HUMAN HEALTH BENEFITS OF STRATOSPHERIC

OZONE PROTECTION

Peer Reviewed Report 

Prepared for: 

Global Programs Division

Office of Air and Radiation

U.S. Environmental Protection Agency

Washington, DC 20460 

Prepared by: 

ICF Consulting

1725 Eye Street NW, Suite 1000 

Washington, DC 20006 

April 24, 2006 
Preface and Peer Review Summary 

This report was prepared by the U.S. Environmental Protection Agency (EPA) with the support of its 

contractor, ICF Consulting, Inc. (ICF). This report describes the analytical and empirical methodologies 

used by the Atmospheric Health Effects Framework (AHEF), a model used to predict changes in human 

health effects that result from changes in the use and release of ozone-depleting substances (ODS). 

The authors of this report consulted with experts from government, industry, and academia in the fields of 

atmospheric chemistry and dynamics, health effects of ultraviolet radiation atmospheric modeling, and 

health effects modeling (see Acknowledgments section). In August and September of 2003, the draft final 

document was peer reviewed for its technical content by Dr. Edward De Fabo of The George Washington 

University in Washington, DC, and by Mr. Archie McCulloch of Marbury Technical Consulting in Cheshire, 

United Kingdom, and visiting research fellow at the School of Chemistry, University of Bristol. The peer 

reviewers were asked to draw upon their expertise in ultraviolet (UV) radiation biological effects 

assessment and atmospheric science, respectively, to comment on whether the methods, tools, and 

approach used in the study reflect sound scientific practice and adequately address the questions at 

hand. 

Written comments were received from peer reviewers. In these comments, the reviewers stated that the 

methodology used in this model represents a sound, state-of-the-art approach to assessing ozone-related 

health effects. A number of comments identified areas for clarification of specific technical items, all of 

which have been considered by the authors. The reviewers stated that the report provides solid analysis 

and discussion of results, given the scope of the work and the uncertainties that currently exist in the 

areas of ozone depletion and UV radiation health impacts estimation. 

Several areas were highlighted during peer review of this report. Dr. De Fabo highlighted the fact that one 

of the greatest sources of uncertainty in estimating UV radiation-induced health impacts is the lack of 

adequate experimental data from which a biological action spectrum for cutaneous malignant melanoma 

(CMM) can be developed. Due to this lack of information, the AHEF predicts cases of malignant 

melanoma based on the SCUP-h action spectrum for squamous cell carcinoma (SCC). Dr. De Fabo 

agreed that the SCUP-h spectrum is the most appropriate action spectrum available to model CMM at this 

time. He noted that the action spectrum for CMM still remains to be determined, and that use of the 

SCUP-h in modeling CMM should be reconsidered if future research reveals that the shape of the action 

spectrum for CMM is not congruent with the SCUP-h action spectrum. EPA acknowledges that further 

scientific research in these and other areas could complement and significantly enhance the information 

presented in this report. 

Dr. De Fabo also agreed that the removal of cataract incidence from the AHEF’s health effects modeling 

reflects a sound decision, in light of recent analyses that suggest a weak correlation between UV 

exposure and cataract incidence in the United States. Dr. De Fabo also affirmed that the paper’s 

discussion on immunosuppression accurately reflects the current state of the science. 

Mr. McCulloch suggested several revisions to the original text to remove ambiguity, and provided 

additional information on the methodologies and assumptions used by WMO in their 1999 and 2003 

reports, to allow for a more accurate and thorough comparison of the projected ozone concentrations 

predicted by WMO and by the AHEF. Mr. McCulloch also commented on the need to clearly justify the 

selection of 55 as the bromine efficiency factor—or alpha factor—for use in the AHEF instead of 45, 

which is the value recommended by WMO (WMO 2003). The selection of an alpha factor of 55 is based 

on the results of state-of-the-art atmospheric models, and is also the value used in a recent report 

prepared for the U.S. Department of Defense (Independent Review Panel 2002, Wuebbles 2003). In 

general, Mr. McCulloch affirmed that the atmospheric science module of the paper provides clear 

descriptions of the methodology and model parameters used, which allow the reader to reach conclusions 

about the way the methods have been applied and how they relate to "mainstream" atmospheric science 

(e.g., WMO Ozone Assessments). 

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All comments of the reviewers were considered, and the document was modified appropriately. 

EPA wishes to acknowledge everyone involved in this report and thank reviewers for their extensive time, 

effort, and expert guidance. The involvement of peer reviewers and other scientific contacts greatly 

enhanced the technical soundness of this report. EPA accepts responsibility for all information presented 

and any errors contained in this document. 

Global Programs Division (6205J) 

Office of Atmospheric Programs 

U.S. Environmental Protection Agency 

Washington, DC 20460 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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TABLE OF CONTENTS

Page

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

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

2. Model Overview...................................................................................................................................3

3. Baseline Incidence and Mortality Projections .................................................................................7

3.1 CMM Mortality Rates ..................................................................................................................7

3.2 CMM Incidence Rates.................................................................................................................8

3.3 NMSC Mortality Rates ..............................................................................................................10

3.4 NMSC Incidence Rates.............................................................................................................10

3.5 U.S. Population Growth Rates..................................................................................................13

4. Stratospheric Ozone Depletion Modeling ......................................................................................14

4.1 Approach for Stratospheric Ozone Concentration Modeling ....................................................14

4.2 Stratospheric Ozone Depletion from ODS Emissions ..............................................................15

4.3 Ozone Protection Policy Scenarios and Projections of Future Ozone Concentrations ............16

5. Modeling Changes in UV Exposure and Health Effects................................................................18

5.1 The Tropospheric Ultraviolet-Visible Radiation (TUV) Model ...................................................18

5.2 Action Spectra...........................................................................................................................19

5.2.1 Selection of Action Spectra for Modeling Skin Cancer.......................................................19

5.3 Dose-Response Relationships..................................................................................................21

5.3.1 Derivation of BAFs for CMM Incidence/Mortality and NMSC Mortality ..............................21

5.3.2 Derivation of BAFs for BCC and SCC Incidence................................................................23

5.4 Resulting BAFs by Health Effect...............................................................................................23

6. Projecting Future Health Effects Under ODS Control Policies ....................................................25

7. Modeling Results ..............................................................................................................................26

7.1 Results Presented by Policy Scenario and by Health Effect ....................................................26

8. Sensitivity Analyses .........................................................................................................................30

8.1 Action Spectra and Exposure/Dose Metrics Sensitivity Analyses ............................................30

8.2 CMM: Effects of Emphasizing Early Life Exposures.................................................................31

9. Uncertainty Analysis ........................................................................................................................33

9.1 Major Sources of Uncertainty....................................................................................................33

9.1.1 Translating ODS Emissions into EESC Concentrations ....................................................34

9.1.2 Translating EESC Concentrations into Stratospheric Column Ozone ...............................35

9.1.3 Translating Column Ozone into Ground-Level UV Radiation.............................................36

9.1.4 Translating UV Exposures into Human Health Effects.......................................................36

9.2 Other Unquantified Sources of Uncertainty ..............................................................................40

9.2.1 Composition of the Future Atmosphere..............................................................................40

9.2.2 Future Conditions of the Ozone Layer ...............................................................................40

9.2.3 Effect of Climate Variations on Ozone Depletion ...............................................................40

9.2.4 Global Compliance with Modeled Policy Scenarios ...........................................................41

9.2.5 Laboratory Techniques and Instrumentation......................................................................41

9.2.6 Demographic and Behavior Changes ................................................................................41

9.2.7 Accuracy of Baseline Information.......................................................................................41

9.3 Summary of Quantified and Unquantified Sources of Uncertainty ...........................................42

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10. Topics for Future Research.............................................................................................................44

10.1Prediction of Impacts on Different Skin Types..........................................................................44

10.2Research on Dose-Response Relationship of Cataract Incidence and UV Exposure .............44

10.3Inclusion of Other Human Health Endpoints.............................................................................44

10.4Inclusion of Impacts of Climate Variations on Ozone Depletion...............................................45

10.5Improved Ground-Level UV Measurements .............................................................................45

10.6Prediction of UV Impacts on Non-Human Endpoints................................................................45

11. References.........................................................................................................................................47

12. Glossary.............................................................................................................................................54

Appendix A. Updates Made to the Original AHEF ................................................................................57

Appendix B: Supplementary Tables for Baseline Incidence and Mortality Projections...................60

Appendix C. Supplementary Tables for Stratospheric Ozone Depletion Modeling..........................63

Appendix D: Comparison of AHEF and WMO Predicted Ozone Concentrations..............................70

Appendix E: Modeling Cataract Incidence ............................................................................................74

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Executive Summary 

Stratospheric ozone protects the biosphere from potentially damaging doses of ultraviolet (UV) radiation. 

Depletion of stratospheric ozone, caused by the release of man-made ozone-depleting substances 

(ODS)—such as chlorofluorocarbons (CFCs), halons, methyl bromide, and hydrochlorofluorocarbons 

(HCFCs)—could lead to significant increases in UV radiation reaching the Earth's surface, which could in 

turn lead to adverse human and animal health effects, as well as ecosystem impacts. 

The Montreal Protocol on Substances That Deplete the Ozone Layer (Montreal Protocol) is a landmark 

international agreement designed to protect the stratospheric ozone layer. The treaty was originally 

signed in 1987 and substantially amended in 1990, 1992, and 1997. The Montreal Protocol stipulates 

phaseout schedules for the production and consumption of compounds that deplete ozone in the 

stratosphere. 

The United States Environmental Protection Agency (EPA) uses its Atmospheric and Health Effects 

Framework (AHEF) to evaluate certain human health impacts associated with reduced emissions of ODS 

under the Montreal Protocol and associated amendments. Specifically, the AHEF estimates the probable 

increases in skin cancer mortality and incidence in the United States that result from ODS emission 

scenarios relative to the baseline. The baseline is defined as the health effects that would have occurred 

if ozone concentrations that existed in 1979-1980 had been maintained through the time period modeled. 

The 1979-1980 concentrations of ozone are used as the baseline because at this date minimal ozone 

depletion had occurred. Differences in health effects can be compared across broad policy scenarios to 

estimate potential benefits of alternative ODS controls. 

The accuracy of the AHEF’s predictions depends upon continual updating of its inputs and methodologies 

to reflect on-going scientific advances since the AHEF’s creation in the mid 1980s. Significant new 

research results that have been incorporated into the revised version of the AHEF include the following: 

 Recalibration and refinement of stratospheric ozone concentration measurements; 

 Updated ODS emission data; 

 Improved forecasts of the impact of emissions of ODS on stratospheric ozone concentrations; 

 New predictions of the impact of changing ozone concentrations on UV radiation intensity at the 

Earth's surface; 

 Updated information on the biological effects of UV radiation of different wavelengths (action 

spectra), and how age and year of birth affect the induction of skin cancers and other human 

health effects; 

 Improved estimation of projected skin cancer mortality rates, based on more recent and reliable 

epidemiological data; 

 Revised health effects modeled by the AHEF, to more accurately predict only those health effects 

for which an agreed upon dose-response relationship is available; and 

 Updated population data. 

While each of these model updates has affected the AHEF output to varying degrees—either slightly or 

significantly increasing or decreasing total projected health effects—each has contributed to more 

accurate modeling results. In addition to these model updates, several other changes have been made to 

enhance model resolution and flexibility. Appendix A details all of the model updates and changes that 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– ES-1 – 
have been made to the AHEF since its inception, and provides explanations and justifications for why 

each one was performed, and its implication on modeling results. 

Despite these model updates, no model or set of results quantifying health effects impacts can be 

considered final, given that research on the atmospherics of ozone depletion and health effects of UV 

exposure is ongoing. Many important issues must continue to be investigated and, as significant new 

findings are incorporated into the AHEF, the accuracy of predictions and the implications for protecting 

stratospheric ozone will be enhanced. For example: 

 Additional research on the effects of UV radiation on darker-skinned populations would enable 

the AHEF to predict the incremental health effects for all populations; 

 Further disaggregation of cataract incidence data by state, and the generation of a populationweighted, geographically distributed dose-response relationship for cataract incidence and UV 

exposure would allow for appropriate modeling of cataract incidence changes in the AHEF; 

 Additional scientific research into the impacts of UV exposure on immune suppression would 

allow for the inclusion of this health endpoint into the model; 

 Improved ground-level UV monitoring would allow the AHEF to incorporate the effects of cloudcover and pollution on UV radiation at ground-level; and 

 Additional research on the effects of UV radiation on non-human endpoints (e.g., aquatic 

systems, agriculture) would allow the AHEF to predict the broader impacts associated with ODS 

emission scenarios. 

The AHEF is a living model, designed with the ability to accept changes in any model input or assumption 

based on new scientific findings, and/or to incorporate any new information as it becomes available. As 

the science on stratospheric ozone depletion and its associated impacts continues to evolve, so too will 

the AHEF. 

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1. Introduction 

Stratospheric ozone protects the biosphere from potentially damaging doses of ultraviolet (UV) radiation. 

Recent depletion of stratospheric ozone, caused by the release of man-made ozone-depleting 

substances (ODS)—such as chlorofluorocarbons (CFCs), halons, methyl bromide, and 

hydrochlorofluorocarbons (HCFCs)—could lead to significant increases in UV radiation reaching the 

Earth's surface, which could in turn lead to increased adverse human and animal health effects, in 

addition to potential ecosystem impacts. 

UV radiation-induced health effects are primarily related to the skin, eyes, and immune system. Skin 

effects include erythema (sunburn), aging of the skin, and various forms of skin cancer—melanoma, 

squamous cell carcinoma (SCC), and basal cell carcinoma (BCC). Eye effects can include cataracts, 

squamous cell cancer of the cornea, conjunctiva, and other damage to the cornea (UNEP 1998, Anduze 

1993). Sunlight exposure also reduces immunological defenses, impeding resistance to infectious 

diseases and skin tumors, and diminishing the effectiveness of vaccines (CIESIN website 2003). Of the 

human health effects from sun exposure, melanoma is the most lethal, causing more than 7,000 deaths 

annually in the United States and showing an alarming rate of increase worldwide (Kannan et al. 2003). 

In addition to human and animal health effects, increased UV exposure can also damage ecological and 

agricultural systems. For example, sunlight is a key ingredient in the formation of photochemical smog, 

and can lower the immunity of vegetation to pest infestation, disrupt nutrient cycles, and cause 

bacterioplankton stress in water, which can lead to the reduction of amphibian and fish populations in 

aquatic ecosystems. Further, UV radiation can damage some plastic materials (Tang et al. 1998, Caldwell 

et al. 1998, Häder et al. 1998, Andrady et al. 1998). 

To protect stratospheric ozone and avoid negative health and environmental impacts, the Montreal 

Protocol on Substances That Deplete the Ozone Layer (Montreal Protocol) was signed in 1987, a 

landmark international agreement designed to reduce production and consumption of compounds that 

deplete ozone in the stratosphere. The Montreal Protocol, now signed by more than 180 countries, was 

amended in 1990, 1992, and 1997, further tightening controls, and ultimately requiring phase out of 

ozone-depleting substances (ODS) listed by the Protocol. However, recovery of stratospheric ozone 

remains uncertain and depends on continued international commitment to limit ODS emissions. 

The United States Environmental Protection Agency (EPA) uses its Atmospheric and Health Effects 

Framework (AHEF) to evaluate certain human health impacts associated with reduced emissions of ODS 

under the Montreal Protocol and associated amendments. Specifically, the AHEF estimates the probable 

increases in skin cancer mortality and incidence in the United States that result from ODS emission 

scenarios. Modeling of cataract incidence, although included in earlier versions of the AHEF, has been 

removed because of the uncertain link between UV exposure and cataract incidence in the United States 

(see text box on next page). 

