8 second wildfire within 1.75 miles of a house in the study area. After the second fire, the housing 

9 prices took between 5 and 7 years to recover. 

10 Figure 5-4 shows the locations of fires in the mixed conifer forest range in 2010. There 

11 were 961 fires detected in these areas, including many in the national parks. While we can’t 

12 conclude that O3 reductions would have prevented these fires because there are many 

13 contributing factors, we can conclude that under the air quality adjusted scenario just meeting the 

14 existing standard will in many areas, decrease the role of O3 as a contributing factor by reducing 

15 the W126 index value to below 7 in most areas. Meeting alternative W126 standards results in 

16 small to no additional reductions in the area of forests above a 7 ppm-hrs W126 standard level. 

17 Additionally, long- term decreases in wildfire would be expected to yield outdoor recreation 

18 benefits consistent with the discussion of scenic beauty in subsequent sections.
5-9 

1 

2 Figure 5-4 Location of Fires in 2010 in Mixed Conifer Forest Areas (under Recent O3 Conditions) 

3 5.4 PROVISIONING SERVICES 

4 Provisioning services include market goods, such as forest and agricultural products. The 

5 direct impact of O3-induced biomass and yield loss can be predicted for the commercial timber 

6 and agriculture markets, respectively, using the Forest and Agriculture Optimization Model 

7 (FASOM). This model provides a national-scale estimate of the effects of O3 on these two 

8 market sectors, including producer and consumer surplus estimates (see Section 6.3 for a 

9 discussion of producer and consumer surplus). Chapter 6 of this document provides detailed 

10 analyses of the potential impact of biomass and yield loss on these services. Non-timber forest 

11 products (NTFP), such as foliage and branches used for arts and crafts or edible fruits, nuts, and 

12 berries, can be affected by the impact of O3 through biomass loss, foliar injury, insect attack, fire 

13 regime changes, and effects on reproduction. Acknowledging that services lost in this sector can 

14 be the result of interacting effects of O3 with other stressors, we also have included details for the 

15 magnitude of the NTFP services in Chapter 6. 
5-10 

1 

2 In addition to the direct effects of 

3 O3 on tree growth, O3 causes increased 

4 susceptibility to infestation by some 

5 chewing insects (U.S. EPA, 2006). This 

6 potentially includes species that are not 

7 considered sensitive to either biomass loss 

8 or foliar injury such as Douglas fir. 

9 Chewing insects include the southern pine 

10 beetle and western bark beetle, species that 

11 are of particular interest to commercial 

12 timber producers and consumers. These infestations can cause economically significant damage 

13 to tree stands and the associated timber production. Figure 5-5 and Figure 5-6 illustrate the 

14 damage caused by southern pine beetles in parts of the south. 

15 According to the USFS Report on the 

16 southern pine beetle (Coulson and Klepzig, 

17 2011), “Economic impacts to timber 

18 producers and wood-products firms are 

19 essential to consider because the SPB causes 

20 extensive mortality in forests that have high 

21 commercial value in a region with the most 

22 active timber market in the world.” The 

23 economic impacts of beetle outbreaks are 

24 multidimensional. In the short-term, the 

25 surge in timber supply caused by owners 

26 harvesting damaged timber depresses prices for timber and benefits consumers. In the long27 term, beetle outbreaks reduce the stock of timber available for harvest, raising timber prices to 

28 the benefit of producers and the detriment of consumers. 

29 The USFS further reports that over the 28 years covered in their analysis (1977-2004), 

30 because of beetle outbreaks, timber producers have incurred losses of about $1.4 billion, or about 

31 $49 million per year, and wood-using firms have gained about $966 million, or about $35 

Figure 5-6 Southern Pine Beetle Damage 

Courtesy: Ronald F. Billings, Texas Forest Service. 

Bugwood.org

Figure 5-5 Southern Pine Beetle Damage 

Courtesy: Ronald F. Billings, Texas Forest Service. 

Bugwood.org
5-11 

1 million per year. This results in a $15 million per year net negative economic impact. (All 

2 dollar values are reported in constant 2010$.) These annual figures mask that most of the 

3 economic impacts result from a few catastrophic outbreaks, causing the impacts to pulse through 

4 the system in large chunks rather than being evenly distributed over the years. It is not possible 

5 to attribute a portion of these impacts resulting from the effect of O3 on trees’ susceptibility to 

6 insect attack; however, such losses are already reflected in the losses cited, and any welfare gains 

7 from decreased O3 would positively impact the net economic impact. 

8 In the western United States, O3-sensitive ponderosa and Jeffrey pines are subject to 

9 attack by bark beetles. Ozone exposure increases susceptibility to these insect infestations in 

10 sensitive species. Figure 5-7 shows areas considered ‘at risk’ of losing 25 percent or more basal 

11 area in the contiguous United States to the top seven pine beetle species over the next 15 years 

12 (pine beetle projections were calculated by the Forest Health Technology Enterprise Team). 

Under recent conditions, approximately 48,000 km2 13 have W126 index values above 15 ppm-hrs. 

14 After just meeting the existing standard, all areas are under a W126 index value of 7 ppm-hrs 

with the exception of about 4,000 km2 15 in Arizona and Colorado. After just meeting an 

16 alternative standard level of 15 ppm-hrs, no area is above 7 ppm-hrs. Table 5-3 and Table 5-4 

17 provide summaries of areas at risk of higher pine beetle loss and millions of square feet of basal 

18 tree area at high risk at various W126 index values. 

19 
5-12 

1 

2 Figure 5-7 W126 Index Values for Just Meeting the Existing and Alternative Standards in Areas 

3 Considered ‘At Risk’ of High Basal Area Loss (>25% Loss) 

4 

5 Table 5-3 Area (km2) ‘At Risk’ of High Pine Beetle Loss at Various W126 Index Values 

<7 ppm-hrs 7-11ppm-hrs 11-15 ppm-hrs >15 ppm-hrs

Recent Conditions 3,456 19,440 13,536 48,096 

Existing Standard 

(75 ppb) 80,640 3,888 0 0 

15 ppm-hrs 84,528 0 0 0 

11ppm-hrs 84,528 0 0 0 

7 ppm-hrs 84,528 0 0 0 

6 
5-13 

1 Table 5-4 Tree Basal Area Considered ‘At Risk’ of High Pine Beetle Loss ByW126 Index Values after 

2 Just Meeting the Existing and Alternative Standard Levels (in millions of square feet) 

<7 ppm-hrs 7-11ppm-hrs 11-15 ppm-hrs >15 ppm-hrs

Recent Conditions 90 368 145 488 

Existing Standard 

(75 ppb) 982 110 0 0 

15 ppm-hrs 1,091 0 0 0 

11ppm-hrs 1,091 0 0 0 

7ppm-hrs 1,091 0 0 0 

3 

4 In 2006, California was the largest producer of ponderosa and Jeffrey pine timber from 

5 public lands. California accounted for 99 million board feet of saw logs – almost 40 percent of 

6 the total U.S. production (U.S. Forest Service, 2009, available at: 

7 http://srsfia2.fs.fed.us/php/tpo_2009/tpo_rpa_int2.php). California also experiences high W126 

8 index values that may contribute to susceptibility to bark beetle attack. It is not possible to 

9 attribute a quantified impact of O3 exposure to economic loss from bark beetle damage because 

10 that impact is already reflected in the loss attributed to bark beetle infestation. Reducing O3

11 impacts would likely reduce economic loss to California timber production.

12 Figures 5-5 and 5-6 illustrate the impact insect outbreaks can have on aesthetic values 

13 such as scenic beauty, as well as to the impacts on timber production. As shown in the NOx/SOx 

14 Policy Assessment (U.S. EPA, 2011e), the value of the impact of O3 and insect attack 

15 susceptibility on aesthetic values may be even greater than the market value of the timber. We 

16 will address timber production effects from reduced growth rates in Chapter 6 and effects of 

17 foliar injury on related ecosystem services in Chapter 7. 

18 5.5 CULTURAL SERVICES 

19 Cultural services include non-use values (i.e., existence and bequest values) that can be 

20 directly or indirectly impacted by O3 exposure. According to responses to the NSRE, a large 

21 majority of Americans wishes to preserve natural or pristine areas, even if they do not intend to 

22 visit these areas. Outdoor recreation is another cultural service that may be affected by O3

23 exposure. Foliar injury caused by O3 exposure and insect attack aided by O3 exposure may have 

24 negative impacts on people’s satisfaction with outdoor activities, especially those activities 

25 associated with the quality of natural environments. 
5-14 

1 According to the National Report on Sustainable Forests (USDA, 2011) there are 

2 approximately 751 million acres of forest lands in the U.S., one-third of which is federally 

3 owned (Figure 5-8). All of these lands are assumed to be protected to some degree, but specific 

4 protections apply to wilderness areas, which comprise about 20 percent of public land. Of the 

5 remaining lands, 7 percent is protected as national parks; 13 percent is designated as wildlife 

6 refuges; and 60 percent is protected, managed forests, including national forests, Bureau of Land 

7 Management lands, and other state and local government lands. The protections afford 

8 preservation of cultural, social, and spiritual values. 

9 

10 Figure 5-8 Percent of Forest Land in the US by Ownership Category, 2007 

11 Source: USFS (Almost all forest lands are open for some form of recreation, although access may be restricted.) 

12 

13 5.5.1 Non-Use Services

14 The NSRE surveys also track American’s attitudes toward various benefits 

15 derived from the environment, including non-use values. When people value a resource even 

16 though they may never visit the resource or derive any tangible benefit from it, they perceive an 

17 existence service. When the resource is valued as a legacy to future generations, a bequest 

18 service exists. Additionally, there exists an option value to knowing that you may visit a 

19 resource at some point in the future. Data provided by the NSRE indicates that Americans have 

20 very strong preferences for existence, bequest, and option services related to forests. 

21 Significantly, according to the survey, only 5 percent of Americans rate wood products as the 

22 most important value of public forests and wilderness areas, and for private forests, only 20 

Corporate forest 

industry, 6.8%

Local, 1.5%

State, 9.2%

Federal, 

33.1%

Other noncorporate, 

2.9%

Family 

individual, 

35.1%

Other corporate, 

11.5%

Forest land ownership (percent)
5-15 

1 percent of respondents rated wood products as most important. Table 5-5 details the survey 

2 responses to these questions. 

3 Table 5-5 NSRE Responses to Non-Use Value Questions For Forests 

Service 

Percent of Respondents Considering the Service Important 

Extremely 

Important Very Important Moderately Important 

Total 

Existence 36 38 18 92 

Option 36 37 17 90 

Bequest 81 12 4 97 

4 *Remaining respondents felt these services were not important. 

5 

6 Studies (Haefele et al., 1991, Holmes and Kramer, 1995) indicate that the American 

7 public places a high value on protecting forests and wilderness areas from the damaging effects 

8 of air pollution. These studies assess willingness-to-pay (WTP) for spruce-fir forest protection in 

9 the southeast from air pollution and insect damage and confirm that the non-use values held by 

10 the survey respondents were in fact greater than the use or recreation values. The survey 

11 presented respondents with a sheet of color photographs representing three stages of forest 

12 decline and explained that, without forest protection programs, high-elevation spruce forests 

13 would all decline to worst conditions. Two potential forest protection programs were proposed. 

14 The first program (minimal program) would protect the forests along road and trail corridors 

15 spanning approximately one-third of the ecosystem at risk. This level of protection may be most 

16 appealing to recreational users. The second level of protection (more extensive program) was for 

17 the entire ecosystem and may be most appealing to those who value the continued existence of 

18 the entire ecosystem. Median household WTP was estimated to be roughly $29 (in 2007 dollars) 

19 for the minimal program and $44 for the more extensive program. Respondents were then asked 

20 to decompose their value for the extensive program into use, bequest, and existence values. The 

21 results were 13 percent for use value, 30 percent for bequest, and 57 percent for existence value 

22 (Table 5-6). 

23 While these studies are specific to damage due to excess nitrogen deposition and the 

24 wooly balsam adelgid (a pest in Fraser fir), the results are relevant to O3 exposure in forests 
5-16 

1 because the effects are similar. In the southeast, loblolly pine is a prevalent species and O3 foliar 

2 injury can cause visible damage. Ozone exposure may also result in trees more susceptible to 

3 insect attack, which in the southeast would include damage caused by the southern pine beetle. 

4 

5 Table 5-6 Value Components for WTP for Extensive Protection Program for Southern Appalachian 

6 Spruce-Fir Forests 

Type of Value Proportion of WTP Component Value ($2007) 

Use 0.13 5.72 

Bequest 0.30 13.20 

Existence 0.57 25.08 

Total 1.0 44.00 

7 

8 5.6 QUALITATIVE ASSESSMENT OF UNCERTAINTY 

9 As noted in Chapter 3, we have based the design of the uncertainty analysis for this 

10 assessment on the framework outlined in the WHO guidance (WHO, 2008). For this qualitative 

11 uncertainty analysis, we have described each key source of uncertainty and qualitatively assessed 

12 its potential impact (including both the magnitude and direction of the impact) on risk results, as 

13 specified in the WHO guidance. In general, this assessment includes qualitative discussions of 

14 the potential impact of uncertainty on the results (WHO Tier1) and quantitative sensitivity 

15 analyses where we have sufficient data (WHO Tier 2). 

16 Table 5-7 includes the key sources of uncertainty identified for the O3 REA. For each 

17 source of uncertainty, we have (a) provided a description, (b) estimated the direction of influence 

18 (over, under, both, or unknown) and magnitude (low, medium, high) of the potential impact of 

19 each source of uncertainty on the risk estimates, (c) assessed the degree of uncertainty (low, 

20 medium, or high) associated with the knowledge-base (i.e., assessed how well we understand 

21 each source of uncertainty), and (d) provided comments further clarifying the qualitative 

22 assessment presented. The categories used in describing the potential magnitude of impact for 

23 specific sources of uncertainty on risk estimates (i.e., low, medium, or high) reflect our 
5-17 

1 consensus on the degree to which a particular source could produce a sufficient impact on risk 

2 estimates to influence the interpretation of those estimates in the context of the secondary O3 

3 NAAQS review. Where appropriate, we have included references to specific sources of 

4 information considered in arriving at a ranking and classification for a particular source of 

5 uncertainty.
5-18 

1 Table 5-7 Summary of Qualitative Uncertainty Analysis in Semi-Quantitative Ecosystem Services Assessments 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: know

uncertainty on 

Direction Magnitude 

A. National W126 surfaces 

The fire risk and bark beetle analyses in this chapter use the national W126 surfaces for recent conditions and adjusted to just meet the existing standard and alternative W126 standards. 

Both Low- Medium Low-Medium KB and INF: See Chapter 4 for m

B. Incremental impact of O3 on ecosystem 

services 

Many ecosystem services 

affected by O3 exposure are 

discussed qualitatively or semiquantitatively, including supporting services (e.g., net primary productivity and community composition), regulating services (e.g., hydrologic cycle and pollination), and cultural services (e.g., recreation and non-use). 

Under High Low 

KB: The O3 ISA concludes that 

between O3 exposure and produ

and biogeochemical cycles, and relationship between O3 exposur

and terrestrial community compHowever, we do not have sufficto adequately quantify the increm

on many ecosystemservices. 

INF: For many services, we can magnitude and, for some, we camonetized value. The estimates will reflect the loss of services ocurrent O3 exposure and provide 

any potential impacts of O3 on th

value of a service is small, the toO3 exposure will also be small. 

large, there is a higher potential the relative contribution of O3 as 
5-19 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: know

uncertainty on 

Direction Magnitude 

C. Areas with fire risk in California 

Maps of areas with moderate and higher fire risk have uncertainty, and thus the potential overlap with areas with higher W126 

index values and mixed conifer forests are also uncertain. 

Unknown Medium High 

KB: California’s fire risk maps aincluding consideration of factornon-flammable roofs, and ignitiofire risk. (See http://www.fire.ca.gov/fire_prev

d_zones_development.php). 

INF: In 2010, over 3 million acr

(NIFC, 2010). The economic vawildfire and fire suppression actmillions of dollars per year in C2007, 2008). 

D. Areas at risk due to 

bark beetle 

In the western U.S., O3-sensitive 

ponderosa and Jeffrey pines are subject to attack by bark beetles. Maps that identify areas considered ‘at risk’ of losing 25 percent or more basal area to pine beetle have uncertainty, and thus the potential area of overlap with areas with higher W126 index values are also uncertain. 

Unknown Medium Medium 

KB: O3 causes increased suscep

chewing insects (U.S. EPA, 200pine beetle and the western bark attribute a portion of these impaO3 on trees’ susceptibility to ins

are already reflected in the lossefrom decreased O3 would positiv

INF: Insect infestations can cau

damage to tree stands and the as

USFS estimates a $15 million peimpact due to bark beetle infesta2011). 
5-20 

1 5.7 DISCUSSION 

2 Ozone damage to vegetation and ecosystems from recent conditions causes widespread 

3 impacts on an array of ecosystem services. Biomass loss impacts numerous services, including 

4 supporting and regulating services such as net primary productivity, community composition, 

5 habitat, and climate regulation. The provisioning services of timber production can be affected 

6 by the increased susceptibility to insect attack caused by O3 exposure. Non-use values, including 

7 existence and bequest values, are also affected by the damage to scenic beauty caused by insect 

8 attack (an indirect effect of O3) and foliar injury (a direct effect). Below we offer a few 

9 observations on the challenges of explicitly valuing ecosystem services, highlight the importance 

10 of continuing to consider the services in our assessments, and indicate where additional analyses 

11 and discussion on valuing the ecosystem services are located in this document. 

12 

13  Most of the impacts of O3 exposure on ecosystem services cannot be specifically 

14 quantified, but it is very important to provide an understanding of the magnitude and 

15 significance of the services that may be harmed by O3 exposure. For many ecosystem 

16 services, we can estimate the current total magnitude and, for some, we can estimate 

17 the current value of the services in question. 

18  The impacts on public welfare from supporting services are generally either indirect 

19 or occur over a long time. The ISA determined that biomass loss due to O3 exposure 

20 may have adverse effects on net primary productivity. But because of data and 

21 methodology limitations, the loss of value to the public from incremental changes in 

22 O3 exposure on NPP on a national level is unquantifiable. Also, we were not able to 

23 quantify the impacts of O3 exposure on community composition. 

24  Regulating ecosystem services include hydrologic cycle, pollination, and fire 

25 regulation. Hydrologic, or water cycling in forests is affected by O3 exposure and has 

26 impacts on ecosystem services associated with both water quality and quantity. 

27 While the NSRE results show that 91 percent of respondents rank water quality 

28 protection as either extremely important or very important, because of data and 

29 methodology limitations, the loss of value to the public from incremental changes in 
5-21 

1 O3 exposure on water cycling is not quantifiable. For pollination services, it is not 

2 possible to explicitly calculate the loss of pollination resulting from O3 exposure, but 

3 the loss is reflected in the current total estimated value of $18.3 billion (2010$) for 

4 pollination services in North America. Lastly, fire regulation is negatively affected 

5 by O3 exposure through forest susceptibility to wildfires, drought stress, and insect 

6 attack. The value of this ecosystem service is reflected in avoided damage to 

7 residential property, timber, rangeland, and wildfire fighting resources, as well as 

8 improved outdoor recreation opportunities. As an example, the California 

9 Department of Forestry and Fire Protection (CAL FIRE) estimated that average 

10 annual losses to homes due to wildfire from 1984 to 1994 were $163 million (CAL 

11 FIRE, 1996) and were over $250 million in 2007 (CAL FIRE, 2008). In fiscal year 

12 2008, CAL FIRE’s costs for fire suppression activities were nearly $300 million 

13 (CAL FIRE, 2008). 

14  Provisioning services include market goods, such as forest and agriculture products. 

15 The direct impact of O3-induced biomass loss can be predicted for the commercial 

16 timber and agriculture markets using the Forest and Agriculture Optimization Model. 

17 Chapter 6 of this document provides detailed analyses of the potential impact of 

18 biomass and yield loss on these services. In addition, non-timber forest products

19 (NTFP), such as foliage and branches used for arts and crafts or edible fruits, nuts, 

20 and berries, can be affected by the impact of O3 through biomass loss, foliar injury, 

21 insect attack, fire regime changes, and effects on reproduction. We include details for 

22 the magnitude of the NTFP services in Chapter 6. 

23  In addition, to estimate the magnitude of insect attacks related to O3 exposure on 

24 provisioning services, such as forest products, we reviewed the USFS Report on the 

25 Southern Pine Beetle (Coulson and Klepzig, 2011). The USFS further reports that 

26 over the 28 years covered in their analysis (1977-2004), because of beetle outbreaks, 

27 timber producers have incurred losses of about $1.4 billion, or about $49 million per 

28 year, and wood-using firms have gained about $966 million, or about $35 million per 
5-22 

year. This results in a $15 million per year net negative economic impact.1 1 While it 

2 is not possible to attribute a portion of these impacts resulting from the effect of O3 on 

3 trees’ susceptibility to insect attack, these losses are reflected in the values cited. 

4  Outdoor recreation is a cultural service that may be affected by O3 exposure. Foliar 

5 injury caused by O3 exposure and insect attack aided by O3 exposure may have 

6 negative impacts on people’s satisfaction with outdoor activities, especially those 

7 activities associated with the quality of natural environments. These impacts are 

8 discussed in Chapter 7 on foliar injury. In addition, some cultural services, such as 

9 existence or bequest services, lend themselves to evaluating total importance and 

10 measuring total value, but assessing the impact of O3 effects on these services is not 

11 currently possible. 

 

1

 All values are reported in constant 2010$. 
 6-1 

1 6 BIOMASS LOSS 

2 6.1 INTRODUCTION 

3 The previous O3 AQCDs (U.S. EPA, 1996, 2006) and current O3 ISA (U.S. EPA, 2013) 

4 concluded that there is strong and consistent evidence that ambient O3 decrease photosynthesis 

5 and growth in numerous plant species, but the magnitude of the effects are variable both across 

6 species and across regions of the U.S. 

7 The ecosystem services most directly affected by biomass loss include: (1) habitat 

8 provision for wildlife, particularly habitat for threatened or endangered wildlife, (2) carbon 

9 storage, (3) provision of food and fiber, and (4) pollution removal (see Figure 6-1). Although we 

10 cannot quantify reduction in habitat provision due to O3 exposure on either a national or case 

11 study scale, there is evidence that this service is important to the public. In the cases of carbon 

12 

13 Figure 6-1 Conceptual Diagram of Relationship of Relative Biomass Loss to Ecosystem 

14 Services [The dashed box indicates those services for which direct 

15 quantification was not possible.] 
 6-2 

1 storage and food and fiber provision, the analyses presented here used the concentration2 response (C-R) functions developed for trees and crops to model, at the national scale, the 

3 approximate loss of services and the marginal benefits of alternative levels of a W126 standard. 

4 We included national parks at the case-study scale, as well as Class I areas. Class I areas 

5 are designated as areas in which visibility has been determined to be of important value (C.F.R. 

6 40, 81.400). The determination is primarily based on air quality limitations on visibility, but in 

7 this assessment we are using them in the context of protected areas of interest to address 

8 potential impacts. The national parks are meant to be preserved for the enjoyment of present and 

9 future generations, as well as for the unique or sensitive ecosystems and species in the parks. 

10 The parks are not a source of food or fiber production and are not included in the analysis of 

11 those services. And although the parks do provide carbon sequestration and storage and 

12 pollution removal, neither of the models for these ecosystem services available for this review 

13 was able to include national parks. The model used for the urban case study areas allows 

14 analysis of carbon sequestration and storage and pollution removal services; it does not include 

15 habitat provision or food and fiber production. 

16 The remainder of this Chapter includes Section 6.2 – Relative Biomass Loss; Section 6.3 

17 – Commercial Timber Effects; Section 6.4 – Non-Timber Forest Products; Section 6.5 – 

18 Agriculture; 6.6 – Climate Regulation; Section 6.7 – Urban Case Study Air Pollution Removal; 

19 and Section 6.8 – Ecosystem Level Effects. 

20 6.2 RELATIVE BIOMASS LOSS 

21 The 1996 and 2006 O3 AQCDs relied extensively on results from analyses conducted on 

22 commercial crop species for the National Crop Loss Assessment Network (NCLAN) and on 

23 analyses of tree seedling species conducted by the EPA’s National Health and Environmental 

24 Effects Laboratory Western Ecology Division (NHEERL/WED). Results from these studies 

25 have been published in numerous publications, including Lee et al. (1994; 1989, 1988b, 1987), 

26 Hogsett et al. (1997), Lee and Hogsett (1999), Heck et al. (1984), Rawlings and Cure (1985), 

27 Lesser et al. (1990), and Gumpertz and Rawlings (1992). Those analyses concluded that a three28 parameter Weibull model is the most appropriate model for the response of absolute yield and 

29 growth to O3 exposure because of the interpretability of its parameters, its flexibility (given the 
 6-3 

1 small number of parameters), and its tractability for estimation. See equation 6-1 for an example 

2 of a three-parameter Weibull model. 

3 



 







 











W 126

Y e 4 

5 Equation 6-1 

6 

7 In addition, if the intercept term, α, is removed, the model estimates relative yield or 

8 biomass without any further reparameterization. Formulating the model in terms of relative yield 

9 or biomass loss (RBL) in relation to the 3-month W126 index is essential for comparing 

10 exposure-response across species or genotypes or for experiments for which absolute values of 

11 the response may vary greatly. See equation 6-2 for the reformulated model. 

12 

RBL = 1 - exp[-(W126/η)

β 13 ]

14 Equation 6-2 

15 In the 1996 and 2006 O3 AQCDs, the two-parameter model of RBL was used to derive 

16 common models for multiple species, multiple genotypes within species, and multiple locations. 

17 Relative biomass loss (RBL) functions for the 12 tree species used in this assessment are 

18 presented in Table 6-1 (see the ISA (U.S. EPA, 2013) for a more extensive review of the 

19 calculation of the C-R functions), and RBL functions for the 10 crop species used in this 

20 assessment are presented in Table 6-2. 

21 
 6-4 

1 Table 6-1 Relative Biomass Loss Functions for Tree Species 

Species RBL Function η (ppm) β

Red Maple (Acer rubrum)

1 – exp[-(W126/η)

β

] 

318.12 1.3756

Sugar Maple (Acer saccharum) 36.35 5.7785 

Red Alder (Alnus rubra) 179.06 1.2377 

Tulip Poplar (Liriodendron tulipifera) 51.38 2.0889 

Ponderosa Pine (Pinus ponderosa) 159.63 1.1900 

Eastern White Pine (Pinus strobus) 63.23 1.6582 

Loblolly Pine (Pinus taeda) 3,966.3 1.000 

Virginia Pine (Pinus virginiana) 1,714.64 1.0000 

Eastern Cottonwood (Populus deltoides) 10.10 1.7793 

Quaking Aspen (Populus tremuloides) 109.81 1.2198 

Black Cherry (Prunus serotina) 38.92 0.9921 

Douglas Fir (Pseudotsuga menzeiesii) 106.83 5.9631 

2 

3 Table 6-2 Relative Biomass Loss Functions for Crop Species 

Species RBL Function η (ppm) β

Barley

1 – exp[-(W126/η)

β

] 

6,998.5 1.388

Field Corn 97.9 2.968 

Cotton 96.1 1.482 

Kidney Bean 43.1 2.219 

Lettuce 54.6 4.917 

Peanut 96.8 1.890 

Potato 99.5 1.242 

Grain Sorghum 205.3 1.957 

Soybean 110.2 1.359 

Winter Wheat 53.4 2.367 

4 

5 Figure 6-2 shows a comparison of W126 median RBL response functions for the tree 

6 species used in this assessment, and Figure 6-3 shows a comparison of W126 median RBL 

7 response functions for the crop species used in this assessment. The figures illustrate how the 

8 two parameters affect the shape of the resulting curves. Differences in the shapes of these curves 
 6-5 

1 are important for understanding differences in the analyses presented later in this chapter. The 

2 two parameters of the RBL equation (Equation 6-2) control the shape of the resulting curve. The 

3 value of η in the RBL function affects the inflection point of the curve, and β affects the 

4 steepness of the curve. Species with smaller values of β (e.g., Virginia Pine) or species with η

5 values that are above the normal range of ambient W126 measurements (e.g., Ponderosa Pine 

6 and Red Alder) have response functions with more gradual and consistent slopes. This results in 

7 a more constant rate of change in RBL over a range of O3 exposure consistent with ambient 

8 exposure concentrations. 

9 In contrast, the species with larger β values (e.g., Sugar Maple) have response functions 

10 that behave more like thresholds, with large changes in RBL over a small range of W126 index 

11 values and relatively small changes at other index values. In these cases the “threshold” is 

12 determined by the η parameter of the model. In the example of Eastern Cottonwood, β is 

13 relatively low, but because η is also very low relative to the other species, the resulting C-R 

14 curve has a very steep gradient relative to other species with similar β values. 

15 

16 Figure 6-2 Relative Biomass Loss Functions for 12 Tree Species 

17 

0 10 20 30 40 50

0.0 0.2 0.4 0.6 0.8 1.0

W126

RBL

Red Maple

Sugar Maple

Red Alder

Tulip Poplar

Ponderosa Pine

White Pine

Loblolly Pine

Virginia Pine

Cottonwood

Aspen

Black Cherry

Douglas Fir
 6-6 

1 

2 Figure 6-3 Relative Biomass Loss Functions for 10 Crop Species 

3 

4 6.2.1 Species-Level Analyses 

5 6.2.1.1 Comparison of seedling to adult tree biomass loss 

6 The response functions for tree species used in this analysis are all based on seedlings 

7 grown in open top chambers (OTC). Since the 2006 O3 AQCD (U.S. EPA, 2006), several studies 

were published based on the Aspen Free-Air Carbon Dioxide Enrichment (FACE)1 8 experiment 

9 using “free air,” O3 and CO2 exposures in a planted forest in Wisconsin. Overall, the studies at 

10 the Aspen FACE experimental site were consistent with many of the open-top chamber (OTC) 

11 studies that were the foundation of previous O3 NAAQS reviews. These results strengthen our 

12 understanding of O3 effects on forests and demonstrate the relevance of the knowledge gained 

13 from Aspen tree seedlings grown in OTC studies. 

14 In the 2006 AQCD (U.S. EPA, 2006), the TREGRO and ZELIG models were used to 

15 simulate growth of adult trees. For this analysis we did not conduct new TREGRO or ZELIG 

16 simulations. We used several existing publications, which modeled tree species used in this 

17 analysis. For this analysis, we calculated the W126 index values from the hourly concentrations 

 1

 The Aspen FACE experiment is a multidisciplinary study to assess the effects of increasing tropospheric O3 and 

carbon dioxide levels on the structure and function of northern forest ecosystems. 

0 10 20 30 40 50

0.0 0.2 0.4 0.6 0.8 1.0

W126

RBL

Barley

Field Corn

Cotton

Kidney Bean

Lettuce

Peanut

Potato

Grain Sorghum

Soybean

Winter wheat
 6-7 

1 at the monitors used in the studies. The seedling RBL was calculated from this W126 and 

2 compared to the study results and adjusted to reflect an annualized RBL. The results are 

3 summarized below in Table 6-3. 

4 

5 Table 6-3 Comparison of Adult to Seedling Biomass Loss 

Study W126 Adult RBL 

TREGRO 

Adult RBL 

ZELIG 

Seedling RBL Comments 

Constable and 

Taylor, 1997 

0.18 

8.98 

46.37 

89.40 

149.22 

0% 

0.3% 

3.1% 

6.4% 

12.1% 

N/A 0.03% 

3.2% 

20.5% 

39.5% 

60.3% 

This study used TREGRO 

and included the western 

and eastern subspecies of 

Ponderosa Pine. O3 data 

were not available for the 

western subspecies, which 

was found to be more 

sensitive than the eastern 

subspecies. The seedling 

C-R function used does 

not differentiate between 

subspecies. 

Weinstein et 

al., 2001 0.32 

15.38 

59.17 

0.32 

15.38 

59.17 

0.32 

15.38 

59.17 

Tulip Polar 

4.7% 

10.6% 

16.8% 

Red Maple 

2.5% 

4.9% 

8.2% 

Black Cherry 

0.2% 

0.3% 

0.5% 

Tulip Polar 

+3.2% 

5.3% 

11.2% 

Red Maple 

0% 

15.6% 

15.6% 

Black Cherry 

11.2% 

4.2% 

+9.1% 

Tulip Poplar 

0% 

7.7% 

73.89% 

Red Maple 

0.01% 

1.5% 

9.4% 

Black Cherry 

0.9% 

32.8% 

78.0% 

This study used TREGRO 

and ZELIG to model 

Tulip Poplar, Red Maple, 

and Black Cherry. 

6 

7 These studies indicate that overall, the seedling biomass loss values are much more 

8 consistent with the adult loss, as estimated by TREGRO and ZELIG, at lower W126 index 

9 values. The Constable and Taylor (1997) study implies that for the eastern subspecies of 

10 Ponderosa Pine, the seedling RBL rate overestimates the adult RBL rate. O3 data for the western 

11 subspecies were not available, but Constable and Taylor (1997) found the western subspecies to 

12 be more sensitive. The Weinstein et al. (2001) study indicates that the seedling RBL estimates 
 6-8 

1 are comparable to the adult estimates, except at higher W126 index values of O3 for Tulip 

2 Poplar. The Black Cherry results are an exception, which tells us that this species is much less 

3 sensitive as an adult than as a seedling. As such, the seedling RBL rate would overestimate RBL 

4 loss in adult trees. One other study (Samuelson and Edwards, 1993) on Red Oak, another 

5 hardwood species, found the exact opposite pattern -- adult trees are much more sensitive to O3-

6 related biomass loss than seedlings. 

7 Mclaughlin et al. (2007) completed a study assessing the interactive effects of O3 and 

8 climate on tree growth and water use. We used the monitored O3 concentrations in this study to 

9 calculate the W126 index value and then used these values to compare the predicted seedling 

10 RBL to observations in the study. The study did not use absolute biomass loss, instead relying on 

11 measurements of circumference to address growth. In addition, the results were presented as 

12 comparisons in growth in 2002 and 2003 relative to 2001. Table 6-4 presents a summary of the 

13 results. 

14 

15 Table 6-4 Comparison of Seedling Biomass Loss to Adult Circumference 

Species

W126 

Study Results 

(% change in 

circumference)

RBL (seedling) Comparison 

2001 2002 2003 2002 2003 2001 2002 2003 2002 2003 

Tulip Poplar 23.31 39.82 20.15 -26% -38% -17.5% -44.4% -13.9% -60.7% 32.4% 

Tulip Poplar 19.78 32.14 11.25 -49.6% 7.5% -12.7% -31.3% -4.1% -59.4% 210% 

Tulip Poplar 14.71 17.50 9.22 -62% N/A -7.1% -10.0% -2.7% -72.8% N/A

Black Cherry 14.71 17.50 9.22 -75% N/A -31.7% -36.4% -21.3% -41.5% N/A

Red Maple 14.71 17.50 9.22 -59.6% N/A -1.5% -1.8% -0.8% -58.4% N/A

Sugar Maple 14.71 17.50 9.22 -43.5% N/A -0.5% -1.5% -0.04% -97.5% N/A

16 

17 Relative to the observed changes in circumference, the seedling RBL estimates are mixed 

18 for Tulip Polar. Loss was overestimated in 2002 but was underestimated in 2003. The results for 

19 Sugar Maple were similar to Tulip Poplar, with loss overestimated in 2002. In contrast to the 

20 TREGRO results presented above, the results in this study found much greater loss in Black 

21 Cherry, and the seedling RBL underestimated the change for adult trees in 2002. The results for 
 6-9 

1 Red Maple were very similar for 2002. Table 6-5 summarizes the uncertainty for all species used 

2 in this study. 

3 

4 Table 6-5 Summary of Uncertainty in Seedling to Adult Tree Biomass Loss 

5 Comparisons 

Species Summary of Seedling-Adult Uncertainty 

Red Maple (Acer rubrum) 

Seedling C-R functions underestimated RBL relative to estimates of adult 

biomass loss from TREGRO and ZELIG. The seedling RBL was comparable 

to field results of changes in circumference. 

Sugar Maple (Acer saccharum) No TREGRO data were available. Seedling RBL overestimated loss compared 

to field results of changes in circumference. 

Red Alder (Alnus rubra) No data were available. 

Tulip Poplar (Liriodendron 

tulipifera) 

Seedling C-R functions underestimated RBL relative to results from TREGRO 

and ZELIG and lower W126 index values of O3, and overestimated RBL at 

the very high index values. Seedling RBL overestimated loss compared to 

field results of changes in circumference in 2002, but underestimated loss in 

2003. 

Ponderosa Pine (Pinus ponderosa) 

Seedling C-R functions overestimated RBL relative to TREGRO results for 

the eastern subspecies. Data were not available for the western subspecies, but 

the western subspecies is known to be more sensitive. 

Eastern White Pine (Pinus strobus) No data were available. 

Loblolly Pine (Pinus taeda) 

No comparable data were available; however this species is very non-sensitive 

as measured by the seedling C-R function, so the risk of overestimating loss is 

low. 

Virginia Pine (Pinus virginiana) 

No comparable data were available; however this species is very non-sensitive 

as measured by the seedling C-R function, so the risk of overestimating loss is 

low. 

Eastern Cottonwood (Populus 

deltoides) 

No data were available. This species is very sensitive as measured by the 

seedling C-R function, so the risk of overestimating loss is high. 

Quaking Aspen (Populus 

tremuloides) 

OTC studies found very consistent biomass loss between seedlings and adult 

trees. 

Black Cherry (Prunus serotina) 

Seedling C-R functions overestimated RBL relative to results from TREGRO 

and ZELIG, except the ZELIG results at the lowest W126 index values. 

Seedling RBL underestimated loss relative to field results of changes in 

circumference. 

Douglas Fir (Pseudotsuga 

menzeiesii) 

No comparable data were available; however this species is very non-sensitive 

as measured by the seedling C-R function, so the risk of overestimating loss is 

low. 
 6-10 

1 

2 6.2.1.2 W126 for Different levels of Biomass Loss 

3 The C-R functions can be plotted as a function of the percent biomass loss against 

4 varying W126 index values. This allows us to compare the W126 index values associated with a 

5 range of biomass loss values. Figure 6-4 and Figure 6-5 reflect two separate graphical 

6 representations of these results for trees and crops respectively. 

7 In each graph, the red line represents the median W126 index value associated with the 

8 percent biomass value on the x-axis when all 54 crop studies or 52 tree seedling studies are 

9 included. The green line is the value when only the composite C-R function is used for each of 

10 the species included (10 crop species and 12 tree species). The grey lines are included as 

11 sensitivity analyses to assess the effect of within-species variability. For each grey line, a C-R 

12 function for each species was randomly selected from the available studies, with the resulting 

13 line representing the median value of the 12 tree species and 10 crops. For some species only one 

14 study was available (e.g., Red Maple), and for other species there were as many as 11 studies 

15 available (Ponderosa Pine). The process was repeated 1,000 times, and the median value is 

16 plotted as the red points for biomass loss values of 1% to 7%, and 10%. The error bar associated 

with the points represents the 25th and 75th 17 percentiles. For tree and crop species, the median 

18 W126 index values are similar, when using all of the studies or just the composite C-R function 

19 for each species; however, the median value is higher when within-species variability is 

20 included. 

21 
 6-11 

1 

2 Figure 6-4 W126 Index Values for Alternative Percent Biomass Loss for Tree Species 

3 

4 

5 Figure 6-5 W126 Index Values for Alternative Percent Biomass Loss for Crop Species 
 6-12 

1 

2 6.2.1.3 Individual Species Analyses 

Using GIS (ESRI®, ArcMAPTM 3 10), we used the C-R functions listed in Table 6-1 to 

4 generate RBL surfaces for the 12 trees species. We created the surfaces using recent ambient O3

5 conditions based on monitored data from 2006 through 2008 and the four O3 rollback surfaces 

simulating just meeting the existing 8-hr secondary standard of 75 ppb (4th 6 highest daily 

7 maximum) and three alternative W126 scenarios of 7, 11 and 15 ppm-hrs (see Chapter 4 for a 

8 more detailed description of the O3 surfaces). We present the maps for one species, Ponderosa 

9 Pine, to illustrate the results (see Figure 6-6, Figure 6-7, Figure 6-8, Figure 6-9, and Figure 6-10). 

10 RBL surfaces for 10 species are presented in Appendix 6A (Maps of Individual Tree Species). It 

11 is important to note that these maps represent the RBL value for one tree species within each 

12 CMAQ grid cell represented, so these maps should be interpreted as indicating potential risk to 

13 individual trees of that species growing in that area. 