The increase in skin cancer mortality and incidence for all ODS emission scenarios are estimated relative 

to the baseline, which is defined as the health effects that would have occurred if ozone concentrations 

that existed in 1979-1980 had been maintained through the time period modeled. These differences can 

be compared across policy and control scenarios to estimate the additional benefits of scenarios with 

increasingly stringent ODS controls. The major scenarios are the four presented in this paper (see 

Section 4.3). The AHEF can also be used to look at the effect of other emission scenarios resulting from 

potential future ODS control policies. This paper reviews the AHEF’s inputs, assumptions, and 

computational methods for estimating future human health effects and reports the estimated benefits of 

four policy/emission scenarios: the Montreal Protocol (1987), the London Amendments (1990), the 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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Copenhagen Amendments (1992), and the Montreal Adjustments (1997).1

 The remainder of the paper is 

organized as follows:2 

 Section 2 provides an overview of how the AHEF works; 

 Section 3 explains how the baseline estimates of incidence and mortality were obtained and 

projected into the future for the health effects 

examined; 

 Sections 4 present the methods used for 

projecting ozone depletion under various 

policy scenarios; 

 Section 5 explains the methods used for 

estimating changes in ground-level solar UV 

irradiance associated with ozone depletion, as 

well as the resulting changes in health effects, 

derived using dose-response relationships 

between UV exposure and health effect 

incidence/mortality; 

 Section 7 presents the modeling results for 

each of the four policy scenarios examined; 

 Section 8 presents a sensitivity analysis for 

action spectra and exposure/dose metrics; 

 Section 9 presents quantitative and qualitative 

analyses of the uncertainties in the AHEF; and 

 Section 10 suggests topics for future 

research. 

Justification for Omission of Cataract Incidence 

Reporting in the AHEF Model 

While cataract incidence historically has been 

assessed in the AHEF model, this health effect is not 

currently modeled. This is a result of a recent analysis 

that has revealed a weak correlation between UV 

exposure and cataract incidence in the United States. 

Specifically, in an effort to determine the statistical 

correlation between UV exposure and age on the 

development of cataract incidence, state-by-state 

average annual UV exposures were correlated with 

cataract incidence data reported by the National Eye 

Institute/Prevent Blindness America (NEI/PBA) 

(2002)—the most recent and comprehensive cataract 

incidence data available—of populations aged 40 and 

above. The results of this analysis suggest that 

cataract incidence is directly correlated with age. 

However, it was not possible to develop a good 

correlation between the intensity of UV exposure and 

cataract incidence using this data. 

One reason for the lack of correlation may be that the 

state-averaged UV values currently available may be 

too aggregated to discern differences in populationbased effects. It may be possible to develop a dose 

response curve using data that is further 

disaggregated. Therefore, if additional data or 

research becomes available on the dose-response 

relationship for cataracts, this health effect will be 

reconsidered for inclusion in the AHEF. (See Appendix 

E for more information on modeling cataract 

incidence.) 

Human behavior can complicate the development of a 

dose response curve for UV. For example, many 

people in the United States wear protective 

sunglasses when in the sun, or may engage in sun 

avoiding behaviors. These behaviors, while beneficial 

to human health, tend to confound analysis of cataract 

incidence versus UV exposure. 

1 The Vienna Amendments (1995) and the Beijing Amendments (1999) are not presented in this paper because the 

ODS phaseout schedules are very similar to that of the Montreal Adjustments. 

2

 A glossary defining terms used in this paper appears at the end of this document. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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2. Model Overview 

The AHEF has five main computational steps that lead to estimated changes in incidence and mortality 

for various UV-related health effects for a given ODS emission scenario. These computational steps are 

as follows: 

1. Projecting baseline incidence and mortality of health effects; 

2. Projecting impacts of future ODS emissions on stratospheric ozone; 

3. Modeling the resulting changes in ground-level UV radiation; 

4. Deriving dose-response relationships for health effect incidence and mortality; and 

5. Projecting future health effects incidence and mortality. 

These steps are described in detail below. 

Step 1. Projections of baseline incidence and mortality are computed based on historical rates 

assuming column ozone concentrations remained constant at 1979-1980 levels. 

The AHEF defines the “baseline” incidence and/or mortality for skin cancer as what would be expected to 

occur in the future if the concentration of stratospheric ozone remained fixed at 1979-1980 levels. This 

baseline provides a standard against which to evaluate increases in mortality and/or incidence for these 

health effects from future ODS emissions and ozone depletion and, under most scenarios, future 

recovery of the ozone layer to 1979-1980 levels.3

 The following data and calculations form the baseline 

estimate of current and future incidence and mortality: 

• Historical data on skin cancer incidence and mortality were used to derive rates (per 100,000 

people) for UV-related health effects in the U.S. population. Rates are based on age, sex, and in 

some cases, birth year. 

• Historical U.S. population estimates (up to 1990) were obtained from the U.S. Census Bureau, 

and national population estimates for 1991-2050 were derived by age and sex groupings from 

U.S. Census Bureau projections. Population projections by state, age, sex, and race—based on 

national population projections for year 2050 and state population projections through 20254

— 

were grouped by latitude-based regions. (Population was assumed to be constant from 2050 to 

2100.) 

• The number of individuals in each age and sex group was multiplied by the appropriate historical 

incidence and/or mortality rate to produce an estimated baseline number of future skin cancer 

cases and deaths per year. 

3 The AHEF assumes that changes in behavior that might confound the establishment of an accurate baseline do not 

occur. For example, a population that becomes less sun-seeking could theoretically have a lower baseline risk than 

the earlier cohort that provided the baseline data, and an increase in cloudiness or rainfall could reduce the number 

of hours spent outdoors, thereby reducing baseline exposures. 

4

 State population projections through 2025 were computed as the sum of the totals for the states in each region, and 

then regional populations (by age, sex, and race) were projected to 2050 based on the national Census projections 

for 2050—under the assumption that the 2025 regional age, sex, and race proportions of the total U.S. population will 

remain unchanged through 2050. In this way, population estimates for 1990-2025 were based on state population 

projections, while population estimates for 2025-2050 were based on national population projections. See Section 3.5 

for more details. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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Because skin cancer and solar UV irradiance vary by latitude, the baseline U.S. health effects data were 

stratified into three latitude regions (i.e., 20 to 30°N, 30 to 40°N, and 40 to 50°N), to correspond with 

satellite data on ozone concentrations. Because skin cancer incidence and mortality among darkerskinned populations are not well understood in terms of rates of responsiveness to increased UV 

exposures, these health effects are only modeled for light-skinned populations. Once the required 

information becomes available, data for darker-skinned U.S. populations may be included. 

Step 2. Impacts of future emissions of ODS on stratospheric ozone concentrations are modeled. 

Since 1978, satellites have provided measurements of stratospheric ozone concentrations using a 

latitudinal grid. Data from the first of these satellites, the Nimbus-7, indicate that during the satellite’s 

lifespan from 1978 to 1993, ozone concentrations declined in a manner that corresponds to an increase 

in the concentration of stratospheric chlorine and bromine released from the dissociation of ODS 

molecules. Using this relationship, the AHEF can use estimated ODS emissions to predict future 

decreases in stratospheric ozone. First, the framework uses regression coefficients to quantify the 

relationship between past ODS emissions and past changes in ozone concentrations. These regression 

coefficients were derived as follows: 

• Historical information on the concentrations of stratospheric ozone by latitude and month was 

obtained from satellite data. 

• Estimates of emissions of ODS were obtained for past time periods that could affect ozone during 

the years for which satellite data were available. These ODS emissions estimates were then 

combined with information on each ODS species’ degree of dissociation and rate of transport to 

the stratosphere. Using this information, total ODS emissions were converted to equivalent 

effective stratospheric chlorine (EESC) for each year and month for which ozone measurements 

were available from the Nimbus-7 satellite. 

• Statistical linear regressions were performed using the 1978-1993 annual EESC estimates and 

stratospheric ozone concentrations, as measured by the Nimbus-7, to estimate the impact of 

ODS on ozone concentrations. These regressions were estimated by month and by latitudinal 

band.5 

• Future changes in ozone associated with projected emissions for each ODS emission scenario 

were converted to EESC estimates which were then multiplied by the estimated regression 

coefficients to predict future ozone concentrations by month and latitude band. 

Step 3. Changes in ground-level UV radiation are estimated. 

Based on projections of stratospheric ozone concentration, UV radiation intensities at the Earth’s surface 

were estimated by latitude, month, year, and time of day using the Tropospheric Ultraviolet-Visible 

radiation model (TUV, v3.9a, as described in Madronich 1993a, Madronich 1993b). The TUV model 

generates look-up tables6

 (see Section 5.1 for more detail) of weighted solar UV irradiance at sea level as 

a function of solar zenith angle and projected total column ozone based on the following assumptions: 

obstruction-free and cloud-free skies; standard profiles of air density, temperature, and tropospheric 

ozone (USSA 1976); typical continental aerosols (Elterman 1968); and 10 percent isotropic ground 

reflectivity. 

5

 A similar procedure has been used in WMO assessments, which also use the Nimbus-7 satellite data (WMO 1995, 

WMO 1999). See Appendix D: Comparison of AHEF and WMO Predicted Ozone Concentrations for more 

information on how AHEF and WMO column ozone estimates compare. 

6 The axes of these look-up tables are solar zenith angle and column ozone concentrations. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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Once solar UV irradiance at the Earth’s surface is calculated, estimates of UV exposure experienced by 

humans can be computed. Peak hour or daily dose on any day of the year, or cumulative doses for a set 

of months or for an entire year are examples of possible dose metrics. The AHEF estimates UV 

exposures for both the entire day of June 21st (i.e., peak day) and the cumulative dose for the entire year 

(calculated as the dose on the 15th day of each month multiplied by 30 days per month summed across 

months) for selected action spectra.7 

Step 4. Dose-response relationships for skin cancer incidence and mortality are selected. 

Determining the health effects caused by UV exposure first requires information on the relative weights to 

be placed on each discrete UV wavelength to reflect the degree to which each wavelength causes 

biologic damage. Such a weighting function is called an action spectrum—an experimentally derived 

function that describes the relative effectiveness of each UV wavelength in the induction of skin cancers. 

Action spectra are normally developed by scientists by exposing a test animal to different UV 

wavelengths and then verifying the effectiveness of each wavelength at inducing a specific health effect. 

For each health effect, an available action spectrum must be selected for use in the AHEF. 

Once the action spectrum for each health effect is selected, it is then possible to explore the relationship 

between those health effects and the intensity of UV exposure. These dose-response relationships are 

typically derived by correlating measurements or estimates of UV exposure received for a specific action 

spectrum and given health effect at various locations, and the level of incidence or mortality for that health 

effect at those same locations. 

For example, the incidence of SCC decreases with distance from the equator (i.e., increasing latitude). It 

is also the case that UV irradiance decreases with distance from the equator. A dose-response 

relationship can thus be derived statistically by correlating the incidence of SCC measured at various 

locations at a variety of latitudes with the UV radiation doses measured or estimated for those same 

locations, as shown in Figure 1. 

Step 5. All inputs are combined to project future skin cancer incidence and mortality. 

The final step in the modeling framework incorporates the inputs from Steps 1-4 to project future 

additional skin cancers generated under a particular emission scenario compared to 1979-1980 baseline 

ozone conditions. This includes two calculations by the AHEF. First, the AHEF projects future baseline 

skin cancer incidence and mortality. Then the AHEF calculates the future annual percentage change in 

UV radiation dose for a given action spectrum across the three latitudinal bands of the United States for 

the specific ODS emission scenario. Multiplying the percentage change in UV exposure in a future year 

by the appropriate dose-response relationship yields the percentage change in future skin cancer 

incidence/mortality attributable to the future change in ozone concentrations. These percentages are then 

multiplied by the baseline incidence and/or mortality for that health effect to compute the absolute number 

of additional future cases or deaths attributable to ozone depletion under various ODS emission 

scenarios relative to the 1979-1980 baseline ozone levels.8 

7

 It is important to note that this analysis does not include a comprehensive listing of all published action spectra that 

may be applicable to the prediction of skin care and cataracts in humans. For example, the derivation of new action 

spectra for UV-mediated health effects not considered in this report (e.g., immunosuppression) is an active field of 

research. The AHEF’s modular structure, described in detail below, enables new action spectra or new information 

on other UV-mediated human health endpoints to be easily incorporated into the modeling framework. 

8 This method of multiplying the changes in UV exposure by the BAF and the underlying baseline incidence or 

mortality is the same as that used by other researchers to estimate changes in health effects based on changes in 

ozone concentrations (e.g., Madronich and de Gruijl 1994, Pitcher and Longstreth 1991). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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Figure 1. Dose-response relationship (BAF) and derivation for 

UV exposure and SCC in light-skinned males. 

(Scotto et al. 1983) 

300 

200 

100 

70 

Legend: 

= 1977-78 

SQUAMOUS CELL 

= 1971-72 

WHITE MALES 

Model: LnRijkl = "ijkl + $jk(Ul) + *ijkl 

(Assumes common slope) 

50 

40 

30 

20 

10 

100 120 140 160 180 200 

The remainder of this paper describes the steps above in greater detail. Specifically, Section 3 on 

Baseline Incidence and Mortality Projections elaborates on Step 1; Section 4 on Stratospheric Ozone 

Depletion Modeling provides more information about Step 2; Section 5 on Modeling Changes in UV 

Exposure and Health Effects further explains Steps 3 and 4; and Section 6 on Projecting Future Health 

Effects Under ODS Control Policies describes Step 5 in more detail. 

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3. Baseline Incidence and Mortality Projections 

Baseline incidence and mortality rates for skin cancer were estimated using data on historical incidence 

and/or mortality for these health effects among the relevant U.S. populations. These rates were then 

applied to annual U.S. population estimates to project future skin cancer incidence and mortality in the 

absence of further stratospheric ozone depletion. A number of different data sources were used to 

calculate historical incidence and mortality rates for cutaneous malignant melanoma (CMM) and nonmelanoma skin cancer (NMSC). The sections below review the historical information for each of the 

health effects rates used and present the data sources and methods for projecting the U.S. populations at 

risk. 

3.1 CMM Mortality Rates 

Baseline CMM mortality data for the years 1950 through 1984 were obtained from the EPA/National 

Cancer Institute (EPA/NCI) data set, which is described in Scotto et al. (1991) and Pitcher and Longstreth 

(1991). The data set used in Scotto et al. (1991) reports deaths from CMM in individuals for 18 age 

groups (birth cohorts), by sex and race, covering every county in the United States during the 35-year 

period. 

To generate CMM mortality rates per 100,000 people per county, the CMM mortality information was 

combined with population data by county. Because the raw mortality data from NCI cover 5-year, rather 

than 1-year, intervals, the five-year mortality total was then divided by the five-year population total. For 

each year in the five-year period, this fraction was multiplied by 100,000 to produce the CMM mortality 

rate per 100,000 people per county (Pitcher and Longstreth 1991). 

Once the mortality rate for each county and time period was calculated, the counties in the EPA/NCI 

CMM mortality data set were aggregated into three geographical regions based on latitude. The three 

geographical regions were formed out of entire states and roughly correspond to latitude bands 20°N to 

30°N, 30°N to 40°N, and 40°N to 50°N (except for California, which was split in half and assigned to the 

30°N to 40°N and 20°N to 30°N geographical regions). Table B-1 of Appendix B lists the states included 

in these geographical regions. The resulting CMM mortality rates by age, birth cohort, sex, and 

geographical region across the United States are also presented in Appendix B. 

This regionally-aggregated data set contains baseline CMM mortality rates for 24 five-year birth cohorts 

(i.e., people born within five years of each other) beginning in 1863 and ending in 1982 observed over the 

35-year history of the underlying data set (see Tables B-2 and B-3 and Appendix B). The shaded areas in 

Table 1 and Table 2 represent actual information from the original EPA/NCI data. 

In order for the AHEF to project future baseline mortality rates, it was necessary to estimate mortality 

rates for populations alive at the time of collection of the NCI data as well as to estimate the future 

mortality rates for those yet to be born. To project future mortality rates for people in the EPA/NCI data 

set, the mortality rates for different age groups and their years of birth for each of the three geographical 

regions were examined using econometric analysis9

 to determine the quantitative influence of age and 

cohort on those mortality rates. In other words, these regressions correlate CMM mortality rates with age 

and year of birth. It should be noted that, in the case of CMM, mortality at any given age appears to have 

9

 As it is used in this document, the term “econometric” refers to a statistical technique that enables analysts to 

determine to what degree one specific variable (e.g., UV exposure) may be responsible for a specific effect (e.g., skin 

cancer) thought to be caused by the interaction of several related variables. For example, it is hypothesized that age, 

birth year, and UV exposure all play a role in the etiology of CMM. Because ODS control policies can only reduce one 

of these risk factors (i.e., the amount of UV-B reaching the ground can be reduced by a thicker ozone layer), 

econometric estimation is used to isolate and quantify the contribution of UV exposure to CMM. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 7 – 
increased from the early 1900s to the mid-1950s, and then gradually declined. Because of this, agespecific mortality rates depend on birth year, especially for people born between approximately the 1920s 

to the 1960s. This effect, known as a ‘cohort effect,’ is included in the regression analyses. The precise 

reasons for this phenomenon are not known, but various factors, including behavior (e.g., use of 

sunscreen, selection of clothing that exposes or covers skin) are believed to play a role.10 

Finally, the actual and predicted mortality rates for each age group in the 1960, 1965, and 1970 birth 

cohorts were averaged for purposes of projecting future cohorts’ mortality rates. These three cohort 

groups were selected because they represent the most recent birth cohorts with statistically robust 

mortality data. Table 1 depicts a portion of the baseline mortality rates derived for CMM using the 

methods described above. 

3.2 CMM Incidence Rates 

A limited set of data on CMM incidence and mortality was extracted from the Surveillance, Epidemiology, 

and End Results (SEER) Program, based within the Cancer Control Research Program at NCI. Nine 

reporting areas were included in the resulting SEER CMM data extract (see Appendix B), covering 

individual cases diagnosed between the years 1973 and 1994 (Ries et al. 1999). This data set was 

aggregated into eighteen age groups by sex, race (“all races,” light-skinned, and darker-skinned), and the 

three latitudinal U.S. regions described in Section 3.1. Again, because of data limitations, only lightskinned populations were included in the analysis. 

Because of the small size of the SEER data set, a different method for projecting future incidence was 

necessary. To do so, the ratio of the SEER-based CMM incidence to mortality was calculated and 

averaged over ten years, 1984 to 1994, for each of the three regions and for each sex. (Results of these 

calculations are provided in Appendix B.) Then, incidence-to-mortality ratios were applied to the EPA/NCI 

CMM mortality rates to generate comprehensive CMM incidence rates by age, cohort, and sex, as 

reported in Appendix B. The results of this process for selected cohort mid-birth years are reported in 

Table 2 for latitudes 40oN- 50oN. Age-, sex-, region-, and cohort- specific rates do not change after 1990. 

The results for additional regions are reported in Appendix B. For very young age groups, incidence and 

mortality information were quite sparse and thus it was assumed that incidence was at least equal to 

mortality. Hence, cohorts and age groups for which the EPA/NCI data set reported positive CMM mortality 

but for which the SEER data set reported zero, the SEER data set was adjusted to report an identical 

percentage of CMM incidence. 

As discussed in Section 3.1, CMM incidence rates exhibit a cohort effect, possibly due to behavioral 

factors (e.g., use of sunscreen, selection of clothing that exposes or covers skin). 

10 CMM is the only health effect modeled in the AHEF for which there is a statistical age-linked component, known as 

a cohort effect. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 8 – 
Table 1. 

o N to 50o

Cohort Mi Age Group 

Birth Yea 

White Male 1865 

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 

1870 

1875 4.713 

1880 3.519 4.398 

1885 2.882 3.673 5.148 

1890 2.484 4.091 4.913 7.729 

1895 1.725 2.604 4.954 5.176 9.091 

1900 1.441 2.332 3.206 4.487 6.029 9.376 

1905 1.683 2.212 2.844 3.544 6.150 8.273 11.701 

1910 1.313 1.834 2.802 3.451 5.148 7.092 9.598 11.400 

1915 1.190 1.456 2.153 3.402 4.049 6.427 9.362 9.590 12.200 

1920 0.937 1.326 1.982 2.749 4.095 5.549 7.342 8.890 10.200 12.800 

1925 0.676 1.214 1.721 2.664 3.183 4.843 6.916 7.320 9.440 10.800 13.400 

1930 0.406 0.724 1.350 1.907 2.428 4.158 5.166 6.060 7.420 9.540 10.900 13.500 

1935 0.113 0.318 0.805 1.191 1.611 2.621 3.898 4.900 5.970 7.330 9.450 10.800 13.400 

1940 0.038 0.103 0.425 0.689 1.235 1.704 3.309 3.820 5.010 6.080 7.440 9.560 10.900 13.500 

1945 0.000 0.020 0.122 0.337 0.815 1.084 2.319 2.890 3.840 5.030 6.110 7.460 9.580 10.900 13.500 

1950 0.018 0.009 0.000 0.166 0.573 0.937 1.741 2.140 3.000 3.950 5.140 6.220 7.580 9.700 11.000 13.600 

1955 0.000 0.008 0.000 0.126 0.444 0.873 1.420 2.060 2.920 3.870 5.060 6.140 7.500 9.620 10.900 13.500 

1960 0.008 0.008 0.024 0.142 0.282 0.824 1.400 2.040 2.890 3.850 5.040 6.110 7.470 9.590 10.900 13.500 

1965 0.009 0.000 0.027 0.080 0.415 0.836 1.410 2.050 2.910 3.860 5.050 6.130 7.480 9.600 10.900 13.500 

1970 0.009 0.006 0.024 0.124 0.376 0.838 1.410 2.050 2.910 3.860 5.050 6.130 7.480 9.600 10.900 13.500 

1975 0.000 0.000 0.024 0.124 0.376 0.838 1.410 2.050 2.910 3.860 5.050 6.130 7.480 9.600 10.900 13.500 

1980 0.000 0.006 0.024 0.124 0.376 0.838 1.410 2.050 2.910 3.860 5.050 6.130 7.480 9.600 10.900 13.500 

hite Fema 1865 

1870 

1875 2.800 

1880 3.294 3.589 

1885 1.890 2.947 3.750 

1890 1.950 2.367 3.718 4.244 

1895 1.492 2.609 3.248 3.957 4.884 

1900 1.087 1.800 2.349 2.814 3.713 5.987 

1905 1.036 1.454 1.957 2.410 3.673 5.195 5.840 

1910 0.999 1.688 1.658 2.160 2.965 5.041 5.505 6.320 

1915 0.826 1.176 1.840 2.109 2.678 3.498 4.028 5.540 6.500 

1920 0.709 0.944 1.471 1.683 2.530 2.863 4.078 4.700 5.850 6.800 

1925 0.548 0.929 1.342 2.010 2.006 2.833 3.113 4.120 5.020 6.160 7.120 

1930 0.344 0.617 1.129 1.219 2.042 2.622 3.234 3.430 4.330 5.230 6.370 7.330 

1935 0.028 0.310 0.591 0.955 1.256 1.871 2.552 2.870 3.320 4.220 5.110 6.260 7.220 

1940 0.000 0.050 0.208 0.498 0.794 1.469 2.124 2.400 2.880 3.320 4.220 5.120 6.260 7.220 

1945 0.000 0.011 0.131 0.253 0.555 0.948 1.373 1.980 2.420 2.900 3.340 4.240 5.140 6.280 7.240 

1950 0.019 0.038 0.018 0.092 0.250 0.634 1.184 1.440 2.030 2.470 2.950 3.390 4.300 5.190 6.340 7.290 

1955 0.017 0.009 0.017 0.094 0.376 0.663 1.080 1.440 2.040 2.480 2.950 3.400 4.300 5.190 6.340 7.300 

1960 0.017 0.008 0.008 0.077 0.329 0.619 1.060 1.420 2.020 2.460 2.930 3.380 4.280 5.170 6.320 7.280 

1965 0.000 0.000 0.009 0.073 0.292 0.607 1.040 1.410 2.000 2.450 2.920 3.360 4.270 5.160 6.310 7.260 

1970 0.006 0.003 0.009 0.078 0.306 0.612 1.050 1.420 2.010 2.450 2.930 3.370 4.270 5.170 6.310 7.270 

1975 0.023 0.003 0.009 0.078 0.306 0.612 1.050 1.420 2.010 2.450 2.930 3.370 4.270 5.170 6.310 7.270 

1980 0.011 0.003 0.009 0.078 0.306 0.612 1.050 1.420 2.010 2.450 2.930 3.370 4.270 5.170 6.310 7.270 

Table 1. CMM Mortality Baseline Cohort Tables by Sex (per 100,000) 

Note: The shaded areas represent actual information from the original EPA/NCI data. Unshaded areas represent 

projected mortality rates. This table can be read in two ways. First, the increase in mortality for the cohort born in a 

given year (e.g., 1960) can be read horizontally as the individuals in that cohort age. For example, a female born in 

1950 would have a baseline mortality rate of 0.018 in 1962 (12 years old) and a 0.092 mortality rate in 1969 (19 

years old). Second, mortality rates for a single age group can be tracked across birth cohorts by reading vertically 

from the selected age group (e.g., 20-24). Baseline mortality rates for males aged 65-69 can be observed to increase 

from 2.882 to 9.600 for individuals born in 1885 and 1980, respectively. It is important to note that the rows for 

these tables are presented in terms of “birth cohort mid-year.” For example, “1980" represents birth years 1978, 

1979, 1980, 1981, and 1982. Similarly, “1985" represents birth years 1983, 1984, 1985, 1986, and 1987. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 9 – 
Table 2 

Table 2: Estimated CMM Incidence Rates for Selected Years 

by Region, Sex, Cohort, and Age Group (per 100,000) 

40o N to 50o N Cohort Mid Age Group 

Birth Year 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 

White Males 1890 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.5 18.2 

1940 0.0 0.0 0.0 0.1 6.5 5.7 13.0 14.2 26.9 35.4 36.6 39.4 54.6 

1990 0.0 0.0 0.0 0.1 5.8 6.9 14.9 17.1 23.6 35.8 36.9 39.7 55.0 

2040 0.0 0.0 0.0 0.1 5.8 6.9 14.9 17.1 23.6 35.8 36.9 39.7 55.0 

2090 0.0 0.0 0.0 0.1 5.8 6.9 14.9 17.1 23.6 35.8 36.9 39.7 55.0 

White Females 1890 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.7 7.3 22.1 

1940 0.0 0.0 0.0 0.1 13.9 18.9 16.2 19.6 36.4 31.6 31.6 35.0 47.8 

1990 0.0 0.0 0.0 0.1 20.4 23.2 21.5 18.9 34.4 32.2 32.1 35.5 48.3 

2040 0.0 0.0 0.0 0.1 20.4 23.2 21.5 18.9 34.4 32.2 32.1 35.5 48.3 

2090 0.0 0.0 0.0 0.1 20.4 23.2 21.5 18.9 34.4 32.2 32.1 35.5 48.3 

Table 3

3.3 NMSC Mortality Rates 

The baseline mortality data by county for basal cell carcinoma (BCC) and squamous cell carcinoma 

(SCC), both forms of non-melanoma skin cancer (NMSC), were obtained from the EPA/NCI data set 

following the same regional aggregation and regression extrapolation procedures described in Section 

3.1 for CMM mortality. Because the underlying data set does not provide disaggregated information by 

tumor type (i.e., BCC is not reported separately from SCC), Table 3 reports all NMSC fatalities. Unlike 

melanoma mortality rates, no cohort-by-cohort historical tabulation is necessary for calculating NMSC 

mortality because NMSC mortality data do not display a cohort trend. NMSC mortality rates for the three 

latitudinal regions by age, sex, and birth cohort are reported in Appendix B. It is possible that the number 

of deaths included in this data set is somewhat uncertain, due to ambiguities in the reporting and 

recording of information on death certificates. It is expected that deaths would generally be underreported, for example, if complications resulting from non-melanoma skin cancer hospitalizations (e.g., 

infections, pneumonia) are erroneously listed as the cause of death on death certificates. Nevertheless, 

this data set is far more comprehensive than any other for non-melanoma mortality and therefore is used 

in the AHEF. 

3.4 NMSC Incidence Rates 

The AHEF uses baseline BCC and SCC incidence rates derived from incidence data for the three latitude 

bands of interest (e.g., 20°N to 30°N, 30°N to 40°N, and 40°N to 50°N). Incidence rates for light-skinned 

males and females, by region and age group, as reported in U.S. EPA (1987) and Fears and Scotto 

(1983), are shown in Table 4 and Table 5. These estimates were originally derived from Scotto et al. 

(1983) and are based on a nation-wide survey in eight cities across the United States during the one-year 

period from June 1, 1977, through May 31, 1978. As the data in the tables indicate, the incidence of both 

SCC and BCC is skewed toward the older age groups, and a strong latitude gradient is apparent. 

Unlike melanoma incidence rates, no cohort-by-cohort historical tabulation is necessary for NMSC 

incidence because non-melanoma skin cancers do not display a cohort trend. Hence, the age-specific 

incidence rates for NMSC by sex and region, as shown in Table 4 and Table 5, were applied to all 

cohorts. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 10 – 
Table 3: NMSC Mortality Baseline Cohort Tables by Sex (per 100,000) 

o N to 50o

Cohort Mi Age Group 

Birth Yea 

White Male 1865 

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 

1870 

1875 13.698 

1880 7.766 9.950 

1885 4.813 6.011 10.695 

1890 3.684 4.773 5.915 8.153 

1895 1.901 3.048 3.240 5.587 4.704 

1900 1.125 1.762 2.893 3.222 3.232 6.368 

1905 0.626 1.237 1.673 2.508 2.104 4.494 6.371 

1910 0.249 0.697 0.914 1.509 1.899 2.613 4.479 5.860 

1915 0.157 0.404 0.411 0.912 1.121 1.882 3.596 3.990 5.850 

1920 0.052 0.129 0.293 0.458 0.661 1.284 2.436 2.770 3.950 5.810 

1925 0.091 0.092 0.079 0.283 0.278 0.913 1.756 2.300 2.840 4.020 5.880 

1930 0.000 0.057 0.081 0.228 0.167 0.443 1.268 1.520 2.350 2.880 4.060 5.920 

1935 0.014 0.030 0.071 0.043 0.058 0.225 0.822 1.020 1.530 2.350 2.890 4.070 5.930 

1940 0.025 0.013 0.000 0.000 0.040 0.133 0.487 0.570 1.020 1.530 2.360 2.890 4.070 5.940 

1945 0.021 0.020 0.011 0.023 0.023 0.022 0.489 0.395 0.612 1.070 1.570 2.400 2.930 4.110 5.980 

1950 0.071 0.009 0.000 0.028 0.010 0.049 0.218 0.267 0.398 0.614 1.070 1.580 2.400 2.940 4.120 5.980 

1955 0.016 0.016 0.008 0.017 0.018 0.156 0.137 0.264 0.395 0.611 1.060 1.570 2.400 2.930 4.110 5.980 

1960 0.008 0.008 0.008 0.025 0.026 0.085 0.125 0.252 0.383 0.599 1.050 1.560 2.390 2.920 4.100 5.970 

1965 0.000 0.008 0.000 0.000 0.011 0.074 0.114 0.241 0.372 0.589 1.040 1.550 2.380 2.910 4.090 5.950 

1970 0.003 0.005 0.003 0.011 0.016 0.078 0.118 0.245 0.376 0.592 1.050 1.550 2.380 2.910 4.090 5.960 

1975 0.000 0.000 0.003 0.011 0.016 0.078 0.118 0.245 0.376 0.592 1.050 1.550 2.380 2.910 4.090 5.960 