14 We based the ranges for the species on data from the Forest Health Technology 

15 Enterprise Team (FHTET) of the USFS (http://www.fs.fed.us/foresthealth/technology/). These 

16 data provide modeled predictions of stand density and basal area. The modeled data were 

17 estimated in 1,000 square meter grids for individual tree species, as well as total basal area. We 

18 summed these values into the larger CMAQ grid cells (12 km x 12 km) used for the O3 surfaces. 

19 For the individual species analyses, these data were used only as a predictor of presence or 

20 absence. In the ecosystem level analysis presented in Section 6.8 these values were used to scale 

21 the biomass loss by the proportion of total basal area for each species. 

22 Overall, the western tree species have more fragmented habitats than the eastern species. 

23 The areas in southern California have the highest W126 index values, which can be seen as the 

24 very high areas of RBL in Figure 6-6. The eastern tree species had less fragmented ranges and 

25 areas of elevated RBL that were more easily attributed to urban areas (e.g., Atlanta, GA and 

26 Charlotte, NC) or to the Tennessee Valley Authority region. In addition to the two western 

27 species not illustrated here, we include maps for the eastern species in Appendix 6A. 
 6-13 

1 

2 Figure 6-6 Relative Biomass Loss of Ponderosa Pine (Pinus ponderosa) Seedlings under 

3 Recent Ambient W126 Index Values (2006 – 2008) 

4 

5 Figure 6-7 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After 

6 Simulating Meeting the Existing (8-hr) Primary Standard (75 ppb) 
 6-14 

1 

2 Figure 6-8 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After 

3 Simulating Meeting an Alternative Secondary Standard of 15 ppm-hrs 

4 (after Meeting Existing O3 Standard) 

5 

6 Figure 6-9 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After 

7 Simulating Meeting an Alternative Secondary Standard of 11 ppm-hrs 

8 (after Meeting Existing O3 Standard) 

9 
 6-15 

1 

2 Figure 6-10 Relative Biomass Loss of Ponderosa Pine with O3 Exposure After 

3 Simulating Meeting an Alternative Secondary Standard of 7 ppm-hrs (after 

4 Meeting Existing O3 Standard) 

5 
 6-16 

Table 6-6 Individual Species Relative Biomass Loss Values – Median, 75th 1 Percentile, Maximum Perc

Relative Biomass Loss 

(Median/75th Percentile/Maximum Percentages) 

Species Recent Conditions 75 ppb 15 ppm-hrs 11 ppm-hrs 

Red Maple 0.95/1.25/3.49 0.08/0.17/0.77 0.08/0.17/0.77 0.08/0.13/0.70 

Sugar Maple 0.06/0.22/3.96 <0.01/<0.01/0.07 <0.01/<0.01/0.07 <0.01/<0.01/0.01 

Red Alder 0.83/1.15/10.10 0.32/0.40/0.78 0.32/0.40/0.78 0.32/0.40/0.78 

Tulip Poplar 5.20/6.88/24.68 0.17/0.35/2.79 0.17/0.35/2.79 0.12/0.21/2.40 

Ponderosa Pine 3.71/5.93/24.34 0.67/1.18/4.05 0.65/0.94/3.25 0.56/0.69/3.25 

White Pine 3.33/5.58/14.70 0.10/0.40/2.66 0.10/0.40/2.66 0.10/0.30/2.05 

Loblolly Pine 0.30/0.36/0.71 0.05/0.07/0.17 0.05/0.07/0.17 0.05/0.06/0.15 

Virginia Pine 0.77/0.88/1.63 0.15/0.20/0.54 0.15/0.20/0.54 0.12/0.16/0.50 

Cottonwood 58.32/74.03/99.79 5.93/11.97/65.90 5.87/11.68/65.90 5.26/8.06/53.33 

Aspen 3.71/6.54/27.51 0.47/1.14/5.85 0.46/1.03/4.22 0.45/0.82/3.89 

Black Cherry 23.97/28.54/51.51 4.89/7.94/23.90 4.89/7.94/23.90 4.51/6.31/19.42 

Douglas Fir <0.01/<0.01/0.46 <0.01/<0.01/<0.01 <0.01/<0.01/<0.01 <0.01/<0.01/<0.01 

2 
 6-17 

1 Table 6-6 above includes individual species relative biomass loss values at the median, 

the 75th 2 percentile, and the maximum for the 12 tree species for which we have C-R functions. 

3 We include the relative biomass loss values for each species at recent conditions, when adjusted 

4 to just meet the existing standard of 75 ppb, and when adjusted to meet potential alternative 

standard levels of 15, 11, and 7 ppm-hrs.2 5 For Ponderosa Pine, at recent conditions, the median 

value is 3.71 percent RBL, the 75th 6 percentile value is 5.93 percent RBL, and the maximum 

7 value is 24.24 percent RBL. When adjusted to just meet the existing standard, the median value 

is 0.67 percent RBL, the 75th 8 percentile value is 1.18 percent RBL, and the maximum value is 

9 4.05 percent RBL; when adjusted to meet a potential alternative standard level of 15 ppm-hrs, 

the median value is 0.65 percent RBL, the 75th 10 percentile value is 0.94 percent RBL, and the 

11 maximum value is 3.25 percent RBL; and when adjusted to meet a potential alternative standard 

level of 7 ppm-hrs, the median value is 0.50 percent RL, the 75th 12 percentile value is 0.58 percent 

13 RBL, and the maximum value is 2.49 percent RBL. In addition, RBL values for each scenario 

14 can be viewed across the entire distribution within each species (Figure 6-11) or as a proportion 

15 of the current standard (Figure 6-12). Figure 6-11 and Figure 6-12 use Ponderosa Pine as an 

16 example - plots for the other 11 species are included in Appendix 6A. 

17 

 2

 W126 calculations are slightly modified in the case of the model adjustment scenarios described in Chapter 4, 

Section 4.3.4. When calculating W126 for the model adjustment cases, we first found the three-year average of each 

three-month period, and then selected the three-month period with the highest three-year average using the same 

three-month period for each of the three years. In this way, the five scenarios are for recent air quality, air quality 

adjusted to just meet the current standard, and air quality further adjusted to just meet three different W126 index 

values: 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. 
 6-18 

1 

2 Figure 6-11 Relative Biomass Loss of Ponderosa Pine at the Existing Primary and 

3 Alternative Secondary Standards [RBL in this figure is plotted as a 

4 proportion relative to no O3 exposure.]

5 

6 
 6-19 

1 

2 Figure 6-12 Proportion of Current Standard, Ponderosa Pine – Recent Conditions and 

3 Alternative Secondary Standards 

4 

5 6.2.1.4 Potential Effects of Compounding RBL 

6 To determine potential uncertainty of using a W126 index value averaged across three 

7 years compared to using separate values for each individual year, we compared the compounded 

8 values for two examples. In both examples, we chose one species (Tulip Polar and Ponderosa 

9 Pine) and one climate region where that species occurred (Southeast and Southwest regions). We 
 6-20 

1 used the values associated with just meeting the existing standard of 75 ppb. Within each region 

2 we calculated both the W126 value at each monitor in the region for each year and the three-year 

3 average W126 value using the method described in Chapter 4. The results, depicted in Figure 

4 6-13 below, show that the use of the three-year average W126 index value may underestimate 

5 RBL values slightly, but the approach does not account for moisture levels or other 

6 environmental factors that could affect biomass loss. Figure 6-14 shows the air quality data that 

7 was used in this analysis. In both regions and in all three years, the three-year average W126 

8 value is sometimes above and sometimes below the individual year W126 index value. 

9 

10 Figure 6-13 Three-Year Compounded Relative Biomass Loss – Southeast and Southwest 

11 Regions 

12 

13 

14 
 6-21 

1 Figure 6-14 Individual and 3-Year Average W126 Index Values – Southeast and 

2 Southwest Regions 

3 6.3 COMMERCIAL TIMBER EFFECTS 

4 We used the Forest and Agricultural Sectors Optimization Model with Greenhouse Gases 

5 (FASOMGHG) (Adams et al., 2005) to calculate the resulting market-based welfare effects of O3

6 exposure in the forestry and agriculture sectors of the United States under the scenarios outlined 

7 below. This section provides a summary of the results of those analyses. The current crop/forest 

8 budgets, which include all inputs to production and the resulting products, included in 

9 FASOMGHG are considered the budgets under recent ambient conditions. To model the effects 

10 of changing W126 index values on the forestry sector, two primary and three alternative 

11 scenarios were constructed and run through the model: 

12  a base scenario, consistent with recent ambient conditions; 

13  a scenario with crop and forest yields for O3 exposures after simulating just meeting 

the existing standard of 75 ppb (4th 14 highest daily maximum) and 

15  three scenarios that represent O3 exposure after just meeting alternative W126-based 

16 standard levels – 15, 11, and 7 ppm-hrs. 
 6-22 

1 We used the O3 C-R functions for tree seedlings to calculate relative yield loss (RYL), 

2 which is equivalent to relative biomass loss, for FASOMGHG trees over their entire life span. To 

3 derive the FASOMGHG region-level RYLs for trees under each scenario, we used FASOMGHG 

4 region O3 values along with the mapping in Table 6-7. For additional details on FASOMGHG, 

5 including a map of the FASOMGHG regions, see Appendix 6B (FASOMGHG Full Results). 

6 We calculate the FASOMGHG region-level RYLs for each tree species listed in the first 

7 column of Table 6-7 by extracting county-level W126 concentrations from the CMAQ air quality 

8 surfaces, using only the portion of each county that is identified as forested in the GIS data 

9 utilized and used the simple average across county O3 values (forested portions of each county) 

10 for all counties falling in a given FASOM region to represent the region-level O3 impacts on 

11 forests. Then the region-level W126 O3 values are applied to tree species present in that region to 

12 calculate RYLs. Then, we calculate a simple average of RYLs for each tree species mapped to a 

13 FASOMGHG forest type in a given region. The mapping of tree species to FASOMGHG forest 

14 types is based on “Atlas of United States Trees” (Little, 1971, 1976, 1977, 1978). 

15 

16 
 6-23 

1 Table 6-7 Mapping O3 Impacts to FASOMGHG Forest Types 

Tree Species Used for 

Estimating O3 Impacts FASOMGHG Forest Type FASOMGHG Region(s) 

Black Cherry, Tulip Poplar Upland Hardwood SC, SE 

Douglas Fir Douglas Fir PNWW 

Eastern White Pine Softwood CB, LS 

Ponderosa Pine Softwood PNWE, PNWW, PSW, RM 

Quaking Aspen Hardwood RM 

Quaking Aspen, Black Cherry, Red Maple, 

Sugar Maple, Tulip Poplar Hardwood CB, LS, NE 

Red Alder Hardwood PNWE, PNWW, PSW 

Red Maple Bottomland Hardwood SC, SE 

Virginia Pine Natural Pine, Oak-Pine, Planted 

Pine SC 

Virginia Pine, Eastern White Pine Natural Pine, Oak-Pine, Planted 

Pine SE 

Virginia Pine, Eastern White Pine Softwood NE 

2 

3 Table 6-8 presents the region-specific RYLs for the forest types by region. At the 

4 existing standard the highest yield loss occurs in upland hardwood forests in the South Central 

5 and Southeast regions at over three percent per year. The next highest yield losses at the existing 

6 standard occur in Corn Belt hardwoods with just over two percent loss per year and in hard- and 

7 softwoods of the Rocky Mountain region at an average loss across all sensitive forests of slightly 

8 over 1 percent loss per year. With the exception of the Rocky Mountain region, which has yield 

9 losses reduced to under 1 percent per year, yield losses do not appreciably change at the 15 ppm10 hrs alternative. This is primarily because most areas have W126 index values lower than 15 

11 ppm-hrs after just meeting the existing standard. The Corn Belt forests remain at about 1.5 

12 percent loss at 11 ppm-hrs and the South Central and Southeastern forests continue to experience 

13 yield losses between 1 and 2 percent even after just meeting an alternative standard level of 7 

14 ppm-hrs. 
 6-24 

1 Table 6-8 Percent Relative Yield Loss for Forest Types by Region for Modeled 

2 Scenarios 

Forest Type Region Existing Standard 

(75 ppb) 

W126 

15 ppm-hrs 11 ppm-hrs 7 ppm-hrs 

Douglas Fir PNWW 0.00 0.00 0.00 0.00 

Natural Pine SC 0.15 0.15 0.12 0.09 

 SE 0.28 0.28 0.24 0.13 

Oak/Pine SC 0.15 0.15 0.12 0.09 

 SE 0.28 0.28 0.24 0.13 

Other Softwoods PNWW 0.49 0.48 0.48 0.48 

Planted Pine SC 0.15 0.15 0.12 0.09 

 SE 0.28 0.28 0.24 0.13 

Softwoods CB 0.78 0.78 0.46 0.23 

 LS 0.13 0.13 0.13 0.13 

 NE 0.05 0.05 0.04 0.02 

 RM 1.13 0.91 0.64 0.53 

 PSW 0.40 0.36 0.31 0.28 

 PNWE 0.52 0.50 0.48 0.47 

Bottomland Hardwoods SC 0.13 0.13 0.10 0.06 

 SE 0.12 0.12 0.10 0.06 

Hardwoods PNWW 0.34 0.34 0.34 0.33 

 CB 2.10 2.10 1.51 0.98 

 LS 0.69 0.69 0.69 0.67 

 NE 0.41 0.41 0.33 0.25 

 RM 1.59 1.27 0.88 0.73 

 PSW 0.27 0.25 0.22 0.19 

 PNWE 0.36 0.35 0.34 0.33 

Upland Hardwoods SC 3.25 3.25 2.71 2.00 

 SE 3.07 3.07 2.79 1.85 
 6-25 

1 Table 6-9 Percent Relative Yield Gain for Forest Types by Region with Respect to the 

2 Existing Standard 

Forest Type Region 

W126 

15 ppm-hrs - ES 11 ppm-hrs - ES 7 ppm-hrs - ES 

Douglas Fir PNWW 0.00 0.00 0.00 

Natural Pine SC 0.00 0.02 0.06 

 SE 0.00 0.04 0.16 

Oak/Pine SC 0.00 0.02 0.06 

 SE 0.00 0.04 0.16 

Other Softwoods PNWW 0.00 0.01 0.01 

Planted Pine SC 0.00 0.02 0.06 

 SE 0.00 0.04 0.16 

Softwoods CB 0.00 0.35 0.59 

 LS 0.00 0.00 0.00 

 NE 0.00 0.01 0.02 

 RM 0.23 0.52 0.63 

 PSW 0.04 0.09 0.13 

 PNWE 0.02 0.04 0.05 

Bottom Hardwoods SC 0.00 0.03 0.06 

 SE 0.00 0.01 0.06 

Hardwoods PNWW 0.00 0.01 0.01 

 CB 0.00 0.65 1.22 

 LS 0.00 0.00 0.02 

 NE 0.00 0.09 0.17 

 RM 0.35 0.77 0.93 

 PSW 0.03 0.06 0.09 

 PNWE 0.01 0.03 0.04 

Upland Hardwoods SC 0.01 0.65 1.48 

 SE 0.01 0.34 1.48 
 6-26 

1 Yield gains associated with meeting alternative W126 standards compared to meeting the 

2 existing standard are relatively small on a percentage change basis, especially in the 15 ppm-hrs 

3 scenario where the highest change is 0.35 percent per year. At 11 ppm-hrs the yield gains are 

4 larger with gains between 0.35 and 0.77 percent for the most affected regions. The 7 ppm-hrs 

5 scenario generates yield gains between 0.59 and 1.48 percent for the Corn Belt, Rocky Mountain, 

6 South Central, and Southeast regions. These results are presented in Table 6-9 and graphically in 

7 Figure 6-15 and Figure 6-16. While the yield gains for the alternative scenarios are small 

8 relative to the baseline of the existing standard, when applied nationally to forest production they 

9 result in increased forest production at every alternative in all years until the last period modeled 

10 in 2040 as shown in Table 6-10. The change in relative yield between the existing standard and 

11 the alternative scenarios results in changes in timber harvests and prices, as shown in Table 6-10. 

12 In general, harvests increase and prices decrease with resulting changes in consumer and 

13 producer welfare. 

14 

15 

16 

17 
 6-27 

1 

2 Figure 6-15 RYG for Softwoods by Region 
 6-28 

1 

2 Figure 6-16 RYG for Hardwoods by Region 
 6-29 

1 Table 6-10 Percentage Changes in National Timber Prices 

Product Policy 2010 2020 2030 2040

Hardwood saw logs 75 ppb 0.69 0.65 0.39 0.19 

 Change with Respect to Existing Standard

15 ppm-hrs -0.28 0.13 -0.16 0.94 

11 ppm-hrs -0.79 0.13 -2.52 -1.51 

7ppm-hrs -1.59 -2.60 -8.72 -7.12 

Hardwood pulp logs 75 ppb 0.24 0.44 0.22 0.12 

 Change with Respect to Existing Standard

15 ppm-hrs 0.00 -0.15 -0.08 -0.08 

11 ppm-hrs -0.87 -1.95 -2.06 -2.64 

7 ppm-hrs -2.10 -3.52 -4.92 -6.23 

Softwood saw logs 75 ppb 2.31 1.91 1.60 1.31 

 Change with Respect to Existing Standard

15 ppm-hrs -0.09 -0.33 -0.44 -0.69 

11 ppm-hrs -0.26 -1.24 -1.32 -1.40 

7 ppm-hrs -0.46 -1.54 -1.91 -2.28 

Softwood pulp logs 75 ppb 1.42 1.12 1.34 0.94 

 Change with Respect to Existing Standard

15 ppm-hrs -0.14 0.12 0.15 0.18 

11 ppm-hrs -0.43 0.13 -0.19 -0.51 

7 ppm-hrs -1.03 -0.42 -0.82 -2.17 

2 

3 Table 6-11 shows the estimated welfare changes brought about by the simulation 

4 scenarios. Consumer and producer welfare in the forest sector are more affected by the 

5 alternative scenario environments than the agricultural sector (see Section 6.5). In general, 

6 consumer welfare increases in both the forest and agricultural sectors as higher productivity 

7 tends to increase total production and reduce market prices. Because demand for most forestry 

8 and agricultural commodities is inelastic, producer welfare tends to decline with higher 

9 productivity as the effect of falling prices on profits more than outweighs the effects of higher 

10 production levels. In other words consumers do not increase their demand for the product enough 

11 in response to the falling prices created by increases production to offset the producer’s loss of 
 6-30 

1 revenue. The increase in consumer welfare is much larger than the loss of producer welfare 

2 resulting in net welfare gains in the forestry sector nationally. 

3 

4 

Welfare economics focuses on the optimal allocation of resources and goods and how 

those allocations affect total social welfare. Total welfare is also referred to as economic surplus, 

which is the overall benefit a society, composed of consumers and producers, receives when a 

good or service is bought or sold, given a quantity provided and a market price. Economic 

surplus is divided into two parts: consumer and producer surplus.

Consumers like to feel like they are getting a good deal on the goods and services they 

buy, and consumer surplus is an economic measure of this satisfaction. For example, assume a 

consumer goes out shopping for a CD player and he or she is willing to spend $250. When the 

shopper finds that the CD player is on sale for $150, economists would say that this shopper has a 

consumer surplus of $100, e.g., the difference between the $150 sale price and the $250 the 

consumer was willing to spend. 

Producer surplus refers to the benefit a producer receives from providing a good or 

service at a market price when they would have been willing to sell that good or service at a lower 

price. For example, if the amount the producer is willing to sell the CD player for is $75, and the 

producer sells the CD player for $150, the producer surplus is $75, e.g., the $150 sale price less 

the $75 price at which the producer was willing to sell. 
 6-31 

1 

2 Table 6-11 Consumer and Producer Surplus in Forestry, Million $2010 

Product Policy 2010 2015 2020 2025 2030 2035 2040

Consumer 

surplus 75 ppb 721,339 793,234 809,271 826,375 875,620 894,705 934,882

 Change with Respect to Existing Standard

15 ppm-hrs 7 31 118 105 2 6 597 

11 ppm-hrs 44 48 360 202 688 56 712 

7ppm-hrs 86 187 694 224 734 91 779 

Producer 

surplus 75 ppb 93,322 121,476 153,997 146,275 145,913 146,115 133,132 

 Change with Respect to Existing Standard

15 ppm-hrs -11 -7 -141 -161 15 -46 -839 

11 ppm-hrs -41 20 -503 -178 -880 55 -858 

7 ppm-hrs -136 -48 -892 -37 -786 156 -766 

3 

4 Key uncertainties in this approach are discussed in Section 6.6.1. It should be noted that 

5 since public lands are not affected within the model, the estimates presented would likely be 

higher if public lands were included. 3 6 See Appendix 6B for a full discussion of the model and 

7 methodology. 

8 6.4 NON-TIMBER FOREST PRODUCTS 

9 Non-timber forest products (NTFP) such as foliage and branches used for arts and crafts, 

10 or edible fruits, nuts, and berries can be affected by the impact of O3 through biomass loss, foliar 

11 injury, insect attack, fire regime changes, and effects on reproduction. The USDA has assessed 

12 the harvest and market value of these products in commercial markets (Emery, 2003). A 

13 significant portion of NTFP is also valuable to subsistence gatherers. Subsistence practices are 

14 much more difficult to assess because these forest users are not required to obtain a permit for 

15 use of federal public lands; as such the impacts are more difficult to enumerate. Because permits 

 3

 The FASOMGHG model includes 348.6 million acres of private, managed forests. The USFS estimates that there 

are approximately 751 million forest acres in the United States (USDA, 2011). 
 6-32 

1 or contracts are not required for gathering activities for personal use the analyses done by USDA 

2 are not able to account for the subsistence use of NTFP. 

3 In Table 6-12 we list some of the uses of the tree species known to be sensitive to the 

4 effects of O3 on biomass. These species have a wide variety of uses ranging from the value of 

5 the timber produced to medicinal uses. 

6 

7 Table 6-12 O3 Sensitive Trees and Their Uses 

Tree Species O3 Effect Uses 

Black Cherry 

Prunus serotina 

Biomass loss, 

Visible foliar 

injury 

Cabinets, furniture, paneling, veneers, crafts, toys 

Cough remedy, tonic, sedative 

Flavor for rum and brandy 

Wine making and jellies 

Food for song birds, game birds, and mammals 

Douglas Fir 

Pseudotsuga 

menziesii 

Biomass loss Commercial timber 

Medicinal uses, spiritual and cultural uses for several Native American tribes 

Spotted owl habitat 

Food for mammals including antelope and mountain sheep 

Eastern 

Cottonwood 

Populus deltoides 

Biomass loss Containers, pulp, and plywood 

Erosion control and windbreaks 

Quick shade for recreation areas 

Beaver dams and food 

Eastern White 

Pine 

Pinus strobus 

Biomass loss Commercial timber, furniture, woodworking, and Christmas trees 

Medicinal uses as expectorant and antiseptic 

Food for song birds and mammals 

Used to stabilize strip mine soils 

Hemlock 

Tsuga canadensis 

Biomass loss Commercial logging for pulp 

Habitat for deer, ruffled grouse, and turkeys 

Important ornamental species 

Hickory Biomass loss Used in furniture and cabinets, fuelwood, and charcoal 

Edible nuts 

Food for ducks, quail, wild turkeys and many mammals 

Ponderosa Pine 

Pinus ponderosa 

Biomass loss, 

Visible foliar 

injury 

Lumber for cabinets and construction 

Ornamental and erosion control use 

Recreation areas 

Food for many bird species, including the red-winged blackbird, chickadee, 

finches, and nuthatches 
 6-33 

Tree Species O3 Effect Uses 

Quaking Aspen 

Populus 

tremuloides 

Biomass loss, 

Visible foliar 

injury 

Commercial logging for pulp, flake-board, pallets, boxes, and plywood 

Products including matchsticks, tongue depressors, and ice cream sticks 

Valued for its white bark and brilliant fall color 

Important as a fire break 

Habitat for variety of wildlife 

Traditional native American use as a food source 

Red Alder 

Alnus rubra 

Biomass loss, 

Visible foliar 

injury 

Commercial use in products such as furniture, cabinets, and millwork 

Preferred for smoked salmon 

Dyes for baskets, hides, moccasins 

Medicinal use for rheumatic pain, diarrhea, stomach cramps – the bark 

contains salicin, a chemical similar to aspirin 

Roots used for baskets 

Food for mammals and birds – dam and lodge construction for beavers 

Conservation and erosion control 

Red Maple 

Acer rubrum 

Biomass loss Revegetation and landscaping especially riparian buffer 

Red Oak 

Quercus rubrum 

Biomass loss Important for hardwood lumber for furniture, flooring, cabinets 

Food, cover, and nesting sites for birds and mammals 

Bark used by Native Americans for medicine for heart problems, bronchial 

infections or as an astringent, disinfectant, and cleanser 

Short Leaf Pine 

Pinus echinata 

Biomass loss Second only to loblolly pine in standing timber volume 

Used for lumber, plywood, pulpwood, boxes, crates, and ornamental 

vegetation 

Habitat and food for bobwhite quail, mourning dove, other song birds and 

mammals 

Older trees with red heart rot provide red-cockaded woodpecker cavity trees 

Sugar Maple 

Acer saccharum 

Biomass loss Commercial syrup production 

Native Americans used sap as a candy, beverage – fresh or fermented into 

beer, soured into vinegar and used to cook meat 

Valued for its fall foliage and as an ornamental 

Commercial logging for furniture, flooring, paneling, and veneer 

Woodenware, musical instruments 

Food and habitat for many birds and mammals 

Virginia Pine 

Pinus virginiana 

Biomass loss, 

Visible foliar 

injury 

Pulpwood, strip mine spoil banks and severely eroded soils 

Nesting for woodpeckers, food for songbirds and small mammals 

Yellow (Tulip) 

Poplar 

Liriodendron 

tulipifera 

Biomass loss, 

Visible foliar 

injury 

Furniture stock, veneer, and pulpwood 

Street, shade, or ornamental tree – unusual flowers 

Food for wildlife 

Rapid growth for reforestation projects 

1 Sources: USDA-NRCS, 2013; Burns, 1990; Hall and Braham, 1998. 

2 
 6-34 

1 6.4.1 Commercial Non-Timber Forest Products 

2 In addition to timber, forests provide many other products that are harvested for 

3 commercial or subsistence activities. These products include: 

4  edible fruits, nuts, berries, and sap, 

5  foliage, needles, boughs, and bark, 

6  transplants, 

7  grass, hay, alfalfa, and forage, 

8  herbs and medicinals, 

9  fuelwood, posts and poles, and 

10  Christmas trees. 

11 For the 2010 National Report on Sustainable Forests (USDA, 2011) these products were 

12 divided into several categories including nursery and landscaping uses; arts, crafts, and floral 

13 uses; regeneration and silviculture uses. Table 6-13 details selected categories of NTFP 

14 harvested by permit in 2007. These harvests are reported in measures relevant to the specific 

15 articles, i.e., bushels of cones, tons of foliage or boughs, or individual transplants. 

16 

17 
 6-35 

1 Table 6-13 Quantity of NTFP Harvested on U.S. Forest Service and Bureau of Land 

2 Management Land 

Product Category Unit Harvest All U.S. 

Arts, crafts, and florals Bushels 70,222 

Pounds 3,442,125 

Tons 620,773 

Christmas trees Each 151,274 

Lineal foot 94.758 

Edible Fruits, nuts, berries, and sap Bushels 250 

Pounds 1,614,565 

 Syrup Taps 10,686 

Fuelwood ccf 35,800 

Cords 417,692 

Grass, hay, and alfalfa Pounds 4,265,952 

Forage Tons 480 

Herbs and medicinals Pounds 101,365 

Nursery and landscape Each 766,645 

Pounds 25,689 

Tons 316 

Regeneration and silviculture Bushels 7,627 

ccf 8 

Each 21,265 

Pounds 247,543 

Tons 110,873 

Posts and poles ccf 5,281 

Each 1,684,618 

Lineal foot 326,312 

3 Note: ccf = 100 cubic feet Source: USDA 2011 

4 

5 According to the ISA, O3 exposure causes biomass loss in sensitive woody and 

6 herbaceous species, which in turn could affect forest products used for arts, crafts, and florals. 

7 For example, Douglas Fir and Red Alder, among others, are used on the Pacific Coast for arts 

8 and crafts, particularly holiday crafts and decorations. The effects of O3 on plant reproduction 
 6-36 

1 (see ISA, Table 9-1, 2013) could affect the supply of seeds, berries, and cones. Foliar injury 

2 impacts on O3-sensitive plants would potentially affect the harvest of leaves, needles, and 

3 flowers from these plants for decorative uses. The visible injury and early senescence caused by 

4 O3 in some evergreens may also reduce the value of a whole tree such as Christmas trees. 

Likewise the same O3 effects would reduce the harvest of edible fruits, nuts, berries, and sap.4 5 

6 The use of native grasses as forage is a significant aspect of forest-land management in the 

7 western U.S. (Alexander et al. 2002). O3 effects on community composition, particularly 

8 changes in the ratio of grasses to forbs (broad-leaved herbs other than a grass), and nutritive 

9 quality of grasses can have effects on rangeland quality for some herbivores (Krupa et al., 2004, 

10 Sanz et al., 2005) and therefore effects on grazing efficiency. The negative impacts of O3 on 

11 plants would similarly affect the harvest in the rest of the categories. 

12 According to the U.S. Census Bureau’s County Business Patterns data from 2006, this 

13 activity is captured in the industry code 1132 -- forest nurseries and gathering of forest products -

14 - and employed 2,098 people, accounting for an annual payroll of $71,657,000 ($2006) with an 

15 average annual income of $34,155 (U.S. Census Bureau, 2006). 

16 The USDA estimates the proportion of the national supply of NTFP represented by USFS 

17 and BLM lands is approximately 10 percent. Retail values for NTFPs harvested on USFS and 

18 BLM lands are approximately $1.4 billion (2010$). These estimates are very rough and are based 

19 only on permit or contract sales. These estimates could be low due to harvests taken without 

20 permit or contract and sold through complex commodity chains that can combine wild-harvested 

21 and agriculturally grown commodities. It is important to note that while we cannot estimate the 

22 loss of production and value to this sector due to O3 exposures, these losses are already reflected 

23 in the harvest and values reported. 

24 6.4.2 Informal Economy or Subsistence Use of Non-Timber Forest Products 

25 Most people gathering NTFPs are doing so for personal use (Baumflek et al., 2010; 

26 USDA, 2011). By one estimate (Baumflek et al., 2010) up to 80 percent of the people collecting 

27 NTFPs in Oregon and Washington are collecting for personal reasons. Such personal use may be 

28 characterized either as part of the informal economy or as subsistence activity. Participants in 

29 the informal economy may earn a wage or salary and participate in gathering NTFPs for reasons 

 4

 To name a few, this category includes blueberries, pine nuts, and sap for maple syrup. 
 6-37 

1 other than recreation (Brown et al., 1998). The term subsistence has usually been applied to 

2 special groups such as Native Americans or the Hmong people and has generally been 

3 understood to imply extreme poverty such that these activities are essential to the necessities of 

4 life (Freeman, 1993). However, Freeman points out researchers stress that economic goals are 

5 only a part of the impetus for these activities. 

6 Brown et al. (1998) proposed a composite definition for the terms that captures both the 

7 informal economy, as practiced by those who are not necessarily a part of a special population, 

8 and subsistence, as generally referenced to those special populations. 

9 

10 “Subsistence refers to activities in addition to, not in place of, wage labor engaged in on a 

11 more or less regular basis by group members known to each other in order to maintain a 

12 desired and/or normative level of social and economic existence.” 

13 

14 This definition allows consideration of the cultural and social aspects of subsistence lifestyles. 

15 These non-economic benefits range from maintenance of social ties and relationships through 

16 shared activity to family cohesiveness to retreatism and a sense of self-reliance for the individual 

17 practitioner (Brown et al., 1998). 

18 While there is general acknowledgement of subsistence activities by Native Americans 

19 and specific treaty rights for tribes guaranteeing access to lands for hunting, fishing, and 

20 gathering, there has been a lack of research focused on other populations (Emery and Pierce, 

21 2005). However, there are some studies that clarify that subsistence activities provide valued 

22 resources for a variety of people in the coterminous United States. Baumflek et al. (2010) and 

23 Alexander et al. (2011) have documented the collection and use of culturally and economically 

24 important NTFPs in Maine and the eastern United States, respectively. Brown et al. (1998) 

25 reports on subsistence activities among residents of the Mississippi Delta. Emery (2003) and 

26 Hufford (2000) examine activities in the Appalachians, and Pena (1999) reports activities by 

27 Latinos in the Southwest. 

28 As with the commercial harvest of NTFPs, subsistence gathering of these forest products 

29 can potentially be affected by the adverse effects of O3 on growth, reproduction, and foliar injury 

30 to the sensitive plants in use for nutrition, medicine, cultural, and decorative purposes. It is 

31 important to note that some plants may have more than one use or significance. For example, the 
 6-38 

1 Mi’kmaq and Maliseet Indian tribes in Maine do not differentiate between blueberries’ 

2 nutritional, medicinal, and spiritual uses. Blueberries are a food and a medicine that is often 

3 incorporated into ceremonies (Baumflek et al., 2010). And while we cannot quantify the size of 

4 the harvest of subsistence-gathered items or monetize the loss of benefit due to O3 effects, a 

5 comparison to the commercial harvest detailed in section 6.4.1 may provide perspective on the 

6 significance of these activities to the people who engage in them. 

7 6.5 AGRICULTURE 

8 6.5.1 Commercial Agriculture 

9 Because the forestry and agriculture sectors are related, and trade-offs occur between the 

10 sectors, we used the same FASOMGHG model runs outlined in the forestry/timber section 

11 (Section 6.3) to calculate the resulting market-based welfare effects of O3 exposure in the 

12 agricultural sector of the United States. This section provides a summary of the results of the 

13 agricultural sector analyses. We have included results at the national scale for both sectors and 

14 at the regional and subregional scale for agriculture. Table 6-14 defines the production and 

15 market regions available in FASOMGHG. The regional-scale analysis provides an estimate of 

16 the changes due to alternative levels of the standard for 63 subregions and indicates the disparate 

17 results between regions. The full model results, including a county-level analysis, are reported in 

18 Appendix 6B. 

19 

20 
 6-39 

1 Table 6-14 Definition of FASOMGHG Production Regions and Market Regions 

Key Market Region Production Region (States/Subregions) 

NE Northeast Connecticut, Delaware, Maine, Maryland, Massachusetts, New 

Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, 

Vermont, West Virginia

LS Lake States Michigan, Minnesota, Wisconsin

CB Corn Belt All regions in Illinois, Indiana, Iowa, Missouri, Ohio (IllinoisN, IllinoisS, 

IndianaN, IndianaS, IowaW, IowaCent, IowaNE, IowaS, OhioNW, 

OhioS, OhioNE)

GP Great Plains (agriculture 

only)

Kansas, Nebraska, North Dakota, South Dakota

SE Southeast Virginia, North Carolina, South Carolina, Georgia, Florida

SC South Central Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, 

Eastern Texas

SW Southwest (agriculture 

only)

Oklahoma, All of Texas but the Eastern Part (Texas High Plains, Texas 

Rolling Plains, Texas Central Blacklands, Texas Edwards Plateau, Texas 

Coastal Bend, Texas South, Texas Trans Pecos)

RM Rocky Mountains Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, 

Wyoming

PSW Pacific Southwest All regions in California (CaliforniaN, CaliforniaS)

PNWE Pacific Northwest—East 

side

Oregon and Washington, east of the Cascade mountain range

PNWW Pacific Northwest—West 

side (forestry only)

Oregon and Washington, west of the Cascade mountain range

2 

3 Using the modeled W126 index values in each subregion under the scenarios, for crops, 

4 we first calculated the RYL in the 63 subregions that have C-R functions. For those crops that do 

5 not have C-R functions, we assign them RYLs for each scenario based on the crop proxy 

6 mapping shown in Table 6-15. In addition, for oranges, rice, and tomatoes, which have O3 C-R 

7 functions that are not W126-based (they are defined based on alternative measures of O3

8 concentrations), we directly used the median RYG values under the “13 ppm-hrs” O3

9 concentration reported in Table G-7 of Lehrer et al. (2007). In addition, we updated RYLs for 

10 crops with county-level production data and specific C-R functions by using production11 weighting. Production weighting applies a county’s share of the region’s total production to the 

12 average so that counties with less production have a smaller impact on the average. 

13 The RYLs for proxy crops were calculated for each FASOMGHG subregion so they 

14 could be used in calculating the yield losses for other crops that occur in those regions. The 
 6-40 

1 weighted RYLs that were used for corn, cotton, winter wheat (hard winter wheat and soft red 

2 winter wheat), sorghum, and soybeans in the model scenarios were calculated only for their 

3 production regions. The values calculated in all 63 regions were weighted by production for 

4 these crops, which eliminated regions with no production. 

5 Table 6-15 Mapping of O3 Impacts on Crops to FASOMGHG Crops 

CROPS FASOMGHG Crops

W126 Crops 

Corn Corn 

Cotton Cotton 

Potatoes Potatoes 

Winter Wheat 

Soft White Wheat, Hard Red Winter Wheat, Soft Red Winter Wheat, Durum Wheat, Hard Red 

Spring Wheat, Oats, Barley, Rye, Wheat Grazing, and Improved Pasture 

Sorghum Sorghum, Silage, Hay, Sugarcane, Sugar Beet, Switchgrass, Energy Sorghum, and Sweet Sorghum 

Soybeans Soybeans, Canola 

Aspen (tree) 

Hybrid Poplar, Willow (FASOMGHG places short-rotation woody biomass production in the crop 

sector rather than in the forest sector) 

Non-W126 Crops 

Oranges Orange Fresh/Processing, Grapefruit Fresh/Processing 

Rice Rice 

Tomatoes Tomato Fresh/Processing 

6 

7 The following figures (Figure 6-17 and Figure 6-18) present the yield loss relative to the 

8 existing standard and yield gains for corn and soybeans under the various adjusted air quality 

9 scenarios. We are using corn and soybeans to illustrate some of the interactions that occur 

10 between crop responses to O3 reductions, production, prices, producer cropping decisions, and 

11 welfare effects for both producers and consumers. For full model results for all crops included in 

12 the analysis see Appendix 6B. In general, the RYL and RYG are unchanged between the 

13 existing 75 ppb standard and the 15 ppm-hrs W126 scenarios. Also, note that in many cases, 

14 subregions that show no change in yield for a given crop have no production of that crop in that 

15 subregion in FASOMGHG. For example, soybeans are relatively sensitive to O3 and there are 

16 large reductions in O3 in California, but there are no impacts on soybean yields in that region 

17 because no soybeans are produced in California in FASOMGHG. 
 6-41 

1 Corn is relatively insensitive to O3-induced yield losses at the existing standard or 15 

2 ppm-hrs. The highest loss occurs in California at 0.88 percent, while in the Corn Belt, Lake 

3 States, and Great Plains the highest loss occurs in southern Ohio with 0.34 percent. Because the 

4 yield losses are small due to corn’s insensitivity to O3 under the alternative W126 standard 

5 scenarios, the yield losses are virtually eliminated at all three alternative W126 standards. Yield 

6 gains associated with the alternative scenarios are almost nonexistent; the highest gain occurs in 

7 Arizona at 0.02 percent at the 7ppm-hrs level. 

8 Soybeans, on the other hand, are relatively sensitive to O3-induced yield losses. The 

9 highest losses at the existing standard or 15 ppm-hrs occur in Colorado, southern Indiana, 

10 Kentucky, and northwest Ohio at over 1 percent. Yield losses remain under all scenarios for 

11 W126, although for the 7 ppm-hrs scenario all losses are less than 0.6 percent. Yield gains 

12 across the alternative W126 standard levels generally range between 0.54 percent and 0.84 

13 percent with northeast Ohio, Tennesse, Kentucky, Illinois, and Indiana on the high end. 