1980 0.000 0.005 0.003 0.011 0.016 0.078 0.118 0.245 0.376 0.592 1.050 1.550 2.380 2.910 4.090 5.960 

hite Fema 1865 

1870 

1875 7.924 

1880 3.466 4.935 

1885 1.964 3.034 3.904 

1890 1.369 1.372 2.626 3.772 

1895 0.799 1.247 1.665 1.906 2.154 

1900 0.472 0.781 0.933 1.736 1.449 2.221 

1905 0.355 0.591 0.754 1.154 1.085 2.168 1.964 

1910 0.311 0.235 0.505 0.825 0.810 1.265 1.492 2.260 

1915 0.113 0.194 0.263 0.612 0.616 1.015 1.410 1.690 2.300 

1920 0.087 0.110 0.241 0.306 0.388 0.656 1.234 1.240 1.680 2.290 

1925 0.075 0.064 0.113 0.232 0.249 0.470 0.813 1.000 1.230 1.670 2.270 

1930 0.053 0.041 0.039 0.110 0.081 0.204 0.278 0.651 0.921 1.150 1.590 2.190 

1935 0.014 0.028 0.000 0.000 0.126 0.073 0.278 0.406 0.663 0.932 1.160 1.600 2.210 

1940 0.013 0.025 0.013 0.025 0.013 0.039 0.069 0.192 0.389 0.646 0.915 1.140 1.580 2.190 

1945 0.011 0.021 0.022 0.000 0.011 0.054 0.056 0.131 0.205 0.402 0.659 0.928 1.150 1.600 2.200 

1950 0.056 0.000 0.009 0.000 0.019 0.020 0.066 0.062 0.132 0.206 0.404 0.661 0.930 1.160 1.600 2.200 

1955 0.050 0.000 0.000 0.009 0.026 0.017 0.025 0.056 0.126 0.200 0.398 0.655 0.924 1.150 1.590 2.200 

1960 0.008 0.017 0.000 0.009 0.000 0.004 0.014 0.045 0.116 0.190 0.387 0.644 0.914 1.140 1.580 2.190 

1965 0.009 0.009 0.009 0.018 0.014 0.010 0.019 0.051 0.121 0.195 0.392 0.650 0.919 1.140 1.590 2.190 

1970 0.023 0.009 0.003 0.014 0.011 0.009 0.019 0.050 0.121 0.195 0.392 0.649 0.919 1.140 1.590 2.190 

1975 0.011 0.000 0.003 0.014 0.011 0.009 0.019 0.050 0.121 0.195 0.392 0.649 0.919 1.140 1.590 2.190 

1980 0.023 0.009 0.003 0.014 0.011 0.009 0.019 0.050 0.121 0.195 0.392 0.649 0.919 1.140 1.590 2.190 

Note: The shaded areas represent actual information from the original EPA/NCI data. Unshaded areas represent 

projected mortality rates. This table can be read in two ways. First, the increase in mortality for the cohort born in a 

given year (e.g., 1960) can be read horizontally as the individuals in that cohort age. For example, a male born in 

1965 would have a baseline mortality rate of 0.008 in 1972 (7 years old) and a 0.074 mortality rate in 1992 (27 years 

old). Second, mortality rates for a single age group can be tracked across birth cohorts by reading vertically from the 

selected age group (e.g., 20-24). Baseline mortality rates for females aged 40-44 can be observed to decrease from 

0.311 to 0.121 for individuals born in 1910 and 1980, respectively. This is consistent with the decline in CMM 

mortality rates observed after the 1950s. It is important to note that the rows for these tables are presented in terms 

of “birth cohort mid-year.” For example, “1980" represents birth years 1978, 1979, 1980, 1981, and 1982. Similarly, 

“1985" represents birth years 1983, 1984, 1985, 1986, and 1987. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 11 – 
Table 4: Estimated BCC Incidence Rates by Region, Sex, and Age Group (per 100,000) 

Age Groups 

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70

Male 40oN to 50oN 0.1 0.1 0.1 2.9 2.9 22.1 22.1 91.1 91.1 259.0 259.0 466.0 466.0 761.0 730oN to 40oN 0.8 0.8 0.8 2.6 2.6 29.4 29.4 120.0 120.0 297.0 297.0 557.0 557.0 872.0 820oN to 30oN 0.3 0.3 0.3 3.8 3.8 49.4 49.4 236.0 236.0 596.0 596.0 1080.0 1080.0 1790.0 17Female 40oN to 50oN 0.5 0.5 0.5 5.6 5.6 22.2 22.2 91.0 91.0 202.0 202.0 287.0 287.0 466.0 430oN to 40oN 0.3 0.3 0.3 5.0 5.0 33.8 33.8 95.8 95.8 198.0 198.0 310.0 310.0 454.0 420oN to 30oN 0.5 0.5 0.5 5.8 5.8 49.8 49.8 175.0 175.0 365.0 365.0 561.0 561.0 815.0 

Source: U.S. EPA (1987) and Fears and Scotto (1983). 

Table 5: Estimated SCC Incidence Rates by Region, Sex, and Age Group (per 100,000) 

Age Groups 

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70

Male 40oN to 50oN 0.2 0.2 0.2 0.3 0.3 1.6 1.6 7.4 7.4 32.6 32.6 87.4 87.4 147.0 130oN to 40oN 0.1 0.1 0.1 0.8 0.8 3.3 3.3 22.2 22.2 67.7 67.7 170.0 170.0 295.0 220oN to 30oN 0.0 0.0 0.0 0.3 0.3 11.2 11.2 43.0 43.0 169.0 169.0 377.0 377.0 640.0 6Female 40oN to 50oN 0.0 0.0 0.0 0.1 0.1 1.4 1.4 4.1 4.1 10.5 10.5 27.5 27.5 54.8 530oN to 40oN 0.1 0.1 0.1 0.1 0.1 2.4 2.4 7.1 7.1 19.5 19.5 48.0 48.0 91.6 920oN to 30oN 0.0 0.0 0.0 0.1 0.4 4.3 4.3 15.3 15.3 55.7 55.7 121.0 121.0 254.0 25

Source: U.S. EPA (1987) and Fears and Scotto (1983). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 12 – 
3.5 U.S. Population Growth Rates 

Data are needed on U.S. population estimates through 2100, disaggregated by 10-degree latitude band, 

as well as by age, sex, and race to provide the basis for developing population baselines for the AHEF. 

Population projections from the U.S. Census Bureau (U.S. Census Bureau Website 2000a, U.S. Census 

Bureau Website 2000b) were used to develop these estimates. 

Specifically, the U.S. Census Bureau provided projections of the total U.S. population by age, sex, and 

race through 2025, as well as for the year 2050. However, because these data were only disaggregated 

by state through 2025, the following calculations were used to estimate future U.S. population by 10­

degree latitude band (by age, sex, and race) through 2100: 

 Step 1: State-by-state population projections through 2025 were grouped by latitude-based 

regions. 

 Step 2: Populations by latitude-based region (by age, sex, and race) were projected for 2050 

by distributing the Census Bureau’s projected 2050 population by age, sex, and race to the 

three latitudinal regions, assuming that the distribution of the 2050 population by region, age, 

sex, and race will be the same as in 2025. This does not imply that, for example, the 

proportion of the total population that is female is the same in 2025 and 2050. Rather, it 

means that if there are proportionately more women in 2050 than in 2025, this greater number 

will be distributed across the regions in the same proportions as their representation in 2025. 

 Step 3: Regional populations (broken out by latitude grouping) for years 2026 through 2049 

were calculated assuming exponential growth for each age/sex/race group in each region from 

2025 through 2050. 

 Step 4: From 2050 through 2100, regional populations were assumed to remain constant. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 13 – 
4. Stratospheric Ozone Depletion Modeling 

This section first reviews the approach used to model future stratospheric ozone concentrations based on 

past, current, and projected future ODS emissions. Next, it describes the ODS policy control scenarios 

underlying the simulations presented in this report. The section concludes by presenting projections of 

future ozone concentrations through 2100. 

4.1 Approach for Stratospheric Ozone Concentration Modeling 

ODS such as chlorofluorocarbons, halons, carbon tetrachloride, methyl chloroform, methyl bromide, and 

HCFCs are used in a variety of applications including refrigeration and air-conditioning, foam blowing, 

aerosols, fire suppression and explosion protection, solvent cleaning, sterilants, tobacco expansion, 

adhesives, coatings, inks, and agricultural uses. 

Some of these sectors immediately emit the ODS used (e.g., fumigation and aerosols). Others emit the 

chemical as it performs a specific function over the course of a number of years. For example, the lifetime 

of some refrigeration equipment can be up to 25 years, some foams can continue emitting their blowing 

agent for up to 45 years, and fire suppression agents can remain in a total flooding system for years until 

discharged. As a result, new equipment production each year generates a stock of each chemical in 

those sectors with multi-year emission profiles. This creates a delay between initial use of some ODS and 

their subsequent emission into the atmosphere. 

Because of this time lag between consumption and emissions, it is necessary to model emissions in two 

separate steps. First, the annual amount of ODS consumed in each of the end uses must be determined. 

Then, this consumption must be translated into emissions into the atmosphere over subsequent years. 

These two steps are examined individually below. 

Step 1. Estimate Global Consumption of ODS 

Annual consumption of ODS is based on historical estimates of ODS use, industry growth rates, and the 

allowable production of ODS for each region. Consumption for 1989 is considered the base year for each 

ODS in each end use for the United States, the rest of the developed world, and the developing world. 

Base year consumption is then allowed to grow for each ODS by end use using market growth rate 

assumptions to approximate a no-controls scenario (i.e., if consumption grew according to demand in the 

absence of regulation or international agreement). Consumption is then reduced from the base year by 

the specified phase-out percentages specific to each ODS and region for each year to model the policy 

scenario (see Appendix C for more detail on these percentages). 

Step 2. Estimate Global Emissions of ODS 

Once consumption has been projected for all years, emissions can be estimated using a series of 

emission profiles for each ODS sector, generated by EPA’s Vintaging Model. The Vintaging Model is a 

tool developed for estimating the annual chemical use and emissions from industrial sectors that have 

historically used ODS in their products. In the context of the AHEF, the Vintaging Model provides 

emission profiles that translate consumption estimates into annual emission profiles for future years 

based on the lifetime and leak rates of each type of equipment. 

To track this information, the Vintaging Model follows one “vintage” of new equipment through its lifetime 

and models emissions at each stage of the equipment lifecycle (i.e., manufacturing, lifetime, 

maintenance, and disposal). Equipment types are divided into two classes, either serviceable or nonserviceable. For non-serviceable equipment, emissions are assumed to take place at disposal and can be 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 14 – 
either “instantaneous” emissions in the year of manufacture (for aerosols and solvents) or a “progressive 

disposal” (for foams) that allows for emissions to extend over a number of years. For serviceable 

equipment, such as refrigeration, air-conditioning, and fire-extinguishing equipment, the Vintaging Model 

applies information on annual leak rates, servicing emissions, and disposal emissions for each end use to 

model the lag between consumption of a chemical and its eventual emission to the atmosphere. 

By aggregating the emission outputs, the Vintaging Model creates annual profiles of emissions for each 

end use. These profiles are then applied by end use to the United States and the rest of global 

consumption for each policy scenario to develop global ODS emission projections. Once emissions are 

estimated, the next step in the methodology is to predict the effect that these emissions will have on 

column ozone. 

4.2 Stratospheric Ozone Depletion from ODS Emissions 

The relationship between stratospheric ozone depletion and releases of ODS is modeled following the 

method implemented at the University of Illinois at Urbana-Champaign (UIUC) by Dr. Don Wuebbles et al. 

(WMO 1995). This approach uses atmospheric models and semi-empirical methods to best quantify the 

temporal evolution of ozone impacts associated with key ODS over specific time horizons relative to the 

reference gas, CFC-11. To measure the ozone-depleting capacity of a certain concentration of ODS while 

accounting for the time delay between consumption and emission of the ODS, the equivalent effective 

stratospheric chlorine (EESC) concentration is estimated.11 

In computing EESC, an adjustment is required to quantify the greater efficiency of bromine in depleting 

ozone relative to chlorine. AHEF uses the UIUC method, which sets the bromine efficiency factor, alpha 

(α), at 55. Using α = 55 means that one bromine atom can deplete as much ozone as 55 chlorine atoms 

(Wuebbles 2003). The results that led to an alpha of 55 are found in a study published by the 

Independent Review Panel (2002) in its assessment of CH3I for the U.S. Department of Defense.12 In 

addition, the AHEF assumes a 3-year lag between tropospheric emissions of ODS and their migration to 

the stratosphere (percolation) where they cause ozone depletion (i.e., contribute to EESC). 

Using the results of the Vintaging Model, modeled global values of annual EESC for the period 1978 to 

1993 were compared to annually- and monthly-averaged stratospheric ozone concentrations at different 

latitudes, as measured by NASA's Nimbus-7 Total Ozone Mapping Spectrometer (TOMS), reported in 

McPeters et al. (1996a, 1996b). The empirical relationship between EESC and ozone was then estimated 

using linear regressions for the period 1978 to 1993 to derive column ozone estimates for each month of 

the year and for the following latitudinal regions: 20°N to 30°N, 30°N to 40°N, 40°N to 50°N, 50°N to 

60°N, and 60°N to 70°N.13 

11 Atmospheric lifetimes (ALT) of ODS and the latitudinal location of the emissions are critical factors used in deriving 

EESC values. For example, short-lived ODS species emitted in higher latitudes will have a lower impact on EESC 

than will the same species emitted near the equator, whereas ODS species with longer atmospheric lifetimes will 

eventually reach the stratosphere and have the same EESC regardless of the latitude of release (Wuebbles 2003). 

12 Earlier work by Wuebbles et al. (1999) and Ko et al. (1998) used slightly different alpha values—of 60 and 58, 

respectively—based on their respective atmospheric models. Although the latest ozone assessment (WMO 2003) 

recommends an alpha value of 45 (Daniel at al. 1999) for long-lived gases, and higher values of 58 and 60 for shortlived gases, it is believed that an alpha value of 55 is most defensible and appropriate for use in the AHEF model. 

This decision is based on the results of state-of-the art atmospheric models, and is also the value used in a recent 

report prepared for the U.S. Department of Defense (Independent Review Panel 2002, Wuebbles 2003). 

13 The AHEF only uses ozone concentrations from the three latitude bands that cover the United States (i.e., 20oN to 

30oN, 30oN to 40oN, and 40oN to 50oN). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 15 – 
The results of these regressions are then used to predict changes in column ozone resulting from 

changes in future EESC by month for the different latitude bands. ODS emission projections for the 

period 1985 to 2100 are based on past ODS use and various future ODS control scenarios, combined 

with end use-specific emission rates developed using the Vintaging Model. Future ozone concentrations 

are then calculated based on projected EESC and the month- and latitude-specific regression 

parameters. These calculations produce estimates of future column ozone concentrations (measured in 

Dobson units) for each latitude band and each month of future years for a given ODS control scenario. 

Each ODS control scenario is compared to a baseline ozone level that assumes no further ozone 

depletion after 1980. Using the satellite measurements of atmospheric ozone, estimates of baseline 

monthly ozone levels for each latitude band are equal to the average of 1979 and 1980 ozone levels, 

which accounts for the observed natural biannual variability of ozone levels (WMO 1992). 

4.3 Ozone Protection Policy Scenarios and Projections of Future Ozone Concentrations 

Since the original Montreal Protocol was signed in 1987, amendments to expand and accelerate the 

controls on ODS have been approved by the nations that are Parties to the Protocol. The health effects 

impacts of four policy scenarios—the original Montreal Protocol, the London Amendments of 1990, the 

Copenhagen Amendments of 1992, and the Montreal Adjustments of 1997—are addressed in this 

analysis.

14 These policies are briefly described below. More detailed information on the specific 

restrictions for all ODS for each policy scenario is presented in Appendix C. 

The original Montreal Protocol, signed in 1987, was the first step in international efforts to protect 

stratospheric ozone. Under this agreement, developed countries were required to begin phasing out 

CFCs in 1993 and achieve a 50% reduction relative to 1986 consumption levels by 1998. Under this 

agreement, CFCs were the only ODS addressed. Skin cancer incidence and mortality modeled under this 

policy reflect decreases in CFC emissions based on control measures adopted by signatories for 

developed and developing (Article 5) countries. 