14 Colorado has the highest gain at 1.01 percent at the 7 ppm-hrs level and most soybean producing 

15 states have at least small gains at every W126 scenario. 
 6-42 

1 

2 Figure 6-17 Percentage Changes in Corn RYG with Respect to 75 ppb 
 6-43 

1 

2 Figure 6-18 Percentage Changes in Soybean RYG with Respect to 75 ppb 
 6-44 

1 In general, increased yield leads to increased supply and lower prices. Because corn does 

2 not lose or gain very much under any scenario one could expect that prices would remain 

3 relatively stable. Soybeans, however, would experience yield gains in any scenario, and prices 

4 would likely fall. In the modeled scenarios soybean prices fall, and since consumer demand does 

5 not increase enough to offset the loss of revenue due to price decreases there is a net decrease in 

6 producer welfare, but consumers always benefit from falling prices. In response to falling 

7 soybean prices, the model predicts that producers would switch to less O3-sensitive crops with 

8 stable prices, such as corn, thereby increasing corn production. See Appendix 6C for an 

9 explanation of the supply curve shift. 

10 Overall, across the full agriculture sector, these changes in production are small, seldom 

11 above 0.5 percent and usually 0.01 percent or less. The production increases lead to generally 

12 lower prices, with price decreases greater than the change in production. The drop in market 

13 prices, while a loss for producers, represents a gain for consumers. In terms of producer and 

14 consumer welfare across the agriculture sector, in nearly all cases producer welfare is negatively 

15 affected. Table 6-16 presents the overall welfare gains and losses. For producers, the W126 

16 alternatives occasion welfare gains in the middle years, 2020-2030, and welfare losses in all 

17 other years. For consumers, however, the changes in production and prices occasion welfare 

18 gains in all scenarios in all years. 

19 Since the forestry and agriculture sectors are interlinked and factors affecting one sector 

20 can lead to changes in the other, it is important to consider the overall effect of O3 changes in the 

21 context of producer and consumer welfare across both sectors. The impacts on consumer surplus 

22 are positive for both sectors, with benefits increasing with lower W126 alternatives. For producer 

23 surplus, however, impacts are negative for the 15 ppm-hrs and 11 ppm-hrs scenarios and positive 

24 for the 7 ppm-hrs case. Table 6-17 presents the annualized surplus for both sectors. 

25 

26 
 6-45 

1 

2 Table 6-16 Consumer and Producer Surplus in Agriculture (Million 2010$) 

Product Policy 2010 2015 2020 2025 2030 2035 2040 

Consumer 

surplus 75 ppb 1,918,082 1,940,673 1,968,142 1,995,346 2,023,022 2,050,791 2,076,018 

 Change with Respect to Existing Standard

15 ppm-hrs 15 -2 1 6 -7 10 3 

11 ppm-hrs 19 24 13 51 42 20 13 

7 ppm-hrs -31 46 36 104 90 26 46 

Producer 

surplus 75 ppb 725,364 831,565 815,072 863,165 878,986 836,692 863,308 

 Change with Respect to Existing Standard

 15 ppm-hrs 612 -1,255 980 -961 90 41 697 

 11 ppm-hrs 1,474 -2,197 1,013 230 232 -3,413 2,189 

7 ppm-hrs 269 -1,873 1,780 423 264 -1,052 2,991 
 6-46 

1 Table 6-17 Annualized Changes in Consumer and Producer Surplus in Agriculture and 

2 Forestry, 2010-2040, Million 2010$ (4% Discount Rate) 

Product Policy Agriculture Forestry Total

Consumer surplus 75 ppb NA NA NA 

Change with Respect to Existing Standard

15 ppm-hrs 4.5 88.1 92.5 

11 ppm-hrs 25.4 236.9 262.3 

7ppm-hrs 36.7 344.0 380.7 

Producer surplus 75 ppb NA NA NA 

Change with Respect to Existing Standard 

15 ppm-hrs -4.7 -112.2 -116.9 

11 ppm-hrs -4.6 -264.4 -269.0 

7 ppm-hrs 194.4 -318.4 -124.0 

Total surplus 75 ppb NA NA NA 

Change with Respect to Existing Standard

15 ppm-hrs -0.2 -24.2 -24.4 

11 ppm-hrs 20.8 -27.5 -6.7 

7 ppm-hrs 231.1 25.6 256.7 

3 

4 6.6 CLIMATE REGULATION 

5 Biomass loss due to O3 exposure affects climate regulation by ecosystems by reducing 

6 carbon sequestration and storage. More carbon stays in the atmosphere because carbon uptake 

7 by forests is reduced. The studies cited in the ISA demonstrate a consistent pattern of reduced 

8 carbon uptake because of O3 damage, with some of the largest reductions projected over North 

9 America. In one simulation (Sitch et al., 2007) the indirect radiative forcing due to O3 effects on 

10 carbon uptake by plants are shown as even greater than the direct effect of O3 on climate change. 

11 6.6.1 National Scale Forest Carbon Sequestration 

12 FASOMGHG can calculate the difference in carbon sequestration by forests and 

13 agriculture due to biomass loss caused by O3 exposure. By comparing equilibriums under the 

14 different scenarios outlined in Section 6.3, we can calculate changes in carbon sequestration 
 6-47 

1 potential over time. Details of FASOMGHG and the methodology for the analyses done for this 

2 risk and exposure assessment are available in Appendix 6B. 

3 The impacts of the simulations of meeting the existing and alternative secondary O3

4 standards on carbon sequestration potential in U.S. forest and agricultural sectors are presented 

5 in Table 6-18, where numbers indicate increased sequestration. As shown in the table, much 

6 greater sequestration changes are projected in the forest sector than in the agricultural sector. The 

7 15 ppm-hrs scenario does not appreciably increase carbon storage relative to just meeting the 

8 existing standard. The vast majority of the enhanced carbon sequestration potential under the 

9 alternative secondary standard scenarios lies in the forest biomass increases over time at the 11 

10 and 7 ppm-hrs standard levels. The forest carbon sequestration potential would increase between 

11 593 and 1,602 million tons of CO2 equivalents over 30 years after meeting the 11 or 7 ppm-hrs 

12 standard level, respectively, compared to just meeting the existing O3 standard. On an annual 

13 basis when just meeting the 11 ppm-hrs W126 standard level, total forestry and agriculture 

14 carbon storage is increased by about 20 million tons per year relative to just meeting the existing 

15 O3 standard; equivalent to taking about 4 million cars off the road as calculated by the EPA 

Greenhouse Gas Equivalencies Calculator5 16 (U.S. EPA, 2013b). When meeting the 7 ppm-hrs 

17 W126 standard level, the increased annual carbon storage is about 53 million tons relative to just 

18 meeting the existing O3 standard, or approximately 11 million fewer cars on the road. 

19 The baseline stock of carbon storage decreases over time for agriculture because the 

20 agriculture sector GHG emissions sources are released every year and soil carbon sequestration 

21 stabilizes over the 30-year period. There are only small increases in net carbon storage compared 

22 to the existing standard for each of the alternative scenarios modeled.

 5

 Available at http://www.epa.gov/cleanenergy/energy-resources/calculator.html. 
 6-48 

1 Table 6-18 Increase in Carbon Storage, MMtCO2e, Cumulative over 30 years 

Product Policy 2010 2020 2030 2040 

Forestry 75 ppb 74,679 79,171 84,863 89,184 

 Change with Respect to Existing Standard

 15 ppm-hrs 1 0 16 13 

 11 ppm-hrs 19 103 312 593 

 7 ppm-hrs 50 305 832 1,602 

Agriculture 75 ppb 18,748 15,363 12,002 8,469 

 Change with Respect to Existing Standard

15 ppm-hrs 0 1 1 4 

11 ppm-hrs 2 5 6 10 

7 ppm-hrs 3 4 6 9 

2 

3 Key uncertainties in using the FASOMGHG in these analyses include: 

4  Although the modeling system applied builds on existing models that have previously 

5 been used for assessments of O3 impacts and reflects what we consider reasonable 

6 and appropriate assumptions, it is very important to recognize the considerable 

7 uncertainties and limitations surrounding the results of this study or any study 

8 assessing the potential impacts of changes in O3 concentrations on forest and 

9 agricultural production. First, the changes in W126 index values being used to 

10 calculate the agricultural and forest productivity responses are assumed to equal point 

11 estimates taken as exogenous to the economic modeling. However, these changes in 

12 the W126 index values were calculated using air quality simulation models where (as 

13 with any model) model parameter values are not known with certainty. 

14  Second, the O3 C-R functions applied to crops and trees used the median parameters 

15 from Lehrer et al. (2007)—the RYLs and RYGs calculated are thus representative of 

16 these median values, whereas there is actually a range of responsiveness to O3. Using 

17 alternative “low” and “high” O3 CRs would present data inputs that exhibit lower or 

18 higher O3 impacts on crop and tree species biomass productivity, and thus the 

19 magnitude of exogenous environmental shocks under different policy scenarios. The 
 6-49 

1 changing of the magnitude of exogenous shocks on U.S. agriculture and forestry 

2 systems would potentially lead to different economic equilibrium outcomes, 

3 especially if the shocks go beyond the buffering or adjustability of the system. 

4  Third, the use of crop proxy mapping and forest type mapping due to incomplete data, 

5 as specified in Section 4, adds to the uncertainty of these model results to the extent 

6 that actual crop-specific impacts differ from those of the proxy crop used. The current 

7 mappings of crops/tree species that have O3 C-R functions, which are a subset of 

8 crops/tree species that are present in U.S. agriculture and forestry systems, present 

9 probable “omission” biases. In particular, forest types that vary by region may have 

10 been underrepresented by just a handful of tree species that have O3 C-R functions 

11 specified. Moreover, due to data limitations, we are using a simple average of tree 

12 RYLs for all forest types within a region, which is an imperfect estimate. For 

13 instance, in southern regions, poplar is far more common than black cherry for 

14 hardwood forests. 

15  Fourth, the potential changes in tree species mixes within a forest type that would be 

16 made by landowners due to differential impacts associated with ground-level O3

17 exposure changes were not considered. Tree species that are less susceptible to 

18 ground O3 damage may gain relative advantage over tree species that are more 

19 sensitive to ground O3. Thus, as time moves forward, the O3 impacts on forests may 

20 get ameliorated because forests adapt to O3 environments – whether via forestry 

21 industry management or through natural processes. 

22  Fifth, the international trade component in FASOMGHG assumes USDA-based 

23 future projections under recent O3 conditions. This may present another uncertainty 

24 for the model results, especially when soybeans and wheat are among the major crop 

25 commodities for U.S. exports and have relatively large responses to changed O3

26 environments. As a result, the exogenous RYGs obtained under O3 policy scenarios 

27 for these crops would present a potentially enhanced supply advantage, relative to 

28 soybeans and wheat produced in the rest of world. The general trade projections thus 

29 may need to reflect these potential changes. 
 6-50 

1  Finally, there is a very large number of parameters contained within the 

2 FASOMGHG modeling system, introducing further uncertainty regarding the best 

3 values to use for each parameter as well as potential interactions between parameters. 

4 To summarize, the uncertainty in crop and tree species’ O3 C-R functions, whether the 

5 crops/forest types are well-represented, the possibly of changes in such representations due to 

6 adaptation, and the potential changes in international trade for O3-sensitive crops present the 

7 known uncertainties for the model results. Careful consideration and sound judgment related to 

8 the potential implications of uncertainties is important for appropriate interpretation of model 

9 results. 

10 In addition, it should be noted that since public lands are not affected within 

11 FASOMGHG the estimates presented would likely be higher if public lands were included. 

12 6.6.2 Urban Case Study Carbon Storage 

13 Urban forests are subject to the adverse effects of O3 exposure in the same ways as 

14 forests in rural areas. These urban forests provide a range of ecosystem services such as carbon 

15 sequestration, pollution removal, building energy savings, and reduced stormwater runoff. The 

16 analyses in this section focus on carbon sequestration. Pollution removal services are discussed 

in section 6.7. The i-Tree model6 17 used in this analysis is a peer-reviewed suite of software tools 

18 provided by USFS. We used data from five urban areas to estimate the effects of O3 (based on 

19 CMAQ modeled W126 index surfaces) on carbon storage. We used the i-Tree Forecast model to 

20 estimate tree growth and ecosystem services provided by trees over a 25-year period, using for 

21 the base case the measured inventory of trees in the area and standard growth rates over the 25-

22 year period. We adjusted the tree growth downward from the base case using the reduced 

23 growth factors for the species present in each area for which we have C-R functions (only 

24 species with W126 C-R functions were reduced). Unlike the FASOMGHG model, C-R 

25 functions were not assigned to species in the study areas that do not have specific C-R functions 

26 available from the literature because the model does not account for dynamic interactions in the 

27 community composition based on increased or decreased competitiveness of the species present. 

28 We contrasted the differences between the scenarios for the 25-year period. We ran six scenarios 

 6

 Available at http://www.itreetools.org/. 
 6-51 

1 simulating a scenario without O3-induced changes in biomass, recent ambient conditions, a 

2 simulation of “just meeting” the existing standard, and just meeting three alternative W126 

3 standards of 15, 11, and 7 ppm-hrs. The model assumed an annual influx of between one and six 

4 trees/hectare/year and a three to four percent annual mortality rate. See Appendix 6D for details 

5 of the model and the methodology employed for these case studies. 

6 We chose the five urban areas based on data availability and presence of species with a 

7 W126 C-R function. No urban area with available vegetation data had more than three qualified 

8 species present. The selected study areas include Baltimore, Syracuse, the Chicago region, 

9 Atlanta, and the urban areas of Tennessee. Table 6-19 shows details of the tree species present, 

10 the percent of sensitive trees in the top ten species present, and the percent of sensitive trees in 

11 the total species in each study area. 

12 
 6-52 

1 Table 6-19 Tree Species with Available C-R Functions in Selected Urban Study Areas 

Study Area 

Top Ten 

Occurring 

Species 

Baltimore Syracuse Chicago Region Atlanta Tennessee 

1 American beech European 

buckthorn 

European 

buckthorn Sweetgum Chinese privet 

2 Black locust Sugar maple Green ash Loblolly pine Virginia pine 

3 American elm Black cherry Boxelder Flowering 

dogwood 

Eastern red 

cedar 

4 Tree of heaven Boxelder Black cherry Tulip tree Hackberry 

5 White ash Norway maple Hardwood Water oak Flowering 

dogwood 

6 Black cherry Northern white 

cedar American elm Boxelder Amur honeysuckle 

7 White mulberry Norway spruce Sugar maple Black cherry Winged elm 

8 Northern red 

oak Staghorn sumac White ash White oak Red maple 

9 Red maple Eastern 

cottonwood 

Amur 

honeysuckle Red maple Black tupelo 

10 White oak Eastern 

hophornbeam Silver maple Southern red oak American beech 

Species w/ C-R 

Function %of 

Top 10 

8.5 18.5 7.7 6.6 9.3 

Species w/ C-R 

Function % of 

Total Trees 

11.2 20.2 10.5 8.9 17.4 

2 Bold – species with C-R function, Italics – species known to be sensitive, no C-R function
 6-53 

1 

2 The largest differences in the modeled air quality are between the recent ambient 

3 conditions and meeting the existing standard. The distribution of O3 air quality is not changed in 

4 most areas in the eastern U.S. when simulating meeting an alternative W126 standard of 15 ppm5 hrs relative to the scenario of just meeting the existing O3 standard. There are small incremental 

6 differences between just meeting the existing O3 standard and just meeting alternative W126 

7 standards of 11 and 7 ppm-hrs. 

8 The model results for changes in carbon storage show substantial reductions in the 

9 capacity of these urban forests to sequester carbon for the simulation of “just meeting” the 

10 existing standard. Estimates for the five modeled areas at the existing standard or an alternative 

11 standard of 15 ppm-hrs are about 3.5 million tons of carbon storage lost over 25 years (about 

12 140,000 tons /year). At an alternative standard of 11 ppm-hrs, loss of carbon sequestration is 

13 128,000 metric tons per year, and at an alternative standard of 7 ppm-hrs, the estimated loss is 

14 112,000 metric tons per year of carbon storage services. 

15 Three of the urban areas show gains in carbon storage at alternative W126 standards 

16 below 15 ppm-hrs. Syracuse and Baltimore do not realize gains because they are currently very 

17 close to meeting the alternative standards. Of the five areas modeled, the combined urban areas 

18 of Tennessee have the largest estimated gains in carbon storage at almost 20,000 tons per year 

19 when meeting the alternative standard of 7 ppm-hrs. The Chicago region gains about 6,400 tons 

20 per year of carbon sequestration when meeting the alternative standard of 7 ppm-hrs. See Table 

21 6-20 for details. 

22 Compared to other activities, the yearly carbon storage gains at 11 ppm-hrs for Atlanta 

23 are only equivalent to taking 50 cars per year off the road or recycling about 90 more tons of 

24 waste every year. At the 7 ppm-hrs standard level, Atlanta would need to remove 250 cars per 

25 year to be equivalent to the gains from reduced O3. The Chicago region would need to take 417 

26 cars per year off the road. At 7 ppm-hrs, Chicago would need to remove more than 1,300 cars. 

27 The urban areas of Tennessee would need about 1,800 fewer cars per year at the 11 ppm-hrs 

28 standard level. To reach the carbon sequestration provided by the urban forests in Tennessee at 

29 the 7 ppm-hrs standard level, Tennessee would need 4,000 fewer cars every year. 

30 Baltimore and Syracuse would realize no gains at the alternative standard levels chosen 

31 for this analysis. Chicago and Atlanta are in the middle of the range of results. In Tennessee, at 
 6-54 

1 recent ambient conditions, the urban areas are all above a W126 standard of 15 ppm-hrs and 

2 comprise a much larger area than the other four case study areas with a far larger tree population. 

3 Thus the relative gains in carbon storage in Tennessee are far larger than the other case study 

4 areas. Keeping in mind that of the 11 tree species for which we have C-R functions, only two to 

5 three species were present in a given area comprising at most 18.5 percent of the total trees 

6 present. It seems reasonable to conclude that the actual effect on carbon storage because of O3

7 exposure would be higher than the estimates modeled here. 

8 These results should not be combined with the results from the FASOMGHG model 

9 discussed in Section 6.7.1. The methodology employed for the FASOMGHG runs assigned 

10 values for O3 exposure C-R functions for species that do not have a function calculated in the 

11 ISA. We did this to ensure the dynamic trade-offs in the model functioned properly. The i-Tree 

12 model does not provide trade-offs between species, so the species that do not have a C-R 

13 function were not assigned values. This could lead to an underestimation of the carbon storage 

14 losses in i-Tree if the other species in the study area are sensitive to O3 exposure effects. 

15 Alternatively assigning C-R functions to species as we did for the FASOMGHG runs would 

16 likely produce an overestimation since many species, even within the same genus, may not be 

17 sensitive to O3 effects. 

18 

19 Table 6-20 O3 Effects on Carbon Storage for Five Urban Areas over 25 Years (in 

20 millions of metric tons)* 

Region 

No O3 

Adjustment 

(NOA) 

Existing 

Standard/15 

ppm-hrs 

(ES/15) 

ES/15 v 

NOA 

11 ppmhrs v 

NOA 

7 ppmhrs v BC 

ES v 

11ppmhrs 

ES v 7 

ppm-hrs 

Atlanta 1.426 1.315 -0.112 -0.106 -0.081 0.006 0.03 

Baltimore 0.578 0.571 -0.007 -0.007 -0.007 0.00 0.00 

Chicago 

Region 19.560 17.053 -2.508 -2.457 -2.346 0.05 0.16 

Syracuse 0.169 0.169 -0.0015 -0.0015 -0.0015 0.00 0.00 

Tennessee 20.568 19.668 -0.900 -0.676 -0.410 0.22 0.49 

Total 42.302 38.607 -3.528 -3.247 -2.845 0.276 0.68 

21 ES = Existing standard 
 6-55 

1 In addition to its direct impacts on vegetation, O3 is a well-known GHG that contributes 

2 to climate warming (U.S. EPA, 2013). A change in the abundance of tropospheric O3 perturbs 

3 the radiative balance of the atmosphere, an effect quantified by the radiative forcing metric. The 

IPCC (2007) reported a radiative forcing of 0.35 W/m2 4 for the change in tropospheric O3 since 

the preindustrial era, ranking it third in importance after the greenhouse gases CO2 (1.66 W/m2 5 ) 

and methane (CH4) (0.48 W/m2 6 ). The earth-atmosphere-ocean system responds to the radiative 

7 forcing with a climate response, typically expressed as a change in surface temperature. Finally, 

8 the climate response causes downstream climate-related ecosystem effects, such as redistribution 

9 of ecosystem characteristics because of temperature changes. While the global radiative forcing 

10 impact of O3 is generally well understood, the downstream effects of the O3-induced climate 

11 response on ecosystems remain highly uncertain. 

12 Since O3 is not emitted directly but is photochemically formed in the atmosphere, it is 

13 necessary to consider the climate effects of different O3 precursor emissions. Controlling 

14 methane, CO, and non-methane VOCs may be a promising means of simultaneously mitigating 

15 climate change and reducing global O3 concentrations (West et al. 2007). Reducing these 

16 precursors reduces global concentrations of the hydroxyl radical (OH), their main sink in the 

17 atmosphere, feeding back on their lifetime and further reducing O3 production. NOx reductions 

18 decrease OH, leading to increased methane lifetime and increased O3 production globally in the 

19 long-term. The resulting positive radiative forcing from increased methane may cancel or even 

20 slightly exceed the negative forcing from decreased O3 globally (West et al. 2007). Of the O3

21 precursors, methane abatement reduces climate forcing most per unit of emissions reduction, as 

22 methane produces O3 on decadal and global scales and is itself a strong climate forcer. Since 

23 they may have different effects on concentrations of different species in the atmosphere, all O3

24 precursors must be considered in evaluating the net climate impact of emission sources or 

25 mitigation strategies. 

26 6.7 URBAN CASE STUDY AIR POLLUTION REMOVAL 

27 In addition to sequestering and storing carbon, urban forests also remove pollutants from 

28 the local atmosphere. The reduction in growth rates resulting from O3 exposure would reduce the 

29 current and future amount of pollutants removed by these forests. We used the i-Tree model 
 6-56 

1 described in Section 6.5.2 to estimate the removal of air pollutants by the forests in the urban 

2 areas discussed. 

3 The preliminary results for changes in air pollution removal estimates for carbon 

4 monoxide, nitrogen dioxide, O3, and sulfur dioxide show reduced capacity for these urban forest 

5 canopies to remove pollution (1) at recent ambient O3 conditions and (2) after adjusting air 

6 quality to just meeting the existing standards and alternative standards. These analyses show that 

7 even at the lowest scenario urban forest capacity to remove pollution is still reduced compared to 

8 a no ozone scenario. Because of the limitations in the availability of C-R functions for all of the 

9 common tree species in urban areas, and because of the limited number of urban areas for which 

10 the i-Tree model has been applied, these reductions only reflect a portion of the impacts on 

11 pollution removal by urban forests in the U.S. Though the model does include estimates for 

12 particulate matter (PM), we do not include those estimates because the model does not yet 

13 distinguish between PM10 and PM2.5, and this distinction is important for evaluating the potential 

14 health and welfare effects associated with PM. Estimates suggest that after meeting the existing 

15 standard about 1,535 tons of air pollution removal capacity is lost annually (or about 38,384 tons 

16 over 25 years) in the five areas modeled. As in the simulations for carbon storage, Syracuse and 

17 Baltimore see the least change in capacity with the urban areas of Tennessee reporting the largest 

18 changes. Syracuse and Baltimore have no change in pollution removal when meeting the 

19 existing and the modeled alternatives. Atlanta and Chicago gain about 470 and 6,500 metric tons 

20 of additional pollution removal after meeting the alternative W126 standard of 7 ppm-hrs 

21 compared to meeting the existing standard, while Tennessee gains almost 12,000 tons of 

22 potential pollution removal annually for the same comparison. For the 7 ppm-hrs scenario, about 

23 51 percent of the pollution removal capacity lost under the existing standard is regained. See 

24 Table 6-21 for details. 

25 We performed a simple analysis of the O3 removal potential to show how this process 

26 might affect ambient air quality values. The analysis makes some general assumptions to 

27 estimate order of magnitude effects of O3 removal by trees on O3 concentrations in the five urban 

28 areas. To make this calculation, the metric tons of O3 removed listed in Table 6-20 are spread 

29 evenly over every hour in the 25-year tree lifetime to achieve an hourly O3 removal. Using the 

30 ideal gas-law, this mass can be converted to an equivalent volume of gas assuming standard 

31 temperature and pressure. Each urban area is treated as a well-mixed volume with the height 
 6-57 

determined as the average maximum daytime boundary layer height7 1 extracted from an April2 October 2007 Weather Research Forecasting (WRF) model simulation for each area of interest. 

The ratio of the O3 volume to the urban area air volume multiplied by 109 3 gives an equivalent 

4 concentration in ppbv. Table 6-21 shows that the effects on O3 concentration are generally 

5 small; deposition to tree surfaces results in ambient O3 concentration reductions ranging from 

6 0.08 ppbv in Tennessee to 0.52 ppbv in Chicago. Differences between the scenarios are minute. 

7 The base case numbers are consistent with previously published values from Song et al. (2008) 

8 who used a photochemical model to show that changes in land use from development in Austin, 

9 TX, might lead to a 0-0.3 ppbv change in O3 concentration due solely to deposition differences. 

10 Some additional benefit may be achieved from cumulative effects, which are not accounted for 

11 here (i.e., O3 removed at 9am will not only decrease concentrations instantaneously, but will also 

12 decrease the starting concentration to some degree at 10am, 11am, etc. throughout the day). In 

13 addition, changing the boundary layer height based on variability in this value could increase or 

14 decrease the ppbv estimates by a factor of two. But in any case, the values would still be small. 

15 

 7

 The maximum daytime boundary layer height is the depth in the atmosphere over which air is well-mixed in the 

afternoon. The WRF modeling simulation showed that this depth was approximately 1700m in Atlanta, 1500m in 

Baltimore, 1150m in Chicago, 1350m in Syracuse, and 1750m in Tennessee. 
 6-58 

1 Table 6-21 Comparison of Pollutant Removal Between an Unadjusted Scenario and 

2 Alternative Simulations and Gains Between the Existing Standard and 

3 Alternatives (metric tons) 

No O3 Adjustment 

(NOA) 

Existing 

Standard/15 

(ES/15) 

NOA 

v 

ES/15 

NOA v 

11 ppmhrs 

NOA v 

7 ppmhrs 

ES/15 v 

11 ppmhrs 

ES/15 v 

7 ppmhrs 

CO 

Atlanta 1,482 1,429 -54 -50 -34 3 9 

Baltimore 186 186 0 0 0 0 0 

Chicago 8,620 8,001 -619 -569 -476 142 235 

Syracuse 55 55 0 0 0 0 0 

Tennessee 12,854 12,626 -227 -97 62 131 290 

NO2

Atlanta 6,852 6,605 -248 -231 -159 16 88 

Baltimore 1,968 1,963 -5 -5 -5 5 5 

Chicago 104,247 96,766 -7,481 -6,883 -5,758 598 1,723 

Syracuse 50 50 0 0 0 0 0 

Tennessee 54,381 53,419 -962 -408 263 554 1,226 

O3

Atlanta 25,495 24,574 -921 -861 -591 60 331 

Baltimore 6,262 6,247 -15 -15 -15 0 0 

Chicago 243,701 226,214 -17,488 -16,090 -13,460 1,398 4,028 

Syracuse 1,544 1,541 -4 -4 -4 0 0 

Tennessee 393,205 386,247 -6,957 -2,953 1902 4,004 8,860 

SO2

Atlanta 3,380 3,257 -122 -114 -78 8 44 

Baltimore 852 850 -2 -2 -2 0 0 

Chicago 29,675 27,546 -2,129 -1,959 -1,639 170 490 

Syracuse 71 71 0 0 0 0 0 

Tennessee 59,371 58,320 -1,050 -446 287 605 1,338 

Total 

Atlanta 37,209 35,865 -1,344 -1,825 -862 87 472 

Baltimore 9,268 9,246 -22 -22 -22 5 0 

Chicago 386,242 358,527 -27,817 -25,501 -21,333 2,308 6,476 

Syracuse 1,721 1,717 -4 -4 -4 0 0 

Tennessee 519,810 510,613 -9,197 -3,904 2,514 5,294 11,714 
 6-59 

1 

2 Key uncertainties in this approach include: 

3  C-R functions are available for only 11 species. The urban areas chosen had a 

maximum of three of the 11 species present.8 4 This limitation neglects the effects of 

5 O3 on species where no C-R function is available. In the areas modeled that means 

6 that the majority of trees in the cities were not accounted for in the O3 damages. 

7 There are 2 species present that we know are sensitive but for which no C-R function 

8 is available. This excludes 80 - 90 percent of the total trees present in the study areas. 

9  Uncertainties inherent within the models, both i-Tree and the CMAQ-generated air 

10 quality surfaces. 

11  In addition this analysis does not account for the fact that many tree species are 

12 biogenic sources of volatile organic compounds (VOC) that contribute to formation 

13 of air pollution. Vegetation may account for as much as two-thirds of the VOC 

14 production (Guenther et al., 2006). Carlton et al. (2010) found, however, that were 

15 man-made pollutants not present biogenic pollution would drop by as a much as 50 

16 percent. 

17 If we were able to account for O3 damages to the species without a C-R function the 

18 estimates would likely be higher. 

19 6.8 ECOSYSTEM-LEVEL EFFECTS 

20 To assess the risk to ecosystems from biomass loss, as opposed to the potential risk to 

21 individual tree species, we attempted to combine the RBL values into one metric. One factor in 

22 assessing the risk to ecosystems is a measure of the overall abundance of each species. As a 

23 measure of overall abundance, we used the basal area estimates described in Section 6.2.1 to 

24 calculate the proportion of basal area for each of the 12 species assessed. Table 6-22 below 

 8

 Because of the timing of this analysis, we did not include Loblolly Pine (it would have been included in Atlanta). 

We did include Loblolly Pine for the other analyses in this draft assessment. 
 6-60 

1 reflects, by region, the total basal area covered by the 12 tree species assessed. We separated the 

2 total basal area covered into different categories of percent cover of the species assessed. For 

3 example, in the Southwest region, 13 percent of the total basal area assessed had less than 10 

4 percent cover of the 12 tree species; 7.1 percent of the total basal area assessed had between 10 

5 and 25 percent cover of the 12 tree species; 8.8 percent of the total basal area assessed had 

6 between 25 and 50 percent cover of the 12 tree species; and 64.9 percent of total basal area 

7 assessed had no data on percent cover of the 12 tree species. The Southwest and West regions 

8 had the largest percentages of total basal area assessed with no data on percent cover of tree 

9 species, and the Central and Northeast regions had the smallest percent of total basal area 

10 assessed with no data on percent cover of tree species. 

11 

12 Table 6-22 Percent of Total Basal Area Covered by 12 Assessed Tree Species 

Percent of Total Basal Area Covered by 12 Assessed Tree Species 

Region < 10% 10% to 25% 25% to 50% 50% to 75% > 75% No Data 

Central 38.4% 32.0% 26.6% 2.2% <0.1% 0.7% 

East North Central 33.4% 25.7% 27.5% 8.9% 0.1% 4.3% 

Northeast 7.0% 22.1% 47.9% 22.2% 0.5% 0.3% 

Northwest 4.5% 7.7% 20.0% 24.5% 15.0% 28.3% 

South 28.6% 4.0% 7.7% 7.7% 0.9% 51.2% 

Southeast 16.0% 14.2% 48.1% 17.7% 0.3% 3.8% 

Southwest 13.0% 7.1% 8.8% 5.1% 1.2% 64.9% 

West 10.0% 3.7% 7.0% 5.5% 0.2% 73.5% 

West North Central 20.2% 8.0% 9.7% 8.2% 6.5% 47.4% 

All Regions 20.3% 12.0% 19.1% 10.0% 2.7% 35.9% 

13 

14 Data on basal area were available in over 64 percent of the cover area assessed, as 

15 measured by the number of grid cells. To understand the potential W126 index values in the 

16 percent of cover area not assessed, Table 6-23 includes information on the (i) number of grid 

17 cells with no data on basal area above a certain amount and (ii) total number of grid cells with no 

18 data on basal area. For those grid cells with no data on basal area, the table also shows, under 

19 recent conditions, the number of grid cells with W126 index values that would exceed potential 

20 alternative standards of 15, 11, and 7 ppm-hrs. In the Southwest, under recent conditions, 52 

21 percent of the grid cells with no data have W126 index values above 15 ppm-hrs, 95 percent 
 6-61 

1 have W126 index values above 11 ppm-hrs, and 100 percent have W126 index values above 7 

2 ppm-hrs. In contrast, in the East North Central, under recent conditions, no grid cells with no 

3 data have W126 index values above 15 ppm-hrs, 1 percent have W126 index values above 11 

4 ppm-hrs, and 3.5 percent have W126 index values above 7 ppm-hrs. 

5 

6 Table 6-23 Grid Cells With No Data That Exceed W126 Index Values under Recent 

7 Conditions 

Region 

Number of Grid 

Cells w/No Data 

Exceeding 10 

sqft/acre Basal Area 

(Total with No Data) 

Number of Grid Cells w/No Data that Exceed W126 

Index Values Under Recent Conditions 

> 7 ppm-hrs > 11 ppm-hrs > 15 ppm-hrs 

Central 35 (35) 34 11 3 

East North Central 193 (198) 7 2 0 

Northeast 11 (11) 11 11 6 

Northwest 709 (1,256) 779 451 189 

South 4,329 (5,239) 4,638 1,945 27 

Southeast 198 (200) 59 15 3 

Southwest 2,854 (4,904) 4,904 4,662 2,572 

West 2,315 (3,550) 3,452 3,274 2,680 

West North Central 3,307 (4,013) 1,870 1,158 283 

All Regions 13,951 (19,406) 15,754 11,529 5,763 

8 

9 We used the proportion of total basal area for each species to weight the RBL value for 

10 that species in each grid cell. The weighted values for all species present in each grid cell were 

11 added to generate a weighted RBL value for each grid cell. Table 6-24 provides a summary of 

12 the percent of total basal area that exceeds a 2 percent weighted biomass loss under recent 

13 conditions and when adjusted to just meet the current standard. Table 6-25 provides a summary 

14 of the percent of total basal area that exceeds a 2 percent biomass loss at potential alternative 

15 standard levels of 15, 11, and 7 ppm-hrs. Note that for biomass loss, CASAC recommended that 

16 EPA should consider options for W126 standard levels based on factors including a predicted 

17 one to two percent biomass loss for trees and a predicted five percent loss of crop yield. Small 

18 losses for trees on a yearly basis compound over time and can result in substantial biomass losses 

19 over the decades-long lifespan of a tree (Frey and Samet, 2012b). We chose to use the 2 percent 
 6-62 

1 biomass loss recommendation in this analysis; however, the weighted RBL value is not the same 

2 as the individual species analysis (Section 6.2.1.3). These data are interpreted in a more relative 

3 manner where higher values represent a larger potential impact on the overall ecosystem. 

4 The data in Table 6-24 and Table 6-25 shows that the total area exceeding two percent 

5 biomass loss decreases, as expected, across air quality scenarios. For example, for the Central 

6 region under recent conditions, a total of 23.7 percent of total basal area assessed would exceed a 

7 2 percent biomass loss and when adjusted to just meet the current standard, a total of 2.7 percent 

8 of total basal area assessed would exceed a 2 percent biomass loss. When adjusted to meet 

9 potential alternative standard levels of 15, 11, and 7 ppm-hrs, 2.7 percent, 1 percent and 0.1 

10 percent, respectively, of total basal area assessed would exceed a 2 percent biomass loss. 

11 While it is not possible to predict overall effects, the results from these analyses show the 

12 weighted average RBL to be a potential predictor of risk in areas with a high proportion of 

13 species included. As such, the percent of area exceeding one and two percent weighted RBL is 

14 most at risk where the species included account for more than 75 percent of the total basal area. 

15 
 6-63 

1 Table 6-24 Percent of Area Exceeding 2% Weighted Biomass Loss – Recent Conditions 

2 and Existing Standard 

Percent of Area Exceeding 2% Weighted Biomass Loss 

(Recent Conditions) 

Cover Categories of 12 Assessed Tree Species 

Region < 10% 10% to 25% 25% to 50% 50 to 75% > 75% Total 

Central 2.4% 11.0% 9.1% 1.2% <0.1% 23.7% 

East North Central 1.1% 8.0% 3.5% <0.1% 0.0% 12.6% 

Northeast 0.1% 0.6% 6.1% 10.3% 0.2% 17.3% 

Northwest 0.0% 0.0% 0.3% 1.0% 1.5% 2.9% 

South 1.2% 0.5% 0.2% 0.1% 0.0% 2.1% 

Southeast <0.1% 0.9% 6.2% 1.4% 0.0% 8.6% 

Southwest 0.1% 0.3% 4.4% 4.3% 1.2% 10.3% 

West <0.1% 0.6% 2.0% 1.8% 0.2% 4.7% 

West North Central 3.0% 3.1% 2.2% 3.0% 3.7% 15.0% 

All Regions 1.1% 2.6% 3.4% 2.2% 0.9% 10.1% 

Percent of Area Exceeding 2% Weighted Biomass Loss 

 (75 ppb Scenario) 

Cover Categories of 12 Assessed Tree Species 

Region < 10% 10% to 25% 25% to 50% 50 to 75% > 75% Total 

Central 0.1% 1.3% 1.3% <0.1% 0.0% 2.7% 

East North Central 0.0% 0.1% 0.6% 0.0% 0.0% 0.6% 

Northeast 0.0% 0.0% 0.1% 0.1% 0.0% 0.2% 

Northwest 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

South 0.0% <0.1% 0.1% 0.0% 0.0% 0.2% 

Southeast 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Southwest <0.1% 0.1% 0.1% 0.1% 0.3% 0.5% 

West 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

West North Central 0.1% 0.5% 1.0% 0.3% 0.3% 2.2% 

All Regions <0.1% 0.2% 0.4% 0.1% 0.1% 0.8% 

3 
 6-64 

1 Table 6-25 Percent of Area Exceeding 2% Weighted Biomass Loss – Alternative W126 

2 Standard Levels 

Percent of Area Exceeding 2% Weighted Biomass Loss 

 (15 ppm-hrs Scenario) 

Cover Categories of 12 Assessed Tree Species 

Region < 10% 10% to 25% 25% to 50% 50% to 75% > 75% Total 

Central 0.1% 1.3% 1.3% <0.1% 0.0% 2.7% 

East North Central 0.0% 0.1% <0.1% 0.0% 0.0% 0.6% 

Northeast 0.0% 0.0% 0.1% 0.1% 0.0% 0.2% 

Northwest 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

South 0.0% <0.1% 0.1% 0.0% 0.0% 0.2% 

Southeast 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Southwest 0.0% 0.1% 0.1% <0.1% <0.1% 0.2% 

West 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

West North Central <0.1% 0.4% 1.0% 0.3% 0.2% 2.0% 

All Regions <0.1% <0.1% 0.2% 0.1% <0.1% 0.7% 

Percent of Area Exceeding 2% Weighted Biomass Loss 

(11 ppm-hrs Scenario)

Cover Categories of 12 Assessed Tree Species

Region < 10% 10% to 25% 25% to 50% 50% to 75% > 75% Total 

Central 0.1% 0.4% 0.4% <0.1% 0.0% 1.0% 

East North Central 0.0% <0.1% 0.4% 0.0% 0.0% 0.4% 

Northeast 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Northwest 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

South 0.0% <0.1% 0.1% 0.0% 0.0% 0.1% 

Southeast 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Southwest 0.0% 0.0% <0.1% <0.1% <0.1% 0.1% 

West 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

West North Central <0.1% 0.4% 0.9% 0.3% 0.2% 1.8% 

All Regions <0.1% 0.1% 0.3% 0.1% <0.1% 0.5% 

Percent of Area Exceeding 2% Weighted Biomass Loss 

(7 ppm-hrs Scenario)

Cover Categories of 12 Assessed Tree Species

Region < 10% 

10% to 

25% 

25% to 50% 50% to 75% > 75% 

Total 

Central <0.1% 0.1% 0.1% 0.0% 0.0% 0.1% 

East North Central 0.0% <0.1% 0.3% 0.0% 0.0% 0.3% 

Northeast 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 
 6-65 

Northwest 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

South 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 

Southeast 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Southwest 0.0% 0.0% 0.0% <0.1% <0.1% <0.1% 

West 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

West North Central 0.0% 0.1% 0.5% 0.3% 0.1% 1.0% 

All Regions <0.1% <0.1% 0.1% 0.1% <0.1% 0.2% 

1 

2 Two important things to note with respect to the weighted RBL analysis. First, the 

3 proportional basal area values do not account for total cover, only for the relative cover of the 

4 tree species present. This is most noticeable with Cottonwood and Ponderosa pine, which are 

5 near 100 percent cover in some areas; however, the absolute cover is very different. Ponderosa 

6 pine occurs in relatively high density in some grids, exceeding 100 square feet per acre, while 

7 Cottonwood is often less than 10 square feet per acre. This affects the direct interpretation of the 

8 values presented because the overall ecosystem effect may be very different, although equally 

9 important. It is important to remember with this data set that these numbers are only useful as a 

10 very general estimate of potential effects. Second, this analysis only accounts for the 12 tree 

11 species with C-R functions; other species may also be sensitive to O3 exposure. It is also 

12 possible other species that are not sensitive may be indirectly affected through changes in 

13 community composition and competitive interactions. 