The London Amendments changed the ODS emission schedule by requiring the complete phaseout of 

CFCs, halons, and carbon tetrachloride by 2000 in developed countries, and by 2010 in developing 

countries. Methyl chloroform was also added to the list of controlled ODS, with phaseout in developed 

countries targeted in 2005, and in 2015 for developing countries. Thus, relative to the original Montreal 

Protocol, this policy scenario decreased U.S. skin cancer incidence and mortality. 

The Copenhagen Amendments significantly accelerated the phaseout of ODS and incorporated an HCFC 

phaseout for developed countries, beginning in 2004. Under this agreement, CFCs, halons, carbon 

tetrachloride, and methyl chloroform were targeted for complete phaseout in 1996 in developed countries. 

The results show further reductions in health effects related to ozone depletion. 

Finally, the Montreal Adjustments, the most stringent control policy accepted by the Parties thus far, 

includes the phaseout of HCFCs in developing countries, as well as the phaseout of methyl bromide in 

developed and developing countries in 2005 and 2015, respectively. 

Projected column ozone concentrations for the four policy control scenarios examined in this paper 

relative to ozone levels prevalent in 1979-1980 (“baseline”) are presented in Figure 2 for the 30°N to 40°N 

degree latitude band. The figure shows that the initial international agreements to curtail ODS emissions 

14 The modular nature of the AHEF enables the model to be easily adapted to predict changes in skin cancer 

incidence and mortality, as well as cataract incidence, resulting from almost any scenario involving a change in ozone 

concentrations. For example, the AHEF was recently used in a peer-reviewed assessment to predict the human 

health impacts resulting from a proposed fleet of supersonic aircraft (U.S. EPA 2001). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 16 – 
Dobson Units

350 

325 

300 

275 

Copenhagen Amendments 

London Amendments 250 

Montreal Adjustments 

Montreal Protocol 

225 Baseline (1979-1980) 

200 

175 

150 

5 

5 

5 

5 

5 

5 

5 

5 

5 

5 

5 

05 

98

99

0

01

03

04

06

07

09

1

1

2

2

202

2

2

205

2

2

208

2

Year 

(i.e., the original Protocol and the London Amendments) did much to reduce the risk of future UV 

radiation-induced health effects. The more recent policy scenarios—the Copenhagen Amendments and 

the Montreal Adjustments—provide additional risk reductions. Under these policies, atmospheric chlorine 

and bromine concentrations (expressed as EESC) are predicted to return to 1979-1980 levels in the mid2040s and to maintain these levels indefinitely in the absence of additional ODS regulation—assuming 

full compliance with these mandated restrictions.

15 

Figure 2. Average Annual Ozone Concentration by ODS 

Scenario for the 30oN to 40oN Latitude Band 

While not addressed in this analysis, the Beijing Amendment (2000) introduces further ODS production 

and consumption controls beyond those of the Montreal Adjustments. Specifically, the Beijing 

Amendment calls for a freeze on HCFC production, bans the import or export of HCFCs between nonParties to the Copenhagen Amendments, and prohibits the non-essential production and consumption of 

bromochloromethane. However, because these additional policy requirements represent small 

incremental changes when compared to the overall ODS emission scenario (relative to those of the 

Montreal Adjustments), this policy scenario is not addressed further in this report or in the AHEF. 

15 The modeling projections assume no impacts from changes in non-ODS atmospheric perturbations (e.g., changes 

in global climate). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 17 – 
F(λ, x, t) = Ftoa(λ) T(λ, x, t; θo, O3, clouds, aerosols, ...) 

5. Modeling Changes in UV Exposure and Health Effects 

This section describes the method for calculating future ground-level UV exposures and relating these 

values to changes in human health effects. Generally, this is done by first using the predicted future 

ozone concentrations under various policy scenarios as discussed in the previous section to model the 

change in UV radiation reaching the ground in the three latitude bands across the United States. 

Incremental changes in health effect incidence and/or mortality are then modeled based on those 

predicted changes in ground-level UV intensity. 

5.1 The Tropospheric Ultraviolet-Visible Radiation (TUV) Model 

The amount of UV radiation reaching the Earth’s surface is computed using the TUV radiation model 

(Madronich 1992, 1993b; Madronich and de Gruijl 1993; Madronich et al. 1996, 1998). The TUV model 

provides “look-up tables” of biologically effective UV radiation as a function of the total ozone column and 

solar zenith angle. These tables are then used to calculate cumulative UV radiation (measured in 

Watts/m2

) by latitude, time of day, day of month, and amount of ozone measured in Dobson units. The 

AHEF has many different look-up tables, one for each different way of weighting the entire UV spectrum 

reaching the ground. The rationale for alternative ways to weight the UV spectrum is discussed fully in 

Section 5.2. 

The TUV model is widely regarded as an accurate tool for calculating surface UV radiation levels and has 

been updated many times to reflect current science. The accuracy of the TUV model has been 

demonstrated in a number of comparisons to direct measurements of UV at the Earth’s surface (e.g., 

Shetter et al. 1992, 1996; Kirk et al. 1994; Lantz et al. 1996; Gao et al. 2001; Bais et al. 2003). Since 

1989, the TUV model has been used in the scientific evaluations of ozone depletion and related 

environmental consequences as mandated under the Montreal Protocol (WMO 1990, 1992, 1995, 1999; 

UNEP 1989, 1991, 1994, 1999). 

The TUV model computes the radiation levels at any height in the atmosphere, including the Earth’s 

surface. The spectral irradiance incident at the Earth’s surface (i.e., the radiation at the Earth’s surface 

measured across the UV spectrum), defined as F(λ, x, t), at time t, location x, and wavelength λ, may be 

represented symbolically as the product of the solar spectral irradiance at the top of the atmosphere, 

Ftoa(λ), and an atmospheric transmission factor, T. 

Equation 1 

Values of Ftoa(λ) are known from direct observations of the Sun by satellite-, balloon-, and ground-based 

instruments. The values of T depend on parameters such as the solar zenith angle (θo), atmospheric 

optical properties—specifically UV radiation absorption by ozone and possibly certain pollutant gases 

(e.g., NO2, SO2)—and UV scattering by air molecules (known as Rayleigh scattering). Atmospheric 

particles (e.g., clouds and aerosols) can contribute to both absorption (e.g., soot) and scattering (e.g., 

water mist) (mathematically described by the Mie theory) and may be used as inputs to the TUV. Surface 

reflections can contribute substantially to the radiation incident at the surface and are also included as 

inputs to the TUV (see, for example, McKenzie et al. 1998). Many of these factors are functions of 

location (i.e., latitude, surface elevation, altitude above the surface) and time (i.e., day, year, and longterm trends), so that F(λ, x, t) is also a strong function of time and geographic site. The TUV model 

calculates T by first subdividing the atmosphere into vertical layers (typically 1 km thick), each having an 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 18 – 
assumed uniform composition. The model uses vertical profiles of air density, temperature, and ozone 

from the United States Standard Atmosphere16 (USSA 1976). The propagation of radiation through the 

atmosphere is then computed using a four-stream discrete ordinates method (Stamnes et al. 1988)—an 

approach that is widely accepted in the radiative transfer community as being accurate to within a few 

percentage points or better. The four-stream method is superior to most other models currently in 

operation, which use a two-stream discrete ordinates method. 

5.2 Action Spectra 

An action spectrum describes the relative effectiveness of energy at different UV wavelengths in 

producing a particular biological response, such as development of CMM, BCC, or SCC. The AHEF and 

the TUV rely on action spectra for each health effect because action spectra provide information 

regarding which wavelengths of the total UV spectrum are most effective at causing the particular health 

effect. For example, UV-B wavelengths (280-320 nm) are known to cause erythema, as well as the 

development of skin cancer, cataracts, and suppression of components of the immune system. UV-A 

radiation (320-400 nm) is not as readily absorbed by ozone (NOAA/National Weather Service 2002), and 

is not as potent as UV-B in the etiology of UV damage-related health effects. To ensure that the 

wavelengths are appropriately weighted when predicting each health effect, it is necessary to measure 

the extent to which wavelengths cause a particular health effect. 

Prior to developing incidence and mortality estimates for health effects, an action spectrum must be 

selected for each health effect. Obtaining action spectra requires heavy investment, in terms of cost, 

labor, and time. Moreover, because it is unethical to expose human subjects to harmful UV radiation to 

test for the induction of skin cancers, action spectra for human health effects are only available for 

generalized DNA damage and a variety of animal subject experiments. Thus, the identification of an 

appropriate action spectrum for each health effect modeled by the AHEF required careful selection from 

the limited available action spectra. The following section describes the action spectra considered and 

selected for each health effect modeled in the AHEF, and explains the reasoning behind the ultimate 

selection. 

5.2.1 Selection of Action Spectra for Modeling Skin Cancer 

For modeling melanoma and NMSC incidence and mortality, three action spectra were considered: 

 DNA-h (1991): This is a generalized DNA damage action spectrum (derived from studies on 

bacteria and other microorganisms), adjusted to account for transmission through untanned 

human skin.17 This may be a good selection for predicting skin cancer incidence and mortality in 

humans, as theoretical analyses suggest that skin cancers result from damaged DNA (Pitcher 

and Longstreth 1991, Setlow 1974). In contrast to other action spectra, the DNA-h action 

spectrum is more tightly focused on the UV-B portion of solar UV irradiance, giving less weight to 

the UV-A portion of solar UV irradiance, which is not significantly altered by ozone depletion. 

 Erythema (1987): This is an action spectrum derived on the basis of erythema (sunburn) 

induction in human volunteers (McKinlay and Diffey 1987). It has been observed in several 

16 The US Standard Atmosphere (USSA) provides average values for a number of atmospheric parameters at 

different altitudes. 

17 Absorbing molecules in the cell layers of human skin selectively filter out some wavelengths of UV light, reducing 

the effective energy that reaches human DNA. Microbial organisms may not have the same degree of protection from 

UV and thus are exposed to a greater amount of radiation. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 19 – 
Figure 3. Action Spectra Considered for Modeling Skin Cancer 

0.00001 

0.00010 

0.00100 

0.01000 

0.10000 

1.00000 

10.00000 

270 290 310 330 350 370 390 410 

Wavelength (nm) 

Response 

Erythema 

DNA-h 

SCUP-h 

Note: The response (plotted on the Y-axis) is a log scale. At the normalizing wavelength, every 1 percent change in UV 

radiation causes a 1 percent change in the health effect. 

epidemiologic studies that CMM risk is associated with intermittent intense UV exposure such as 

that acquired through sunburning. 

 SCUP-h (1993): The SCUP spectrum was derived on the basis of the induction of SCC in 

hairless mice (denoted as SCUP-m). Because mouse skin and human skin have different 

absorption spectra for UV light, the action spectrum was corrected for human skin transmission 

by making adjustments to account for differences in epidermal thickness and the number of hair 

follicles per unit area. This adjusted action spectrum is denoted as SCUP-h (de Gruijl et al. 1993). 

Figure 3 graphically presents the three action spectra described above. As can be seen, all of these 

action spectra show strong sensitivity at UV-B wavelengths (hence the sensitivity of skin cancer to ozone 

changes), and much less so to UV-A (which has a much lower sensitivity of skin cancer to ozone 

changes). 

Based on the available action spectra, the SCUP-h action spectrum was selected for modeling all types of 

skin cancers. Rationales for selecting this action spectrum for each type of skin cancer are as follows: 

 SCC: The scientific community is largely in agreement that the SCUP-h is the appropriate action 

spectrum for SCC. Because there is sufficient scientific evidence to indicate that SCUP-h is the 

correct choice to predict SCC, the SCUP-h action spectrum was chosen to predict incidence of 

SCC in the AHEF. 

 BCC: Because a biological action spectrum has not been developed for BCC, the SCUP-h was 

selected for use in the AHEF to predict incidence of this cancer, as it is the only available action 

spectrum based on the induction of skin cancer. SCUP-h is currently believed to be the most 

appropriate for modeling BCC (Longstreth et al. 1998). Nevertheless, because there is less 

scientific certainty in applying SCUP-h to predict BCC than SCC, this approach can be 

reconsidered if future studies investigating the action spectrum for BCC reveal that it is not 

congruent with the SCUP-h action spectrum for SCC. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 20 – 
 CMM: As is the case with BCC, the SCUP-h action spectrum was selected for use in the AHEF to 

predict CMM because action spectra specific to CMM have not yet been derived. However, this 

analysis may be reconsidered if future studies reveal that the action spectrum for CMM is not 

congruent with the SCUP-h action spectrum for SCC (i.e., if it does not have similar shape). To 

date, however, an action spectrum for CMM has not yet been developed. 

5.3 Dose-Response Relationships 

Once the appropriate action spectrum is selected for each health effect and the UV dose of biologically 

active radiation and morbidity or mortality across latitudes are identified, statistical regression analyses 

are used to estimate the dose-response relationship, known in technical terms as the biological 

amplification factor (BAF), for each health effect. The dose response relationship, or BAF, measures the 

degree to which changes in UV exposure weighted by the appropriate action spectrum (as measured in 

Watts/m2

) cause incremental changes in health effects (incidence or mortality), estimated after accounting 

for the influence of birth year and age, as necessary. 

BAFs are defined as the percent change in a health effect resulting from a one-percent change in the 

intensity of UV radiation (weighted by the chosen action spectrum). For each health effect, the AHEF 

applies the BAF to predict future incidence and mortality. Estimated ground-level effective UV irradiance 

from the TUV model is combined with a selected BAF to translate changes in UV exposure to a 

percentage change in expected health effects. 

The remainder of this section provides a description of how the available data and existing studies are 

utilized to develop BAFs for each health endpoint, as well as the uncertainties these estimated BAFs 

introduce to the AHEF’s results. 

5.3.1 Derivation of BAFs for CMM Incidence/Mortality and NMSC Mortality 

BAFs for CMM incidence were not estimated separately from the BAFs for CMM mortality because, as 

described in Section 3.2, CMM incidence rates do not vary independently of CMM mortality rates in the 

model. The is due to the fact that CMM incidence rates were derived based on the EPA/NCI CMM 

mortality data set and the ratio of incidence to mortality for CMM. Therefore, BAFs used to predict the 

incidence of CMM are the same as those used to predict CMM mortality (i.e., the percentage change in 

CMM incidence is the same as for CMM mortality). 

The general method for estimating BAFs for CMM incidence and mortality and NMSC mortality is as 

follows: 

 Detailed EPA/NCI data on CMM and NMSC mortality provided raw mortality information by sex, 

age, and birth cohort year for a large set of locations across the United States. (Because the 

EPA/NCI data for NMSC do not provide a distinction between BCC and SCC, it is only possible to 

derive a BAF for total NMSC mortality using these data.) 

 Combining CMM and NMSC incidence data with the associated total population by sex for each 

location, the rate of mortality per 100,000 people was computed for both CMM and NMSC. 

 UV exposure at each location was then calculated based on latitude. Specifically, Serafino and 

Frederick’s UV irradiance model was used to estimate ground-level UV exposures for each of the 

639 U.S. sites surveyed by EPA/NCI, based on techniques, input data, and modeling methods 

described in Pitcher and Longstreth (1991). 

 A statistical technique was then used to determine the influence of UV exposure and age on 

CMM and NMSC mortality rates. In the case of CMM, the influence of birth cohort year was also 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 21 – 
determined, to account for the cohort effect.18 The functional form of the regression equation 

used is expressed in Equation 2, below: 

[B0+ BCi

Ci

+ B A B ln( ) UV ] Rij = e ∑ ∑ Aj j +

UV ij +ε ij 

Equation 2 

where: 

Rij = mortality rate (CMM or NMSC) for the i

th cohort and the jth age group19 

UVij = percent change in UV radiation exposure for the ith cohort and the jth age group 

Ci and Aj = cohort and age group categorical variables20 

BCi and BAj = coefficients that control for the influence of cohort and age factors on CMM and 

NMSC mortality21 

B0 = regression coefficient (intercept) 

BUV = BAF for either CMM or NMSC 

εij = Error term, used to measure how accurate the fitted curve is overall (i.e., how close 

on average the curve is to the observations). 