14 6.8.1 Potential Biomass Loss in Federally Designated Areas 

15 6.8.1.1 Class I Areas 

16 We analyzed federally designated Class I areas in relation to the W126 air quality surface 

17 and the weighted RBL values. We completed the analyses of Class I areas in the same manner as 

18 the analyses across the entire range of data; however, we present the results as a count of the 

19 Class I areas and not as a percentage of area. We treated each Class I area as an individual 

20 geographic endpoint and calculated an average weighted RBL for all Class I areas with at least 

21 one grid cell that had a non-zero weighted RBL. Data were available in 145 of the 156 Class I 

22 areas. A complete list of Class I areas and the weighted RBL values at the current standard and 

23 alternative W126 standard levels is included in Appendix 6E. 
 6-66 

1 Table 6-26 summarizes the number of Class I areas exceeding 1 percent and 2 percent 

2 weighted RBL across varying percent cover of species and under recent conditions and when 

3 adjusted to just meet the existing standard and potential alternative standard levels of 15, 11, and 

4 7 ppm-hrs. The number of areas exceeding 1 percent and 2 percent decreases across air quality 

5 scenarios. 

6 Table 6-26 Weighted RBL and Percent Cover in Class I Areas 

Percent 

of Total 

Basal 

Area 

Class I 

Areas 

Covered 

Number of Class I Areas Exceeding 1% 

Weighted RBL 

Number of Class I Areas Exceeding 2% 

Weighted RBL 

Recent 

Conditions 

75 

ppb 

15 

ppmhrs 

11 

ppmhrs 

7 

ppmhrs 

Recent 

Conditions 75 

ppb 

15 

ppmhrs 

11 

ppmhrs 

7 

ppmhrs 

No Data 11 - - - - - - - - - - 

<10 54 6 2 2 2 1 2 1 1 1 0 

10 to 25 35 8 0 0 0 0 2 0 0 0 0 

25 to 50 48 20 1 1 0 0 7 0 0 0 0 

50 to 75 6 1 0 0 0 0 1 0 0 0 0 

> 75 2 1 1 1 1 1 1 1 1 1 1 

Total 

Areas 156 36 4 4 3 2 13 2 2 2 1 

7 

8 6.9 QUALITATIVE ASSESSMENT OF UNCERTAINTY 

9 As noted in Chapter 3, we have based the design of the uncertainty analysis for this 

10 assessment on the framework outlined in the WHO guidance (WHO, 2008). For this qualitative 

11 uncertainty analysis, we have described each key source of uncertainty and qualitatively assessed 

12 its potential impact (including both the magnitude and direction of the impact) on risk results, as 

13 specified in the WHO guidance. In general, this assessment includes qualitative discussions of 

14 the potential impact of uncertainty on the results (WHO Tier1) and quantitative sensitivity 

15 analyses where we have sufficient data (WHO Tier 2). 

16 Table 6-27 includes the key sources of uncertainty identified for the O3 REA. For each 

17 source of uncertainty, we have (a) provided a description, (b) estimated the direction of influence 

18 (over, under, both, or unknown) and magnitude (low, medium, high) of the potential impact of 

19 each source of uncertainty on the risk estimates, (c) assessed the degree of uncertainty (low, 
 6-67 

1 medium, or high) associated with the knowledge-base (i.e., assessed how well we understand 

2 each source of uncertainty), and (d) provided comments further clarifying the qualitative 

3 assessment presented. The categories used in describing the potential magnitude of impact for 

4 specific sources of uncertainty on risk estimates (i.e., low, medium, or high) reflect our 

5 consensus on the degree to which a particular source could produce a sufficient impact on risk 

6 estimates to influence the interpretation of those estimates in the context of the secondary O3

7 NAAQS review. Where appropriate, we have included references to specific sources of 

8 information considered in arriving at a ranking and classification for a particular source of 

9 uncertainty. 

10 
 6-68 

1 Table 6-27 Summary of Qualitative Uncertainty Analysis in Relative Biomass Loss Assessments 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

A. National W126 surfaces 

The biomass loss analyses in this chapter use the national W126 surfaces for recent conditions and adjusted to just meet the existing standard and alternative W126 standards. 

Both Low- Medium Low-medium KB and INF: See Chapter 4 fo

B. Shape of the C-R function for biomass loss for different species 

Biomass loss and yield loss estimates are highly sensitive to the parameters in the C-R function. 

Unknown High Medium 

KB: We conducted sensitivity studies) and 12 tree species (in high intraspecific and interspeonly had one study, while otheINF: The resulting C-R functiowere mostly of intermediate sespecies considered very sensitlittle or no sensitivity to O3. Th

consistent with the additional further studies are needed to dreflects the larger suite of tree 

C. Absence of C-R functions for many O3-sensitive species 

C-R functions are available for only 12 tree species, thus the majority of trees in the modeled urban areas and Class I areas were not incorporated. 

Under Medium- High Medium-Low 

KB: We are certain that there based on studies cited in the ISHowever, the studies of additiprovide sufficient information Therefore, we are certain that biomass loss in urban areas anINF: Eighty to 90 percent of thstudy areas are excluded from species in the case study areas for which no C-R function is ainfluence is dependent on the area. 
 6-69 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

D. Using C-R functions for tree seedlings rather than adult trees 

C-R functions for trees are based on analyses of tree seedlings, but most biomass impacts are from effects on adult trees. 

Both Low- Medium Medium 

KB and INF: In general, estim(RBL) in tree seedlings are coadult trees, with a few exceptispecies overestimate RBL in aunderestimate RBL. 

E. Urban tree inventory in iTree 

The base inventory of urban trees, including species and distribution, in iTree has uncertainty. 

Unknown Low High 

KB: The urban tree inventorieare based on field counts and mspecific urban areas analyzed Nowak, 6/2011). Tree census Chicago, and Atlanta) are genthan modeled tree inventories Tennessee). INF: The iTree model estimatepollution removal services proservices are based on tree growfunctions that are specific to tharea, including the species comdiameter distribution of trees. inventory are propagated into sequestration and pollution reminventories. 

F. Pollution removal functions in iTree 

The functions applied in iTree to estimate pollution removal are uncertain and vary by species. 

Unknown Medium Medium 

KB: Pollution removal is calcuconcentration, and meteorologremoval functions in iTree are INF: iTree estimates that 1,53removed from the urban case sstandard. Nowak et al. (2006) ranges of pollution removal in 
 6-70 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

G. VOC emissions from trees 

Many tree species are biogenic sources of volatile organic compounds (VOC) that contribute to formation of ozone. Additional VOC emissions associated with biomass gains are not addressed. 

Over (generally) Medium High 

KB: According to the O3 ISA 3.2.1), vegetation emits substathe 2005 NEI approximately 2were from biogenic sources. INF: Vegetation may account VOC production (Guenther et found, however, that if man-mO3 attributable to biogenic em

much as 50 percent. 

H. Carbon sequestration functions in iTree and FASOM 

The functions applied in the models to estimate carbon sequestration are uncertain and vary by species. 

Unknown Medium Medium 

KB: The studies in the ISA shreduced carbon uptake due to 

reductions projected over Nortaccounting component of FASfrom the U.S. Forest Service’s modeling system, which is an carbon budgets simulated acroclasses, forest age classes, ownpools. Multiple equations for icombined to produce one predof diameters for individual speto prevent disjointed sequestrawhen calculations switch betwequations. If no allometric equindividual species, the average the same genus is used. If no gaverage of results from all broused. INF: We estimate that carbon million metric tons and 1.6 bilafter just meeting the existing level of 7 ppm-hrs, respectivelthe individual formulas producwithin 2% of the original estim
 6-71 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

I. Use of median C-R functions for crops in FASOM 

FASOMGHG incorporates median parameters from Lehrer et al. (2007) in the C-R functions for oranges, rice, and tomatoes. Using alternative C-R functions would result in lower or higher O3 impacts on crop 

and tree species biomass productivity, which would potentially lead to different economic equilibrium outcomes. 

Both Low Low 

KB: These 3 crops have C-R f

other than W126, as reported iINF: Use of the median functithose crops specifically. No otthese functions. 

J. Crop proxy and forest type assumptions 

The crops/tree species modeled are only a subset of species present in U.S. agriculture and forestry systems. Actual impacts may differ from those of the crop proxy or the forest type. Further, FASOMGHG modeling used a simple average of tree RYLs for all forest types within a region. 

Both Medium- High Low 

KB: Aggregation of crop and tbased on recommendations fro2012a). As stated by CASAC, functions for all species, and thto infer C-R relationships in a knowledge of a closely-related INF: Total economic surplus imillion or increase by as much and 2040. It is unclear how usrather than proxy species wouHowever, consistent with CASnot assign the most sensitive Cspecies. 
 6-72 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

K. FASOMGHG does not model agriculture/ forestry on public lands 

Because public lands are not affected within the model, the estimates of changes in consumer and producer surplus would likely be higher if public lands were included. 

Under Medium Medium-Low 

KB: The model assumes that O

little influence on harvest decion public lands are set by the rbody (Forest Service, Bureau 

INF: The FASOMGHG model 

private, managed forests. The approximately 751 million forsmall portion of this public lan

L. Forest adaptation to O3

FASOMGHG modeling does not reflect changes in tree species mixes within a forest type made by natural adaptation and adaptive management by landowners due to O3. Less 

sensitive tree species may gain relative advantage over more sensitive species. 

Unknown Low Low 

KB: The ISA finds that the evthat O3 causes changes in com

O3 tolerant species over sensit

natural adaptation and adaptivand the relative dominance of depending on the degree of acINF: Over time, the O3 impact

forests adapt to O3 environmen

or natural processes. 

M. International trade projections in FASOMGHG 

FASOMGHG reflects future international trade projections by USDA based on recent O3

conditions. Soybeans and wheat are major crop exports and have relatively large responses to O3, 

which are not reflected in the trade projections. 

Both Medium Medium 

KB: Although FASOMGHG imajor commodities, the internreflect the potential for increasincreased yield from reduced O

quantities data in the model harecent trade data for specific csince the original data from th(Roningen, 1986). INF: Increased exports could i

the impacts on consumer surpl
 6-73 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

N. Estimates of tree basal area used to assess larger scale ecosystem effects 

Estimates of basal area were modeled by the FHTET at a scale of 240 m2

. These values 

were aggregated to the 144 (12x12) km2 CMAQ grid. 

Unknown Low- Medium Low 

KB: USDA’s FHTET has been their models to estimate basal and total basal area nationwideINF: The effect on risk estimaecosystems, depending on combasal area and the ecosystem sthe overall large number of CMspecies, the overall estimates psmall. 
 6-74 

1 6.10 DISCUSSION 

2 Relative Biomass Loss: 

3  We compared seedling RBL to results from several studies with mixed results. The 

4 studies indicate that overall the seedling biomass loss values are much more 

5 consistent with the adult loss at lower W126 index values. 

6  The Constable and Taylor (1997) study implies that for the eastern subspecies of 

7 Ponderosa Pine, the seedling RBL rate could possibly overestimate the adult RBL 

8 rate. 

9  The Weinstein et al. (2001) study indicates that the seedling RBL estimates are 

10 comparable to the adult estimates, except at higher W126 index values for Tulip 

11 Poplar. The Black Cherry results are an exception, which tells us that this species is 

12 much less sensitive as an adult than as a seedling. 

13  Another study (Samuelson and Edwards, 1993) on Red Oak found the exact opposite 

14 pattern -- adult trees are much more sensitive to O3-related biomass loss than 

15 seedlings. 

16  Overall, the western tree species have more fragmented habitats than the eastern 

17 species. The areas in southern California have the highest W126 index values. The 

18 eastern tree species had less fragmented ranges and areas of elevated RBL that were 

19 more easily attributed to urban areas (e.g. Atlanta, GA and Charlotte, NC) or to the 

20 Tennessee Valley Authority Region. 

21 Commercial Timber Effects: 

22  At the existing standard of 75 ppb the highest yield loss occurs in upland hardwood 

23 forests in the South Central and Southeast regions at over 3 percent per year. The next 

24 highest yield losses occur in Corn Belt hardwoods with just over 2 percent loss per 

25 year and in hard- and softwoods of the Rocky Mountain region at an average loss 

26 across all sensitive forests of slightly over 1 percent loss per year. With the exception 

27 of the Rocky Mountain region, yield losses do not appreciably change when meeting 
 6-75 

1 the 15 ppm-hrs alternative incremental to meeting the existing standard. Yield gains 

2 associated with meeting alternative W126 standards are relatively small on a 

3 percentage change basis, especially in the 15 ppm-hrs scenario where the highest 

4 change is 0.35 percent per year. 

5  Consumer and producer welfare in the forest sector are more affected by meeting 

6 alternative W126 standards incremental to meeting the existing standard than the 

7 agricultural sector. In general, consumer welfare increases in both the forest and 

8 agricultural sectors as higher productivity tends to increase total production and 

9 reduce market prices. Because demand for most forestry and agricultural 

10 commodities is inelastic, producer welfare tends to decline with higher productivity 

11 as the effect of falling prices on profits more than outweighs the effects of higher 

12 production levels. 

13 Climate Regulation: 

14  For national-scale carbon sequestration, much greater changes in carbon sequestration 

15 are projected in the forest sector than in the agricultural sector. The 15 ppm-hrs 

16 scenario does not appreciably increase carbon storage relative to just meeting the 

17 existing standard. The vast majority of the enhanced carbon sequestration potential 

18 under the scenarios is from increased forest biomass due to the yield increases 

19 accruing to forests over time at the 11 and 7 ppm-hrs alternative W126 standards. 

20 The forest carbon sequestration potential would increase between 593 and 1,602 

21 million tons of CO2 equivalents over 30 years after meeting the 11 or 7 ppm-hrs 

22 W126 standard level, respectively. 

23  For the urban case study areas, estimates suggest that in the five modeled areas 

24 relative to recent conditions, at the existing standard or at an alternative W126 

25 standard level of 15 ppm-hrs about 3.5 million tons of carbon storage will be lost over 

26 25 years (about 140,000 tons/year). At an alternative W126 standard level of 11 

27 ppm-hrs, loss of carbon sequestration is approximately 128,000 metric tons per year, 

28 and meeting an alternative W126 standard of 7 ppm-hrs results in the loss of 112,000 

29 metric tons per year of carbon storage services. 
 6-76 

1  Of the five areas modeled, the combined urban areas of Tennessee have the largest 

2 estimated gains in carbon storage at almost 20,000 tons per year when meeting an 

3 alternative W126 standard of 7 ppm-hrs relative to the existing standard. 

4 Urban Case Study Air Pollution Removal: 

5  Estimates from i-Tree indicate that at the existing standard about 1,535 tons of air 

6 pollution removal capacity is lost annually in the five areas modeled. Syracuse and 

7 Baltimore have no change in pollution removal when meeting the existing standard 

8 and the modeled alternatives. Atlanta and Chicago gain about 470 and 6,500 metric 

9 tons of additional pollution removal when meeting the 7 ppm-hrs W126 alternative 

10 standard compared to the existing standard, while Tennessee gains almost 12,000 tons 

11 of potential pollution removal annually for this scenario. Under the 7 ppm-hrs 

12 scenario, about 51 percent of the pollution removal capacity lost under the existing 

13 standard is regained. 

14 Agriculture: 

15  Among the major crops, winter wheat and soybeans are more sensitive to ambient O3

16 levels than corn and sorghum. California, the Northeast, and the Rocky Mountain 

17 regions generally have the highest yield losses. 

18  For winter wheat, the highest loss occurs in California at 15 percent. In the 

19 Northeast, the losses range from 7.65 percent in Maryland to 3.69 percent in 

20 Pennsylvania, with 6.43 percent in Delaware and 6.55 percent in New Jersey. In the 

21 Rocky Mountain region, the losses in Utah are 7.26 percent. When the W126 

22 scenarios are modeled, the yield losses are almost eliminated at all values of W126. 

23  For soybeans, the highest loss occurs in Maryland at 8.3 percent. In the Northeast, 

24 the losses range from 8.3 percent in Maryland to 5.38 percent in Pennsylvania, with 

25 7.65 percent in Delaware and 7.76 percent in New Jersey. In the Corn Belt the 

26 highest loss occurs in southern Indiana at 5.1 percent. In the Rocky Mountain region, 

27 the losses in Colorado are 6.73 percent. Yield losses remain under all scenarios for 

28 W126, although for the 7 ppm-hrs scenario all losses are less than 0.6 percent. 
 6-77 

1  For corn, the highest loss occurs in California at 0.88 percent. In the Northeast, the 

2 losses range from 0.68 percent in Maryland to 0.26 percent in Pennsylvania, with 

3 0.56 percent in Delaware and 0.48 percent in New Jersey. In the Corn Belt, Lake 

4 States, and Great Plains the highest loss occurs in southern Ohio at 0.34 percent. And 

5 in the Rocky Mountain region, the losses range from 0.67 percent in Utah to 0.42 

6 percent in Nevada, with 0.45 percent in Colorado. When the W126 scenarios are 

7 modeled, the yield losses are virtually eliminated at all values of W126 and 

8 subsequent yield gains are almost nonexistent. 

9  In general, increased yield leads to increased supply and lower prices. Because corn 

10 does not lose or gain very much under any scenario prices are likely to remain 

11 relatively stable. Soybeans, however, would experience yield gains in any scenario 

12 and prices would likely fall. In response to falling soybean prices, the model predicts 

13 that producers would switch to less O3-sensitive crops with stable prices, such as 

14 corn, thereby increasing corn production. 

15  For producers, the W126 alternatives results in welfare gains in the middle years, 

16 2020-2030, and welfare losses in all other years. For consumers, however, the 

17 changes in production and prices results in welfare gains in all scenarios in all years. 
 7-1 

1 7 VISIBLE FOLIAR INJURY 

2 7.1 INTRODUCTION 

3 Visible foliar injury resulting from exposure to ozone (O3) has been well characterized 

4 and documented over several decades on many tree, shrub, herbaceous, and crop species (U.S. 

5 EPA, 2013, 2006, 1996, 1984, 1978). Visible foliar injury symptoms are considered diagnostic 

6 as they have been verified experimentally in exposure-response studies using exposure 

7 methodologies such as continuous stirred-tank reactors (CSTRs), open-top chambers (OTCs), 

8 and free-air fumigation (see Section 9.2 of the ISA for more detail on exposure methodologies). 

9 Although the majority of O3-induced visible foliar injury occurrence has been observed on 

10 seedlings and small plants, many studies have reported visible injury of mature coniferous trees, 

11 primarily in the western U.S. (Arbaugh et al., 1998), and of mature deciduous trees in eastern 

12 North America (Schaub et al., 2005; Vollenweider et al., 2003; Chappelka et al., 1999a; 

13 Chappelka et al., 1999b; Somers et al., 1998; Hildebrand et al., 1996). 

14 The ecosystem services most likely to be affected by O3-induced foliar injury are 

15 aesthetic value and outdoor recreation. Aesthetic value and recreation services depend on the 

16 perceived scenic beauty of the environment. Studies of Americans’ perception of scenic beauty 

17 are quite consistent (Ribe, 1994) in their findings -- people tend to have a reliable set of 

18 preferences for forest and vegetation with fewer damaged or dead trees and plants. Aesthetic 

19 value not related to recreation includes the scenic value of vistas observed as people go about 

20 their daily lives and the scenic value of the views of open space near and around homes. Many 

21 outdoor recreation activities directly depend on the scenic value of the area, in particular scenic 

22 viewing, wildlife watching, hiking, and camping. These activities are enjoyed by millions of 

23 Americans every year and generate millions of dollars in economic value (OIF, 2012; NPS, 

24 2002a, 2002b, 2002c). Figure 7-1 illustrates the relationship between foliar injury and ecosystem 

25 services as discussed in this chapter. 
 7-2 

1 

2 Figure 7-1 Relationship between Visible Foliar Injury and Ecosystem Services 

3 

4 The significance of O3 injury at the leaf and whole-plant levels depends on how much of 

5 the total leaf area of the plant has been affected, as well as the plant’s age, size, developmental 

6 stage, and degree of functional redundancy among the existing leaf area. Previous O3 Air 

7 Quality Criteria Documents (AQCDs) and the O3 Integrated Science Assessment (ISA) for have 

8 noted the difficulty in relating visible foliar injury symptoms to other vegetation effects such as 

9 individual plant growth, stand growth, or ecosystem characteristics (U.S. EPA, 2013, 2006, 

10 1996). As a result, it is not currently possible to determine, with consistency across species and 

11 environments, what degree of injury at the leaf level has significance to the vigor of the whole 

12 plant. However, in some cases, visible foliar symptoms have been correlated with decreased 

13 vegetative growth (Somers et al., 1998; Karnosky et al., 1996; Peterson et al., 1987; Benoit et al., 

14 1982) and with impaired reproductive function (Chappelka, 2002; Black et al., 2000). 

15 Conversely, the lack of visible injury does not always indicate a lack of phytotoxic effects from 

16 O3 or a lack of non-visible O3 effects (Gregg et al., 2006). 

17 The National Park Service (NPS) published a list of trees and plants considered sensitive 

18 because they exhibit foliar injury at or near ambient concentrations in fumigation chambers or 

19 they have been observed to exhibit symptoms in the field by more than one observer. This list 

20 includes many species not included in Table 6-10, such as various milkweed species, asters, 
 7-3 

1 coneflowers, huckleberry, evening primrose, Tree-of-heaven, redbud, blackberry, willow, and 

2 many others. Many of these species are important for non-timber forest products, recreation, and 

3 aesthetic value among other ecosystem services. 

4 Based on the NPS sensitive species list (NPS, 2003), data from the Forest Health 

5 Technology Enterprise Team of the U.S. Forest Service (described in Chapter 6, Section 6.2.1.3) 

6 were available for 15 tree species. Figure 7-2 illustrates the percent of total basal area that is 

7 accounted for by these 15 species, which include Ponderosa Pine, Loblolly Pine, Virginia Pine, 

8 Red Alder, Tulip Poplar, Aspen, Black Cherry, Jack Pine, Table Mountain Pine, Pitch Pine, 

9 White Ash, Green Ash, Sweetgum, California Black Oak, and Sassafrass. 

10 Figure 7-2 Tree Species Sensitive to Foliar Injury 

11 

12 Table 7-1 summarizes the overall cover of the 15 tree species and the percent of area in 

13 each cover category that exceeds varying W126 index values. It is important to note that there 
 7-4 

1 are additional tree species that are known to be sensitive for which cover data were not available, 

2 and there are many non-tree species listed in the NPS report that are not addressed in this 

3 analysis. 

4 Table 7-1 Percent of Cover Category Exceeding W126 Index Values 

Cover Category 

(percent of total basal 

area accounted for by 

the 15 species 

included) 

National 

Distribution 

Percent of Cover Category Area Exceeding W126 Index 

Values 

> 7 ppm-hrs >10 ppm-hrs > 15 ppm-hrs 

None Present 34.5% 85.0% 71.1% 31.4% 

Less than 10% 26.0% 65.4% 39.7% 9.4% 

10% to 25% 17.0% 73.4% 52.7% 13.9% 

25% to 50% 12.8% 79.3% 60.9% 20.7% 

50% to 75% 7.9% 83.6% 57.2% 15.7% 

Greater than 75% 1.8% 82.4% 41.6% 9.5% 

5 

6 In addition to direct impacts on foliar injury, O3 exposure contributes to trees’ 

7 susceptibility to insect infestation. These infestations can affect scenic beauty and the services 

8 associated with the perceived beauty of the environment. Foliar injury and insect attack can 

9 occur separately or in conjunction with one another and are briefly discussed together in the next 

10 section of this chapter, Section 7.1.1, on ecosystem services impacts. The remainder of this 

11 chapter provides details on the analyses we conducted and includes Section 7.2 – National-Scale 

12 Analysis of Foliar Injury; Section 7.3 –Screening-level Assessment of Visible Foliar Injury in 

13 National Parks; and Section 7.4 – National Park Case Study Areas, including Great Smoky 

14 Mountains National Park, Rocky Mountain National Park, and Sequoia and Kings Canyon 

15 National Parks. The national park case studies include discussions of the potential value of the 

16 ecosystem services affected by foliar injury resulting from O3 exposure. 

17 7.1.1 Ecosystem Services 

18 7.1.1.1 Aesthetic Value 

19 Aesthetic value services not related to recreation include the view of the landscape from 

20 houses, as individuals commute, and as individuals go about their daily routine in a nearby 

21 community. Studies find that scenic landscapes are capitalized into the price of housing. Studies 

22 also document the existence of housing price premiums associated with proximity to forest and 
 7-5 

1 open space (Acharya and Bennett, 2001; Geoghegan, Wainger, and Bockstael, 1997; Irwin, 

2 2002; Mansfield et al., 2005; Smith et al., 2002; Tyrvainen and Miettinen, 2000). In addition, 

3 according to Butler (2008), approximately 65 percent of private forest owners rate providing 

4 scenic beauty as either a very important or important reason for their ownership of forest land. 

5 These aesthetic value services are at risk of impairment because of O3-induced damage: 

6 directly due to foliar injury, and indirectly due to increased susceptibility to insect attack. Data 

7 are not available to explicitly quantify these negative effects; however, the damage is included in 

8 the price premium mentioned. In other words, without such damage, the associated price 

9 premium for scenic beauty that is incorporated into housing prices is likely higher. 

10 7.1.1.2 Recreation 

11 With few exceptions, publicly owned forests are open for some form of recreation. 

12 Based on the analysis done for the USDA National Report on Sustainable Forests (USDA, 2011), 

13 almost all of the 751 million acres of forest lands in the U.S. are at least partially managed for 

14 recreation. Of these 751 million acres, 44 percent are publicly owned (federal, state, or local). 

15 Scenic quality has been found to be strongly correlated to recreation potential and the likelihood 

16 of visiting recreation settings, and the correlations apply to both active and passive recreational 

17 pursuits (Ribe, 1994). According to Ribe (1994), differences in scenic beauty account for 90 

18 percent of the variation in participant satisfaction across all recreation types. 

19 Americans enjoy a wide variety of outdoor pursuits many of which are subject to 

20 negative impacts resulting from O3 exposure, especially the effects on foliage, insect 

21 susceptibility, habitat, and community composition. The effects related to scenic beauty (foliar 

22 injury and insect damage) affect not only the scenery viewing, but also satisfaction with other 

23 scenery-dependent activities. Ninety-seven percent of National Survey on Recreation and the 

24 Environment (NSRE) survey respondents rated scenic beauty as an important or extremely 

25 important aspect of their wilderness experience. 

26 Perceptions of scenic beauty depend on a number of forest attributes, including the 

27 appearance of forest health, the effects of air pollution and insect damage, visual variety, species 

28 variety, and lush ground cover (Ribe, 1989). The ISA concludes that there is a causal 

29 relationship between O3 exposure and visible foliar injury. Figure 7-3 shows the effects of foliar 

30 injury on ponderosa pine, milkweed, and tulip poplar. 
 7-6 

1 The presence of downed wood, whether caused by O3 mortality, insect attack, or other 

2 causes, has a negative impact on scenic beauty assessments (Ribe, 1989; Buyhoff, et al., 1982). 

3 Figure 7-4 shows the effects of southern bark beetle damage. Species composition of forests 

4 may also influence preferences. According to Ribe (1994) these preferences may be affected by 

5 cultural, regional, or contextual expectations, which would include the expectation of the 

6 presence of certain species in specific areas (e.g., the presence of ponderosa pine in California). 

7 In addition, there is a positive effect on preferences for ground cover rather than bare or 

8 disturbed soil (Brown and Daniel, 1984, 1986). Thus, the reduced value of scenic beauty from 

9 O3-induced effects on sensitive plants, by way of foliar injury, extends beyond large trees to the 

10 grasses, forbs, ferns, and shrubs that comprise the understory of a forest setting. 

11 In Peterson et al. (1987), where O3-exposure had resulted in foliar injury to ponderosa 

12 pines in the San Bernardino Forest, survey participants were asked to: (1) rank preferences for 

13 scenic views, (2) rate their recreation experiences, (3) state how decreases in tree quality would 

14 affect their visitation, and (4) specify whether they would be willing to pay for programs to 

15 mitigate the damage. This survey showed that visible foliar injury had a negative impact on 

16 perceptions of scenic beauty and a nonzero value for willingness to pay for programs to improve 

17 forest aesthetics damaged by O3. 

18 

19 

20 Figure 7-3 Examples of Foliar Injury from O3 Exposure 

21 Courtesy: NPS, Air Resources Division 

22 
 7-7 

1 

2 Figure 7-4 Examples of Southern Bark Beetle Damage 

3 Courtesy: Ronald F. Billings, Texas Forest Service. Bugwood.org 

4 

5 The NSRE provides estimates of participation for many recreation activities. According 

6 to the survey some of the most popular outdoor activities are walking, including day hiking and 

7 backpacking; camping; bird watching; wildlife watching; and nature viewing. Participant 

8 satisfaction with these activities depends wholly or partially on the quality of the natural scenery. 

9 Table 7-2 summarizes the survey results, for these and other popular activities, including the 

10 percent participation and the number of participants nationally, the number of days participants 

11 engage in recreation activities annually, and their willingness-to-pay (WTP) for their 

12 participation. 

13 

14 
 7-8 

1 Table 7-2 National Outdoor Activity Participation 

Activity Percent 

Participation 

Number of 

Participants

(in millions) 

Number of 

Activity 

Days(in 

millions) 

Mean 

WTP/Day 

(in 2010$) 

Mean Total 

Participation 

Value 

(in millions of 

2010$) 

Day Hiking 32.4 69.1 2,508 $60.63 $152,060 

Backpacking 10.4 22.2 224.0 $13.33 $2,986 

Picnicking 54.9 116.9 935.2 $20.70 $19,359 

Camping (Developed 

and Primitive Sites) 42.3 90.1 757.5 $19.98 $15,135 

Visit a Wilderness 

Area 32.0 68.2 975.4 N/A N/A 

Birdwatching/ 

Photography 31.8 67.7 5,828.1 $49.74 $289,773 

Wildlife Watching/ 

Photography 44.2 94.2 3,616.5 $48.72 $176,196 

Natural Vegetation 

Viewing/ 

Photography 

43.9 93.6 5,720.8 N/A N/A 

Natural Scenery 

Viewing/ 

Photography 

59.6 126.9 7,119.7 N/A N/A 

Sightseeing 50.8 108.2 2,055.0 $45.94 $94,407 

Gathering 

(Mushrooms, Berries, 

Firewood) 

28.6 60.9 852.7 N/A N/A 

2 Source: NSRE 2010 and 2003 National Report on Sustainable Forest Management. 2003 National 

3 Report: Documentation for Indicators 35, 36, 37, 42, and 43 available at: 

4 http://warnell.forestry.uga.edu/nrrt/NSRE/MontrealIndDoc.PDF and Recreation Values Database 

5 available at: http://recvaluation.forestry.oregonstate.edu/

6 N/A = not available

7 

8 The relationship between scenic beauty and recreation satisfaction for camping was 

9 quantified by Daniel et al. (1989) in a contingent valuation study. The authors surveyed campers 

10 regarding their perceptions of scenic beauty, as indicated by a photo array of scenes along a 

11 spectrum of scenic beauty, and their WTP to camp in certain areas. All else being equal, scenic 

12 beauty and WTP demonstrated a nearly perfect linear relationship (correlation coefficient of 
 7-9 

1 0.96). This suggests that campers would likely have a greater WTP for recreation experiences in 

2 areas where scenic beauty is less damaged by O3. Since as mentioned previously, Ribe (1994) 

3 found that scenic beauty plays a strong role in recreation satisfaction and explains 90 percent of 

4 the difference in recreation satisfaction among all types of outdoor recreation, there is reason to 

5 believe that this linear relationship between scenic beauty and WTP would hold across all 

6 recreation types. We believe that it would follow that decreases in O3 damage would generate 

7 benefits to all recreators. We cannot estimate the incremental impact of reducing O3 damage to 

8 scenic beauty and subsequent recreation demand; however, given the large number of outdoor 

9 recreation participants and their substantial WTP for recreation, even very small increments of 

10 change in WTP or activity days should generate significant benefit to these recreators. 

11 Another resource for estimating the economic value of consumers’ recreation experiences 

12 is the data available on actual expenditures for recreation and the total economic impact of 

13 recreation activities. Economic impacts across the national economy can be estimated using the 

IMPLAN® model (MIG Inc, 1999).1 14 For this document we refer to analyses done for the 2011 

15 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation (FHWAR) (U.S. 

16 Department of the Interior, U.S. Fish and Wildlife Service, and U.S. Department of Commerce, 

17 2011) and an analysis performed by Southwick and Associates for the Outdoor Industry 

18 Foundation (OIF), The Economic Contribution of Active Outdoor Recreation – Technical Report 

19 on Methods and Findings (OIF, 2012). 

20 The FHWAR and the OIF report provide estimates of trip and equipment-related annual 

21 expenditures for wildlife watching activities in the U.S. The OIF report also provides estimates 

22 of recreators’ annual expenditures on trail-related activities, camping, bicycling, snow-related 

23 sports, and paddle sports. For this review, we include the data on trail-related activities and 

24 camping as the most relevant for analysis of O3-related damages. 

25 As shown in Table 7-3, the total expenditures across wildlife watching activities, trail26 based activities, and camp-based activities are approximately $230 billion dollars annually. 

27 While we cannot estimate the magnitude of the impacts of O3 damage to the scenic beauty, the 

28 losses are reflected in the values reported. 

 1

 IMPLAN® is a commercially available input-output model that has been used by the Department of Interior, the 

National Park Service, and other government agencies in their analyses of economic impacts. 
 7-10 

1 Table 7-3 National Expenditures for Wildlife Watching, Trail, and Camp-Related 

2 Recreation (in billions of 2010$) 

Expenditure Type Wildlife-Watchingb Trailc Campc Totalc 

Trip-Related $16.7 $53.7 $109.3 $179.7 

Equipment & Services $26.3 $6.3 $8.3 $40.9 

Other Expenditures $10.2 N/R N/R $10.2 

Total for All Expenditures $230.8 

a 3 Data from 2011 FHWAR 

b 4 Data from 2012 OIF report 

5 N/R = not reported

6 

7 The impact of these expenditures has a multiplier effect through the economy, which was 

estimated by OIF using the IMPLAN® model.2 8 The model estimates the flow of goods and 

9 money through the economy at scales from local to national. According to the OIF report 

10 (2012), trail activities generated over $190 billion in total economic activity, including $97 

11 billion in salaries, and wages. The same report estimates the total economic activity generated 

12 by camping-related recreation at $346 billion, including $175 billion in salaries, and wages. The 

13 total economic activity estimates also include state and federal tax revenues. 

14 7.2 NATIONAL-SCALE ANALYSIS OF FOLIAR INJURY 

15 To assess foliar injury at a national scale, we compared data from the Forest Health 

16 Monitoring Network (USFS, 2011) with O3 exposure estimates for individual years, described in 

17 Section 4.3.1.2, and soil moisture data, which was estimated using NOAA’s Palmer Z drought 

18 index (NCDC, 2012b). 

19 7.2.1 Forest Health Monitoring Network 

20 The only national-scale data set pertaining to foliar injury is from the USDA Forest 

21 Service’s (USFS) Ozone Biomonitoring Program (OBP). This effort was completed as part of 

22 the Forest Inventory and Analysis (FIA) and Forest Health Monitoring (FHM) programs (see 

23 Figure 7-5 for O3 biomonitoring sites). The OBP used a number of bioindicator species (O3-

24 sensitive plants) to monitor the potential impacts of O3 on our nation’s forests. The field 

 2 Assumptions and Caveats to the IMPLAN® Results: Statistics on the precision of the final economic impacts were 

not produced by OIF because of feasibility issues. Harris Interactive survey results combine several parameters 

from the data, and outside data from the U.S. Bureau of the Census’ population estimates and IMPLAN multipliers 

were used. 
 7-11 

1 methods, sampling procedures, and analytical techniques were consistent across sites and 

2 between years (USFS, 2011). 

3 We obtained data on foliar injury from the USFS for the five years from 2006 to 2010. 

4 Because of privacy laws that require the exact location information of sampling sites 

5 (“biosites”) to not be made public, the data were assigned to the CMAQ grid used for the O3

6 exposure surface by the USFS (USFS, 2013). Data were not available for California, Oregon, 

7 and Washington, so we used the publically available data. In those states we assigned the data 

8 to the CMAQ grid based on the publically available geographic coordinates, which are masked 

9 for privacy concerns as mentioned above; the data in those states have additional uncertainty 

10 relating the O3 and Palmer Z drought index data to the foliar injury data. Also, because 

11 sampling was discontinued in some states prior to this analysis, we did not include data for 

12 most of the western states (Montana, Idaho, Wyoming, Nevada, Utah, Colorado, Arizona, New 

13 Mexico, Oklahoma, and portions of Texas). 

14 The biosite index is calculated from a combination of the proportion of leaves affected 

15 on individual bioindicator plants. In order to calculate the biosite index, at least 30 individual 

16 plants of two bioindicator species must be present at each biosite. The mean severity of 

17 symptoms ranges from a score of zero to a score of 100 (USFS, 2011). 
 7-12 

1 

2 Figure 7-5 O3 Biomonitoring Sites 

3 Note: Sites are shown as the CMAQ grid cell in which they occur. Some biosites were sampled in more than one 

4 year, but are indicated on this figure only as the most recent year sampled. 

5 7.2.2 NOAA Palmer Z Drought Index 

6 The Palmer Z drought index represents the difference between monthly soil moisture and 

7 long-term average soil moisture (Palmer, 1965). These data typically range from -4 to +4, with 

8 positive values representing more wetness than normal and negative values representing more 

9 dryness than normal. Values between -1.25 and +1.0 could be interpreted as normal soil 

10 moisture, whereas values beyond the range from -2.75 to +3.5 could be interpreted as extreme 

11 drought and extremely moist, respectively (NCDC, 2012c). 

12 The soil moisture index is calculated for each of the 344 climate regions divisions within 

13 the contiguous U.S. defined by the National Climatic Data Center (NCDC) (NOAA, 2012a). 

14 Because we did not have soil moisture data outside of the continental contiguous U.S., we did 

15 not evaluate parks in Alaska, Hawaii, Puerto Rico, or Guam. We identify the NCDC climate 

16 divisions with Palmer Z data in Figure 7-6. 
 7-13 

1 

2 

3 Figure 7-6 344 Climate Divisions with Palmer Z Soil Moisture Data 

4 Source: NCDC, 2012a 

5 7.2.3 Results 

6 Data were available for a total of 5,284 biosites across the five years from 2006 – 2010 

7 (Table 7-4, Figure 7-5). Table 7-4 summarizes the biosite index values for each year. The 

8 categories used in Table 7-4 follow the USFS risk categories with the exception of including a 

9 separate category for a biosite index of zero, or no damage. We included the data to highlight 

10 that across all of the sites, over 81 percent of the observations recorded no foliar injury. This 

11 percentage was similar across all of the years, with a low value of 78 percent and a high value of 

12 85 percent. The data showed no clear relationship between O3 and biosite index (Figure 7-7), as 

13 well as no clear relationship between O3 and the Palmer Z drought index (measured as an 

14 average value of the months from April to August (Figure 7-8)). 

15 

16 

17 
 7-14 

1 Table 7-4 Summary of Biosite Index Values for 2006 to 2010 O3 Biomonitoring Sites. 

2 Categories modified from USFS (Smith et al., 2008) 

 Biosite Index Damage 2006 2007 2008 2009 2010 Total 

0 None 744 769 796 902 1,075 4,286 

< 5 Very Light 139 131 98 135 183 686 

5 to 15 Light 41 29 29 61 65 225 

15 to 25 Moderate 15 6 8 6 12 47 

> 25 Heavy 12 4 4 8 12 40 

Total 951 939 935 1,112 1,347 5,284 

3 

4 Figure 7-7 General Relationship of O3 (ppm-hrs) and Biosite Index 

5 

0 10 20 30 40 50 60

0 50 100 150

W126 (ppm-hrs)

Biosite Index
 7-15 

1 

2 Figure 7-8 General Relationship of Average Palmer Z (April to August) and Biosite 

3 Index 

4 The lack of a clear relationship is partly because of the high number of observations with 

5 no foliar injury, which may in part be due to different resolutions between the O3 exposure 

6 surface, NCDC climate divisions, and the biosite size. Because of these values, we use a 

7 censored regression to account for the non-injury observations and focus on the sites where 

injury was observed.3 8 

9 The results of the regression (Table 7-5) support what is known about foliar injury (ISA 

10 Section 9.4.2), which is that there is a significant relationship between foliar injury and both O3

11 and moisture (as measured by Palmer Z), and there is also a significant interaction between O3

12 and moisture. The censored regression does not provide a “goodness of fit” statistic as easily 

13 interpreted as the r-squared value associated with a standard regression, so the results are more 

14 difficult to interpret. We used the regression coefficients to calculate estimated biosite index 

15 values, but when we compared those to observed values this did not provide a good estimate, 

16 again in part due to the large number of non-injury observations (data not included). 

 3

 A censored regression is used in cases where the variable of interest is only observable under certain conditions. 

-2 0 2 4

0 50 100 150

Average Palmer Z (April to August)

Biosite Index
 7-16 

1 Table 7-5 Censored Regression Results 

Coefficient Intercept Estimate Std. Error t-value p 

Intercept -22.5967 0.8934 -25.293 < 0.0001 

W126 0.7307 0.0613 11.919 <0.0001 

Palmer Z (Apr-Aug) 1.8357 0.4850 3.785 0.0002 

W126: Palmer Z 0.1357 0.0437 3.104 0.0019 

 Marginal Effect 

W126 0.1178 0.0099 11.918 <0.0001 

Palmer Z (Apr-Aug) 0.2960 0.0777 3.812 0.0001 

W126: Palmer Z 0.0219 0.0070 3.093 0.0020 

2 

3 To further assess the relationship between O3 and foliar injury, we conducted a 

4 cumulative analysis (Figures Figure 7-9 through Figure 7-12). In these analyses, we ordered the 

5 data by W126 index value, then for each W126 index value we calculated the proportion of sites 

6 exceeding the selected biosite index value for all observations at or below that W126 index 

7 value. We repeated this using an index value greater than zero, indicating presence of any foliar 

8 injury, and an index value ≥ 5, corresponding to a USFS cutoff for elevated injury (USFS, 2011). 

9 In this analysis, we split the data into individual years, as well as into moisture categories; the 

10 moisture categories followed NOAA’s Palmer Z drought index, with values less than -1.24 

11 considered dry, values greater than or equal to 1 considered wet, and values between those 

12 considered normal. 

13 When looking only at presence/absence of foliar injury (“any injury”) (Figure 7-9), with 

14 the exception of 2008, the proportion of sites across all W126 index values exceeds 15 percent; 

15 in 2006, it exceeds 20 percent, while in 2008 the proportion of sites with foliar injury across all 

16 W126 index values was just below 15 percent. 

17 Although the overall percentages are much lower, when we use a biosite index of 5 or 

18 greater as the benchmark (Figure 7-10), we see a similar pattern in which there is a rapid increase 

19 in the proportion of sites exceeding a biosite index of 5 at W126 index values below 10 ppm-hrs. 

20 In both cases, presence/absence and biosite index ≥ 5, the data for 2007 show a more gradual 
 7-17 

1 increase in proportion. The more gradual increase and relatively low overall proportions in 2007 

2 can at least be partly explained by 2007 being the driest year in the analysis. In contrast, 2008 

3 was a normal moisture and average O3 year among years in this analysis, which does not explain 

4 the consistently low proportions in 2008. 

5 There are two important observations that can be made in both of these analyses: (1) The 

6 proportion of sites exhibiting foliar injury rises rapidly at increasing W126 index values below 

7 10 ppm-hrs, and (2) there is relatively little change in the proportions above W126 index values 

8 of 20 ppm-hrs. 

9 

10 Figure 7-9 Cumulative Proportion of Sites with Foliar Injury Present, by Year 

11 

0 10 20 30 40

0.00 0.05 0.10 0.15 0.20 0.25

Biosite Index > 0

W126 (ppm-hrs)

Proportion of sites

All

2006

2007

2008

2009

2010
 7-18 

1 

2 Figure 7-10 Cumulative Proportion of Sites with Elevated Foliar Injury, by Year 

3 

4 When categorized by moisture categories, as defined by the average Palmer Z drought 

5 index, the data show a more distinct pattern. Similar to the analysis by individual years, the most 

6 rapid increase in the proportion occurs at W126 index values below 10 ppm-hrs, but the moisture 

7 category has a much greater effect on the overall proportion (Figure 7-11 and Figure 7-12). In 

8 both analyses, there is again relatively little change in the proportion beyond a W126 of 20 ppm9 hrs in normal and dry years. 

10 The data for normal moisture sites are very similar to the dataset as a whole, with an 

11 overall proportion of close to 18 percent for presence/absence, and close to 6 percent for sites 

12 exceeding a biosite index of 5. Sites classified as wet (average Palmer Z ≥ 1) have much higher 

13 overall proportions at both any injury and elevated injury and a much more rapid increase in 

14 proportion of sites with foliar injury present, exceeding 20 percent at W126 index values under 5 

15 ppm-hrs. At sites considered dry (average Palmer Z < -1.24), the overall proportions are much 

16 lower, around 10 percent and 4 percent for presence/absence and exceeding an index of 5. This 

17 indicates that drought does provide protection from foliar injury as discussed in the ISA (U.S. 

18 EPA, 2013), but not entirely. 

0 10 20 30 40

0.00 0.02 0.04 0.06 0.08

Biosite Index > 5

W126 (ppm-hrs)

Proportion of sites

All

2006

2007

2008

2009

2010
 7-19 

1 

2 Figure 7-11 Cumulative Proportion of Sites with Foliar Injury Present, by Moisture 

3 Category 

4 

5 Figure 7-12 Cumulative Proportion of Sites with Elevated Foliar Injury, by Moisture 

6 Category 

0 10 20 30 40

0.00 0.05 0.10 0.15 0.20 0.25

Biosite Index > 0

W126 (ppm-hrs)

Proportion of sites

All

Wet

Normal

Dry

0 10 20 30 40

0.00 0.02 0.04 0.06 0.08

Biosite Index > 5

W126 (ppm-hrs)

Proportion of sites

All

Wet

Normal

Dry
 7-20 

1 In Figure 7-13, we provide the data separated by NOAA climate regions (Karl and Koss, 

2 1984). Although we had data for most regions of the contiguous U.S., we did not have data for 

3 the Southwest and limited data for the West and West North Central regions. For example, from 

4 2006 to 2010, there were over 1,000 biosite index values each for the Northeast and Central 

5 regions and no biosite index values for the Southwest. In general, the regions show a similar 

6 pattern: the proportion of biosites showing foliar injury increases steeply with W126 index 

7 values up to approximately10 ppm-hrs and is relatively constant at W126 index levels above10 

8 ppm-hrs. 

9 

10 Figure 7-13 Cumulative Proportion of Sites with Foliar Injury Present, by Climate 

11 Region 

12 7.3 SCREENING-LEVEL ASSESSMENT OF VISIBLE FOLIAR INJURY IN 

13 NATIONAL PARKS 

14 A study by Kohut (2007) assessed the risk of O3-induced visible foliar injury on O3

15 bioindicators (i.e., O3-sensitive vegetation) in 244 parks managed by the NPS. Specifically, 

16 Kohut (2007) estimated O3 exposure using hourly O3 monitoring data collected at 35 parks from 

17 1995 to 1999, estimated O3 exposure at 209 additional parks using kriging, a spatial interpolation 
 7-21 

1 technique, and qualitatively assessed risk. Kohut applied a subjective evaluation based on three 

criteria: (1) the frequency of exceedance of foliar injury “thresholds”4 2 using several O3 exposure 

3 metrics (i.e., SUM06, W126 and N100), (2) the extent that low soil moisture constrains O3 

4 uptake during periods of high exposure, and (3) the presence of O3 sensitive species within each 

5 park. Based on these criteria, Kohut (2007) concluded that the risk of visible foliar injury was 

6 high in 65 parks (27 percent), moderate in 46 parks (19 percent), and low in 131 parks (54 

7 percent). 

8 In this assessment, we applied a modified screening-level approach using more recent O3 

exposure and soil moisture data for 214 parks in the contiguous U.S.5 9 Consistent with advice 

10 from CASAC (Frey and Samet, 2012a), we modified the approach used by Kohut (2007) to 

11 apply the W126 metric alone, and, in doing so, we chose foliar injury benchmarks derived from 

the analysis in section 7.2 that assesses soil moisture quantitatively.6 12 

13 7.3.1 Screening Assessment Methods 

14 7.3.1.1 O3 Exposure 

15 As described in Section 4.3.1.3, we used recent O3 monitoring data (2006-2010) to create 

16 spatial surfaces of O3 exposure using the VNA interpolation method, which covers the 

17 contiguous U.S. with a spatial resolution of 12 km by 12 km for each of the five years. This 

18 method allowed us to assess parks in the contiguous U.S., including parks without O3 monitors 

19 located within their park boundaries. We provide the W126 estimates at each park by year in 

20 Appendix 7A. 

21 7.3.1.2 Soil Moisture 

22 As described in section 9.4.2 of the ISA (U.S. EPA, 2013), soil moisture is a major 

23 modifying factor for O3-induced visible foliar injury. Low soil moisture can limit the amount of 

24 O3 entering the leaf, which can decrease the incidence and severity of foliar injury during periods 

 4

 Kohut (2007) uses the term “foliar injury thresholds”. In this assessment, we use the term “benchmarks” in order to 

avoid implying that foliar injury could not occur below these levels. 

5

 The parks assessed here include lands managed by the NPS in the continental U.S., which includes National Parks, 

Monuments, Seashores, Scenic Rivers, Historic Parks, Battlefields, Reservations, Recreation Areas, Memorials, 

Parkways, Military Parks, Preserves, and Scenic Trails. 6

 We applied different foliar injury benchmarks in this assessment after further investigation into the benchmarks 

applied in Kohut (2007), which were derived from biomass loss rather than visible foliar injury. Kohut cited a 

threshold of 5.9 ppm-hrs for highly sensitive species from Lefohn (1997), which was based on the lowest W126 

estimate corresponding to a 10% growth loss for black cherry. For soil moisture, Kohut (2007) qualitatively 

assessed whether there appeared to be an inverse relationship between soil moisture and high O3 exposure. 
 7-22 

1 of drought. To incorporate short-term soil moisture into the screening-level assessment, we 

2 applied Palmer Z data for 2006 to 2010 (NCDC, 2012b). These data are for the contiguous U.S. 

3 only. 

4 Short-term estimates of soil moisture are highly variable over time, even from month to 

5 month within a single year. For this reason, we used an average estimate of soil moisture to 

6 reflect the cumulative nature of foliar injury in each park in each year. To determine the 

7 appropriate timeframe for the soil moisture average, we identified the months corresponding to 

8 the highest W126 estimate at each park with an O3 monitor. As shown in Figure 7-14, the 

9 highest 3-month W126 estimate for 98 percent of monitored parks occurred between March and 

10 September, which roughly corresponds to the growing season. Based on this information, we 

11 applied the 7-month average from March to September for each year in the screening-level 

12 assessment for all parks. For parks with O3 monitors, we also conducted sensitivity analyses 

13 applying the 5-month soil moisture average from April to August and the 3-month soil moisture 

14 average corresponding to the specific 3-months in the highest W126 estimate at that monitor. We 

15 provide the average soil moisture estimates for each park by year and the timeframe of W126 for 

16 monitored parks in Appendix 7A. We also provide figures illustrating the difference in soil 

17 moisture across the 7-month, 5-month, and 3-month timeframes by year in Appendix 7A. 

18 

19 Figure 7-14 Timeframe of W126 Estimates for 57 Monitors Located in Parks 

20 

0%

10%

20%

30%

40%

JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND

Percent of Parks

3 months used in W126 estimate

All years

2006

2007

2008

2009

2010
 7-23 

1 7.3.1.3 GIS Analysis 

Using GIS (ESRI® ArcMAPTM 2 10), we spatially overlaid the interpolated O3 exposure 

3 surface and soil moisture data (NCDC, 2012b) with NPS boundaries (USGS, 2003) to link these 

4 data to each park. First, we dissolved all of the internal boundaries for each park such that each 

5 park only had one park boundary. Next, we spatially joined the soil moisture data and the 

6 gridded O3 exposure data with the park boundaries, creating an average soil moisture estimate 

7 and O3 exposure estimate at each park. To identify the parks with O3 monitors, we spatially 

8 overlaid the O3 monitor data with the NPS park boundaries and included only those monitors 

located within the park boundaries.7 9 We excluded all parks outside of the contiguous U.S. 

10 because of the absence of soil moisture data, resulting in 42 parks with O3 monitors and 214 

parks with O3 exposure estimated from the interpolated surface.8 11 Figure 7-15 identifies the 214 

12 parks included in this assessment, including the 42 parks with O3 monitors. In Figure 7-16, we 

13 provide the distribution of O3 exposure and average soil moisture estimates for the 214 parks for 

14 each year in this assessment, noting the range of “near normal” soil moisture conditions as 

15 defined by NCDC (NOAA, 2012c). 

 7

 There are 57 O3 monitors located within NPS parks, and an additional 7 monitors are located within 1km of the 

park boundaries. Some monitors (e.g., at Rocky Mountain National Park) have addresses that imply locations 

within park boundaries but are actually located just outside the NPS boundary. We did not include the monitors 

located just outside of the parks in the monitored park assessment. In addition, nine parks contained more than one 

O3 monitor. We provide the O3 exposure and soil moisture data for the 57 monitors located within NPS parks in 

Appendix 7A. 

8

 Along coastlines, the shapefile for soil moisture is more generalized than the shapefile for O3 exposure. Therefore, 

we manually linked the soil moisture data to (a) 8 seashore parks in order to include them in the 214 park 

assessment and (b) 4 park monitors for the 42 park assessment. 
 7-24 

1 

2 (*Parks identified by park code, which are provided in Appendix 7A. Not all park labels shown due to overlap. 

3 National Parks are prioritized in mapping.) 

4 Figure 7-15 214 National Parks included in the Screening-Level Assessment 

Not monitored (172 parks, 80%)

Monitored (42 parks, 20%) 
 7-25 

1 

2 (Shaded area represents “near normal” soil moisture (-1.25 > Palmer Z > 1) 

3 Figure 7-16 Distribution of O3 and Soil Moisture in 214 Parks by Year 

4 

‐4.00

‐3.00

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

0 10 20 30 40

Palmer Z (average Mar‐Sep)

W126

2006

Dry

Wet

‐4.00

‐3.00

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

0 10 20 30 40

Palmer Z (average Mar‐Sep)

W126

2007

Dry

Wet

‐4.00

‐3.00

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

0 10 20 30 40

Palmer Z (average AMar‐Sep)

W126

2008

Dry

Wet

‐4.00

‐3.00

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

0 10 20 30 40

Palmer Z (average Mar‐Sep)

W126

2009

Dry

Wet

‐4.00

‐3.00

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

0 10 20 30 40

Palmer Z (average Mar‐Sep)

W126

2010

Dry

Wet

‐4.00

‐3.00

‐2.00

‐1.00

0.00

1.00

2.00

3.00

4.00

0 10 20 30 40

Palmer Z (average Mar‐Sep)

W126

2006‐2010 2006

2007

2008

2009

2010

Dry

Wet
 7-26 

1 7.3.1.4 Benchmark Criteria 

2 For each park, we evaluated whether O3 exposure exceeded certain foliar injury 

3 benchmark criteria in each year (2006-2010). Specifically, we derived 6 scenarios from the foliar 

4 injury assessment in section 7.2 for evaluation in this screening-level assessment. The base 

5 scenario as representing the W126 above which there was a consistent percentage (17.7 percent) 

6 of all biosites showing foliar injury, regardless of soil moisture. The other 5 scenarios explicitly 

7 consider soil moisture and represent W126 benchmarks corresponding to different percentages of 

8 biosites showing injury and the degree of injury (e.g., any injury or elevated injury). Four of 

9 these scenarios reflect the special status of parks as areas designated for protection, and thus 

10 apply benchmarks corresponding to any visible foliar injury at certain percentages of biosites 

11 (i.e., 5%, 10%, 15%, and 20%). One scenario represents elevated injury (i.e., visible foliar injury 

12 exceeded a biosite index of 5), which is consistent with a USFS’s biosite index cut-off for foliar 

injury.9 13 These scenarios represent the full range of percentages of biosites showing visible foliar 

14 injury in the assessment in section 7.2. In total, we evaluated 13 different W126 benchmarks 

15 associated with the 6 foliar injury risk scenarios. 

16 Table 7-6 provides the benchmark criteria for O3 exposure (as W126) and short-term, 

17 relative soil moisture (Palmer Z) for each of these 6 scenarios. We provide the figures 

18 corresponding to the benchmark criteria for each scenario in Appendix 7A. 

19 

20 

 9

 For further discussion of the biosite index, refer to section 7.2. 
 7-27 

1 Table 7-6 W126 Benchmark Criteria for O3 Exposure and Relative Soil Moisture in 6 

2 Scenarios used in Screening-Level Assessment of Parks 

Scenario Description 

Normal 

(Palmer Z 

between -1.25 

and 1) 

Wet 

(Palmer Z ≥ 1) 

Dry 

(Palmer Z < -1)

Base 

17.7% of all biosites in foliar injury analysis 

showed any injury (the W126 index value 

above which a consistent percentage of all 

biosites in the foliar injury analysis showed 

any injury) 

W126>10.46 

(soil moisture not considered) 

5% of 

biosites 

5% of biosites in foliar injury analysis showed 

any injury, reflects soil moisture 

categorization 

W126>3.05 W126>3.76 W126>6.16 

10% of 

biosites 

10% of biosites in foliar injury analysis 

showed any injury, reflects soil moisture 

categorization 

W126>5.94 W126>4.42 W126>24.61 

15% of 

biosites 

15% of biosites in foliar injury analysis 

showed any injury, reflects soil moisture 

categorization 

W126>8.18 W126>4.69 N/A 

20% of 

biosites 

20% of biosites in foliar injury analysis 

showed any injury, reflects soil moisture 

categorization 

N/A W126>5.65 N/A 

5% of 

biosites, 

Injury ≥ 5 

5% of biosites in foliar injury analysis showed 

injury equal or greater than 5 on the biosite 

injury index (e.g., 5% of leaf shows injury in 

10% of the leaves), reflects soil moisture 

categorization 

W126>12.23 W126>7.02 W126>46.87 

3 

4 7.3.1.5 Sensitive Vegetation Species 

5 Consistent with Kohut (2007), we identify the parks containing O3-sensitive vegetation 

6 species (NPS, 2003, 2006b) and consider the results for parks without species as potential until 

7 species are identified in field surveys at these parks. In addition, we conducted a sensitivity 

8 analysis where parks without sensitive species are assumed to not exceed the benchmark criteria. 

9 Based on the NPS lists, 95 percent of the parks in this assessment contain at least one sensitive 

10 species. We identify the parks with and without currently identified sensitive species in Figure 

11 7-17. NPS (2003) defines a sensitive species as “species that typically exhibit foliar injury at or 
 7-28 

1 near ambient ozone concentrations in fumigation chambers and/or are species for which ozone 

2 foliar injury symptoms in the field have been documented by more than one expert observer.” 

3 According to NPS (2003), the list of sensitive species is limited in number of species because 

4 few species from natural ecosystems have been fumigated in chambers or examined in the field 

5 for O3 symptoms. 

6 

7 

8 (Parks identified by park code. Not all park labels shown due to overlap. National Parks are prioritized in mapping. 

9 Data source: NPS, 2003, 2006b)

10 Figure 7-17 Presence of O3-Sensitive Species in Parks 

11 

12 7.3.2 Screening Assessment Results and Discussion 

13 Similar to Kohut (2007), we evaluated how often O3 exposure exceeded certain 

14 benchmark criteria, the soil moisture conditions during high exposure periods, and the presence 

15 of sensitive vegetation species. However, we updated the data for O3 exposure, soil moisture, and 

16 benchmark criteria. 

17 As shown in Figure 7-18, in the assessment of 214 parks, 11 percent exceeded the 

18 benchmark criteria in the base scenario for all 5 years, 39 percent for at least 4 years, 58 percent 

Species present (203 parks, 95%)

Species not present (11 parks, 5%) 
 7-29 

1 for at least 3 years, 70 percent for at least 2 years, and 83 percent for at least 1 year. Results for 

2 each of the other scenarios vary. As the required percentage of biosites showing foliar injury 

3 increases, the percentage of parks exceeding the benchmark criteria decreases. Similarly, as the 

4 degree of injury increases (i.e., from any injury to elevated injury ≥ 5), a smaller percentage of 

5 parks exceed the benchmark criteria. As shown in Table 7-7, the percentage of parks exceeding 

6 the benchmark criteria in any given year varies by scenario because O3 exposure and soil 

7 moisture vary by year. 

8 To compare geographic differences across the scenarios, we provide a graph of the 

9 geographic breakdown for parks exceeding the benchmark criteria for at least 3 years in Figure 

10 7-19 and maps of the full results for each scenario in Figure 7-20 to compare geographic 

11 differences across the scenarios. Detailed results for each park and scenario, including additional 

12 figures, are provided in Appendix 7A. 

13 

14 Table 7-7 Percent of 214 Parks that Exceed Benchmark Criteria in Each Year (2006-

15 2010) in 6 Scenarios 

Scenario 2006 2007 2008 2009 2010 

Base 80% 69% 58% 12% 41% 

5% of biosites, injury≥5 65% 31% 43% 7% 29% 

5% of biosites, any injury 97% 96% 95% 96% 96% 

10% of biosites, any injury 81% 56% 83% 53% 80% 

15% of biosites, any injury 77% 48% 72% 33% 65% 

20% of biosites, any injury 8% 7% 12% 13% 4% 

16 
 7-30 

1 

2 Figure 7-18 Screening-Level Results for Foliar Injury in 214 Parks in 6 Scenarios 

3 

All 5 

years

At least 4 

years

At least 3 

years

At least 2 

years 

At least 1 

year No years

Base 11% 39% 58% 70% 83% 17%

5% of biosites, 

Injury=5 0% 13% 34% 52% 78% 22%

5% of biosites, 

any injury 91% 95% 98% 98% 98% 2%

10% of biosites, 

any injury 27% 59% 80% 90% 95% 5%

15% of biosites, 

any injury 11% 46% 68% 82% 90% 10%

20% of biosites, 

any injury 0% 0% 2% 9% 34% 66%

0%

20%

40%

60%

80%

100%

% Parks

# Years Exceeding Criteria
 7-31 

1 

2 Figure 7-19 Parks Exceeding Benchmark Criteria for at least 3 years by Scenario and 

3 Climate Region 

4 

5 

All 214

Parks Base

5%

biosites,

any injury

10%

biosites,

any injury

15%

biosites,

any injury

20%

biosites,

any injury

5%

biosites,

Injury=5

East North Central 8083100

Northwest 11 2 9 3 3 0 1

West North Central 20 6 20 14 9 0 0

West 19 12 16 12 8 0 4

Central 17 8 17 16 15 0 3

South 21 1 21 19 12 1 3

Northeast 27 19 27 26 26 3 15

Southeast 39 27 39 29 22 0 10

Southwest 52 49 52 50 49 0 36

0

50

100

150

200

# parks

Scenario
 7-32 

1 

2 

3 

4 

5 Figure 7-20 Foliar Injury Results Maps for 6 Scenarios for 214 Parks 

6 (Parks identified by park code. Not all park labels shown due to overlap. National Parks are prioritized in mapping. 

7 Larger maps available in Appendix 7A.) 

8 

Key: All 5 years 4 years 3 years 2 years 1 year No years 

5% of biosites, 

injury=5

Base 

5% of biosites, 

any injury

10% of biosites, 

any injury

20% of biosites, 

any injury

15% of biosites, 

any injury
 7-33 

1 In the assessment of 42 parks with O3 monitors based on the interpolated surface, 24 

2 percent exceeded the benchmark criteria in the base scenario for all 5 years, 36 percent for at 

3 least 4 years, 57 percent for at least 3 years, 69 percent for at least 2 years, and 81 percent for at 

4 least 1 year. These results are generally similar to the results for the 214 park assessment for the 

5 base scenario, except that the monitored park analysis showed a higher fraction of parks that 

6 exceeded the benchmark criteria for all 5 years rather than at least 4 years. We provide the results 

7 of the monitored park assessment in Table 7-8. We also evaluated three different methods for 

8 assigning O3 exposure to parks with monitors: interpolated surface, highest monitor, and average 

9 monitor. The results using these methods are discussed in more detail in section 7.3.3.1. 

10 

11 
 7-34 

1 Table 7-8 Screening-level Foliar Injury Results in 42 Parks with an O3 Monitor using 3 

2 Methods for Assigning O3 Exposure to Each Park in Base Scenario.* 

Park Name State 

Years with 

Monitoring 

Data 

# Years Exceeding Benchmark 

Criteria for Base Scenario 

Interpolation Highest 

Monitor 

Average 

Monitor 

Acadia National Park* ME 5 0 1 0 

Agate Fossil Beds National Monument NE 3 2 1 1 

Badlands National Park* SD 5 1 1 0 

Big Bend National Park TX 5 1 3 3 

Blue Ridge Parkway NC 5 3 1 1 

Canyonlands National Park UT 5 5 5 5 

Cape Cod National Seashore MA 5 3 3 3 

Carlsbad Caverns National Park NC 4 1 2 2 

City of Rocks National Reserve ID 1 4 0 0 

Colorado National Monument CO 4 3 2 2 

Congaree National Park SC 5 3 2 2 

Cowpens National Battlefield SC 5 2 2 2 

Craters of the Moon National Monument ID 4 3 1 1 

Cumberland Gap National Historical Park KY 4 3 1 1 

Death Valley National Park CA 5 5 5 5 

Devil's Tower National Monument WY 3 2 0 0 

Dinosaur National Monument CO 4 4 2 2 

Glacier National Park* MT 5 0 0 0 

Grand Canyon National Park AZ 5 5 4 4 

Great Basin National Park NV 5 5 4 4 

Great Smoky Mountains National Park* TN 5 3 4 3 

Indiana Dunes National Lakeshore IN 5 1 1 1 

Joshua Tree National Park* CA 5 5 5 5 

Lassen Volcanic National Park CA 5 4 3 3 

Mesa Verde National Park CO 5 5 5 5 

Mojave National Preserve CA 4 5 4 4 

Mount Rainier Wilderness WA 5 0 0 0 
 7-35 

Park Name State 

Years with 

Monitoring 

Data 

# Years Exceeding Benchmark 

Criteria for Base Scenario 

Interpolation Highest 

Monitor 

Average 

Monitor 

Olympic National Park* WA 1 0 0 0 

Padre Island National Seashore TX 2 0 0 0 

Petrified Forest National Park AZ 5 4 4 4 

Pinnacles National Monument CA 5 3 4 4 

Saguaro National Park AZ 5 4 5 5 

Saratoga National Historical Park NY 5 0 0 0 

Scotts Bluff National Monument NE 1 3 0 0 

Sequoia-Kings Canyon National Park* CA 5 5 5 5 

Shenandoah National Park VA 5 2 4 4 

Theodore Roosevelt National Park* ND 5 0 0 0 

Tonto National Monument AZ 5 5 5 5 

Voyageurs National Park MN 5 0 0 0 

Wind Cave National Park SD 5 2 2 2 

Yellowstone National Park WY 5 1 2 2 

Yosemite National Park* CA 5 5 5 5 

Summary Results by O3 Exposure Method 

All 5 years 71% 24% 19% 19% 

At least 4 years 86% 36% 36% 33% 

At least 3 years 93% 57% 43% 43% 

At least 2 years 95% 69% 60% 60% 

At least 1 year 100% 81% 76% 71% 

No years 0% 19% 24% 29% 

1 * More than one O3 monitor within park boundaries (shown as bold). 

2 

3 7.3.3 Limitations and Uncertainty Characterization for Screening-Level 

4 Assessment 

5 This is a screening-level assessment that primarily relies on national-level data with 

6 coarse spatial resolution. As such, these results should be interpreted within the context of the 

7 analytical limitations and with the appropriate uncertainty characterization, as noted below. 
 7-36 

1 7.3.3.1 O3 Exposure 

2 As noted by Kohut (2007), monitoring provides the most accurate assessment of O3 

3 exposure in specific locations, but a single monitor may not reflect the differences in exposure 

4 throughout the park. For this reason, we compared the results of the assessment for parks with O3

5 monitors located within the park boundaries using the interpolated surface with the results based 

6 on O3 monitor data. As noted above, we conducted sensitivity analyses to evaluate the impact of 

7 this analytical choice for parks with more than one monitor, using both the highest monitor in the 

8 park, and the average of the monitors in the park for the base scenario. As shown in Table 7-8, 

9 the results using the highest monitor and average monitor were generally similar but tended to 

10 exceed the benchmark criteria for fewer years. This result is primarily due to the absence of 

11 monitoring data for a few years in a few parks, which lowered the maximum number of years 

12 that a park could exceed the benchmark criteria using the highest monitor and average monitor 

13 methods. For the 9 parks with multiple monitors, only 2 parks exceeded the benchmark criteria 

14 for a different number of years using the highest monitor compared to using the average of the 

15 monitors. For both Acadia National Park and Badlands National Park, the benchmark criteria 

16 were exceeded for 1 year using the highest monitor but for no years based on the average of the 

17 monitors. For the 30 parks with all 5 years of monitoring data, 17 parks had the same results 

18 using all 3 methods, 5 parks had more years using the interpolation, 5 parks had more years 

19 using either monitor method, and 3 parks had more years using the highest monitor. 

20 In the 214-park assessment, we used interpolated surfaces to estimate O3 exposure at each 

21 of the parks. As such, we are not able to identify which 3 months are included in the W126 

22 estimate at all of the parks. This limitation has two important implications. First, we have to 

23 make assumptions regarding the months included in the O3 exposure estimate to match with the 

24 months in the soil moisture estimate. Second, a few areas in the West, such as Utah, can 

25 experience high O3 episodes during the winter, when many plants are dormant with limited 

opportunities for O3 uptake.10 26 However, because only a few areas of the country have high O3 

27 episodes in winter when many plants are dormant and some sensitive vegetation species do not 

28 shed leaves in the winter (e.g., Pinus ponderosa), we believe that this limitation contributes only 

29 a small amount of uncertainty to the overall results of the 214-park assessment. In addition, 

 10 There are 11 parks in Utah, which all exceeded the benchmark criteria for at least 4 years in the base scenario. 

Only one park has a monitor (Canyonlands), and the 3-month timeframes corresponding to the highest W126 

estimates for 2006 to 2010 occurred between March and July. 
 7-37 

1 based on the assessment of 42 parks with O3 monitors, less than 2 percent of the highest W126 

2 estimates occurred outside of the March to September timeframe. 

3 Because W126 estimates can be highly variable from year to year, the selection of 

4 different analysis years for this analysis could lead to different results. In Table 7-9, we provide 

5 the sensitivity of the results for the base scenario by splitting the data into two timeframes. In 

6 general, more parks show higher O3 exposure during the first 3 years of the assessed timeframes 

7 (i.e., 2006-2008) than the last 3 years (i.e., 2008-2010). However, assessing the exceedances in 

8 specific years across scenarios in Table 7-9 shows that the scenario affects which years have the 

9 highest percentage of parks that exceed the benchmark criteria. 

10 For more information regarding uncertainty in the O3 exposure estimates, see Chapter 4.
 7-38 

1 Table 7-9 Foliar Injury Sensitivity Analyses for 214 Parks 

Screening Criteria 

Percent of Parks Exceeding Benchmark Criteria by # 

Years (2006-2010) 

All 5 

years 

At 

least 4 

years 

At 

least 3 

years 

At 

least 2 

years 

At 

least 1 

year 

No 

years 

O3

Exposure 

and Soil 

Moisture 

Scenarios 

Base (W126>10.46) 11% 29% 19% 12% 13% 17% 

5% of biosites, injury≥5 0% 13% 21% 18% 26% 22% 

5% of biosites 91% 4% 2% 0% 0% 2% 

10% of biosites 27% 32% 21% 10% 5% 5% 

15% of biosites 11% 35% 22% 14% 8% 10% 

20% of biosites 0% 0% 2% 7% 24% 66% 

O3

Exposure 

only 

W126>15 (alternative standard) 3% 9% 23% 39% 58% 42% 

W126>13 (alternative standard) 3% 21% 36% 51% 72% 28% 

W126>11 (alternative standard) 8% 33% 52% 66% 81% 19% 

W126>9 (alternative standard) 21% 55% 70% 77% 87% 13% 

W126>7 (alternative standard) 41% 73% 80% 86% 92% 8% 

W126>46.87 (5% of biosites, injury≥5, 

dry) 0% 0% 0% 0% 0% 100% 

W126>24.61 (10% of biosites, dry) 1% 1% 3% 3% 4% 96% 

W126>12.23 (5% of biosites, injury≥5, 

normal) 5% 23% 41% 57% 75% 25% 

W126>8.18 (15% of biosites, normal) 27% 63% 73% 81% 89% 11% 

W126>7.02 (5% of biosites, injury≥5, 

wet) 40% 72% 80% 86% 92% 8% 

W126>6.16 (5% of biosites, dry) 51% 77% 82% 89% 95% 5% 

W126>5.94 (10% of biosites, normal) 57% 78% 84% 91% 95% 5% 

W126>5.65 (20% of biosites, wet) 61% 80% 87% 92% 95% 5% 

W126>4.69 15% of biosites, wet) 80% 87% 92% 95% 97% 3% 

W126>4.42 (10% of biosites, wet) 85% 90% 93% 95% 97% 3% 

W126>3.76 (5% of biosites, wet) 91% 94% 97% 98% 98% 2% 

W126>3.05 (5% of biosites, normal) 96% 97% 98% 98% 98% 2% 

Timeframe 

2006-2008 0% 0% 55% 70% 83% 17% 

2008-2010 0% 0% 11% 40% 61% 39% 

Sensitive 

Species Base (0 if no species) 10% 37% 54% 66% 79% 21% 

2 
 7-39 

1 7.3.3.2 Soil Moisture 

2 Evaluating soil moisture is more subjective than evaluating O3 exposure because of its 

3 high spatial and temporal variability within the O3 season. Due to the size of the NCDC climate 

4 divisions (e.g., potentially hundreds of miles wide), soil moisture will vary within each region 

5 and potentially even within a park. For example, some vegetation along riverbanks may still 

6 experience sufficient soil moisture during periods of drought to exhibit foliar injury. Due to the 

7 spatial resolution of the soil moisture regions, the inability to capture within-region variability in 

8 soil moisture adds some uncertainty to this assessment, but we are currently unable to quantify 

9 the magnitude of this uncertainty. Regarding temporal variability, averaging the monthly values 

10 from May to October for each year also adds some uncertainty to this assessment. For example, 

11 the average is sensitive to skew by a single very wet or very dry month within that timeframe or 

12 even a single precipitation episode within a month. To evaluate the sensitivity of the results to 

13 different averaging times, we conducted an analysis using the 7-month, 5-month, and 3-month 

14 soil moisture average for parks with O3 monitors. As shown in Table 7-10, the results for the 57 

15 O3 monitors in parks are not very sensitive to the different timeframes for soil-moisture data for 

16 the 6 scenarios, although the specific 3-month data tend to show slightly fewer parks that exceed 

17 the benchmark criteria for more years. On balance, we believe that the spatial and temporal 

18 resolution for the soil moisture data is likely to underestimate the potential of foliar injury that 

19 could occur along some areas such as stream banks. 

20 
 7-40 

1 Table 7-10 Foliar Injury Sensitivity Analyses for Soil Moisture in 57 O3 Monitors in 

2 Parks* 

Scenario and Soil Moisture Method 

Percent of Parks Exceeding Benchmark Criteria by # Years 

(2006-2010) 

All 5 

years 

At least 4 

years 

At least 3 

years 

At least 2 

years 

At least 1 

year 

No years 

Base 18% 33% 40% 56% 74% 26% 

7-month 

Palmer Z 

(MarSept) 

5% of biosites, injury≥5 4% 12% 28% 37% 63% 37% 

5% of biosites 54% 74% 79% 86% 96% 4% 

10% of biosites 14% 42% 60% 75% 86% 14% 

15% of biosites 4% 26% 46% 68% 81% 19% 

20% of biosites 0% 2% 2% 12% 32% 68% 

5-month 

Palmer Z 

(Apr-Aug) 

5% of biosites, injury≥5 4% 11% 25% 39% 63% 37% 

5% of biosites 53% 72% 79% 88% 96% 4% 

10% of biosites 12% 33% 58% 75% 84% 16% 

15% of biosites 4% 18% 44% 67% 79% 21% 

20% of biosites 0% 0% 2% 7% 40% 60% 

Specific 3-

Month 

Palmer Z 

(based on 

monitor) 

5% of biosites, injury≥5 4% 11% 19% 37% 58% 42% 

5% of biosites 56% 72% 79% 88% 96% 4% 

10% of biosites 9% 25% 54% 74% 86% 14% 

15% of biosites 2% 9% 37% 65% 81% 19% 

20% of biosites 0% 0% 0% 7% 32% 68% 

3 *Includes multiple monitors in 9 parks. The results for the base scenario are constant across soil moisture methods 

4 because this scenario does not incorporate soil moisture. 

5 

6 In addition, we are unaware of a clear threshold for drought below which visible foliar 

7 injury would not occur. In general, low soil moisture reduces the potential for foliar injury, but 

8 injury could still occur because plants must open their stomata even during periods of drought. In 

9 addition, the degree of drought necessary to reduce potential injury is not clear. As shown in 

10 Figure 7-11, foliar injury occurs even at lower soil moisture. In addition, we applied NOAA’s 

11 categorization for “near normal” for Palmer Z data in defining 5 of the 6 scenarios, as described 

12 in section 7.2. However, NOAA’s categorization for Palmer Z data has been described as “rather 
 7-41 

arbitrary” (Karl, 1986).11 1 Because we use these scenarios to assess the impact of soil moisture on 

2 the potential for foliar injury, any uncertainties in interpreting the underlying soil moisture data 

3 are embedded within those scenarios. Because using a different categorization would lead to 

4 different benchmark criteria for O3 exposure, it is not clear whether this uncertainty could 

5 underestimate or overestimate the potential foliar injury. 