The form of this equation is known as a power function, expressed as ex

, where x is the power. This is 

obtained by deriving a best-fit equation that describes the appropriate dose-response curve, where the 

dose is the yearly amount of weighted UV radiation, and the response is mortality for either CMM or 

NMSC. The BAF is equal to the slope of the dose-response curve for each of these health effects in 

percentage terms. 

Because there are no significant cohort effects associated with NMSC mortality, the cohort term in 

Equation 2 is not estimated as part of the BAF derivation. In addition, although the derived BAFs for 

NMSC do not distinguish between SCC and BCC (given that the EPA/NCI data are not disaggregated 

into BCC and SCC), evidence suggests that 75 percent of the mortality from NMSC is attributable to SCC 

and 25 percent to BCC (Scotto et al. 1983). 

Furthermore, new scientific research has enabled a more thorough understanding of the mechanisms of 

UV radiation-induced NMSC tumor induction that provides a theoretical foundation for the BAFs. 

Research suggests that a gene (p53) may play a significant role in suppressing NMSC skin cancer tumor 

18 More specifically, UV exposure estimates were combined with historical mortality data for both CMM and NMMSC 

mortality, as provided in the EPA/NCI data set. The resulting data set contained CMM and NMSC mortality rates per 

100,000 people by sex, age, and cohort birth year for a large number of sites covering the United States, as well as 

estimated UV exposures at each location. 

19 As explained in Section 5.3, BAFs for CMM mortality and incidence and NMSC mortality were estimated directly 

from primary data sources. BAFs for NMSC and cataract incidence were estimated by other researchers using 

methods similar to those used to calculate BAFs for skin cancer mortality. 

20 Of the health effects of concern, cohort effects are only significant for CMM (i.e., statistics on incidence and 

mortality demonstrate an age-linked component). 

21 This regression is run using the maximum likelihood non-linear Poisson method because the error term is no longer 

normally distributed after the natural log is applied to both sides of the equation. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 22 – 
induction resulting from UV-B radiation (D’errico et al. 1996).22 Certain portions of this gene appear to be 

more susceptible to UV-B-induced mutation than others. These “mutation hotspots” are easily affected by 

UV radiation, as evidenced by the more than 280 of the 393 amino acids sequenced on this gene that 

have been found to be mutated in tumors (Pfeifer and Denissenko 1998). 

Uncertainty associated with the derived BAFs for CMM incidence and mortality and NMSC mortality are 

discussed in detail in Section 9.1.4. 

5.3.2 Derivation of BAFs for BCC and SCC Incidence 

The BAFs for both BCC and SCC incidence are based on analyses conducted by de Gruijl and Forbes 

(1995)—the most up-to-date estimates provided in the literature, derived using the SCUP-h action 

spectrum. In calculating BCC and SCC incidence, De Gruijl and Forbes assumed that humans are 

steadily exposed to UV radiation throughout their lifetimes, and that various skin sites of humans are 

exposed to specific fractions of annual ambient UV exposure. The uncertainty associated with the derived 

BAFs for BCC and SCC incidence is discussed in Section 9.1.4. 

5.4 Resulting BAFs by Health Effect 

The resulting BAFs for all health effects are presented in Table 6, as well as the key inputs used to derive 

each one. For CMM incidence and mortality and NMSC mortality, BAFs for light-skinned males and 

females are shown for both annual exposures with typical cloud conditions and for a peak clear day 

exposure (i.e., June 21). Generally, peak clear day BAFs are substantially greater than annual dose BAFs 

because the annual value is based on exposure across the entire year, while the peak day is only for 

exposure on the sunniest day of the year. Because the variation in UV radiation across latitudes is greater 

on an annual basis than it is on June 21st (when all latitudes across the United States are in the summer 

season and UV radiation is relatively high everywhere), underlying variations in UV radiation are much 

less for peak clear days than for cumulative annual exposure. Therefore, BAFs are larger on peak clear 

days, as they must account for the underlying fixed variation in cancer and mortality across latitudes. In 

both cases, the results will be similar in magnitude: if the peak day UV exposure metric is used, a 

relatively smaller variation in UV exposure across latitudes will be multiplied by a relatively higher BAF, 

while the reverse is true if the cumulative annual UV exposure metric is used. Because annual dose BAFs 

better reflect underlying UV radiation variations across latitudes, they are considered to be more 

representative of actual exposures and are, therefore, used in the AHEF. It should be noted, however, 

that the AHEF is capable of using peak clear day exposures as well. 

For most health effects, BAFs are higher for males than for females, implying that skin cancer incidence 

and mortality in males are typically more affected by a given change in the intensity of UV radiation. This 

may be due to differences in UV exposure patterns between men and women. 

22 Mice lacking one functional copy of gene p53 were shown to be more susceptible to skin cancer induction than 

those with two functional copies. Mice without a functional copy of this gene were even more susceptible to tumor 

induction on both the skin and the eyes (Jiang et al. 1999). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 23 – 
Table 6. Summary of Calculated BAFs and Key Inputs 

Health Effect Data Sourcesa Selected 

Action 

Spectrum 

BAF: Used in AHEF 

(Annual Exposures) 

BAF: Not Used in AHEF 

(Peak Clear Day 

Exposures) 

Males Females Males Females 

CMM Incidence/ 

Mortality 

Incidence: Ratios from SEER 

data set (see Section 3.2) 

Mortality: EPA/NCI data set 

(see Section 3.1) 

BAF: Developed using 

econometric analysis 

(described in Section 5.3.1) 

SCUP-h 

(1993) 

0.5846 0.5047 1.444 1.310 

BCC Incidence Incidence: Based on methods 

used in U.S. EPA (1987) and 

Fears and Scotto (1983) (see 

Section 3.4) 

BAF: de Gruijl and Forbes 

(1995) 

SCUP-h 

(1993) 

1.5 1.3 NA NA 

SCC Incidence Incidence: Based on methods 

used in U.S. EPA (1987) and 

Fears and Scotto (1983) (see 

Section 3.4) 

BAF: de Gruijl and Forbes 

(1995) 

SCUP-h 

(1993) 

2.6 2.6 NA NA 

NMSC Mortality Mortality: EPA/NCI data set 

(see Section 3.3) 

BAF: Developed using 

econometric analysis 

(described in Section 5.3.1) 

SCUP-h 

(1993) 

0.7094 0.4574 2.068 1.565 

NA = Not available.

a For all health effects, the Serafino and Frederick model (1987) was used by independent researchers to derive the BAFs used 

in the AHEF. The Serafino and Frederick model is a radiative transfer model that translates ozone measurements from the 

Nimbus-7 satellite into ground-level solar UV irradiance, similar to the TUV model used in the AHEF. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 24 – 
6. Projecting Future Health Effects Under ODS Control Policies 

The final step in the modeling framework integrates baseline estimates of health effects, estimates of 

future changes in ozone concentration, and the associated changes in solar UV irradiance, appropriate 

UV spectrum weighting functions, and BAFs to project future additional skin cancer incidence and 

mortality for any ODS emission control scenario. Scenario estimates are then compared to the incidence 

and mortality estimates expected under 1979-1980 ozone conditions. 

For example, suppose that it is necessary to estimate the absolute increase in BCC incidence for lightskinned males aged 50-54 living in the southern latitude band of the United States. The BAF for lightskinned male BCC incidence is 1.4 (see Section 5.3.2) and the future percentage change in cumulative 

lifetime SCUP-h-weighted UV exposure of the age 50-54 group is 1 percent (a hypothetical estimate). 

Multiplying these two numbers together indicates that the increase in BCC incidence among this 

population group is estimated to be 1.4 percent for a 1 percent change in weighted UV exposure. When 

this percentage change in incidence is multiplied by the baseline light-skinned male BCC incidence for 

the 50-54 age group (suppose this is 100,000 cases), the result is an estimate of the absolute number of 

additional future cancers of this type among this population group attributable to a 1 percent change in 

UV radiation dose. These calculations are summarized in Equation 3: 

(Cumulative Percentage Increase in UV Exposure) x (BAF for BCC) x (Baseline Incidence of 

BCC for the Population Group) = Absolute Increase in BCC Incidence: 

(0.01) x (1.4) x (100,000) = 1,400 cases 

Equation 3 

Performing these calculations for each health effect and for each future population group produces 

predictions of the additional health effects for a particular ODS emission scenario relative to those 

prevalent during the baseline (1979-1980). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 25 – 
Table 7. Summary of Incremental Skin Cancer Incidence/Mortality for ODS Policy Scenarios 

Relative to Baseline 

Cohort Group/ 

Scenario 

CMM Incidence 

(Cases) 

CMM Mortality 

(Deaths) 

BCC Incidence 

(Cases) 

SCC Incidence 

(Cases) 

NMSC Mortality 

(Deaths) 

Montreal Protocol 

1890-1980 301,687 44,582 8,814,835 5,050,875 30,859 

1985-2010 794,121 109,352 21,250,450 11,517,066 66,829 

2015-2050 2,042,358 265,759 50,978,569 26,627,765 147,554 

2055-2100 3,228,517 409,876 78,708,574 40,793,037 220,452 

London Amendments 

1890-1980 101,523 13,774 2,785,732 1,514,657 7,960 

1985-2010 113,885 13,854 2,688,789 1,375,322 6,926 

2015-2050 80,379 9,527 1,830,867 924,516 4,602 

2055-2100 31,569 3,831 734,634 377,381 1,946 

Copenhagen Amendments 

1890-1980 76,048 10,118 2,047,391 1,096,153 5,593 

1985-2010 66,922 7,815 1,495,278 743,682 3,634 

2015-2050 18,026 2,023 379,285 186,009 906 

2055-2100 0 0 0 0 0 

Montreal Adjustments 

1890-1980 68,816 9,076 1,834,142 974,827 4,923 

1985-2010 54,940 6,356 1,210,046 599,467 2,925 

2015-2050 10,308 1,155 216,245 105,993 517 

2055-2100 0 0 0 0 0 

Note: The numbers presented above indicate the number of cases in excess of the baseline (1979-1980) for each scenario. 

7. Modeling Results 

This section presents the projected changes in incidence and/or mortality for each of the health effects 

and policy scenarios examined. 

7.1 Results Presented by Policy Scenario and by Health Effect 

Table 7 presents the incremental number of skin cancer cases/deaths in excess of the baseline (i.e., 

those associated with changes in column ozone concentrations from levels observed in 1979-1980) that 

are projected to occur under each ODS control scenario. Decreasing incidences/mortalities that result as 

more stringent control scenarios are implemented illustrates the benefits of each further amendment 

and/or adjustment to the Montreal Protocol. Table 8 presents the avoided health effects realized in 

moving from one ODS policy scenario to the next (e.g., from the Montreal Protocol to the London 

Amendments). Figure 4 illustrates that as ODS controls are tightened, additional incidence and mortality 

estimates for each health effect relative to baseline move closer to zero on the y-axis (i.e., closer to the 

incidence and mortality that would be expected if 1979-1980 ozone concentrations had been maintained 

throughout the time period modeled). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 26 – 
Table 8. Incremental Number of Avoided Skin Cancer Incidence/Mortality Under ODS Policy

Scenarios with Increasingly Stringent Controls 

Cohort 

Group/Scenario 

CMM Incidence 

(Cases) 

CMM Mortality 

(Deaths) 

BCC Incidence 

(Cases) 

SCC Incidence 

(Cases) 

NMSC Mortality 

(Deaths) 

Incremental Number of Avoided Cases/Deaths: From Montreal Protocol to London Amendments 

1890-1980 200,164 30,809 6,029,103 3,536,217 22,898 

1985-2010 680,236 95,498 18,561,661 10,141,745 59,903 

2015-2050 1,961,979 256,232 49,147,703 25,703,249 142,952 

2055-2100 3,196,948 406,045 77,973,940 40,415,656 218,506 

Total 6,039,327 788,584 151,712,406 79,796,866 444,258 

Incremental Number of Avoided Cases/Deaths: From London Amendments to Copenhagen Amendments 

1890-1980 25,475 3,655 738,342 418,504 2,368 

1985-2010 46,963 6,038 1,193,511 631,640 3,292 

2015-2050 62,353 7,504 1,451,582 738,507 3,697 

2055-2100 31,569 3,831 734,634 377,381 1,946 

Total 166,360 21,028 4,118,068 2,166,033 11,303 

Incremental Number of Avoided Cases/Deaths: From Copenhagen Amendments to Montreal Adjustment 

1890-1980 7,232 1,042 213,249 121,326 670 

1985-2010 11,982 1,459 285,232 144,215 708 

2015-2050 7,718 868 163,040 80,017 389 

2055-2100 0 0 0 0 0 

Total 26,932 3,369 661,520 345,557 1,767 

Note: The numbers presented above indicate the number of avoided cases from one policy scenario to another. 

Based on data presented in Table 7, Figure 4 through Figure 7 graphically present the incremental health 

benefits for successively more stringent scenarios for CMM incidence, CMM mortality, NMSC incidence, 

and NMSC mortality.23 As shown in Figure 4, the curve representing impacts associated with the Montreal 

Adjustments most closely approaches the baseline (1979-1980) ozone concentration (at zero on the yaxis) after a number of years, but there remain significant opportunities for further reducing health effects. 

Because the recovery of ozone (i.e., the return to pre-depletion levels prevalent in the 1979-1980 

timeframe) is projected to occur around 2050, no exposures attributable to ozone depletion will accrue for 

people born after this recovery date. Incremental UV exposures for people born before 2050, however, 

will continue to result in health effects incidence and mortality after 2050, albeit at a lower rate than in 

earlier years. By approximately 2150, it is predicted that there will be no living population that experienced 

incremental exposure associated with depleted ozone levels, and hence, no additional health effects 

incidence or mortality above those expected to occur under “normal” conditions (i.e., 1979-1980 ozone 

levels). 

23 These estimates do not include effects on ozone from climate variation and other factors. How climate may 

ultimately affect the recovery of stratospheric ozone is unclear and beyond the scope of the AHEF. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 27 – 
Figure 4. Annual Incremental U.S. CMM Incidence through 2100 

Under Different ODS Control Policies (SCUP-h Action Spectrum) 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

9,000 

10,000 

1985

1995

2005

2015

2025

2035

2045

2055

2065

2075

2085

2095 

Year 

Excess Incidence 

Montreal 

Protocol 

London 

Amendments 

Copenhagen 

Amendments 

Montreal 

Adjustments 

Note: Because this graph shows the incremental CMM incidence relative to the 1979-

1980 baseline, the level of CMM incidence in the baseline is represented by zero on the 

y-axis. 

Figure 5. Annual Incremental U.S. CMM Mortality through 2100 

Under Different ODS Control Policies (SCUP-h Action Spectrum) 

0 

50 

100 

150 

200 

250 

300 

350 

400 

450 

500 

1985

1995

2005

2015

2025

2035

2045

2055

2065

2075

2085

2095 

Year 

Excess Mortality 

Montreal 

Protocol 

London 

Amendments 

Copenhagen 

Amendments 

Montreal 

Adjustments 

Note: Because this graph shows the incremental CMM mortality relative to the 1979-

1980 baseline, the level of CMM mortality in the baseline is represented by zero on the yaxis. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 28 – 
Figure 6. Annual Incremental U.S. NMSC Incidence through 2100 

Under Different ODS Control Policies (SCUP-h Action Spectrum)

0 

25,000 

50,000 

75,000 

100,000 

125,000 

150,000 

175,000 

200,000 

1985 

1995 

2005 

2015 

2025 

2035 

2045 

2055 

2065 

2075 

2085 

2095 

Year 

Excess Incidence 

Montreal 

Protocol 

London 

Amendments 

Copenhagen 

Amendments 

Montreal 

Adjustments 

Note: Because this graph shows the incremental NMSC incidence relative to the 1979- 

1980 baseline, the level of NMSC incidence in the baseline is represented by zero on the 

y-axis. 