6 7.3.3.3 Foliar Injury Benchmarks in 6 Scenarios 

7 This assessment relies upon the foliar injury benchmarks derived from the analysis in 

8 section 7.2. The precision in these benchmarks reflects the precision in the underlying soil 

9 moisture and O3 exposure data. Each of the uncertainties identified in that analysis extend to this 

10 assessment. In particular, due to the absence of biosite injury data in the southwest region and 

11 limited biosite data in the west and west north central regions (see Figure 7-5), the benchmarks 

12 applied may not be applicable to these regions. This absence of data results in additional 

13 uncertainty in extrapolating the national-level benchmarks to parks, particularly in the southwest 

14 region. As shown in Figure 7-19, many of the parks that exceed the benchmark criteria for at 

15 least 3 years are located in the southwest region across many scenarios. In Table 7-9, we provide 

16 the sensitivity of the results for 214 parks to 6 different scenarios reflecting different 

17 considerations of O3 exposure, soil moisture, and degree of foliar injury. This analysis indicates 

18 that the results are highly sensitive to the selected foliar injury benchmarks in the 6 scenarios. 

19 7.3.3.4 Sensitive Species 

20 As noted by NPS (2003), relatively few vegetation species have been evaluated for O3-

21 sensitive foliar injury in the field, and continuing fieldwork will likely identify additional 

22 sensitive species. Because we did not exclude parks without identified sensitive species, some 

23 parks that exceed the benchmark criteria may not actually have high potential for foliar injury. 

24 However, due to the small number of parks without sensitive species (i.e., only 11 parks, or 5 

25 percent) and on-going fieldwork, the magnitude of this uncertainty is likely to be small. As 

 11 From Karl (1986), (p.83): “It should be emphasized that these qualitative descriptions are rather arbitrary. It is 

important to realize that the Z-index is standardized across all 12 months. This means that it is quite possible and 

common for some months which typically have low precipitation, and/or low moisture reserves, and/or high 

potential evapotranspiration, and/or low run-off (i.e. northern locations in winter, arid areas during the dry season) 

to never have Z-indices less than minus two. Contrarily, areas and times of the year which typically have favorable 

moisture conditions, high reserves, ample precipitation, low potential evapotranspiration, and high runoff, can have 

very low Z indices (<minus five) during a dry month. Additionally, the Z-index has no theoretical upper limit (i.e., 

precipitation has no upper bound), but it does have a theoretical lower limit (i.e., precipitation is zero with little or 

no moisture reserve).” 
 7-42 

1 shown in Table 7-8, assuming that parks without sensitive species do not exceed the benchmark 

2 criteria does not significantly change the results for the base scenario. 

3 7.3.3.5 Evaluation of Alternative Standards 

4 This screening-level assessment does not evaluate the model-adjusted W126 spatial 

surfaces for the scenarios of just meeting the existing 75 ppb (4th 5 highest daily maximum) or 

alternative W126 standards.12 6 Because this screening-level assessment relies on year-by-year 

7 estimates of O3 exposure and soil moisture, it would not be possible to evaluate these year-by8 year impacts using the attainment scenario surfaces, which were derived from 3 years of model9 adjusted W126 data. Nevertheless, we can make a few observations regarding the potential 

10 implications of just meeting existing and alternative standards. For example, as shown in Table 

11 7-8, 42 percent of parks did not exceed 15 ppm-hrs during 2006-2010 using annual W126 data. 

12 In addition, none of the 214 parks would exceed the annual benchmark criteria for the base 

13 scenario (W126>10.46 ppm-hrs) after adjusting air quality to meet the existing standard (3-year 

average W126 data).13 14 Similarly, Figure 7-21 shows that only 8 parks exceed 7 ppm-hrs after 

15 adjusting air quality to just meet the existing standard (3-year average). Figure 7-22 shows O3

16 exposure in the 10 most-visited parks after adjusting air quality to meet the existing and 

17 alternative standards. We provide the results for just meeting the existing standard and 

18 alternative W126 standards at each of the 214 parks in Appendix 7A. 

 12 W126 calculations are slightly modified in the case of the model adjustment scenarios described in Chapter 4, 

Section 4.3.4. When calculating W126 for the model adjustment cases, we first found the three-year average of each 

three-month period, and then selected the three-month period with the highest three-year average using the same 

three-month period for each of the three years. In this way, the five scenarios are for recent air quality, air quality 

adjusted to just meet the current standard, and air quality further adjusted to just meet three different W126 index 

values: 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. 

13 See Sections 4.3.1, 4.3.2, and 4.3.4 for more information regarding the air quality adjustments. 
 7-43 

1 

2 Figure 7-21 Percent of 214 Parks at Different W126 Levels with Adjustments for 

3 Existing and Alternative Standards (3-year average) 

4 

36

206 211 212 214

13

6 3 2

24

2

38

33

70

0%

20%

40%

60%

80%

100%

2006-2008 Just meeting 75 

ppb

15 ppm-hrs 11 ppm-hrs 7 ppm-hrs

Percent of Parks (# parks provided on each bar)

Adjustment Scenarios

>15 ppm-hrs

13-15 ppm-hrs

11-13 ppm-hrs

9-11 ppm-hrs

7-9 ppm-hrs

<7 ppm-hrs
 7-44 

1 

2 Figure 7-22 O3 Exposure in Ten Most Visited National Parks after Just Meeting Existing 

3 and Alternative Standards (3-year average) 

4 7.4 NATIONAL PARK CASE STUDY AREAS 

5 The national parks represent a set of resources the public has agreed are special areas in 

need of protection for this and future generations to experience and enjoy.14 6 Because of this 

7 status risks to park resources are of special concern, particularly for bequest and option services 

8 because these services are specifically referenced in the creation of the parks. The NPS is 

9 responsible for the protection of all resources within the national park system. These resources 

10 include those that are related to and/or dependent upon good air quality, such as whole 

11 ecosystems and ecosystem components. 

12 Several laws and policies protect the natural resources in national parks. The NPS, in its 

13 Organic Act (16 U.S.C. 1), is directed to conserve the scenery, natural and historic objects and 

 14 C.F.R. 40, 81.400 provides for visibility protection for federal Class I areas. 

0

5

10

15

20

25

30

2006‐2008 Just meeting 75

ppb

15 ppm‐hrs 11 ppm‐hrs 7 ppm‐hrs

W126 (ppm‐hrs)

Great Smoky Mountains National Park Grand Canyon National Park

Yosemite National Park Yellowstone National Park

Rocky Mountain National Park Zion National Park

Olympic National Park Grand Teton National Park

Acadia National Park Cuyahoga Valley National Park
 7-45 

1 wildlife and to provide for the enjoyment of these resources unimpaired for current and future 

2 generations. The Wilderness Act of 1964 (Public Law 88-577, 16 U.S. C. 1131-1136) asserts 

3 wilderness areas will be administered in such a manner as to leave them unimpaired and preserve 

4 them for the enjoyment of future generations. NPS Management Policies (2006) guide all NPS 

5 actions including natural resources management. In general, the NPS Management Policies 

6 reiterate the NPS Organic Act’s mandate to manage the resources “unimpaired.” Although we 

7 have not quantified the monetary value of the bequest or option services given the data and 

8 methodology limitations inherent in such an effort, the status afforded these special areas through 

9 these laws and policies is indicative of their value to the public. 

10 The ecosystem service we can quantify, with some qualifications, is the recent monetary 

11 value of the total recreation opportunity provided by the parks. We cannot quantify the loss in 

12 monetary value for these services associated with O3; however, the magnitude of the overall 

13 value is informative in understanding the potential significance of any O3 damage (see Chapter 5 

14 for more discussion). The NPS has collected data on visitation, recreational activities, and 

15 expenditures for trips to parks and modeled the economic impacts to local communities around 

16 parks. The NSRE provides WTP estimates for the value of recreation activities specific to the 

17 regions where parks are located. Together these data allow us to estimate the magnitude of the 

18 recreation services provided by parks. The loss of service provision or visitor satisfaction due to 

19 O3 injury to sensitive species in the case study parks is reflected in these estimates. 

20 The three parks we are highlighting for case study analysis, Great Smoky Mountains NP, 

21 Rocky Mountain NP, and Sequoia/Kings Canyon NP, represent different regions of the country, 

22 different ecosystems, and O3 conditions. Each park contains species sensitive to O3 injury. The 

23 text boxes accompanying each section highlight some of the reasons these parks were chosen for 

24 special protection. 

25 For the case study areas, we used the O3-sensitive species list from the preceding section 

26 and cover data from VegBank plots (see Section 7.2). The resulting maps give cover estimates 

27 for O3-sensitive species at the finer scale of the NPS vegetation map. It is important to note that 

28 the cover estimates are separated into vegetation stratum (e.g., herb, shrub, tree) and it is possible 

29 to have more than one vegetation strata present in a location. As such, it is possible to have 

30 sensitive species cover at a higher cumulative proportion than is shown here. We also used the 

31 benchmarks presented in section 7.2 to assess the effect of just meeting the existing and 
 7-46 

1 alternative standards on W126 index values in the case study parks. We used a benchmark of 10 

2 percent of biosites exhibiting injury in a normal year as the basis for the analysis, which is 

3 depicted in Figure 7-23. 

4 

5 Figure 7-23 Identification of W126 Index Value where 10 Percent of Biosites show Any 

6 Foliar Injury 

7 

8 

0 10 20 30 40

0.00 0.05 0.10 0.15 0.20 0.25

Biosite Index > 0

W126 (ppm-hrs)

Proportion of sites

All

Wet

Normal

Dry
 7-47 

1 7.4.1 Great Smoky Mountains 

2 National Park 

3 In 2010, the Great Smoky Mountains National 

4 Park (GRSM) welcomed approximately 9.5 million 

5 visitors (NPS, 2010) making it the most visited national 

6 park in America. 

7 The “whole park” services affected by potential 

8 O3 impacts include the existence, option, and bequest 

9 values and habitat provision discussed in Chapter 5. 

10 Recreation value specific to the park is discussed later in 

11 this section. 

12 The extent of sensitive species coverage in 

13 GRSM is substantial. Showing the percent cover of 

14 species sensitive to foliar injury and focusing the analysis 

15 on areas where recreation services are provided can 

16 provide some perspective on the potential level of harm 

17 to scenic beauty and recreation satisfaction within the 

18 Park. 

19 The NPS 2002 Comprehensive Survey of the 

20 American Public, Southeast Region Technical Report 

21 includes responses from recent visitors to southeast parks 

22 about the activities they pursued during their visits (NPS, 

23 2002a). Using the 2010 annual visitation rate from the 

24 NPS survey (NPS, 2010) and the regional results from 

25 the Kaval and Loomis (2003) report on recreational use 

26 values compiled for the NPS, we estimated visitors’ 

27 WTP for various activities; we present the estimates in 

28 Table 7-11. In addition to the activities listed in the 

29 table, 19 percent, or 1.8 million park visitors, benefited 

30 from educational services offered at the park by 

Mount Le Conte, Summer 

Great Smoky Mountains National Park 

Courtesy: NPS 

http://www.nps.gov/grsm/photosmultimedia/index.htm

Great Smoky Mountains National Park is 

the most visited national park in America 

and a UNESCO World Heritage Site. The 

Park is valued for the diversity of its 

vegetation and wildlife; the scenic beauty 

of its mountains, including the famous fogs 

that give the Smoky Mountains their name; 

and the preservation of the remnants of 

Southern Appalachian culture. It is also 

subject to high ambient O3 levels. The 

park has recent O3 levels ranging between 

W126 levels of 10 – 18 ppm-hrs with a 

mean level of 14.7 ppm-hrs. 
 7-48 

1 participating in a ranger-led nature tour, which suggests that visitors wish to understand the 

2 ecosystems preserved in the park. 

3 Table 7-11 Value of Most Frequent Visitor Activities at Great Smoky Mountains 

4 National Park 

Activity Percent 

Participation 

Number of 

Participants 

(thousands) 

Mean WTP 

(in 2010$) 

Total Value of 

Participation 

(millions of 2010$) 

Sightseeing 82 7,790 53.34 416 

Day Hiking 40 3,800 69.93 266 

Camping 19 1,805 29.87 54 

Picnicking 50 4,750 42.42 201 

Total 937 

5 

6 The report Economic Benefits to Local Communities from National Park Visitation and 

7 Payroll (NPS, 2011) provides estimates of visitor spending and economic impacts for each park 

8 in the system. Visitor spending and its economic impact to the surrounding area are provided in 

9 Table 7-12 for the GRSM. In addition, Table 7-13 includes data on the median value that 

10 visitors spend on food, gas, lodging, and other items. 

11 Table 7-12 Visitor Spending and Local Area Economic Impact of GRSM 

Public Use Data Visitor Spending 2010a Impacts on Non-Local Visitor 

Spending

2010 Recreation 

Visits 

2010 Overnight 

Stays 

All 

Visitors 

Non-Local 

Visitors Jobs Labor 

Incomea 

Economic 

Impacta 

9,463,538 393,812 $818,195 $792,547 11,367 $303,510 $504,948 

a 12 ($000s) 

13 Source: NPS (2011) 

14 
 7-49 

1 Table 7-13 Median Travel Cost for GRSM Visitors 

Expense Median Expenditures (2010$) 

Gas and Transportation $73 

Lodging $182 

Food and Drinks $73 

Clothes, Gifts, and Souvenirs $61 

Total Per Visitor Party $389 

2 Source: NPS (2002a) 

3 

4 Each of the activities discussed above is among those shown in the national-scale 

5 analysis to be strongly affected by visitor perceptions of scenic beauty. As discussed in Section 

6 7.1.1.2 for visible O3 damage (Peterson, 1987) and for visible nitrogen and adelgid damage (a 

7 pest in Fraser fir) (Haefele et al., 1991 and Holmes and Kramer, 1996) visitors have a non-zero 

8 WTP for reductions in the described scenic impairments. As in the national analysis, it is not 

9 possible to assess the extent of loss of services from impairment of scenic beauty by O3; 

10 however, for the park these losses are captured in the estimated values for spending, economic 

11 impact, and WTP. 

12 GRSM is prized, in part, for its rich species diversity. The large mix of species includes 

13 37 O3-sensitive species across vegetative strata, and many areas contain several sensitive species. 

14 For instance, there may be a sensitive tall shrub occurring under the canopy of a sensitive tree 

15 and various sensitive short shrubs or herbaceous plants occurring in the area of the tall shrub. In 

16 areas where sensitive species overlap, it is possible to have sensitive species coverage 

17 substantially higher than coverage for any one category of vegetation. Figure 7-24 shows the 

park coverage of various sensitive species. Nearly 40 percent of the Park’s 2,185 km2 18 total area 

has sensitive tree cover (canopy and subcanopy) greater than 20 percent. Of that, 232 km2 19 has 

sensitive tree species cover between 20 percent and 40 percent. Shrubs account for 491 km2 20 of 

sensitive vegetation, with over 100 km2 21 having over 80 percent of the species present as 

22 sensitive. While sensitive herbaceous species occur throughout the park, the percent cover rarely 

23 exceeds 20 percent. 

24 We can quantify the extent of the hiking trails in areas where sensitive species are at risk 

25 for foliar injury. Of the approximately 1,287 km of trails in GRSM, including approximately 
 7-50 

1 114 km of the Appalachian Trail, over 1,040 km, or about 81 percent of trail area, are in areas 

2 where species sensitive to foliar injury occur. Figure 7-25 shows a summary of the overlap of 

3 the hiking trails in the GRSM, including a portion of the Appalachian Trail, with the species 

4 cover index. The accompanying pie charts in Figure 7-26 show the number of trail kilometers in 

5 each cover category. The categories likely most visible to hikers are subcanopy trees, shrubs, 

6 and herbaceous vegetation. There are 311 km, or about 24 percent, of trail area where sensitive 

7 subcanopy tree cover accounts for over 20 percent of the tree species present. Sensitive shrubs 

8 cover over 20 percent of 549 km of trail area, or about 43 percent of total area. 

9 Although we cannot quantify the incremental loss of hiker satisfaction with their 

10 recreation experience, this analysis illustrates that very substantial numbers of trail kilometers 

11 are potentially at risk. With 3.8 million hikers using the trails every year and those hikers willing 

12 to pay over $266 million for that activity, even a small benefit of reducing O3 damage in the park 

13 could result in a significant value. 
 7-51 

1 2 Figure 7-24 Cover of Sensitive Species in GRSM
 7-52 

1 2 Figure 7-25 Trail Cover of Sensitive Species in GRSM
 7-53 

1 

2 Figure 7-26 GRSM Trail Kilometers by Species Cover Category 

3 

4 One of the amenities provided by GRSM is the scenic views from the roads and trails -- 

5 the views from the scenic overlooks are one of the major park attractions. On a day with natural 

6 viewing conditions visitors can see about 150 km across the mountain ridges of North Carolina 

7 and Tennessee, far outside the borders of the park itself. On average viewing days visitors can 

8 still see about 40 km, again outside the park itself. Figure 7-27 shows the sensitive tree canopy 

cover within a 3 km buffer of the overlooks. Within these small buffers 78 km2 9 have sensitive 

10 species cover over 20 percent. While there are no data on the number of visitors stopping at the 

11 overlooks, almost 8 million visitors identify sightseeing as one of their activities in the Park. 

12 With their collective WTP for this activity over $400 million, it seems reasonable to conclude 

13 that park visitors substantially value the scenic quality of the overlooks. O3 concentrations in 

14 GRSM have been among the highest in the eastern U.S., sometimes twice as high as neighboring 

cities such as Atlanta and Knoxville. Under recent conditions 44 percent, or 959 km2 15 , of the park 

16 has W126 index values above 15 ppm-hrs. After just meeting the existing standard at 75 ppb, 

135.2

288.8

738.6

315.6

177.4

Tree Canopy

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

135.2

395.4

948.9

176.2

Tree Subcanopy

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

135.2

269.0

1032.5

155.0 64.0

Tall Shrub

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

135.2

112.1

1,133.7

155.0

55.6 64.0

Short Shrub

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

135.2

1,211.0

253.9

55.6

Herbaceous

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%
 7-54 

1 W126 index values are reduced such that no area is over 7 ppm-hrs. Just meeting the alternative 

2 of 15 ppm-hrs produces the same result as meeting the existing standard. The lower alternative 

3 standards of 11 and 7 ppm-hrs result in the park having W126 index values under 3 ppm-hrs for 

4 the entire park, with most of the park under 2 ppm-hrs after just meeting the 7 ppm-hrs standard 

5 level. See Table 7-14 for additional details. 

6 

7 

8 Figure 7-27 Sensitive Vegetation Cover in GRSM Scenic Overlooks (3km) 

9 

10 Table 7-14 Geographic Area of GRSM after Just Meeting Existing and Alternative 

Standard Levels (km2 11 ) 

Under 5.94 

ppm-hrs 

Between 5.95 

and 7 ppm-hrs 

Between 7-

11ppm-hrs 

Between 11-15 

ppm-hrs 

Over 15 

ppm-hrs 

Recent conditions 

(2006-2008) 0 0 48 1,178 959 

Just meeting 75 ppb 2,185 0 0 0 0 

15 ppm-hrs 2,185 0 0 0 0 

11 ppm-hrs 2,185 0 0 0 0 

7 ppm-hrs 2,185 0 0 0 0 

12 
 7-55 

Sheep Lakes 

Courtesy: NPS 

http://www.nps.gov/romo/photosmulti

media/index.htm

Rocky Mountain National Park 

features riparian ecosystems 

with 150 lakes and 450 stream 

miles that support lush 

vegetation. The montane 

ecosystem includes pine 

forests and grasslands, while 

subalpine elevations present 

spruce and fir trees weathered 

by the elements. The alpine 

ecosystems are too harsh for 

trees, but support low growing 

plants. The park has recent O3

levels ranging between W126 

levels of 2 – 54 ppm-hrs with a 

mean level of 14.2 ppm-hrs. 

1 7.4.2 Rocky Mountain National Park 

2 In 2010 Rocky Mountain National Park (ROMO) welcomed 

3 million visitors (NPS, 2010) to its 1,075 km2 3 of mountain 

4 ecosystems. ROMO allows visitors to enjoy vegetation and 

5 wildlife unique to these ecosystems along over 483 km of hiking 

6 trails. 

7 The NPS 2002 Comprehensive Survey of the American 

8 Public, Intermountain Region Technical Report includes responses 

9 from recent visitors to intermountain parks about the activities they 

10 pursued during their visit (NPS, 2002b). As in the GRSM case 

11 study, using the 2010 visitation rate from the NPS survey (NPS, 

12 2010) and the regional results from the Kaval and Loomis (2003) 

13 report on recreational use values compiled for the NPS, we present 

14 estimates for visitors’ WTP for various activities in Table 7-15. 

15 Table 7-15 Value of Most Frequent Visitor Activities at 

16 ROMO 

Activity Percent 

Participation 

Number of 

Participants 

(thousands) 

Mean 

WTP 

(in 

2010$) 

Total Value of 

Participation 

(millions of 

2010$) 

Sightseeing 85 2,550 $28.17 $72 

Day Hiking 51 1,520 $46.03 $70 

Camping 27 810 $41.47 $34 

Picnicking 38 1,140 $33.77 $38 

Total $214 

17 

18 In addition to the activities listed in Table 7-15, 11 percent 

19 of, or 330,000, park visitors took advantage of educational services 

20 offered at the park by participating in a ranger-led nature tour. 

21 Each of the activities discussed above are among those 

22 shown in the national-scale analysis to be strongly affected by 

23 visitor perceptions of scenic beauty. As in the national analysis it is 
 7-56 

1 not possible to assess the extent of loss of services due to impairment of scenic beauty due to O3

2 damage; however those losses are captured in the estimated values for spending, economic 

3 impact, and WTP for the park. If O3 impacts were lower these estimated values would likely be 

4 higher. 

5 The report Economic Benefits to Local Communities from National Park Visitation and 

6 Payroll (NPS, 2011) provides estimates of visitor spending and economic impacts for each park 

7 in the system. Visitor spending and its economic impact to the surrounding area are given in 

8 Table 7-16 for the ROMO. Table 7-17 includes data on the median value that visitors spend on 

9 food, gas, lodging, and other items. 

10 Table 7-16 Visitor Spending and Local Area Economic Impact of ROMO 

Public Use Data Visitor Spending 2010 Impacts on Non-Local Visitor Spending

2010 

Recreation 

Visits 

2010 

Overnight 

Stays 

All Visitors Non-Local 

Visitors Jobs Labor Incomea 

Economic 

Impacta 

2,955,821 174,202 229,032 221,896 3,316 $89,975 $ 155,157 

a 11 ($000s) 

12 Source: NPS (2011) 

13 

14 Table 7-17 Median Travel Cost for ROMO Visitors 

Expense Median Expenditures (in 2010$) 

Gas and Transportation $63 

Lodging $100 

Food and Drinks $63 

Clothes, Gifts, and Souvenirs $45 

Total per Visitor Party $271 

15 Source: NPS (2002b) 

16 

17 Unlike GRSM, only 7 sensitive species provide cover in ROMO as depicted in Figure 

18 7-28. The most notable of these is Quaking Aspen, or Populus tremuloides. This is significant 

19 in that many of the visitors to ROMO visit specifically to see this tree in its fall foliage. In some 

20 areas of the park, cover of this species can reach 80 percent. The species is found, along with the 

21 other sensitive tree species silver wormwood and Scouler’s willow, in all vegetative layers in the 
 7-57 

1 park. Sensitive species cover in just the tree canopy, subcanopy, and tall shrub layers is over 40 

percent in 328 km2 2 , or 30 percent, of the park. 

3 We were able to quantify the extent of the hiking trails present in areas where sensitive 

4 species are at risk for foliar injury. Of the approximately 562 km of trails in ROMO, including 

5 approximately 87 km of the Continental Divide National Scenic Trail, over 242 km, or about 43 

6 percent of trail area, are in areas where species sensitive to foliar injury in the canopy, subcanopy 

7 or tall shrub category occur in greater than 20 percent coverage. Figure 7-29 maps the hiking 

8 trails in ROMO, including the relevant portion of the Continental Divide National Scenic Trail 

9 overlaid with the species cover index. The accompanying pie charts in Figure 7-30 show the 

10 number of trail km in each cover category. 

11 Again, although we are not able to quantify the impact of this scenic damage on hiker 

12 satisfaction, given 1.5 million hikers in ROMO and their $70 million WTP for the hiking 

13 experience, even a small improvement in the scenic value could be significant. While we did not 

14 map the scenic overlooks in ROMO, given the 2.5 million visitors who come to the park to 

15 sightsee and the $72 million they are willing to pay for this activity, it is reasonable to conclude 

16 that any improvement in the scenic quality of the vistas at the overlooks would be of significant 

17 value. 

Under recent conditions, all 1,067 km2 18 of the park have W126 index values over 15 ppm19 hrs. Meeting the existing standard would bring about 59 percent of the Park into the 7-15 ppmhrs range, with the remaining 440 km2 20 under 7 ppm-hrs. Assessing an alternative standard of 15 

21 ppm-hrs would bring the entire park under 7 ppm-hrs. See Table 7-18 for a summary of full 

22 results. 

23 
 7-58 

1 Table 7-18 Geographic Area of ROMO after Just Meeting Existing and Alternative 

Standard Levels (km2 2 ) 

Under 5.94 

ppm-hrs 

Between 

5.95-7 

ppm-hrs 

Between 7-11 

ppm-hrs 

Between 11-15 

ppm-hrs 

Over 15 

ppm-hrs 

Recent conditions 

(2006-2008) 0 0 0 0 1,067 

Just meeting 75 ppb 37 403 627 0 0 

15 ppm-hrs 986 81 0 0 0 

11 ppm-hrs 1,067 0 0 0 0 

7 ppm-hrs 1,067 0 0 0 0 

3 
 7-59 

1 2 Figure 7-28 Sensitive Species Cover in ROMO
 7-60 

1 2 Figure 7-29 ROMO Sensitive Species Trail Cover
 7-61 

1 

2 

3 Figure 7-30 ROMO Trail Cover by Sensitive Species Type 

4 

5 

6 

4.0

231.5

291.0

24.3

11.7 19.1 Trees Canopy

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

4.0

262.7

168.4

106.4

39.9

Tree Subcanopy

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

4.02

471.1

106.44

Emergent Trees

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

4.0

234.5

301.1

0.0

2.5 39.4 Tall Shrubs

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

4.0

249.6

327.9

Short Shrubs

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

4.02

188.2

384.09

4.13 1.13

Dwarf Shrubs

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

4.0

273.2

304.3

Herbaceous

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%
 7-62 

1 7.4.3 Sequoia and Kings Canyon 

2 National Parks 

3 Sequoia and Kings Canyon National Parks (SEKI) 

4 are located in the southern Sierra Nevada Mountains east of 

5 the San Joaquin Valley in California. The two parks 

6 welcomed 1.6 million visitors in 2010 (NPS, 2010) to 

7 experience the beauty and diversity of some of California’s 

8 iconic ecosystems. 

9 The NPS 2002 Comprehensive Survey of the 

10 American Public, Pacific West Region Technical Report 

11 includes responses from recent visitors to western parks 

12 about the activities they pursued during their visit (NPS, 

13 2002c). By using the 2010 annual visitation rate from the 

14 NPS survey and the regional results from the Kaval and 

15 Loomis (2003) report on recreational use values compiled 

16 for the NPS, we estimated visitors’ WTP for various 

17 activities; the results are presented in Table 7-19. 

18 

19 Table 7-19 Value of Most Frequent Visitor Activities 

20 at Sequoia and Kings Canyon National 

21 Parks 

Activity Percent 

Participation

Number of 

Participants 

(thousands) 

Mean 

WTP 

(in 

2010$) 

Total Value of 

Participation 

(millions of 

2010$) 

Sightseeing 81 1,300 $24.21 $31 

Day 

Hiking 58 928 $27.77 $26 

Camping 33 528 $124.65 $66 

Picnicking 45 720 $76.72 $55 

Total $178 

22 

23 In addition to the activities listed in Table 7-19, 14 

24 percent of, or 224,000 park visitors availed themselves of 

Kings Canyon 

Courtesy: NPS, 

http://www.nps.gov/seki/photosmultim

edia/index.htm

The Sequoia and Kings 

Canyon National Parks share a 

boundary and natural 

resources. The natural 

resource features include the 

giant sequoia trees (and other 

species, including ponderosa 

and Jeffrey pine). The varied 

ecosystems from the top of 

Mount Whitney to the marble 

caverns provide habitat for a 

rich diversity of species. The 

park has recent O3 levels 

ranging between W126 levels 

of 34 – 53 ppm-hrs with a 

mean level of 43ppm-hrs. 
 7-63 

1 educational services offered at the park by participating in a ranger-led nature tour, which 

2 suggests that visitors wish to understand the ecosystems preserved in the park. 

3 Each of the activities discussed above is among the activities shown in the national-scale 

4 analysis to be strongly affected by visitor perceptions of scenic beauty. As in the national 

5 analysis, it is not possible to assess the extent of loss of services resulting from impairment of 

6 scenic beauty due to O3 damage; however, these losses are captured in the estimated values for 

7 spending, economic impact, and WTP for the parks. If O3 impacts were lower these estimated 

8 values would likely be higher. 

9 The report Economic Benefits to Local Communities from National Park Visitation and 

10 Payroll (NPS, 2011) provides estimates of visitor spending and economic impacts for each park 

11 in the system. Visitor spending and its economic impact to the surrounding area are provided in 

12 Table 7-20 for SEKI. In addition, Table 7-21 includes data on the median value that visitors 

13 spend on good, gas, lodging, and other items. 

14 Table 7-20 Visitor Spending and Local Area Economic Impact of SEKI 

Public Use Data Visitor Spending 2010a Impacts on Non-Local Visitor Spending

2010 

Recreation 

Visits 

2010 

Overnight 

Stays 

All Visitors Non-Local 

Visitors Jobs Labor Incomea 

Economic 

Impacta 

1,320,156 438,677 $97,012 $89,408 1,283 $37,299 $60,504 

a 15 ($000s) 

16 Source: NPS (2011) 

17 

18 Table 7-21 Median Travel Cost for SEKI Visitors 

Expense Median Expenditures (in 2010$) 

Gas and Transportation $75 

Lodging $150 

Food and Drinks $98 

Clothes, Gifts, and Souvenirs $63 

Total per Visitor Party $386 

19 Source: NPS (2002c) 

20 

21 
 7-64 

1 There are 12 identified sensitive species in SEKI. The percent coverage of these species 

2 is depicted in Figure 7-31. Areas of the parks with sensitive species cover of over 20 percent in 

the canopy comprise 646 km2 3 , or about 20 percent of the total area of SEKI. This area 

4 encompasses about 285 km of the 1,287 km (22 percent) of hiking trails available to 

5 approximately 928,000 hikers in the parks. Figure 7-32 depicts the sensitive species cover across 

6 the trail system, including the portion of the John Muir Trail that crosses the Parks’ 19 km, 

7 which has sensitive species coverage over 20 percent. Figure 7-33 shows the sensitive species 

8 by type. 

9 Again, although we are not able to quantify the impact of this scenic damage on hiker 

10 satisfaction for hikers in SEKI and their $26 million WTP for the experience, even a small 

11 improvement in the scenic value could be significant. 

12 As in the previous case studies, moving from recent conditions to meeting the existing O3

13 standard results in a large change in the area of the parks with exposures above 15 ppm-hrs. For 

14 SEKI, this means the parks move from all areas experiencing exposures above 15 ppm-hrs to all 

15 areas in the SEKI having exposures below 7 ppm-hrs. At lower alternative standards, SEKI 

16 moves to exposures below 3 ppm-hrs. See Table 7-22 for additional details. 

17 Table 7-22 Geographic Area of SEKI after Just Meeting Existing and Alternative 

Standard Levels (km2 18 ) 

Under 5.94 

ppm-hrs 

Between 5.95-7 

ppm-hrs 

Between 7-11 

ppm-hrs 

Between 11-15 

ppm-hrs 

Over 15 

ppm-hrs 

Recent conditions 

(2006-2008) 0 0 0 0 3,466 

Just meeting 75 ppb 3,466 0 0 0 0 

15 ppm-hrs 3,466 0 0 0 0 

11 ppm-hrs 3,466 0 0 0 0 

7 ppm-hrs 3,466 0 0 0 0 

19 
 7-65 

1 2 Figure 7-31 Sensitive Species Cover in SEKI 
 7-66 

1 2 Figure 7-32 Sensitive Species Trail Cover in SEKI
 7-67 

1 

2 

3 Figure 7-33 SEKI’s Sensitive Species Cover by Type 

4 7.5 QUALITATIVE ASSESSMENT OF UNCERTAINTY 

5 As noted in Chapter 3, we have based the design of the uncertainty analysis for this 

6 assessment on the framework outlined in the WHO guidance (WHO, 2008). For this qualitative 

7 uncertainty analysis, we have described each key source of uncertainty and qualitatively assessed 

8 its potential impact (including both the magnitude and direction of the impact) on risk results, as 

9 specified in the WHO guidance. In general, this assessment includes qualitative discussions of 

10 the potential impact of uncertainty on the results (WHO Tier1) and quantitative sensitivity 

11 analyses where we have sufficient data (WHO Tier 2). 

12 Table 7-23 includes the key sources of uncertainty identified for the O3 REA. For each 

13 source of uncertainty, we have (a) provided a description, (b) estimated the direction of influence 

14 (over, under, both, or unknown) and magnitude (low, medium, high) of the potential impact of 

15 each source of uncertainty on the risk estimates, (c) assessed the degree of uncertainty (low, 

16 medium, or high) associated with the knowledge-base (i.e., assessed how well we understand 

17 each source of uncertainty), and (d) provided comments further clarifying the qualitative 

18 assessment presented. The categories used in describing the potential magnitude of impact for 

19 specific sources of uncertainty on risk estimates (i.e., low, medium, or high) reflect our 

20 consensus on the degree to which a particular source could produce a sufficient impact on risk 

21 estimates to influence the interpretation of those estimates in the context of the secondary O3

22 NAAQS review. Where appropriate, we have included references to specific sources of 

23 information considered in arriving at a ranking and classification for a particular source of 

24 uncertainty. 

138.0

463.7 289.3

177.7

107.6

25.1 6.3

Tree Canopy

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

138.0

507.4

547.2

11.5

1.9

1.9

Tall Shrub

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%

138.0

383.0 681.8

5.0

Herbaceous

No Data

0%

<20%

20% to 40%

40% to 60%

60 % to 80%

>80%
 7-68 

1 Table 7-23 Summary of Qualitative Uncertainty Analysis in Visible Foliar Injury Assessments. 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

A. National W126 surfaces 

The foliar injury analyses in this chapter use the interpolated W126 surfaces for individual years (2006-2010), as well as the surfaces for recent conditions and adjusted to just meet the existing standard and alternative W126 standards. 

Both Low- Medium Low-medium KB and INF: See Chapter 4 for 

B. Surveys of recreational activities 

Survey estimates of participation rates, visitor spending/economic impacts, and willingness-to-pay are inherently uncertain. These surveys potential double-count impacts based on the allocation of expenditures across activities but also potentially exclude other activities with economic value. 

Both Medium Medium 

KB: Each survey (NSRE, FHWdifferent survey methods, so it across the surveys. In general, standard approaches, which miINF: Since the surveys are in aof outdoor recreationists and bvarious recreation types even schanges in recreation satisfactirecreation sites could potentialvalue of outdoor recreation. 

C. Ozone sensitive species 

Only species identified as O3-

sensitive by NPS are included in the analyses. 

Under Medium Medium 

KB: Relatively few vegetation O3-sensitive foliar injury in the 

will likely identify additional sINF: The identification of addilikely increase the extent of follocations and the percentage of location. 
 7-69 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

D. Spatial assignment of foliar injury biosite data to 12x12 km grids 

Because of privacy laws that require the exact location information of sampling sites to not be made public, the data were assigned to the CMAQ grid by the USFS. Data in California, Oregon, and Washington were assigned to the CMAQ grid based on publically available geographic coordinates; thus, these data have a higher level of uncertainty. 

Both Low Medium-Low 

KB: The FHM biosites are smaCMAQ grids. The publically aand longitude fuzzed by up to 7California, Washington and Orwere assigned to the wrong CMstates, the CMAQ grid was assdata. INF: Having precise geographiuncertainty, but the direction is likely be assigned to an adjaceninterpolation of the surfaces, dcells are relatively small, so the likely small. 

E. Availability of biosite sampling data 

Because sampling was discontinued in some states prior to this analysis, we did not include data for many western states (Montana, Idaho, Wyoming, Nevada, Utah, Colorado, Arizona, New Mexico, Oklahoma, and portions of Texas). 

Unknown Medium Low 

KB: Due to the discontinued sain these areas. It appears unlikethose regions at this time. INF: It is unclear how the addistates would affect the risk estisampling data in the southwest west and west north central regbenchmarks that may not be apsouthwest in particular has genvalues than other regions, so daimportant. In addition, the sout

F. Soil moisture threshold for foliar injury 

Low soil moisture reduces the potential for foliar injury, but injury could still occur because plants must open their stomata even during periods of drought. 

Over High Medium 

KB: We are unaware of a clear which visible foliar injury woufoliar injury analysis did not prmoisture threshold for injury. INF: If there is a threshold for foliar injury at lower levels of 
 7-70 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

G. Spatial resolution of soil moisture data 

Some vegetation such as along riverbanks may experience sufficient soil moisture during periods of drought to exhibit foliar injury. In addition, we did not have soil moisture data for Alaska, Hawaii, Puerto Rico, or Guam. 

Under Medium Medium 

KB: Soil moisture has substantsource for soil moisture are NOwhich can be hundreds of milewithin-division variability in souncertainty to this assessment, However, we are currently unaof this uncertainty. INF: Soil moisture can vary, evareas. It is most likely that soil areas considered to be in drougthese areas exhibited foliar injuunderestimated, which underesmoisture’s effect on foliar inju

H Time period for soil moisture data 

Short-term estimates of soil moisture are highly variable over time, even from month to month within a single year. Using averages contributes to a potential temporal mismatch between soil moisture and injury. 

Unknown Low- Medium Low 

KB: The average of monthly vsingle very wet or very dry moeven a single precipitation episin a sensitivity analysis, parks different timeframes for soil-mINF: Without much more preciassess the effect of the soil mooverall effect of averaging apphigh and very low moisture conthese results in opposite directi

I. Drought categories 

The soil moisture categories used to derive the foliar injury benchmarks (i.e., wet, normal, and dry) are uncertain. 

Unknown Unknown Low 

KB: NOAA’s categorization fohas been described as “rather aINF: Using a different categoribenchmark criteria for O3 expo

injury, but it is not clear whethunderestimate or overestimate 
 7-71 

Source Description 

Potential influence of 

uncertainty on risk 

estimates Knowledge- Base Comments (KB: knowled

uncertainty on 

Direction Magnitude 

J. Spatial resolution for combining soil moisture, biosite, and ozone exposure data 

For the national-scale foliar injury assessment, we combined data from different spatial resolutions. 

Unknown Medium Low 

KB: In general, the biosite data (usually ~ .02 km2 than the ozo

moisture data (hundreds of milINF: We used data at the finest minimize this uncertainty. 

K. Maps of vegetation and recreational areas within parks 

Maps of vegetation and recreational areas that overlap with areas with higher W126 index values are uncertain. 

Unknown Low High 

KB and INF: VegBank is the vEcological Society of AmericaClassification, and it consists ovegetation types, and (3) all plahttp://vegbank.org/vegbank/ge

the data quality of the vegetatioacross the park using plant comunquantified variation in the deresolution of the vegetation maozone exposure maps (12km2). 
 7-72 

1 7.6 DISCUSSION 

2 National-Scale Analysis of Foliar Injury: 

3  Using the data on biosites and the Palmer Z drought index, across all of the biosites 

4 (5,284 over five years from 2006-2010) over 81 percent of observations showed no 

5 foliar injury. Using the full dataset including all observations with or without injury, 

6 the analysis showed no clear relationship between O3 and the biosite index and no 

7 clear relationship between O3 and the Palmer Z drought index. This largely reflects 

8 the fact that O3 is not a good predictor of the presence or absence of foliar injury, but 

9 not necessarily that there is no relationship between the degree of injury and O3 in 

10 plants that do show injury. 

11  To better understand the relationship between O3 and those biosites that did show 

12 foliar injury, we conducted a cumulative analysis. When analyzed by individual year 

13 and looking at the presence/absence of foliar injury, the proportion of sites exhibiting 

14 foliar injury rises rapidly (over 20 percent in 2010) at increasing W126 index values 

15 up to 10 ppm-hrs. Similarly, when looking at an elevated biosite index of ≥ 5, the 

16 proportion of sites exhibiting foliar injury rises rapidly (over 6 percent in 2010) at 

17 increasing W126 index values below approximately 10 ppm-hrs. 

18  When categorized by moisture category, the results show a more distinct pattern. 

19 Looking at both the presence/absence of foliar injury and an elevated biosite index of 

20 ≥ 5, there is a rapid increase in the proportion of sites exhibiting foliar injury at O3

21 below a W126 index value of 10 ppm-hrs. Sites classified as wet have much higher 

22 overall proportions at both any injury and elevated injury and a much more rapid 

23 increase in proportion of sites with foliar injury present. At sites considered dry, the 

24 overall proportions are much lower for presence/absence and an elevated biosite 

25 index of ≥ 5, potentially indicating that drought may provide protection from foliar 

26 injury as discussed in the ISA. 

27  This analysis suggests that reductions in W126 index values at or above the W126 

28 benchmark of 10.46 ppm-hrs are unlikely to substantially reduce the prevalence of 
 7-73 

1 foliar injury. Similarly, this analysis suggests that reductions in W126 index values 

2 below the base scenario benchmark are likely to relatively sharply reduce the 

3 prevalence of foliar injury. 

4 Screening-level Assessment of Visible Foliar Injury in National Parks: 

5  Based on NPS lists, 95 percent of the parks contain at least one O3-sensitive species. 

6  During 2006 to 2010, 58 percent of parks exceeded the benchmark W126 

7 corresponding to the base scenario (W126>10.46 ppm-hrs, 17.7 percent of biosites, 

8 without consideration of soil moisture, any injury) for at least three years. This 

9 analysis suggest that in order to substantially reduce the risk of foliar injury in these 

10 parks, the W126 index values would need to be reduced to be below 10.46 ppm-hrs. 

11  During 2006 to 2010, 98%, 80%, 68% and 2% of parks would exceed the benchmark 

12 criteria corresponding to the 5%, 10%, 15%, and 20% prevalence scenarios for at 

13 least 3 years. 

14  For the elevated injury scenario, 34 percent of parks would exceed the benchmark 

15 criteria (five percent of biosites, multiple moisture categories, elevated foliar injury) 

16 for at least three years. 

17  During 2006-2010, 42 percent of parks did not exceed 15 ppm-hrs. 

18  None of the 214 parks would exceed the benchmark criteria for the base scenario 

19 (W126>10.46 ppm-hrs) after adjustments to meet the existing standard at 75 ppb. 

20 Only 8 parks exceed 7 ppm-hrs after adjustments to meet the existing standard at 75 

21 ppb. 

22 National Park Case Study Areas: 

23  GRSM is prized, in part, for its rich species diversity. The large mix of species 

24 includes 37 O3-sensitive species and many areas contain several sensitive species. 

25 With 3.8 million hikers using the trails every year and those hikers willing to pay over 

26 $266 million for that activity, even a small benefit of reducing O3 damage in the park 

27 could result in a significant value. 
 7-74 

1  W126 index values in GRSM have been among the highest in the eastern U.S. – at 

2 times twice as high as neighboring cities such as Atlanta. Under recent conditions, 44 

3 percent of the Park has W126 index values over 15 ppm-hrs. After just meeting the 

4 existing standard, W126 index values are reduced such that no area is over 7 

5 ppm-hrs. 

6  Unlike GRSM, sensitive species cover in ROMO is driven by a few O3-sensitive 

7 species (7 species) and most notably by Quaking Aspen. This is significant in that 

8 many of the visitors to ROMO visit specifically to see this tree in its fall foliage. 

9 Given 1.5 million hikers in ROMO and their $70 million WTP for the hiking 

10 experience, even a small improvement in the scenic value could be significant. 

 Under recent O3 conditions, all 1,067 km2 11 of ROMO have W126 index values over 15 

12 ppm-hrs. Meeting the existing standard would bring about 59 percent of the Park into 

13 the 7-15 ppm-hrs range, with the remaining 41 percent under 7 ppm-hrs. Assessing 

14 an alternative standard of 15 ppm-hrs would bring the entire park under 7 ppm15 hrs. 

16  SEKI is home to 12 identified sensitive species. Again, although we are not able to 

17 quantify the impact of this scenic damage on hiker satisfaction for hikers in SEKI and 

18 their $26 million WTP for the experience, even a small improvement in the scenic 

19 value could be significant. 

20  As in the previous national park case studies, moving from recent conditions to 

21 meeting the existing O3 standard of 75 ppb results in a large change in the area of 

22 SEKI with exposures above 15 ppm-hrs. For SEKI this means the parks move 

23 from all areas experiencing exposures above 15 ppm-hrs to the SEKI having 

24 exposures below 7 ppm-hrs. 
8-1 

1 8 SYNTHESIS OF RESULTS 

2 8.1 Introduction 

3 The goals for this welfare risk and exposure assessment include characterizing ambient 

4 ozone (O3) exposure and its relationship to ecological effects and estimating the resulting 

5 impacts to several ecosystem services. In particular, we characterize ambient O3 exposures on 

6 two important ecological effects – biomass loss and foliar injury – and estimate impacts to the 

7 following ecosystem services: supporting, regulating, provisioning, and cultural services. In the 

8 assessment, we conduct national- and regional-scale analyses to (1) characterize ambient O3

9 exposure (Chapter 4); (2) quantify the effects of insect damage related to foliar injury (cultural 

10 services) (Chapter 5); (3) consider the overall risk to a subset of ecosystem services by 

11 combining the relative biomass loss (RBL) rates for multiple tree species into one metric and 

12 evaluating weighted RBL rates (Chapter 6); (4) estimate the market effects of biomass loss on 

13 timber production and agricultural harvesting (provisioning services) and quantify the associated 

14 economic effects (Chapter 6); (5) estimate the effect of biomass loss on carbon sequestration 

15 (regulating service) (Chapter 6); (6) estimate the effect of foliar injury and its impact on national 

recreation (cultural services) (Chapter 7); (7) derive potential W1261 16 benchmarks associated with 

17 different combinations of the prevalence of biosites showing injury, the degree of foliar injury, 

18 and different soil moisture considerations; and (8) apply these benchmark criteria to a screening19 level assessment of foliar injury in 214 national parks (cultural services) (Chapter 7). In 

20 addition, we conduct case study-scale analyses to (1) characterize the effect of foliar injury on 

21 forest susceptibility and fire regulation in California (regulating services) (Chapter 5); (2) 

22 quantify the effects of biomass loss on carbon sequestration and pollution removal (regulating 

23 services) in five urban areas (Chapter 6); (3) quantify the effects of relative biomass loss in Class 

24 I areas (Chapter 7); and (4) assess the impacts of foliar injury on recreation in three national 

25 parks (Chapter 7). In addition, in Chapters 5, 6, and 7 we also qualitatively assess additional 

26 ecosystem services, including regulating services such as hydrologic cycle and pollination; 

 

1 The W126 metric is a seasonal sum of hourly O3 concentrations, designed to measure the cumulative effects of O3

exposure on vulnerable plant and tree species. The W126 metric uses a sigmoidal weighting function to place less 

emphasis on exposure to low concentrations and more emphasis on exposure to high concentrations. 
8-2 

1 provisioning services such as commercial non-timber forest products; and cultural services with 

2 aesthetic and non-use values. 

To evaluate risk for the existing 8-hour daily maximum standard2 3 and alternative W126 

4 standards in this welfare risk and exposure assessment, we (1) quantified ecological effects based 

5 on relationships between ecological effect and the W126 metric, (2) quantified the impact of 

6 these ecological effects on ecosystem services, and (3) qualitatively assessed potential impacts to 

7 several additional ecosystem services. The results from these assessments will help inform 

8 consideration of the adequacy of the existing O3 standards and potential risk reductions 

9 associated with several alternative levels of the standard, using the W126 form. In addition, the 

10 assessment (1) includes information (e.g., foliar injury analyses) that could be relevant to a three11 year average of a W126 standard, (2) addresses how just meeting alternative W126 standard 

12 levels would affect exposures and welfare risks and associated ecosystem services, and (3) 

13 addresses uncertainties and limitations in the available data. 

14 To facilitate interpretation of these results, this chapter provides a synthesis of the various 

15 results, focusing on comparing and contrasting results to identify common patterns or important 

16 differences. These comparisons focus on patterns across different geographic areas of the U.S., 

17 across years of analysis, and across alternative W126 standard levels. We evaluate the degree to 

18 which the integrated results are representative of overall patterns of exposure and risk across 

19 different types of ecosystems. We also summarize overall confidence in the results, as well as 

20 relative confidence between the different analyses. The chapter concludes with an overall 

21 integrated characterization of risk in the context of key policy relevant questions. The remainder 

22 of this chapter summarizes the results (Section 8.2) and includes discussions on patterns of risk 

23 (Section 8.3), representativeness (Section 8.4), confidence in the results (Section 8.5), and 

24 integrated risk characterization (Section 8.6). 

25 8.2 Summary of Analyses and Key Results 

26 We conducted a variety of analyses to assess O3 welfare risk and exposure and to 

27 estimate the relative change in risk and exposure resulting from air quality adjustments to just 

28 meeting existing and alternative standards. These analyses included national- and case study-

 

2

 The existing secondary standard for O3 is identical to the existing primary health-based standard, which is set at 75 

ppb for the 4th highest 8-hour daily maximum averaged over three years. 
8-3 

1 scale analyses addressing air quality, biomass loss, foliar injury, insect damage, fire risk, and 

2 recreation. The remainder of this section briefly summarizes the national- and case study-scale 

3 analyses and key results. 

4 8.2.1 National-Scale Analyses 

5 8.2.1.1 Air Quality Analyses 

6 The analyses used ambient air quality data from 2006 through 2008, as well as data 

adjusted to meet the current and potential alternative secondary standard levels. 3 7 An HDDM 

8 adjustment methodology, similar to the one used in the Health Risk and Exposure Assessment 

9 (see Chapter 4, Section 4.3.4.1 for a discussion of the methodology), independently adjusted air 

10 quality for nine climate regions as defined by the National Oceanic and Atmospheric 

Administration (NOAA) and shown in Figure 8-1 below (reproduced from Chapter 4).4 11 We 

12 considered these regions an appropriate delineation for our analyses because geographic patterns 

13 of both O3 and plant species are often largely driven by climatic features such as temperature and 

14 precipitation patterns. The NOAA climate regions were used for all of the adjustments between 

15 observed air quality concentrations and air quality adjusted to just meet the existing and 

16 alternative W126 standards. 

17 In the air quality analyses in Chapter 4, we consider the changes across the distribution of 

18 W126 index values after adjusting air quality to just meet the existing standard and just meet 

19 alternative W126 standard levels, all 3-year averages. As indicated above, each climate region 

20 was adjusted independently such that the entire region was adjusted based on the magnitude of 

21 across-the-board reductions in U.S. anthropogenic NOx emissions required to bring the highest 

22 monitor down to the targeted level. For the biomass loss analyses, we generated a national-scale 

23 air quality surface that just meets the existing standard using the Voronoi Neighbor Averaging 

24 (VNA) interpolation technique to fill in values between monitor locations. VNA national 

 

3

 W126 calculations are slightly modified in the case of the model adjustment scenarios described in Chapter 4, 

Section 4.3.4. When calculating W126 for the model adjustment cases, we first found the three-year average of each 

three-month period, and then selected the three-month period with the highest three-year average using the same 

three-month period for each of the three years. In this way, the five scenarios are for recent air quality, air quality 

adjusted to just meet the current standard, and air quality further adjusted to just meet three different W126 index 

values: 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. 

4

 Many of the models and analytical tools used in the analyses include different definitions of geographic areas. To 

the extent possible, we will refer to geographic areas by the nine climate regions based on National Oceanic and 

Atmospheric Administration (NOAA) National Climate Data Center (NCDC) regions in this chapter and note where 

definitions differ. 
8-4 

1 surfaces were also created for monitors adjusted to meet the current standard and for monitors 

2 adjusted to meet alternative W126 standard levels of 15, 11, and 7 ppm-hrs. During the last O3

3 National Ambient Air Quality Standards review, the Clean Air Scientific Advisory Committee 

4 (CASAC) recommended and supported a range of alternative W126 standard levels from 15 to 7 

5 ppm-hrs. The adjusted surfaces, based on monitored, three-year average W126 index values 

6 from 2006 through 2008, are used as inputs to several assessments (described below), including 

7 the geographic analysis to assess the effects of insect damage related to foliar injury, the 

8 national- and case study-scale biomass loss assessments, and the national park case studies for 

9 foliar injury. For the national-scale and screening-level foliar injury analyses, we generated five 

10 national-scale air quality surfaces from the monitored annual W126 index values (unadjusted) for 

11 the individual years from 2006 through 2010, also using VNA. See Chapter 4, Section 4.3 for 

12 more detailed discussions of the air quality analyses. 

13 The largest reduction in W126 index values occurs when moving from recent ambient 

14 conditions to meeting the existing secondary standard of 75 ppb (8-hour daily maximum). After 

15 adjusting to just meet the current standard, only two of the nine U.S. regions have W126 index 

16 values remaining above 15 ppm-hrs (West -- 18.9 ppm-hrs and Southwest – 17.7 ppm-hrs). The 

17 Central region would meet an alternative W126 standard level of 15 ppm-hrs, but further air 

18 quality adjustment would be needed for the Central region to meet alternative standards of 11 

19 and 7 ppm-hrs. In addition, when adjusting to just meeting the existing standard, four regions 

20 (East North Central, Northeast, Northwest, and South) would meet 7 ppm-hrs, and two regions 

21 (Southeast and West North Central) have index values between 9 and 12 ppm-hrs (Southeast – 

22 11.9 ppm-hrs and West North Central – 9.3 ppm-hrs). 
8-5 

1 

2 Figure 8-1 Map of the 9 NOAA climate regions (Karl and Koss, 1984) used in the 

3 national-scale air quality adjustments (Chapter 4, Figure 4-6) 

4 

5 8.2.1.2 Forest Susceptibility to Insect Infestation 

6 In Chapter 5, we review information on O3 exposure and the increased susceptibility of 

7 forests to insect infestations. O3 exposure results in increased susceptibility to infestation by 

8 some chewing insects, including the southern pine beetle and western bark beetle. These 

9 infestations can cause economically significant damage to tree stands and the associated timber 

10 production. In the short term, the immediate increase in timber supply that results from the 

11 additional harvesting of damaged timber depresses prices for timber and benefits consumers. In 

12 the longer term, the decrease in timber available for harvest raises timber prices, harming 

13 consumers and potentially benefitting some producers. The United States Forest Service (USFS) 

14 reports timber producers have incurred losses of about $1.4 billion (2010$), and wood-using 
8-6 

1 firms have gained about $966 million, due to beetle outbreaks between 1977 to 2004 (Coulson 

2 and Klepzig, 2011). It is not possible to attribute a portion of these impacts resulting from the 

3 effect of O3 on trees’ susceptibility to insect attack; however, the losses are embedded in the 

4 estimates cited. 

5 In addition, in Chapter 5 we provide summaries of area at risk of high pine beetle loss 

6 (i.e., high loss due to pine beetle damage), as well as millions of square feet of basal tree area at 

7 risk of high pine beetle loss after just meeting the existing and alternative standards. For area at 

8 risk of high pine beetle loss, under recent ambient conditions approximately 57 percent of the at9 risk area is at or above a W126 index value of 15 ppm-hrs; approximately 16 percent of the at10 risk area is at a W126 index value between 15 and 11 ppm-hrs; approximately 23 percent of the 

11 at-risk area is at a W126 index value between 11 and 7 ppm-hrs; and approximately four percent 

12 of the at-risk area is at a W126 index value below 7 ppm-hrs. After just meeting the 

13 existing standard, approximately five percent of the at-risk area has W126 index value between 

14 11 and 7 ppm-hrs, and no at-risk area is above a W126 index value of 11 ppm-hrs. When 

15 adjusting to an alternative standard level of 15 ppm-hrs, no at-risk area is above a W126 index 

16 value of 7 ppm-hrs. In terms of millions of square feet of tree basal area at risk of high pine 

17 beetle loss, under recent ambient conditions, approximately 45 percent of the "at-risk square 

18 feet" is at or above a W126 index value of 15 ppm-hrs; approximately 13 percent of "at19 risk square feet" is between 15 and 11 ppm-hrs; approximately 34 percent is between 11 and 7 

20 ppm-hrs; and approximately eight percent is at a W126 index value below 7 ppm-hrs. After just 

21 meeting the existing standard, approximately ten percent of the "at-risk square feet" is at a W126 

22 index value between 11 and 7 ppm-hrs, and no square feet are above 11 ppm-hrs. 

23 8.2.1.3 Biomass Loss 

24 We reviewed several studies that modeled vegetation growth for several tree and crop 

25 species. For trees, we calculated seedling RBL associated with W126 index values and 

26 compared the seedling RBL values to the study results for adult trees. Overall, seedling biomass 

27 loss values are much more consistent with adult biomass loss at lower W126 index values. For 

28 example, for Tulip Poplar, at a W126 index value of 15 ppm-hrs, the adult biomass loss rate is 

29 estimated to be 10.5 percent, and the seedling biomass loss rate is estimated to be 7.7 percent; at 

30 a W126 index value of 59 ppm-hrs, the adult biomass loss rate is estimated to be 16.8 percent, 
8-7 

1 and the seedling biomass loss rate is estimated to be 74 percent. See Chapter 6, Section 6.2.1.1 

2 for additional information. 

3 For biomass loss, CASAC recommended that EPA should consider options for W126 

4 standard levels based on factors including a predicted one to two percent biomass loss for trees 

5 and a predicted five percent loss of crop yield. Small losses for trees on a yearly basis compound 

6 over time and can result in substantial biomass losses over the decades-long lifespan of a tree 

7 (Frey and Samet, 2012b). To assess overall ecosystem-level effects from biomass loss, we 

weighted the RBL values for multiple tree species using basal area5 8 and combined them into a 

9 weighted RBL value and considered the weighted value in relation to the proportion of basal area 

10 accounted for by the tree species. A weighted RBL value is a relatively straight-forward metric 

11 to attempt to understand the potential ecological effect on some ecosystem services. We 

12 separated results into categories of different percentages of total basal area (e.g., <10 percent, 10 

13 to 25 percent)and compared weighted RBL values against the one and two percent biomass loss 

14 for trees recommended by CASAC. In each category, the results indicate that of the area being 

15 assessed the portion exceeding benchmarks of one to two percent biomass loss decreases as 

16 W126 index values decrease. For example, after just meeting the existing standard, 20.8 percent 

17 and 12.4 percent of the total area being assessed exceeds benchmarks of one percent and two 

18 percent biomass loss in trees, respectively. After just meeting an alternative standard level of 7 

19 ppm-hrs, 11.5 percent and 7.7 percent of the total area being assessed still exceeds benchmarks 

20 of one and two percent biomass loss in trees, respectively. It is important to note that the 

21 proportional basal area values do not account for total cover, but rather the relative cover of the 

22 tree species present. See Chapter 6, Section 6.8 for additional information. We also analyzed 

23 federally designated Class I areas by calculating an average weighted RBL value for 119 of the 

24 156 Class I areas. The number of Class I areas that exceed one and two percent relative biomass 

25 loss decreases as the alternative W126 standard levels become more stringent. See Chapter 6, 

26 Section 6.8.1 for additional information. 

27 Using the concentration-response (C-R) functions for tree seedlings and crops, we 

28 determined the range of biomass loss associated with just meeting the existing 8-hour daily 

 

5

 Basal area is the term used in forest management that defines the area of a given section of land that is occupied by 

a cross-section of tree trunks and stems at their base. This typically includes a measurement taken at the diameter at 

breast height of a tree above the ground and includes the complete diameter of every tree, including the bark. 
8-8 

1 maximum standard and alternative W126 standard levels. To compare different levels of 

2 biomass loss to different W126 index values, we plotted the C-R functions as a function of the 

3 percent biomass loss against varying W126 index values. For a one percent biomass loss for 

4 trees, the estimated W126 index values were between 4 and 10 ppm-hrs; for a two percent 

5 biomass loss for trees the estimated W126 index values were between 7 and 14 ppm-hrs; and for 

6 a five percent biomass loss for crops the estimated W126 index values were between 12 and 17 

7 ppm-hrs. See Chapter 6, Section 6.2.1.2 for additional information. 

8 Using the Forest and Agricultural Optimization Model with Greenhouse Gases 

9 (FASOMGHG), we conducted national-scale analyses to quantify the effects of biomass loss on 

timber production and agricultural harvesting, as well as on carbon sequestration.6 10 We used the 

11 O3 C-R functions for tree seedlings and crops to calculate relative yield loss (RYL), which is 

12 equivalent to relative biomass loss. Because the forestry and agriculture sectors are related, and 

13 trade-offs occur between the sectors, we simultaneously calculated the resulting market-based 

14 welfare effects of O3 exposure in the forestry and agriculture sectors. 

15 In the analyses for commercial timber 

16 production, because most areas have W126 

17 index values lower than 15 ppm-hrs when 

18 simulating meeting the existing standard, 

19 relative yield losses (RYL) are below one 

20 percent, with the exception of the Southwest, 

21 Southeast, Central, and South regions (see text 

22 box below for clarification on region names). 

23 Relative yield losses remain above one percent 

24 for the parts of the Southeast, Central, and 

25 South regions at alternative W126 standard 

26 levels of 15 and 11 ppm-hrs, and for the 

27 Southeast and South regions at an alternative W126 standard level of 7 ppm-hrs. 

 

6

 FASOMGHG is a national-scale model that provides a complete representation of the U.S. forest and agricultural 

sectors’ impacts of meeting alternative standards. FASOMGHG simulates the allocation of land over time to 

competing activities, e.g., production of different crops or livestock, in both the forest and agricultural sectors. 

The states included in the NOAA NCDC regions 

and the states included in the FASOMGHG model 

regions differ slightly. Below we align the 

different region names. To be consistent across 

summary discussions, we use the NCDC region 

names. 

NCDC FASOMGHG

West primarily Pacific Southwest 

Southwest primarily Rocky Mountain 

Central primarily Cornbelt 

South primarily South West and South 

 Central 

Southeast primarily South Central and 

Southeast 

Northeast primarily Northeast 
8-9 

1 In the analyses for agricultural harvest, the largest yield changes occur when comparing 

2 recent ambient conditions to just meeting the existing standard. Under recent ambient 

3 conditions, the West, Southwest, and Northeast regions generally have the highest yield losses. 

At alternative W126 standard levels of 15, 11, and 7 ppm-hrs, for winter wheat7 4 relative yield 

5 losses are less than the 5 percent loss recommended by CASAS, as well as less than one percent. 

6 For soybeans, when the W126 scenarios are modeled, yield losses above both 5 and 1 percent 

7 remain at 15 ppm-hrs for the Southwest and Central regions. Yield losses are reduced to below 

8 one percent at alternative W126 standard levels of 11 and 7 ppm-hrs. 

9 In addition to estimating changes in forestry and agricultural yields, FASOMGHG 

10 estimates the changes in consumer and producer/farmer surplus associated with the change in 

yields.8 11 Changes in yield affect individual tree species and crops, but the overall effect on forest 

12 ecosystem productivity depends on the composition of forest stands and the relative sensitivity of 

13 trees within those stands. Overall effect on agricultural yields and producer and consumer 

14 surplus depends on the (1) ability of producers/farmers to substitute other crops that are less O3 

15 sensitive and (2) responsiveness, or elasticity, of demand and supply. Relative to just meeting 

16 the existing standard, W126 index values decrease in the Southwest, West, Central, Southeast, 

17 South, East North Central, and West North Central regions at alternative standard levels of 15, 

18 11, and 7 ppm-hrs. These decreases in W126 index values are estimated to result in changes in 

19 patterns for agricultural production and resulting consumer and producer surplus. For example, 

20 with reductions W126 index values, wheat crops would likely increase in one of its major 

21 production regions, the Southwest region. This expansion of wheat production may result in a 

22 decrease in wheat production in the East North Central region. The East North Central region 

23 would likely see production changes for other crops because the contraction in wheat production 

24 makes room for alternatives. Soybean production in the East North Central region would likely 

25 expand, and this expansion would induce regional shifts of soybean production at the national 

26 level, including decreases in soybean production in the West North Central and Central regions. 

27 Generally the crop producers’ surplus in the Central and Southwest regions would increase and 

28 in the South region would decrease. Crop producers’ surplus in the West North Central and East 

29 North Central regions would fluctuate over time. 

 

7

 Among the major crops, because winter wheat and soybeans are more sensitive to ambient O3 levels than other 

crops we include these crops for this discussion. 

8

 See Chapter 6, Section 6.3 for a brief discussion of economic welfare and consumer and producer surplus. 
8-10 

1 Economic welfare impacts resulting from just meeting the existing and alternative 

2 standards were largely similar between the forestry and agricultural sectors -- consumer surplus, 

3 or consumer gains, generally increased in both sectors because higher productivity under lower 

4 W126 index values increased total yields and reduced market prices. Because demand for most 

5 forestry and agricultural commodities is not highly responsive to changes in price, there were 

6 more cases where producer surplus, or producer gains, decline. In some cases, lower prices 

7 reduce producer gains more than can be offset by higher yields. For example, in 2040, the year 

8 with maximum changes in consumer and producer surplus, in the forestry sector at just meeting 

9 the existing standard, total producer surplus is estimated to be $133 billion and total consumer 

10 surplus is estimated to be $935 billion, or 7 times greater than producer surplus. For the forestry 

11 sector, when adjusting to meeting alternative W126 standard levels of 15, 11, and 7 ppm-hrs, 

12 consumer surplus increases $597 million, $712 million, and $779 million (i.e., 0.06, 0.08, and 

13 0.08 percent), respectively, while producer surplus decreases $839 million, $858 million, and 

$766 million, (i.e., about 0.6 percent), respectively. All estimates are in 2010$.9 14 

15 In the analysis for changes in carbon sequestration related to biomass loss, relative to just 

16 meeting the existing standard, the 15 ppm-hrs W126 alternative standard does not appreciably 

17 increase carbon sequestration. The majority of the enhanced carbon sequestration potential is in 

18 the forest biomass increases over time under alternative secondary W126 standard levels at 11 

19 and 7 ppm-hrs. In the forestry sector, relative to just meeting the existing standard (with 

20 sequestration of 89 billion metric tons of CO2 equivalents), at alternative W126 standard levels 

21 of 11 and 7 ppm-hrs carbon sequestration potential is projected to increase 593 million and 1.6 

22 billion metric tons of CO2 equivalents over 30 years (i.e., 0.66 and 1.79 percent) respectively. 

23 For the agricultural sector, relative to just meeting the existing standard (with sequestration of 8 

24 billion metric tons of CO2 equivalents), at alternative W126 standard levels of 11 and 7 ppm-hrs 

25 carbon sequestration potential is projected to increase 9 and 10 million metric tons of CO2

26 equivalents respectively over 30 years, or about 0.1 percent. 

 

9

 FASOMGHG is an international model and the increase in productivity caused by a reduction in O3 results in a net 

increase in the present value of total global economic surplus (consumer + producer surplus). The reported producer 

surplus here is for U.S. producers only and benefits and costs accruing overseas are not included. Also, for any 

given year, there may be a decline in global consumer and producer surplus due to the effects on the dynamics of 

planting and harvesting decisions in the forestry sector. 
8-11 

1 8.2.1.4 Visible Foliar Injury 

2 To assess the effects of visible foliar injury on recreation, we reviewed the National 

3 Survey on Recreation and the Environment (NSRE), as well as the 2006 National Survey of 

4 Fishing, Hunting, and Wildlife-Associated Recreation (FHWAR) and a 2006 analysis done for 

5 the Outdoor Industry Foundation (OIF). According to the NSRE, some of the most popular 

6 outdoor activities are walking, including day hiking and backpacking; camping; bird watching; 

7 wildlife watching; and nature viewing. Participant satisfaction with these activities can depend 

8 on the quality of the natural scenery, which can be adversely affected by O3-related visible foliar 

9 injury. According to the FHWAR and the OIF reports, the total expenditures across wildlife 

10 watching activities, trail-based activities, and camp-based activities are approximately $200 

11 billion dollars annually. While we cannot quantify the magnitude of the impacts of O3 damage 

12 to the scenic beauty and outdoor recreation, the existing losses associated with current O3-related 

13 foliar injury are reflected in reduced outdoor recreation expenditures. 

14 To assess foliar injury at a national scale and identify potential W126 benchmarks, we 

15 conducted several analyses using a national data set on foliar injury from the USFS’s Forest 

16 Health Monitoring Network. We conducted the analyses using presence/absence of foliar injury, 

as well as using a cutoff for elevated foliar injury.10 17 We also conducted analyses across years 

and different soil moisture categories in NOAA climate divisions.11 18 Across years, when 

19 assessing the presence or absence of foliar injury, at an alternative W126 standard level of 15 

20 ppm-hrs between 12 and over 18 percent of sites indicated the presence of foliar injury; at an 

21 alternative W126 standard level of 11 ppm-hrs between 12 and over 20 percent of sites indicated 

22 the presence of foliar injury; and at an alternative W126 standard level of 7 ppm-hrs between 4 

and over 20 percent of sites indicated the presence of foliar injury.12 23 Across years, when 

24 assessing elevated foliar injury, at an alternative W126 standard level of 15 ppm-hrs between 3 

25 and over 6 percent of sites show elevated foliar injury; at an alternative W126 standard level of 

26 11 ppm-hrs between 2 and over 6 percent of sites show elevated foliar injury; and at an 

27 alternative W126 standard level of 7 ppm-hrs between approximately 2 and over 6 percent of 

 

10 The elevated foliar injury corresponds to a biosite index of 5, which is consistent with a USFS cut-off for foliar 

injury. 

11 See Chapter 7, Section 7.2 for a more detailed discussion of the data on biosites and foliar injury from the USFS 

and the Palmer Z drought index data from NOAA. 

12 See Chapter 7, Section 7.2.3 for additional discussion and Figure 7-8 for additional information. The proportion 

of sites with foliar injury present varies by year, creating these ranges for percent of sites with foliar injury present. 
8-12 

1 sites show elevated foliar injury. Generally, the results of all of these foliar injury analyses 

2 demonstrate a similar pattern – the proportion of biosites showing foliar injury increases steeply 

3 with W126 index values up to approximately10 ppm-hrs and is relatively constant at W126 index 

4 levels above10 ppm-hrs. This analysis suggests that reductions in W126 index values at or 

5 above this benchmark (W126 > 10.46 ppm-hrs) are unlikely to substantially reduce the 

6 prevalence of foliar injury. Similarly, this analysis suggests that reductions in W126 index 

7 values below the base scenario benchmark are likely to relatively sharply reduce the prevalence 

8 of foliar injury. Figure 8-2, which originally appears as Figure 7-10 in Chapter 7, shows the 

9 pattern seen in the foliar injury analyses stratified by soil moisture category. In addition, we see 

10 similar patterns when the foliar injury is stratified by year and geographic region. See Section 

11 7.2.3 for a more detailed discussion of the analyses. 

12 

13 Figure 8-2 Cumulative Proportion of Sites with Visible Foliar Injury Present, by 

14 Moisture Category 

15 

16 We used the results of the national analysis to derive benchmarks for visible foliar injury 

17 that we apply in a screening-level assessment and case studies of national parks. 

18 We define six scenarios for evaluating potential W126 benchmarks, representing the full range of 

19 the percentages of biosites showing visible foliar injury (i.e., any injury and elevated injury), 
8-13 

1 including five scenarios considering soil moisture. We defined the W126 benchmark for the 

2 “base scenario” as representing the point above which there was a consistent percentage (17.7 

3 percent) of biosites showing foliar injury, regardless of soil moisture. This analysis suggests that 

4 reductions in W126 index values at or above this benchmark (W126 > 10.46 ppm-hrs) are 

5 unlikely to substantially reduce the prevalence of foliar injury. Similarly, this analysis suggests 

6 that reductions in W126 index values below the base scenario benchmark are likely to relatively 

7 sharply reduce the prevalence of foliar injury. We also looked at alternative scenarios based on 3 

8 different categories of soil moisture and the W126 index values associated with four different 

9 prevalences (e.g., 5%, 10%, 15% and 20% of biosites) of any foliar injury, and a final one based 

10 on a 5% prevalence of foliar injury index greater than or equal to 5. In total, the WREA 

11 evaluated 13 different W126 benchmarks associated with the 6 foliar injury risk scenarios. The 

12 W126 benchmarks across the six scenarios range from 3.05 ppm-hrs (five percent of biosites, 

13 normal moisture, any injury) up to 46.87 ppm-hrs (five percent of biosites, dry, elevated injury). 

14 See Table 7-5 for the specific benchmark criteria corresponding to each of the six scenarios. 

15 The general approach in the screening-level assessment of national parks is derived from 

16 Kohut (2007), but we apply more recent O3 exposure and soil moisture data for 214 national 

17 parks in the contiguous U.S. combined with the benchmarks derived from the national analysis. 

18 Generally, benchmark scenarios corresponding to higher percentages of biosites showing foliar 

19 injury show fewer parks that exceed the benchmark criteria for those scenarios. During 2006 to 

20 2010, 58 percent of parks exceeded the benchmark W126 corresponding to the base scenario 

21 (W126>10.46 ppm-hrs, 17.7 percent of biosites, without consideration of soil moisture, any 

22 injury) for at least three years. This analyses suggest that in order to substantially reduce the risk 

23 of foliar injury in these parks, the W126 index values would need to be reduced to be below 

24 10.46 ppm-hrs. In addition, 98%, 80%, 68% and 2% of parks would exceed the benchmark 

25 criteria corresponding to the 5%, 10%, 15%, and 20% prevalence scenarios for at least 3 years 

26 within the 2006-2010 period. For the elevated injury scenario, 34 percent of parks would exceed 

27 the benchmark criteria (five percent of biosites, multiple moisture categories, elevated foliar 

28 injury) for at least three years. Because the screening-level assessment relies on annual estimates 

29 of W126 index values and soil moisture, we cannot fully evaluate just meeting the existing and 

30 alternative standards because they are based on the 3-year average air quality surfaces. 

31 However, we can observe that after adjusting the W126 surfaces to just meet the existing 
8-14 

1 standard, all of the 214 parks are below 10.46 ppm-hrs, which corresponds to the benchmark 

2 criteria for the base scenario. 

3 8.2.2 Case Study-Scale Analyses 

4 8.2.2.1 Fire Regulation 

5 As indicated in Chapter 5, fire regime regulation is also negatively affected by O3

6 exposure. For example, Grulke et al. (2009) reported various lines of evidence indicating that O3

7 exposure may contribute to southern California forest susceptibility to wildfires by increasing 

8 leaf turnover rates and litter, increasing fuel loads on the forest floor. According to the National 

9 Interagency Fire Center, in the U.S. in 2010 over 3 million acres burned in wildland fires and an 

10 additional 2 million acres were burned in prescribed fires. From 2004 to 2008, Southern 

11 California alone experienced, on average, over 4,000 fires per year burning, on average, over 

12 400,000 acres per year. The California Department of Forestry and Fire Protection (CAL FIRE) 

13 estimated that losses to homes due to wildfire were over $250 million in 2007 (CAL FIRE, 

14 2008). In 2008, CAL FIRE’s costs for fire suppression activities were nearly $300 million (CAL 

15 FIRE, 2008). 

16 We developed maps that overlay the mixed conifer forest area of California with areas 

17 of moderate or high fire risk defined by CAL FIRE and with surfaces of recent conditions and 

18 surfaces adjusted to just meet existing and alternative standards. The highest fire risk and 

19 highest W126 index values overlap with each other, as well as with significant portions of mixed 

20 conifer forest. Under recent conditions, over 97 percent of mixed conifer forest area has O3

21 W126 index values over 7 ppm-hrs with a moderate to severe fire risk, and 74 percent has O3

22 W126 index values over 15 ppm-hrs with a moderate to severe fire risk. When adjusted to just 

23 meeting the existing standard, almost all of the mixed conifer forest area with a moderate to high 

24 fire risk shows a reduction in O3 to below a W126 index value of 7 ppm-hrs. At the alternative 

25 W126 standard level of 15 ppm-hrs, all but 0.18 percent of the area is less than 7 ppm-hrs, and at 

26 alternative standard levels of 11 and 7 ppm-hrs all of the moderate to high fire threat area is less 

27 than 7 ppm-hrs. 

28 8.2.2.2 Biomass Loss 

29 Using the iTree model to estimate tree growth and ecosystem services provided by trees 

30 over a 25-year period, we conducted case-study scale analyses to quantify the effects of biomass 
8-15 

loss on carbon sequestration and pollution removal in five urban areas.13 1 See Appendix 6D for 

2 details on the iTree model and the methodology used for the case study analyses. 

3 We estimated the effects of O3-related biomass loss on carbon sequestration and ran six 

4 scenarios, including just meeting the existing standard and just meeting alternative W126 

5 standards of 15, 11, and 7 ppm-hrs. While both urban and non-urban forests have the potential to 

6 remove pollutants from the atmosphere, using iTree we also estimated the effects of O3-related 

7 biomass loss on the potential to remove carbon monoxide, nitrogen dioxide, O3, and sulfur 

8 dioxide pollution in the five urban areas (1) at recent ambient O3 conditions and (2) after 

9 adjusting air quality to just meeting the existing standards and alternative W126 standard levels 

10 of 15, 11, and 7 ppm-hrs. As a supplement to the iTree analysis, we also performed a simple 

11 analysis of the O3 pollution removal potential to show how this process might affect ambient air 

12 quality values. This analysis made some general assumptions to estimate order of magnitude 

13 effects of O3 removal by trees in the five urban areas. The results indicate that the effects on O3

14 concentrations are small; when meeting the current standard, deposition to tree surfaces results in 

15 ambient O3 concentration reductions ranging from 0.08 parts per billion by volume (ppbv) in 

16 Tennessee to 0.52 ppbv in Chicago compared to O3 concentrations that would occur without any 

deposition to trees in these cities. 14 17 Relative changes in ambient O3 concentrations due to 

18 changes in deposition to tree surfaces were much smaller. 

19 Relative to just meeting the existing standard, three of the urban areas (Atlanta, Chicago, 

20 and the urban areas of Tennessee) show gains in carbon sequestration at alternative W126 

21 standard levels of 11 and 7 ppm-hrs. For example, relative to just meeting the existing standard, 

22 Chicago gains about 6,400 tons of carbon sequestration per year at 7 ppm-hrs, and the urban 

23 areas of Tennessee gain about 8,800 tons of carbon sequestration per year at 11 ppm-hrs and 

24 20,000 tons of carbon sequestration per year at 7 ppm-hrs. Syracuse and Baltimore do not 

25 realize gains in carbon sequestration because recent air quality almost meets the alternative 

26 standards levels in those areas. Similar to changes in carbon sequestration, Syracuse and 

27 Baltimore have no change in pollution removal when just meeting the existing standard and the 

28 W126 alternative standards. Atlanta, Chicago, and the urban areas of Tennessee show gains in 

 

13 The iTree model is a peer-reviewed suite of software tools provided by USFS. 14 The ratio of O3 volume to urban area air volume multiplied by 10^9 gives the concentration in ppbv. 
8-16 

1 potential pollution removal at alternative W126 standard levels of 11 and 7 ppm-hrs compared to 

2 meeting the existing standard. For example, relative to just meeting the existing standard, 

3 Chicago gains about 2,300 metric tons of pollution removal annually at 11 ppm-hrs and 6,500 

4 metric tons of pollution removal annually at 7 ppm-hrs, and the urban areas of Tennessee gain 

5 about 5,300 metric tons of pollution removal annually at 11 ppm-hrs and 11,700 metric tons of 

6 pollution removal annually at 7 ppm-hrs. 