Figure 7. Annual Incremental U.S. NMSC Mortality through 2100 

Under Different ODS Control Policies (SCUP-h Action Spectrum) 

0 

50 

100 

150 

200 

250 

300 

350 

400 

450 

500 

1985 

1995 

2005 

2015 

2025 

2035 

2045 

2055 

2065 

2075 

2085 

2095 

Year 

Excess Mortality 

Montreal 

Protocol 

London 

Amendments 

Copenhagen 

Amendments 

Montreal 

Adjustments 

Note: Because this graph shows the incremental NMSC mortality relative to the 1979- 

1980 baseline, the level of NMSC mortality in the baseline is represented by zero on the 

y-axis. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 29 – 
Table 9. Summary of Results for Selected Health Effects by Action Spectrum Under the Montreal 

Adjustments Scenario 

Action Cohort Group Health Effect 

Spectrum CMM CMM NMSC 

Incidence Mortality Mortality 

(Cases) (Deaths) (Deaths) 

1890-1980 98,914 13,025 7,342 

1985-2010 78,922 9,106 4,354 

DNA-h 2015-2050 14,658 1,636 760 

2055-2100 0 0 0 

1890-1980 68,289 8,983 4,865 

1985-2010 54,652 6,295 2,892 

Erythema 2015-2050 10,258 1,143 510 

2055-2100 0 0 0 

1890-1980 68,816 9,076 4,923 

1985-2010 54,940 6,356 2,925 

SCUP-h 2015-2050 10,308 1,155 517 

2055-2100 0 0 0 

8. Sensitivity Analyses 

This section describes the results of sensitivity analyses conducted concerning the selection of action 

spectra for CMM incidence and CMM and NMSC mortality and the effects of modeling early life exposure 

on CMM mortality. 

8.1 Action Spectra and Exposure/Dose Metrics Sensitivity Analyses 

Because there are uncertainties associated with selecting an appropriate UV action spectrum and dose 

metric for induction of the UV radiation -induced health effects discussed in this paper, it is important to 

explore the impacts of alternative action spectra and dose metrics on these estimates. Table 9 presents 

results of simulations performed under the Montreal Adjustments policy scenario to estimate CMM 

incidence and mortality and NMSC mortality using three different action spectra (i.e., erythema, DNA-h, 

and SCUP-h). 

The incremental health effects are comparable for all action spectra, with the DNA-h action spectrum 

having somewhat higher values for the selected health effect end-points. As discussed in Section 5.2.1, 

the DNA-h action spectrum is tightly focused on the UV-B portion of solar UV irradiance. In contrast, the 

other action spectra give more weight to the UV-A portion of solar UV irradiance, which is not altered by 

ozone depletion. Thus, BAFs generated from the DNA-h action spectrum are likely to be different from 

those generated by the more UV-A-focused action spectra, resulting in differences in estimates of 

predicted skin cancer cases. For reasons outlined in Section 5.2.1, selection of the SCUP-h action 

spectrum to model CMM and NMSC is the most scientifically valid choice. It also yields estimated health 

benefits that are lower than would be modeled using the DNA-h action spectrum, for a more conservative 

modeling approach. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 30 – 
Figure 8. Excess CMM Mortality for the Montreal Adjustments Scenario for Equal Age Exposure 

Weighting and Weighting of Exposures Only for Ages 1-20: Annual and Peak Day Exposures 

0 

100 

200 

300 

400 

1985 2005 2025 2045 2065 2085 2105 2125 2145 

Year 

Excess CMM Mortality 

Cumulative Exposures, All 

Ages Weighted Equally 

Cumulative Yearly Exposure, 

Ages 1-20 Only 

Cumulative Peak Exposure, All 

Ages Weighted Equally 

Cumulative Peak Exposure, 

Ages 1-20 Only 

8.2 CMM: Effects of Emphasizing Early Life Exposures 

Another area of uncertainty for which sensitivity was modeled involves the weight that is given to UV 

exposures at different points in an individual’s life. Cumulative UV exposure over a lifetime is thought to 

be the most important risk factor for NMSC. However, early life exposure has been identified as a 

potentially important risk factor associated with increased susceptibility to CMM (Harrison et al. 1994, 

Zanetti et al. 1992). It has been estimated that people receive 50 percent of their cumulative lifetime UV 

exposure before the age of 18 (Bergfeld 1997). 

Because there is a long latency period associated with the onset of CMM, disease manifestation for 

exposure to UV may not be seen for decades (Geller et al. 1998). While the current simulation for CMM 

does not model latency, it does offer the option of weighting UV exposures by age and by type of 

exposure (e.g., peak day exposure and annual exposure) summed over the lifetime of the individual. 

Figure 8 presents the results of these simulations for CMM mortality using annual and peak day 

exposures, each computed either by weighting all exposures equally over a person’s lifetime, or by 

weighting only the exposures received between age one and age twenty. More specifically, the following 

approach was used for estimating whole-life versus early-life exposures: 

 For whole life exposure: exposures throughout the individual’s lifetime are given equal weighting 

(i.e., each year’s exposure is counted in the results). 

 For early life exposure: only exposures received between the ages of one and 20 are considered 

(i.e., later life exposures do not contribute to the results). 

Table 10 tabulates excess CMM mortality using the SCUP-h action spectrum for both whole lifetime 

exposure and early life exposure assumptions for the Montreal Adjustments policy scenario, assuming 

peak and annual dose exposure scenarios. The table indicates that CMM mortality changes by up to 11 

percent when the age-weighting exposure assumptions are changed. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 31 – 
a Table 10. Incremental CMM Mortality Under Different Dose and Exposure Weighting Assumptions

(SCUP-h Action Spectrum) 

CMM MORTALITY 

Cohort Group Annual Dose Peak Dose 

Whole Life Exposure Early Life Exposure Whole Life Exposure Early Life Exposure 

1890-1980 9,100 2,100 7,100 1,700 

1985-2010 6,400 12,600 5,000 9,900 

2015-2050 1,200 3,500 900 2,800 

2055-2100 0 0 0 0 

Total 16,700 18,200 13,000 14,400 

Change 9% 11% a

 Results are rounded to the nearest 100. 

The timing of the health effects changes, however, is more dramatic. Using the age 1-20 cumulative 

exposure scenario shifts the incremental skin cancer risk substantially forward in time from current 

generations to future ones. Therefore, uncertainty concerning the appropriate exposure dose manifests 

itself less in the total incremental risks predicted, than in when those incremental effects are predicted to 

occur and, thus, who will bear them (i.e., this shifts the risk to children born after 1980). More specifically, 

for the whole life exposure assumption, the risks of ozone depletion are borne primarily by the present 

population of adults who will experience these health effects as they age. It is children and future 

generations that will experience increased early life UV exposures and the associated incremental health 

effects later in their lives. It should be noted that this shift of health risks does not reflect a formal 

modeling of CMM latency, which would involve an elaborate method for assigning different weights to 

exposures incurred at different ages or some other yet-to-be-developed approach. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 32 – 
9. Uncertainty Analysis 

The AHEF, like any complex modeling framework, uses inputs and computational procedures that 

introduce uncertainty to the results. These inputs come from various existing sources and are combined 

with other inputs and procedures derived specifically for this analytical framework. Proper interpretation 

and use of the human health effects results generated by the AHEF requires some understanding of the 

nature and magnitudes of the major sources of uncertainty involved. This section uses a combination of 

empirical analyses and theoretical reasoning to roughly characterize the quantifiable and unquantifiable 

uncertainties associated with the AHEF’s incidence and mortality predictions. 

The remainder of this section is organized as follows: 

 Section 9.1 focuses on four major sources of uncertainty in the AHEF’s estimates of health 

effects that are considered to be central to its structure, and that have been quantified to the 

extent possible; 

 Section 9.2 presents a discussion of other unquantified sources of uncertainty that affect the 

AHEF’s results, but that are not considered to be central to its structure; and 

 Section 9.3 summarizes the quantified and unquantified sources of uncertainty. 

9.1 Major Sources of Uncertainty 

The AHEF uses past and future ODS emissions to generate equivalent effective stratospheric chlorine 

(EESC) concentrations, which in turn are used to estimate stratospheric column ozone changes. These 

column ozone changes then are used to compute changes in ground-level UV radiation, from which 

estimated changes in human health effects can be calculated. Figure 9 illustrates these model inputs. 

Figure 9. Central Computational Procedures Associated with Uncertainty in the AHEF 

ODS Emissions 

EESC Concentrations 

Column Ozone Changes 

Ground-Level UV Changes 

Changes in Human Health Effects 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 33 – 
Each of the linkages identified in the figure is the source of some degree of uncertainty. Although some 

might attempt to combine these different sources using statistical techniques, it is best to consider each 

source separately for two reasons. First, the quantitative estimates of the levels of uncertainty of the 

AHEF's many inputs and modeling components were derived using different techniques of varying levels 

of precision. Second, and perhaps more important, is that uncertainties concerning some of the inputs 

and computations might be inversely related. For example, if the TUV's estimated ground-level UV 

radiation is biased upward, so that variations in UV exposures are too high, then the estimated BAFs 

(which are derived based on correlation with ground-level UV radiation variation) will be biased 

downward. 

From a purely statistical standpoint, the largest source of uncertainty in the AHEF is the EESC-to-column 

ozone component, with standard errors around the mean effects ranging from about 25 to over 100 

percent. However, as will be discussed in Section 9.1.2, this is a product of the limited data available for 

the regression analysis and likely does not reflect the true uncertainty that would be revealed with 

substantially more data. 

By contrast, the TUV's estimates of changes in ground-level UV radiation due to changes in column 

ozone impart statistical uncertainty of up to 10 percent. Similarly, the choice of action spectrum for each 

health endpoint yields very small variations in the health effects results, with the exception of the DNA-h 

action spectrum (which is not used in the AHEF), as explained in more detail in Section 9.1.4. The last 

two sources of quantified uncertainty—the age-weighted exposure scenario assumption and the 

estimated BAFs—also introduce relatively modest variation in the estimated health effects, of about 11 

percent and up to 30 percent, respectively. 

Thus, as is true of any complex modeling framework with multiple inputs and computational procedures, 

the AHEF does contain uncertainties. Perhaps over time, these can be reduced as additional data and 

research become available. At present, however, the AHEF embodies the best inputs, assumptions, and 

computational procedures that are known. The remainder of this section discusses the five major areas of 

uncertainty in greater detail. 

9.1.1 Translating ODS Emissions into EESC Concentrations 

One source of uncertainty in the AHEF methodology is that the magnitude of ozone depletion and 

recovery based on ODS emissions could be different from those predicted under the international controls 

in place now or scheduled for the future. This could occur because ODS use might be less than allowed 

under the various current and future phaseout requirements, or ODS use could be higher in the future if 

ODS use exceeds allowable amounts due to non-compliance with the phaseout targets. However, to 

date, countries have reportedly tended to over-comply with Montreal Protocol obligations (i.e., they have 

generally undertaken ODS phaseout efforts before the limits imposed by the Protocol take effect), as 

described in WMO (2003). For example, in 1999, reports of CFC production indicated that production of 

CFCs was 20,000 ODP-tons less than allowable consumption in that year (WMO 2003).24 Thus, the 

scenario of total compliance used in the AHEF may potentially represent the maximum ODS emissions 

scenario. 

Similarly, the parameters that characterize the process of how ODS emissions translate into EESC are 

also taken to be given, despite the fact that the reaction kinetics of these transitions and the composition 

24 Although scientific measurements of actual CFC-11 and CFC-12 emissions have indicated that mixing ratios were 

5 to 10 percent higher than ratios that would have been expected if production levels were identical to those reported, 

the discrepancy between measurements and reported values could be related to differences in measured and 

reported values that have occurred throughout the entire measurement period for CFC-11 and CFC-12, rather than 

as a result of under-reporting in 1999. Supporting this hypothesis, measurement and production values have been 

closer in recent years (WMO 2003). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 34 – 
Table 11. Means and Standard Errors of EESC to Column Ozone Coefficients for Select Months 

and Latitudes (Change in Dobson Units for a 1 ppb Change in EESC) 

Month 30°N-20°N Latitude Band 40°N-30°N Latitude Band 50°N-40°N Latitude Band 

Mean Standard Error Mean Standard Error Mean Standard Error 

January -0.0160 0.0104 (65%) -0.0344 0.0124 (36%) -0.0431 0.0122 (28%) 

April 

July

October 

-0.0142 

 -0.0032 

-0.0077 

0.0096 (68%) 

0.0055 (172%) 

0.0053 (69%) 

-0.0268 

-0.0080 

-0.0076 

0.0108 (40%) 

0.0060 (75%) 

0.0045 (59%) 

-0.0400 

-0.0103 

-0.0122 

0.0107 (27%) 

0.0074 (72%) 

0.0045 (37%) 

of the future atmosphere are also subject to uncertainty (as discussed in more detail in Section 9.2). 

These inputs are as up-to-date as the available complex atmospheric models can provide. Moreover, 

undertaking a sensitivity analysis for all of the relevant parameters that translate ODS emissions at the 

ground into EESC would be prohibitively resource intensive. Hence, the uncertainties in ODS 

use/emissions to EESC portion of the AHEF’s structure are noted, but not quantitatively examined. 

9.1.2 Translating EESC Concentrations into Stratospheric Column Ozone 

From a statistical standpoint, the largest source of uncertainty in the AHEF is introduced by the limited 

data points available for use in predicting changes in column ozone resulting from changes in EESC. The 

reason these factors are statistically uncertain is that they are estimated from a very limited data set of 

satellite-measured stratospheric ozone concentrations and estimated EESC for the years that 

stratospheric ozone data are available from NASA's Nimbus-7 Total Ozone Mapping Spectrometer 

(TOMS) (i.e., 1978 to 1993). Relatively few observations in a data set can lead to large standard errors in 

any statistical analysis. 

Furthermore, UV radiation changes resulting from ozone depletion and ground level pollution (i.e., 

tropospheric ozone generation) are more accurately measured by spectrally resolved ground-based 

monitors than by satellite measurements on which the AHEF currently relies (this is discussed further in 

Section 10.5). Satellite data are not as accurate for measuring ozone concentrations at ground-level as 

they are at higher altitudes because of the coupling between UV absorption by ozone and UV scattering 

by aerosols and particulate matter. These considerations are important when the ozone perturbations 

occur in the lower-to-middle troposphere, where soot and other aerosols are prevalent. When ozone 

perturbations occur in the stratosphere (i.e., well above the region where scattering occurs), absorption 

predominates. Thus, the altitude at which ozone perturbations occur can affect UV radiation at the ground 

level. These effects are not well captured by satellite data and hence, ground level UV monitoring data 

could help to improve modeling estimates, particularly in urban areas. 

Table 11 presents the estimated mean impacts of EESC on column ozone, along with the standard 

errors, for four different months and for each of the three latitude bands modeled by the AHEF. Because 

the AHEF estimates EESC by year and then estimates column ozone by month and latitude based on 

regression analyses using TOMS data, the variation in the AHEF’s predicted ozone by month and latitude 

is attributable in large part to the data source and not the regressions that estimate the impact of EESC 

on column ozone. EESC is measured in parts per billion and column ozone is measured in Dobson units. 

Table 11 illustrates that an EESC increase of 1,000 parts per billion (ppb) results in an estimated 

reduction of 16 Dobson Units of column ozone in January in the 30°N to 20°N latitude band. 

Standard errors of roughly 25 to over 150 percent indicate large statistical uncertainty of the column 

ozone coefficients. Until additional data on column ozone from satellite or ground-level measurements are 

obtained to refine these estimates, such uncertainty cannot be reduced. For additional discussion on the 

uncertainty associated with the AHEF’s column ozone estimates, see Appendix D, which compares AHEF 

and WMO (1999) predicted ozone concentrations. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 35 – 
9.1.3 Translating Column Ozone into Ground-Level UV Radiation 

Uncertainty in the estimation of weighted UV exposure at the Earth’s surface was not explicitly quantified. 

Experts generally agree that the uncertainty contributed by the column ozone-to-UV calculations is 

relatively small compared to those introduced by other inputs and components of the analysis. 