7 8.2.2.3 Foliar Injury – Three National Parks 

8 In addition to the national-scale analysis, we also assess foliar injury at a case-study scale 

9 because national parks are designated as special areas in need of protection. Specifically, we 

10 assess O3-exposure risk at three national parks – Great Smoky Mountains National Park 

11 (GRSM), Rocky Mountain National Park (ROMO), and Sequoia/Kings Canyon National Parks 

12 (SEKI). For each park, we assess the potential impact of O3-related foliar injury on recreation 

13 (cultural services) by considering information on visitation patterns, recreational activities and 

14 visitor expenditures. We include percent cover of species sensitive to foliar injury and focus on 

15 the overlap between recreation areas within the park and elevated W126 index values. 

16 In GRSM, there are 37 sensitive species across vegetative strata, and 2011 visitor 

17 spending exceeded $800 million. W126 index values in GRSM have been among the highest in 

18 the eastern U.S. -- under recent ambient conditions, 44 percent of GRSM has W126 index values 

19 over 15 ppm-hrs. After adjustments to just meet the existing standard of 75 ppb, no area in 

20 GRSM exceeds an alternative W126 standard level of 7 ppm-hrs. ROMO has seven sensitive 

21 species, including Quaking Aspen. In 2011 visitor spending at ROMO was over $170 million. 

22 Under recent ambient conditions, all of ROMO has W126 index values over 15 ppm-hrs. When 

23 adjusted to just meet the existing standard, 41 percent of the park would meet an alternative 

24 W126 standard level of 7 ppm-hrs and 59 percent of the park would meet an alternative W126 

25 standard level between 7 and 11 ppm-hrs. In SEKI there are 12 sensitive species across 

26 vegetative strata, and 2011 visitor spending was over $97 million. When adjusted to just meet 

27 the existing standard, no area in SEKI has W126 index values above 7 ppm-hrs. 

28 8.3 Patterns of Risk 

29 Considering the national- and case study-scale analyses and appropriate benchmarks for 

30 biomass loss and foliar injury, we reviewed whether there were patterns or trends in the risk and 
8-17 

1 risk reductions – between geographic areas and across years and alternative standards. For 

2 biomass loss, CASAC recommended that EPA should consider options for W126 standard levels 

3 based on factors including a predicted one to two percent biomass loss for trees and a predicted 

4 five percent loss of crop yield. Small losses for trees on a yearly basis compound over time and 

5 can result in substantial biomass losses over the decades-long lifespan of a tree (Frey and Samet, 

6 2012b). For trees, annual W126 index values for a one percent biomass loss range from 

7 approximately 4 to 10 ppm-hrs and for a two percent biomass loss range from approximately 7 to 

8 14 ppm-hrs. For crops, annual W126 index values for a five percent biomass loss range from 

9 approximately 12 to 17 ppm-hrs. Based on this assessment, the pattern is that crops exceed 

10 CASAC’s benchmarks at higher W126 index values than trees, and suggests that meeting 

11 alternative standards that are protective of trees will also protect crops. Unlike biomass, CASAC 

12 did not recommend a benchmark for foliar injury. As a result, we developed a set of W126 

13 benchmark criteria (“scenarios”) associated with different combinations of the prevalence of 

14 biosites showing injury, the degree of foliar injury, and different soil moisture considerations. 

15 8.3.1 Risk Patterns Across or Between Geographic Areas 

16 The geographic or spatial patterns of changes in W126 index values and changes in 

17 ecosystem services and related economic welfare are slightly different. Figure 8-3 and Figure 

18 8-4, which originally appear as Figures 4-9 and 4-11 in Chapter 4, show the W126 index values 

19 after being adjusted to just meeting alternative standards of 15 and 11 ppm-hrs. After adjusting 

20 to just meeting an alternative standard of 15 

21 ppm-hrs, the West, Southwest, and Central 

22 regions show the highest W126 index values 

23 between 11 and 15 ppm-hrs; after adjusting to 

24 just meeting an alternative standard level of 11 

25 ppm-hrs, all areas show W126 index values 

26 below 11 ppm-hrs. The analyses of biomass loss 

27 and affected timber and agricultural yields show 

28 that most of the remaining risk after adjusting to 

29 just meeting an alternative standard level of 15 

30 ppm-hrs is in the Southwest, South, Southeast, 

General references to the eastern and western 

U.S. and the states included in the NOAA 

NCDC regions differ. For ease of discussion, 

below we align the general U.S. region and 

NCDC region references. 

General U.S. NCDC 

Western U.S. Northwest 

 West 

 Southwest 

 West North Central 

Eastern U.S. East North Central 

 Central 

 South 

 Southeast 

 Northeast 
8-18 

1 and Central regions; after adjusting to just meeting an alternative standard level of 11 ppm-hrs, 

2 most of the remaining risk is in the South, Southeast, and Central regions. 

3 There is substantial heterogeneity in plant responses to O3, both within species, between 

4 species, and across regions of the U.S. The O3-sensitive tree species are different in the eastern 

5 and western U.S. -- the eastern U.S. has far more total species (see text box for clarification on 

6 region names). O3 exposure and risk are somewhat easier to assess in the eastern U.S. because of 

7 the availability of more data and the greater number of species to analyze. In addition, there are 

8 more O3 monitors in the eastern U.S. but fewer national parks. In the national-scale analyses for 

9 commercial timber production, because most areas have W126 index values below 15 ppm-hrs 

10 after simulating just meeting the existing standard, relative yield losses (RYL) are below one 

11 percent, with the exception of the Southwest, Southeast, Central, and South regions. In part 

12 because the South and Southeast regions have more forest land, RYL remain above one percent 

13 for parts of those regions even after just meeting an alternative W126 standard level of 7 ppm14 hrs. 
8-19 

1 

2 Figure 8-3 National Surface of 2006-2008 Average W126 Index values Adjusted to 

3 Just Meet the Alternative Standard of 15 ppm-hrs 
8-20 

1 

2 Figure 8-4 National Surface of 2006-2008 Average W126 Index values Adjusted to Just 

3 Meet the Alternative Standard of 11 ppm-hrs 

4 

5 The largest improvements in agricultural harvesting resulting from reduced O3 exposure 

6 are likely to occur in the West, Southwest, South, Southeast, and Central regions because those 

7 regions (1) have the most sensitive crop species present, (2) have significant agricultural 

8 production, and (3) will experience the most significant air quality improvement between recent 

9 conditions and just meeting the existing secondary standard. For soybeans, when the W126 

10 scenarios are modeled, yield losses above both five and one percent remain at 15 ppm-hrs for the 

11 Southwest and Central regions. For all regions, yield losses are reduced to below five and one 

12 percent at alternative W126 standard levels of 11 and 7 ppm-hrs. 
8-21 

1 In the analyses using presence/absence of foliar injury and cutoff for elevated foliar 

2 injury, we analyzed the data sets across NOAA climate divisions. We did not have foliar injury 

3 data for the Southwest, and we had limited data for the West and West North Central. 

4 Similar to the analyses across years and moisture categories, across NOAA climate divisions the 

5 proportion of biosites showing foliar injury increases steeply with W126 index values up to 

6 approximately 10 ppm-hrs and is relatively constant at W126 index levels above 10 ppm-hrs. 

7 8.3.2 Risk Patterns Across Years 

8 Using the FASOMGHG model to calculate forestry and agricultural yield changes, we 

9 estimated changes in consumer and producer surplus from 2010 through 2040 for alternative 

10 standard levels of 15, 11, and 7 ppm-hrs. Over the period in the forestry sector, changes in 

11 consumer surplus are always positive and range from <0.01 percent in 2010 for alternative 

12 standard levels of 15 and 11 ppm-hrs up to 0.08 percent in 2040 for alternative standard levels of 

13 11 and 7 ppm-hrs (relative to consumer surplus at just meeting the existing standard of $721 

14 billion in 2010 and $934 billion in 2040 (2010$)). Consumer surplus does not consistently 

increase between 5-year periods from 2010 to 2040.15 15 For example, while always a positive 

16 value, consumer surplus decreases between 2025 and 2030, increases slightly between 2030 and 

17 2035, and increases significantly between 2035 and 2040. Changes in producer surplus are 

18 generally negative and range from <-0.1 percent in 2010 for an alternative standard level of 7 

19 ppm-hrs to -0.6 percent in 2040 for alternative standard levels of 15 and 11 ppm-hrs (relative to 

20 producer surplus at just meeting the existing standard of between $93 billion in 2010 and $133 

21 billion in 2040). 

22 In the agricultural sector over the period, changes in consumer surplus are generally 

23 positive and <0.01 percent (relative to consumer surplus at just meeting the existing standard of 

24 between $1.9 trillion in 2010 and $2.1 trillion in 2040 (2010$)). Changes in producer surplus 

25 vary and range from -0.2 percent in 2015 for alternative standard levels of 11 and 7 ppm-hrs to 

26 0.25 and 0.35 percent in 2040 for alternative standard levels of 11 and 7 ppm-hrs (relative to 

27 producer surplus at just meeting the existing standard of between $725 billion in 2010 and $863 

28 billion in 2040). At just meeting the existing standard, total consumer and producer surplus 

 

15 FASOMGHG results include multi-period, multi-commodity results over 60 to 100 years in 5-year time intervals 

when running the combined forest-agriculture version of the model. 
8-22 

1 values are much higher in the agricultural sector than in the forestry sector. As a result, absolute 

2 changes in consumer and producer surplus values at alternative standard levels are much larger 

3 in the agricultural sector. In the agricultural sector, over time and by alternative standard, 

4 changes in consumer surplus are largely positive, with approximately 15 percent of the estimates 

5 being minor negative changes. Over time and by alternative standard, changes in producer 

6 surplus are mixed, with approximately 30 percent of the estimates being significant negative 

7 changes. See Section 6.5 and Appendix 6B for additional discussion of these analyses. 

8 In the national-scale assessment to identify foliar injury benchmarks, we conducted 

9 analyses using a national data set on foliar injury. Across years in the data set, we analyzed 

10 presence/absence of foliar injury, as well as a cutoff for risk of elevated foliar injury. Generally, 

11 2010 showed a more dramatic rise in the proportion of sites showing the presence of foliar injury 

12 or elevated foliar injury at W126 index values below 10 ppm-hrs, and 2006 through 2009 

13 showed a more subtle pattern. Figure 8-5 below, which originally appears as Figure 7-8 in 

14 Chapter 7, shows the pattern for presence/absence of foliar injury across years. 

15 

16 Figure 8-5 Cumulative Proportion of Sites with Foliar Injury Present, by Year 

17 

0 10 20 30 40

0.00 0.05 0.10 0.15 0.20 0.25

Biosite Index > 0

W126 (ppm-hrs)

Proportion of sites

All

2006

2007

2008

2009

2010
8-23 

1 In addition to the above foliar injury analyses, the screening-level assessment for 214 

2 national parks assessed foliar injury in individual years. This assessment, which was based on 

3 W126 index values and soil moisture that varied temporally, concluded that O3-related foliar 

4 injury risk in parks was generally lower in the 2008-2010 time period than in the 2006-2008 time 

5 period. For the base scenario, 2009 represented the year with the lowest percentage of parks 

6 exceeding the benchmark criteria (i.e., only 12 percent of parks) and 2006 represented the year 

7 with the highest percentage of parks exceeding the benchmark criteria (i.e., 80 percent of parks). 

8 Further, this assessment determined that the 3-month timeframe corresponding to the highest 

9 W126 estimates in monitored parks occurred between March and September, which roughly 

10 corresponds to the vegetation growing season. 

11 8.3.3 Risk Patterns Across Alternative W126 Standard Levels 

12 For the ecological effect of biomass loss, O3-related exposure and risk decrease at lower 

13 alternative W126 standard levels. For the ecological effect of foliar injury, changes in O3-related 

14 exposure and risk at lower alternative W126 standard levels are more challenging to directly 

15 assess because we do not have concentration-response (C-R) functions to assess changes in foliar 

16 injury across different W126 index values. However, we observe that after just meeting the 

17 existing standard, all of the 214 parks are below 10.46 ppm-hrs, which corresponds to the W126 

18 benchmark for the base scenario. See Table 8-1 and Table 8-2 for a summary of risk across 

19 alternative W126 standard levels for these two ecological effects. 
8-24 

1 Table 8-1 Summary of O3-Exposure Risk Across Alternative W126 Standards Relative to Just Meeting Existing 2 National-Scale Analyses 3 

 15 ppm-hrs 11 ppm-hrs Ecological Effect 

Biomass Loss Average Weighted RBL Loss 

for Tree Seedlings (Section 6.8) 

Percent of Covered Area exceeding 1 and 2 percent weighted RBL declines by about 0.3 percent

Percent of Covered Area exceeding 1 and 2 percent weighted RBL declines by between 0.5 and 1.3 percent

P1 

dp

Ecosystem Services 

Provisioning Timber Production 

(Section 6.3) 

For hardwoods and upland hardwoods, RYL between 1 and 3.25 percent for Southeast, Central, and South regions. All other regions RYL below 1 percent. 

For hardwoods and upland hardwoods, RYL between 1 and 3 percent for 

Southeast, Central, and South regions. All other regions RYL below 1 percent. 

Farb

 Consumer and Producer 

Surplus (2010$) - Forestry (Section 6.3) 

Consumer surplus – in 2010 is $7 million, or 0.01% and in 2040 is $597 million, or 0.06% Producer surplus – in 2010 is -$11 million, or -0.01% and in 2040 is -$839 million, or -0.6% 

Consumer surplus – in 2010 is $44 million, or 0.01% and in 2040 is $712 million, or 0.08% Producer surplus – in 2010 is -$41 million, or -0.04% and in 2040 is -$858 million, or -0.6% 

Cm$P-2

 Agricultural Harvest 

(Section 6.5) 

For some sensitive crops (soybeans), RYL remain > 1 percent in the Southwest and Central regions. All other regions RYL below 1 percent. 

For most sensitive crops, RYL < 1 percent. 

Fp

 Consumer and Producer 

Surplus (2010$) - Agriculture (Section 6.5) 

Consumer surplus – in 2010 is $15 million, or <0.01% and in 2040 is $3 million, or <0.01% Producer surplus – in 2010 is $612 million, or 0.08%; in 2015 is -$1,255 million, or -0.15%; and in 2040 is $697 million, or 0.08% 

Consumer surplus – in 2010 is $19 million, or <0.01% and in 2040 is $13 million, or <0.01% Producer surplus – in 2010 is $1,474 million, or 0.2%; in 2015 is -$2,197 million, or -0.26%; and in 2040 is $2,189 million, or 0.25% 

C-2Pm-2

Regulating Carbon Sequestration 

(Section 6.6.1) 

Little change compared to just meeting existing standard 

In forestry sector, storage potential is projected to increase 593 million metric tons of CO2 equivalents (CO2e), or 0.66 

percent, over 30 years. In agricultural sector, storage potential is projected to increase 9 million metric tons of CO2e, or about 0.1 percent, over 

30 years. 

Iim

pIpm

p

4 
8-25 

1 

2 Table 8-1 Summary of O3-Exposure Risk Across Alternative W126 Standards Relative to Just Meeting Existing S3 National-Scale Analyses, continued 4 

 15 ppm-hrs 11 ppm-hrs Ecological Effect 

Foliar Injury National Foliar Injury 

Screening16

(Section 7.2) 

Depending on year, between 12 and 18 percent of sites show presence/absence of foliar injury and between 3 and 6 percent of sites show elevated foliar injury Depending on moisture category, between 7 and >20 percent of sites show presence/absence of foliar injury and between 3 and >6 percent of sites show elevated foliar injury 

Depending on year, between 12 and >20 percent of sites show presence/absence of foliar injury and between 2 and 6 percent of sites show elevated foliar injury Depending on moisture category, between 7 and >20 percent of sites show presence/absence of foliar injury and between 3 and 5 percent of sites show elevated foliar injury 

D> pasDbsipf

5 

16 This analysis is not relative to just meeting the existing standard, but is a national-scale analysis that summarizes foliar injury at different levels. 
8-26 

1 Table 8-2 Summary of O3-Exposure Risk Across Alternative Standards Relative to Just Meeting Existing Stand

2 Case Study-Scale Analyses

 15 ppm-hrs 11 ppm-hrs 

Ecosystem Services 

Regulating (Biomass 

Loss) 

Carbon Sequestration (Section 6.6.2) 

W126 levels and carbon storage potential do not change relative to just meeting existing standard 

Atlanta, Chicago, and the urban areas of Tennessee show gains in carbon sequestration. For example, urban areas of Tennessee gain about 8,800 tons of sequestration annually. Syracuse and Baltimore do not realize gains because recent W126 index values almost meet the alternative standards levels. 

Atlanof Tesequeof TesequeSyracgains valuestand

 Pollution Removal 

(Section 6.7) 

W126 index values and pollution potential do not change relative to just meeting existing standard 

Atlanta, Chicago, and the urban areas of Tennessee show gains in pollution removal. For example, urban areas of Tennessee gain about 5,300 tons of pollution removal annually. Syracuse and Baltimore do not realize gains because recent W126 index values almost meet the alternative standards levels. 

Atlanof TeremovTennepollutSyracgains valuestand

Ecosystem Services 

Cultural (Foliar Injury) Recreation 

(Section 7.4) 

Rocky Mountain National Park – No 

area of park exceeds a W126 standard level of 15 ppm-hrs when the W126 index values are adjusted to just meeting the existing standard Great Smoky Mountains National 

Park and Sequoia/Kings National Park -- No area of parks exceeds a 

W126 standard level of 15 ppm-hrs when the W126 index values are adjusted to just meeting the existing standard In screening-level assessment, of 214 parks, 3 parks remain above 7 ppmhrs after W126 index values are adjusted to 15 ppm-hrs 

Rocky Mountain National Park – 59 

percent of the park would meet alternative W126 standard level of between 11 and 7 ppm-hrs when the W126 index values are adjusted to just meeting the existing standard Great Smoky Mountains National Park and 

Sequoia/Kings National Park -- No area of 

parks exceeds a W126 standard level of 11ppm-hrs when the W126 index values are adjusted to just meeting the existing standard In screening-level assessment, of 214 parks, 2 parks remain above 7 ppm-hrs after W126 index values are adjusted to 11 ppm-hrs 

Rock

percealternbetweW126 meetiGreat 

and S

area standW126 meetiIn scrparkshrs afadjus
8-27 

1 8.4 Representativeness 

2 In conducting the national and case-study scale analyses of ecological effects and 

3 resulting impacts on ecosystem services, we worked to reflect appropriate representation of 

4 vegetation species, geographic regions, and timeframes. The following briefly discusses the 

5 representativeness across species, geography, and time in our analyses. 

6 8.4.1 Species Representativeness 

7 To estimate the effect of O3 exposure on biomass loss, we used data on 12 tree species 

8 and 10 crop species. The 12 species represent a range of sensitivities normally distributed 

9 around intermediately sensitive species. Several species are very non-sensitive, two species are 

10 relatively more sensitive, and the remainder is between non-sensitive and moderately sensitive 

11 species. The data on the 12 species facilitate representation of species for which we do not have 

12 data. For tree species, we used data for areas with at least one of the tree species present, 

13 resulting in approximately 46.6 percent of the contiguous U.S. constituting the area being 

14 assessed. For 74 percent of the area being assessed, the species we know about made up 50 

15 percent or less of total basal area cover. For another 12 percent of the area being assessed, the 

16 species we know about made up between 50 and 75 percent of total basal area cover. For the 

17 remaining 14 percent of the area being assessed, the species we know about made up over 75 

18 percent of total basal area cover. Although we know that there are additional O3-sensitive 

19 species, we do not have C-R functions for those species. We also used these C-R functions for 

20 the tree and crop species in FASOMGHG, and to better employ the dynamic tradeoffs within the 

21 model, FASOMGHG assigns proxy functions for O3 exposure C-R functions for additional 

22 species. For the iTree case-study scale analysis on carbon sequestration and pollution removal, 

23 we chose the five urban areas based on data availability and presence of species with a W126 C24 R function. No urban areas with available vegetation data had more than three sensitive species 

25 present. Unlike FASOMGHG, the iTree model does not provide tradeoffs between species, so 

26 the species that do not have a C-R function were not assigned values, and thus were not part of 

27 the carbon sequestration and pollution removal estimates. Therefore, the majority of trees in 

28 those urban areas were not accounted for in the O3 damages. For example, there are three tree 

29 species present in these areas that we know are sensitive but for which no C-R function is 
8-28 

1 available, excluding 80 - 90 percent of the total trees present in these two study areas. The 

2 species include northern red oak in Baltimore and southern red oak and tulip tree in Atlanta. 

3 We also qualitatively discuss many additional ecological effects and ecosystem services 

4 for which we do not have data to assess quantitatively; those ecological effects and related 

5 ecosystem services include supporting services such as net primary productivity; regulating 

6 services such as hydrologic cycle and pollination; provisioning services such as commercial non7 timber forest products; and cultural services such as recreation, aesthetic and non-use values. In 

8 addition, other ecological effects that are causally or likely causally associated with O3 exposure 

9 are not directly addressed in this risk and exposure assessment. These ecological effects include 

terrestrial productivity, water cycle, biogeochemical cycle, and community composition.17 10 

11 8.4.2 Geographic Representativeness 

12 Nine of the 12 tree species used in the biomass analyses were in the eastern U.S. and 

13 three were in the western U.S., with a few species such as Aspen and Cottonwood in both the 

14 eastern and western U.S. For the biomass loss analyses, by region we include the total basal area 

15 covered by the 12 tree species assessed. In parts of the eastern U.S. – the Central, East North 

16 Central, and Northeast regions -- from less than 1 percent to 4 percent of basal area assessed had 

17 no data on percent cover of the 12 tree species. In contrast, in parts of the western U.S. – 

18 Southwest, West, West North Central regions -- from 47 percent to 74 percent of basal area 

19 assessed had no data on percent cover of the 12 tree species. 

20 We applied C-R functions for 12 tree species and 10 crop species in FASOMGHG to 

21 estimate nationwide effects on timber production, agricultural harvest, and carbon sequestration. 

22 While we used available C-R functions for tree and crop species, as well as the available models, 

23 we had differential and inconsistent species coverage across the U.S., e.g., data were available 

24 for more species in the eastern U.S. than in the western U.S., limiting our analyses. In addition, 

25 to assess overall ecosystem-level effects from biomass loss, we combined the RBL values for 

26 multiple tree species into a weighted RBL value and considered the weighted value in relation to 

27 proportion of basal area covered, both nationally and in Class I areas. 

 

17 For additional details on these other ecological effects, see Table 2-4 of the Integrated Science Assessment for 

Ozone and Related Photochemical Oxidants (U.S. EPA, 2013). 
8-29 

1 Also, in estimating the effect of O3 exposure on biomass loss and resulting changes in 

2 carbon sequestration and pollution removal capacity, for case-study scale analysis we used the 

3 iTree model and data from five urban areas. The urban areas represent diverse geography in the 

4 Northeast, Southeast, and Central regions, but we did not assess an urban area in the western part 

5 of the U.S. Based on the monitored data from 2006 to 2008, Atlanta, Baltimore, and the urban 

6 areas in Tennessee have W126 index values over 20 ppm-hrs, with Atlanta having the highest 

7 index value. After adjusting to just meeting the existing standard, all of the urban areas show 

8 W126 index values between 5 and 7 ppm-hrs. Because there are more monitors in urban areas in 

9 the eastern U.S., we focused on urban areas in the eastern U.S. for the case-study analyses. 

10 For the national-scale foliar injury analysis, we were limited by the available foliar injury 

11 data. Biosite sampling was discontinued in some states prior to our analysis. Although we had 

12 data for most regions of the contiguous U.S., we did not have data for the Southwest and limited 

13 data for the West and West North Central regions. For example, over 2006 to 2010 there were 

14 over 1,000 biosite index values each for the Northeast and Central regions and no biosite index 

15 values for the Southwest. In assessing foliar injury at parks, we conducted national scale 

16 analyses, as well as a case-study scale analysis of national parks. In assessing foliar injury at the 

17 case-study scale, the three national parks represent diverse geographic areas -- in the 

18 Southeast/Central (GRSM), the Southwest (ROMO), and the West (SEKI). In the screening19 level assessment of foliar injury, we included 214 national parks in the contiguous U.S. 

20 8.4.3 Temporal Representativeness 

21 The biomass loss analysis relied upon the national-scale air quality surfaces described in 

22 section 8.2.1.1 and in Chapter 4. A separate set of surfaces were created for the national-scale 

23 analyses of presence/absence of and elevated foliar injury, for which national-scale surfaces were 

24 generated by interpolating the unadjusted monitored annual W126 index values for the individual 

25 years 2006 through 2010. Monitored O3 index values in those years vary considerably, and those 

26 years represent a reasonable range of meteorological conditions that affect O3 formation. The 

27 period also includes years with varying categories of soil moisture, which impacts the sensitivity 

28 of plants to foliar injury. 

29 Because the forestry and agriculture sectors are interlinked and factors affecting one 

30 sector can lead to changes in the other, we considered overall effects on producers and 
8-30 

1 consumers associated with just meeting alternative W126 standard levels over time and across 

2 sectors. In estimating the effect of O3 exposure on biomass loss and ecosystem services, we used 

3 the C-R functions for 12 tree seedlings in FASOMGHG to calculate relative yield changes over 

4 the entire lifespan of the trees, including percentage changes in national timber product market 

5 prices through 2040. 

6 At the national scale, we also used FASOMGHG to calculate changes in carbon 

7 sequestration by forests and agriculture through 2040. At the case-study scale, we used iTree to 

8 estimate tree growth and calculate changes in carbon sequestration and pollution removal 

9 capacity in the five urban areas over a 25-year period. 

10 8.5 Overall Confidence in Welfare Exposure and Risk Results 

11 There are several important factors to consider when evaluating the overall confidence 

12 we can express about the estimates of exposures and risks associated with just meeting the 

13 existing and potential alternative W126 secondary standards. As with any complex analysis 

14 using estimated parameters and inputs from numerous data sources and models, there are many 

15 sources of uncertainty that may affect estimated results. These sources of uncertainty are 

16 discussed in each of the chapters related to air quality, biomass loss, visible foliar injury and 

17 ecosystem services. 

18 The overall effect of the combined set of uncertainties on confidence in the interpretation 

19 of the results of the analyses is difficult to quantify. Due to differences in available information, 

20 the degree to which each analysis was able to incorporate quantitative assessments of uncertainty 

21 differed. In general, we followed the WHO tiered approach to uncertainty characterization, 

22 which includes both quantitative and qualitative assessments. Chapters 4, 5, 6, and 7 include 

23 tables identifying and characterizing the potential impact of key uncertainties on risk estimates, 

24 including the degree to which we were able to quantitatively address those uncertainties. 

25 Below we discuss several key limitations and uncertainties, which may have a large 

26 impact on both overall confidence and confidence in individual analyses. 

27 8.5.1 Uncertainties in Air Quality Analyses 

28 The national W126 surface was created using the VNA technique to interpolate recent air 

29 quality measurements of O3. In general, spatial interpolation techniques perform better in areas 
8-31 

1 where the O3 monitoring network is denser. Therefore, we have lower confidence in the W126 

2 estimated in the rural areas in the West, Northwest, Southwest, and West North Central with few 

3 or no monitors. 

4 An additional uncertainty comes from the adjustment methodology, which used U.S.-

5 wide NOx emissions reductions to adjust air quality to just meet the existing and alternative 

6 standard levels. Consequently, meeting the standard levels at the highest monitor in each region 

7 (which generally occurs in or near a major urban area) leads to substantial reductions below the 

8 targeted level through the rest of the region. These across-the-board NOx cuts do not represent 

9 an actual control strategy, and it should be noted that resulting air quality could look different if 

10 we used different assumptions about emissions reductions strategies. However, the assumption 

11 of broad regional or national NOx reductions is not unreasonable given current EPA regulations 

12 such as (i) the Clean Air Interstate Rule (CAIR), which requires NOx emissions reductions 

13 across the Eastern U.S. to reduce regional ozone transport, and (ii) the multitude of onroad and 

14 offroad mobile source rules that will lead to reductions in NOx emissions from these sources 

15 across the country in future years. 

16 Because the W126 estimates generated in the air quality analyses are inputs to the 

17 vegetation risk analyses for biomass loss and foliar injury, any uncertainties in the air quality 

18 analyses are propagated into those analyses. 

19 8.5.2 Uncertainties in Biomass Loss Analyses 

20 Even though we are certain that there are additional species adversely affected by O3-

21 related biomass loss, we only have C-R functions available to quantify this loss for 12 tree 

22 species and 10 crop species. This absence of information only allows a partial characterization 

23 of the O3-related biomass loss impacts in trees and crops associated with recent O3 index values 

24 and with just meeting the existing and potential alternative secondary standards. In addition, 

25 there are uncertainties inherent in these C-R functions, including the extrapolation of relative 

26 biomass loss rates from tree seedlings to adult trees and information regarding within-species 

27 variability. The overall confidence in the C-R function varies by species based on the number of 

28 studies available for that species. Some species have low within-species variability (e.g., many 

29 agricultural crops) and high seedling/adult comparability (e.g., Aspen), while other species do 

30 not (e.g., Black Cherry). The uncertainties in the C-R functions for biomass loss and in the air 
8-32 

1 quality analyses are propagated into the analysis of the impact of biomass loss on ecosystem 

2 services, including provisioning and regulating services. 

3 In the national-scale analyses of timber production, agricultural harvesting, and carbon 

4 sequestration, we used the FASOMGHG model, which includes functions for carbon 

5 sequestration, assumptions regarding proxy species, and non-W126 C-R functions for three 

6 crops. However, FASOMGHG does not include agriculture and forestry on public lands, 

7 changes in exports due to O3 into international trade projections, or forest adaptation. Despite 

8 the inherent limitations and uncertainties, we believe that the FASOMGHG model reflects 

9 reasonable and appropriate assumptions for a national-scale assessment of changes in the 

10 agricultural and forestry sectors due to changes in vegetation biomass associated with O3

11 exposure. 

12 In the case study analyses of five urban areas, we used the iTree model, which includes 

13 an urban tree inventory for each area and species-specific pollution removal and carbon 

14 sequestration functions. However, iTree does not account for the potential additional VOC 

15 emissions from tree growth, which could contribute to O3 formation. Despite the inherent 

16 limitations and uncertainties, we believe that the iTree model reflects reasonable and 

17 appropriate assumptions for a case study assessment of pollution removal and carbon 

18 sequestration for changes in biomass associated with O3 exposure. 

19 8.5.3 Uncertainties in Visible Foliar Injury Analyses 

20 To develop benchmarks for evaluating visible foliar injury, we conducted a national-scale 

21 analysis using biosite sampling data combined with O3 exposure and soil moisture. Evaluating 

22 soil moisture is more subjective than evaluating O3 exposure because of its high spatial and 

23 temporal variability within the O3 season, and there is considerable subjectivity in the 

24 categorization of relative drought. On balance, we believe that the spatial and temporal 

25 resolution for the soil moisture data is likely to underestimate the potential of foliar injury that 

26 could occur in some areas. In addition, we are unaware of a clear threshold for drought below 

27 which visible foliar injury would not occur. In general, low soil moisture reduces the potential 

28 for foliar injury, but injury could still occur, and the degree of drought necessary to reduce 

29 potential injury is not clear. Due to the absence of biosite injury data in the Southwest region and 

30 limited biosite data in the West and West North Central regions, the benchmarks applied may not 
8-33 

1 be applicable to these regions. We applied the benchmarks from the national-scale analysis to a 

2 screening-level analysis of 214 national parks and case studies of three national parks. Therefore, 

3 uncertainties in the foliar injury benchmarks and in the air quality analyses are propagated into 

4 the national park analyses. Additional uncertainties in the national park analyses are primarily 

5 related to the mapping, including park boundaries, vegetation species cover, and park amenities, 

6 such as scenic overlooks and trails. In general, we have high confidence in the park mapping. 

7 8.6 Conclusions 

8 This welfare risk and exposure assessment provides information to further inform the 

following policy-relevant questions18 9 : (1) in considering alternative standards, to what extent do 

10 alternative standard levels, averaging times, and forms reduce estimated exposures and welfare 

11 risks attributable to O3; (2) what range of alternative standard levels should be considered based 

12 on the scientific information evaluated in the ISA, air quality analyses, and the welfare risk and 

13 exposure assessment; and (3) what are the important uncertainties and limitations in the evidence 

14 and assessments and how might those uncertainties and limitations be taken into consideration in 

15 identifying alternative secondary standards for consideration. To develop information to help 

16 inform these questions, we quantified ecological effects based on the relationship with the W126 

17 metric and assessed the associated impacts on ecosystem services. For some ecosystem services, 

18 such as commercial non-timber forest products, recreation, and aesthetic and non-use values, we 

19 qualitatively assessed potential impacts to services. We assessed impacts on ecosystem services 

20 at the national and case-study scales, as well as across species, U.S. geographic regions and 

21 future years. Throughout the assessment, we characterized the uncertainties inherent in the 

22 analyses. 

23 To assess the ecological effect of biomass loss, we used C-R functions for tree seedlings 

24 and crops to determine the range of biomass loss associated with just meeting alternative W126 

25 standard levels relative to just meeting the existing 75 ppb 8-hour standard. To compare 

26 different levels of biomass loss to different W126 index values, we plotted the C-R functions as a 

27 function of the percent biomass loss against varying W126 index values. For a one percent 

28 biomass loss for trees, the estimated W126 index values were between 4 and 10 ppm-hrs; for a 

29 two percent biomass loss for trees the estimated W126 index values were between 7 and 14 ppm-

 

18 The policy-relevant questions were identified in the Integrated Review Plan for the Ozone National Ambient Air 

Quality Standards (IRP, US EPA, 2011a). 
8-34 

1 hrs; and for a five percent biomass loss for crops the estimated W126 index values were between 

2 12 and 17 ppm-hrs. We also conducted an analysis to estimate the ecosystem-level impacts of 

3 biomass loss from 12 tree species using weighted average RBL functions. These results indicate 

4 that of the area being assessed, the portion exceeding benchmarks of one to two percent biomass 

5 loss decreases as W126 index values decrease. For example, after just meeting the existing 

6 standard, 20.8 percent and 12.4 percent of the total area being assessed exceeds benchmarks of 

7 one percent and two percent biomass loss in trees, respectively. After just meeting an alternative 

8 standard level of 7 ppm-hrs, 11.5 percent and 7.7 percent of the total area being assessed still 

9 exceeds benchmarks of one and two percent biomass loss in trees, respectively. 

10 We also used the FASOMGHG model to conduct national-scale analyses to quantify the 

11 effects of biomass loss on timber production and agricultural harvesting, as well as on carbon 

12 sequestration. Because the forestry and agriculture sectors are related, and trade-offs occur 

13 between the sectors, we also calculated the resulting market-based welfare effects of O3 exposure 

14 in the forestry and agriculture sectors. For commercial timber production, because most areas 

15 have W126 index values lower than 15 ppm-hrs when simulating meeting the existing standard, 

16 the RYLs are below one percent, with the exception of the Southwest, Southeast, Central, and 

17 South regions. For some sensitive crops, such as soybeans, RYLs above 1 percent remain at 15 

18 ppm-hrs for the Southwest and Central regions; RYLs are reduced to below one percent at 

19 alternative W126 standard levels of 11 and 7 ppm-hrs. Economic welfare impacts resulting from 

20 just meeting the existing and alternative standards were largely similar between the forestry and 

21 agricultural sectors -- consumer surplus, or consumer gains, generally increased in both sectors 

22 because higher productivity under lower W126 index values increased total production and 

23 reduced market prices. For producer surplus, there were many examples for which producer 

24 surplus declines. For example, in 2040, in the forestry sector when adjusting to meeting 

25 alternative W126 standards of 15, 11, and 7 ppm-hrs, consumer surplus increases $597 million, 

26 $712 million, and $779 million (i.e., 0.06, 0.08, and 0.08 percent), respectively, while producer 

27 surplus decreases $839 million, $858 million, and $766 million, (i.e., about 0.6 percent), 

28 respectively. 

29 To assess the ecological effect of foliar injury at a national scale, we conducted several 

30 analyses based on a national data set on foliar injury from the USFS’s Forest Health Monitoring 

31 Network. We conducted the analyses using presence/absence of foliar injury, as well as using a 
8-35 

1 cutoff for elevated injury; we conducted analyses across years in the data set, according to 

2 different moisture categories and across different geographic regions. Generally, the results of 

3 these foliar injury analyses demonstrate a similar pattern – the proportion of biosites showing 

4 foliar injury increases steeply with W126 index values up to approximately 10 ppm-hrs and is 

5 relatively constant at W126 index levels above 10 ppm-hrs. Using benchmarks derived from the 

6 national analysis, we conducted a screening-level assessment of foliar injury at national parks for 

7 214 national parks in the contiguous U.S. Generally, as the percentage of biosites showing foliar 

8 injury increases, the percentage of parks exceeding that benchmark decreases; similarly as the 

9 degree of foliar injury is elevated, the percentage of parks exceeding that benchmark decreases. 

10 During 2006 to 2010, 58 percent of parks exceeded the benchmark criteria corresponding to the 

11 base scenario (W126>10.46 ppm-hrs, 17.7 percent of biosites, without consideration of soil 

12 moisture, any injury) for at least three years, and 34 percent of parks would exceed the 

13 benchmark criteria for the elevated injury scenario (five percent of biosites, multiple moisture 

14 categories, elevated foliar injury) for at least three years. Because the screening-level assessment 

15 relies on annual estimates of W126 index values and soil moisture, we cannot fully evaluate just 

16 meeting the existing and alternative standards because they are based on the 3-year average air 

17 quality surfaces. However, we can observe that after adjusting the W126 surfaces to just meet 

18 the existing standard (3-year average), all of the 214 parks are below 10.46 ppm-hrs, which 

19 corresponds to the annual benchmark criteria for the base scenario. 

20 In conclusion, we estimated that some exposures and risks remain after just meeting the 

21 existing standard and that in many cases, just meeting alternative standard levels results in 

22 reductions in those remaining exposures and risks. Overall, the largest reduction in O3 exposure23 related welfare risk occurs when moving from recent ambient conditions to meeting the existing 

24 secondary standard of 75 ppb (equal to the existing primary standard). When using monitored 

25 W126 index values and adjusting for meeting the existing O3 standard of 75 ppb, only two of the 

26 nine U.S. regions have 3-year index values remaining above 15 ppm-hrs (West -- 18.9 ppm-hrs 

27 and Southwest – 17.7 ppm-hrs). Four regions (East North Central, Northeast, Northwest, and 

28 South) would meet 7 ppm-hrs, and two regions (Southeast and West North Central) have 3-year 

29 index values between 9 and 12 ppm-hrs (Southeast – 11.9 ppm-hrs and West North Central – 9.3 

30 ppm-hrs). When adjusting to just meeting the existing standard, the Central region would meet 

31 an alternative W126 of 15 ppm-hrs, but further air quality adjustment would be needed for the 
8-36 

1 Central region to meet alternative standards of 11 and 7 ppm-hrs – alternate standard levels that 

2 would protect against the recommended one to two percent biomass loss for trees and five 

3 percent for crops. At an alternative W126 standard level of 15 ppm-hrs, ambient conditions and 

4 related risk are not appreciably different than they are after just meeting the existing standard of 

5 75 ppb, and the risk decreases at alternative W126 standard levels of 11 ppm-hrs and 7 ppm-hrs, 

6 just not as much as the decrease in risk from recent conditions to the existing standard of 75 ppb. 

7 
 9-1 

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United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication No.