Uncertainties in translating column ozone to ground-level UV radiation are dominated by uncertainties in 

the following: 

• Clear sky radiation model. The accuracy of the TUV model has been evaluated extensively by 

comparisons with other models (e.g., Koepke et al. 1998) and with direct measurements of UV 

radiation (e.g., Shetter et al. 1992, 1996; Kirk et al. 1994; Lantz et al. 1996; Gao et al. 2001; Bais 

et al. 2003). For spectrally resolved radiation, the agreement is 10% or better for all wavelengths 

of biological relevance (e.g., Kirk et al. 1994, Bais et al. 2003). For integrated quantities (e.g. 

biologically effective UV and atmospheric photolysis coefficients), agreement improves to roughly 

5% or better due to averaging over the relevant wavelength ranges. These small errors are 

believed to result primarily from uncertainties in the extraterrestrial irradiance (approximately 5

10% in the UV-B band), the ozone absorption cross-section (less than 2% in the UV-B, De More 

et al. 1997), and from incomplete knowledge of the atmosphere (e.g., exact aerosol amount) at 

the time of the measurements. 

• UV perturbations due to clouds and air pollutants. Clouds and air pollutants generally reduce the 

UV radiation incident at the Earth’s surface. However, as long as cloud cover and pollutant levels 

remain constant, the relative (percent) changes in UV radiation due to changes in stratospheric 

ozone are expected to be identical to those computed for cloud-free, pollution-free conditions 

(WMO 1990). This is because the absorption of photons by stratospheric ozone occurs at 

altitudes far above those of clouds and air pollutants. Any future systematic changes in cloud 

cover (e.g., related to climate change) or air pollutants are highly uncertain and speculative, and 

are not included in the AHEF at the present time. It is recognized, however, that such putative 

changes could either increase or decrease the average UV radiation levels incident at the Earth’s 

surface. 

9.1.4 Translating UV Exposures into Human Health Effects 

The final major modeling step in the AHEF’s structure that introduces some uncertainty to the estimated 

health effects is the translation of changes in ground-level UV exposure into incremental skin cancers. 

This step involves multiplying the percentage change in estimated UV exposure by the BAF for a 

particular action spectrum, exposure scenario (discussed in Section 5), and health effect. Specifically, 

three sources of uncertainty come into play: (i) uncertainty associated with choice of action spectrum, (ii) 

uncertainty regarding exposure period, and (iii) uncertainty in the BAF. Each of these sources of 

uncertainty is explored further below. 

9.1.4.1 Uncertainty associated with choice of action spectrum 

An important source of uncertainty in the AHEF’s estimates of UV-related health effects is related to a 

lack of complete understanding regarding the correct weighting for the portions of the UV spectrum that 

are most effective in causing health effects. Several candidate action spectra have been developed 

based on both human observations (e.g., erythema) and from laboratory experiments on animals (e.g., 

SCUP-h), but precisely which spectrum weighting causes particular human health effects remains 

unknown. 

Despite some uncertainty regarding selection of an appropriate action spectrum for each health effect, it 

is possible to choose among the available spectra based on certain parameters. For example, as Table 

12 illustrates for various health effects endpoints under the Montreal Adjustments ODS control scenario, 

­

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 36 – 
Table 12. Incremental CMM Incidence and Mortality and 

NMSC Mortality for Three Action Spectra 

Action 

Spectrum/ 

All Cohorts 

CMM Incidence CMM Mortality NMSC Mortality 

Excess 

Incidence 

Difference 

from SCUP-h 

Excess 

Mortality 

Difference 

from SCUP-h 

Excess 

Mortality 

Difference 

from SCUP-h 

DNA-h 192,494 41.5% 23,767 41.3% 12,457 79.8% 

Erythema 133,199 -1.2% 16,421 -1.4% 8267 -2.7% 

SCUP-h 134,064 - 16,587 - 8,365 -

there is a range of expected incidence and mortality estimates for CMM and NMSC based on which 

action spectrum is selected. Both the SCUP-h and erythema spectrum have good correlation (within a 

few percentage points) for the examined health effects, while the DNA-h spectrum has wider variability. 

This divergence is because the DNA-h action spectrum is more tightly focused on the UV-B portion of the 

spectrum. Furthermore, there is a poor understanding of the correction factors needed to adjust between 

viral/bacterial DNA (for which the spectrum was originally developed) and human DNA (i.e., DNA-h). 

As additional data become available on the dose-response relationship for CMM and NMSC, use of the 

SCUP-h action spectrum may be re-evaluated. 

9.1.4.2 Uncertainty regarding exposure period 

Another source of uncertainty in the AHEF’s health effects estimates is associated with the exposure 

period over a person’s lifetime that is most likely to be the cause of UV-related health effects. This is 

especially relevant for CMM, since it has been hypothesized that CMM is largely the product of intense 

exposures early in life (e.g., through age 20) rather than cumulative lifetime exposure. As discussed on 

page 32 (see Table 10), CMM mortality changes by ±11 percent when the exposure assumptions are 

changed, with uncertainty concerning the appropriate exposure dose manifesting itself less in the total 

incremental risks predicted, than in when those incremental effects are predicted to occur, and who will 

bear them (i.e., shifting the risk to children born after 1980). 

9.1.4.3 Uncertainty in the BAFs 

Uncertainty in the BAFs is associated with (1) the accuracy of the BAFs themselves, as measured by the 

uncertainty ranges, and (2) whether or not the BAF can be appropriately calculated for the health effect of 

concern, which depends on the selection of the action spectrum. As described in detail below, the 

uncertainty in the AHEF’s predicted excess UV-related human health effects is 6 percent for CMM 

mortality, 5 percent for NMSC mortality, and 30 percent for NMSC incidence. These uncertainty ranges 

are small and not significant compared to the levels of uncertainty that are common in health effects 

assessments for other hazards. 

CMM Incidence/Mortality 

The BAFs used by the AHEF for CMM incidence and mortality were estimated econometrically by 

correlating data on latitudinal variations in UV exposure and skin cancer mortality. As with any statistical 

estimate, these estimated BAFs have standard errors. The estimated BAFs for CMM mortality and their 

standard errors for the SCUP-h UV action spectrum using the cumulative lifetime UV exposure 

assumption are shown in Table 13. At a 95 percent confidence interval, the BAF for light-skinned males 

based on annual exposures ranges from 0.55 to 0.62. This yields an uncertainty range of approximately 

±6 percent around the central value (median). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 37 – 
Table 13. Estimated Mean, Standard Errors, and Confidence Intervals for the BAFs for CMM 

Mortality for the SCUP-h Action Spectrum and Exposure Scenarios, by Sex 

Annual With Clouds Peak Clear Day (June 21) 

Light-Skinned 

Males 

Light-Skinned Females Light-Skinned 

Males 

Light-Skinned Females 

Mean 0.5846 0.5047 1.444 1.310 

Standard Error 0.02 0.02 0.05 0.06 

95% Confidence 

Limit 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

0.62 0.55 0.55 0.46 1.55 1.34 1.43 1.19 

97.5% 

Confidence 

Limit 

Upper 

Bound 

0.63 

Lower 

Bound 

0.54 

Upper 

Bound 

0.56 

Lower 

Bound 

0.45 

Upper 

Bound 

1.56 

Lower 

Bound 

1.33 

Upper 

Bound 

1.45 

Lower 

Bound 

1.17 

Although researchers’ understanding of the biology and pathogenesis of CMM tumors has improved in 

recent years (Nesbit et al. 1998, Fidler 1998), uncertainty remains about the etiology and mechanism(s) 

of induction of these tumors (Longstreth 1998). While most researchers agree that the primary 

environmental risk factor for CMM is exposure to sunlight, there is uncertainty about three important 

aspects of this relationship: 

• Effects of Early Life Exposure. Some studies indicate that exposures early in life could increase 

the risk of adult cases of CMM, although preliminary results suggest that high childhood 

exposures are only important in the context of high adult exposures (Harrison et al. 1994, Zanetti 

et al. 1992, Autier and Dore 1998). It has also been hypothesized that chronic low-level UV 

exposure may even be protective (Holman and Armstrong 1984). Depending on how and if early 

childhood exposure does indeed influence CMM incidence, and on whether chronic low-level UV 

exposure may be protective, CMM incidence rates may be under- or over-projected in the AHEF. 

However, the overall impacts on results are not expected to be great (i.e., up to 11 percent, as 

explained in Section 8.2). 

• Choice of Appropriate Action Spectrum. There are no studies on CMM induction in test animals 

and, as such, an action spectrum specific to CMM has not yet been developed. However, recent 

studies suggest that the appropriate action spectrum to predict tumor induction may be more 

dependent on UV-A radiation than previously suspected (Setlow et al. 1993, Ley 1997). The lack 

of adequate experimental data from which to derive an action spectrum for CMM is one of the 

greatest sources of uncertainty in estimating UV-induced health impacts. Due to this lack of 

information, the AHEF predicts CMM cases and deaths based on the SCUP-h action spectrum for 

SCC. However, this analysis should be reconsidered if future studies aimed at developing an 

action spectrum for CMM reveal that its shape is not similar to the SCUP-h action spectrum for 

SCC (DeFabo 2001). 

• Effects of UV-B on Tumor Suppression. One important variable confounding the dose-response 

relationship is the effect of UV-B on human tumor suppression genes. It is hypothesized that UVB may inactivate tumor suppression genes (i.e., the p21 gene), making humans more susceptible 

to UV-related cancers. More specifically, research indicates that UV light targets the 

retinoblastoma (RB) pathway of the p21 genetic locus, which contains genes that encode kinase 

inhibitors and act as tumor suppressors (Kannan et al. 2003, Chin et al. 1997, Hutchinson 2003). 

This introduces uncertainty into the AHEF, as the model does not consider how UV independently 

affects tumor suppression genes and how this may lead to increased UV-related health impacts. 

Thus, because it is not possible to separate the effects of UV radiation on DNA and the p21 gene, 

there is some uncertainty regarding the dose-response relationship derived from incidence and 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 38 – 
Table 14. Estimated Mean, Standard Errors, and Confidence Intervals for the BAFs for NMSC 

Mortality for the SCUP-h Action Spectrum and Exposure Scenarios, by Sex

Annual With Clouds Peak Clear Day (June 21) 

Light-Skinned 

Males 

Light-Skinned Females Light-Skinned 

Males 

Light-Skinned Females 

Mean 0.7094 0.4574 

Standard Error 0.03 0.03 0.07 0.09 

95% Confidence 

Limit 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

0.77 0.65 0.52 0.40 2.21 1.93 1.74 1.39 

97.5% 

Confidence 

Limit 

Upper 

Bound 

0.78 

Lower 

Bound 

0.64 

Upper 

Bound 

0.53 

Lower 

Bound 

0.39 

Upper 

Bound 

2.22 

Lower 

Bound 

1.91 

Upper 

Bound 

1.77 

Lower 

Bound 

1.36 

mortality data. Although the degree of uncertainty is not quantified, it is not expected to be 

significant. 

NMSC Mortality 

The BAFs used by the AHEF for NMSC mortality were estimated econometrically by correlating data on 

latitudinal variations in UV exposure and skin cancer mortality. The estimated BAFs for NMSC mortality 

and their standard errors for the SCUP-h UV action spectrum using the cumulative lifetime UV exposure 

assumption are shown in Table 14. At a 95 percent confidence interval, the BAF for light-skinned males 

based on annual exposures ranges from 0.65 to 0.77. This yields an uncertainty range of approximately 

±5 percent around the central value (median). 

SCC and BCC Incidence 

Table 15 presents the mean BAF values and associated standard errors for SCC and BCC incidence, 

which were derived by de Gruijl and Forbes (1995) using similar statistical techniques. Sources of 

uncertainty associated with the BAFs calculated by de Gruijl and Forbes include confounding factors, 

such as migration, patient reporting delay, high early life exposure, and potential exposure to other 

carcinogens. Relative error for carcinogenicity caused by wavelengths over 340 nm was still very 

substantial in 1995. In addition, the model was unable to account for epidermal thickening and 

pigmentation that alter spectral sensitivity of the skin, although corrections for thicker human epidermises 

could be applied. Also, differences between mice and humans (e.g., better adaptation of humans to 

increases in UV exposure) may have influenced the results of applying the hairless mouse model to 

humans. This yields an upper uncertainty range of approximately 30 percent for the BCC and SCC 

incidence AHEF estimates. 

Table 15. BAFs and Standard Errors for BCC and SCC Incidence 

SCC BCC 

U.S. Males U.S. Females U.S. Males U.S. Females 

2.6 ± 0.7 2.6 ± 0.8 1.5 ± 0.5 1.3 ± 0.4 

Source: de Gruijl and Forbes 1995. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 39 – 
9.2 Other Unquantified Sources of Uncertainty 

There are a number of other sources of uncertainty in the AHEF’s health effects predictions. Some of 

these sources of uncertainty are possible to quantify, but are not central to the structure of the AHEF. 

Others cannot be quantified because any assumptions or estimates would be simply speculative. These 

other sources of uncertainty include: 

 Composition of the future atmosphere; 

 Future conditions of the ozone layer; 

 Effect of climate change on ozone depletion; 

 Global compliance with modeled policy scenarios; 

 Laboratory techniques and instrumentation for deriving action spectra; 

 Demographic and human behavioral changes; and 

 Baseline information. 

Accurate prediction of future changes in human health effects would require consideration of the net 

effect of all of the factors described above. Although this challenge is beyond the ability of the current 

state of atmospheric and epidemiological science, these uncertainties are described qualitatively in more 

detail below. This section concludes with a summary of these uncertainties. 

9.2.1 Composition of the Future Atmosphere 

The exact composition of the future atmosphere as a result of compliance with different ODS phaseout 

policies is unknown. As levels of atmospheric chlorine are reduced, the impact of ozone depletion from 

chlorine and bromine radical species generated from ODS would change. In addition, long-term 

systematic changes in atmospheric opacity (e.g., clouds, aerosols, other pollutants) will also impact the 

AHEF’s ability to model changes in ozone. Likewise, future changes in climate could result in changes in 

the atmospheric circulation patterns and therefore could change cloud cover. The impacts of such 

changes on the predicted recovery of the ozone layer are unknown. All of these uncertainties could 

influence the AHEF’s ability to model atmospheric processes accurately. 

9.2.2 Future Conditions of the Ozone Layer 

Uncertainties also can be contributed by assumptions regarding the future conditions of the ozone layer in 

response to the phaseout of ODS. Some computer models predict that the phaseout of ODS will slow and 

eventually stop the rate of ozone depletion, and suggest that natural ozone-making processes will enable 

stratospheric ozone to return to 1979-1980 ozone conditions. These models also predict that the recovery 

will eventually result in increased concentrations beyond 1979-1980 levels25 (see Chapter 12 in WMO 

1999 for more detail). Because there is incomplete knowledge about the behavior of ozone prior to the 

satellite measurements taken in 1979-1980, the AHEF imposes a limit on future ozone recovery to the 

conditions observed in 1979-1980. 

9.2.3 Effect of Climate Variations on Ozone Depletion 

The effects of global climate variations on stratospheric temperature and, in turn, on ozone depletion, are 

not well understood, and have therefore not been modeled in the AHEF. While this effect is not 

incorporated into any other international models used to assess future global ozone depletion, it does 

represent a modeling constraint that should be noted. 

25 Whether this recovery scenario, called “ozone superabundance,” is likely to occur is open to debate, particularly 

because of the potential for complex interactions between global climate change and stratospheric ozone dynamics. 

Model computations have predicted both higher and lower amounts of ozone in the future. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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9.2.4 Global Compliance with Modeled Policy Scenarios 

This analysis assumes global compliance with each of the modeled policy scenarios. To the extent that 

these limitations are not adhered to, future ODS emissions could be different in both composition and 

quantity. 

9.2.5 Laboratory Techniques and Instrumentation 

Additional uncertainty can be contributed by the laboratory techniques and instrumentation used for 

deriving the action spectra used to weight UV exposure. Discrepancies between the wavelengths of UV 

radiation intended to be administered and the wavelengths actually received by the test organism can 

result in orders of magnitude differences in the measured response. In addition, many action spectra are 

derived using monochromatic light sources that do not fully simulate the polychromatic light received 

directly from the sun. 

9.2.6 Demographic and Behavior Changes 

Future demographic and behavior changes that could affect the accuracy of the AHEF include: 

• Changes in human UV exposure behavior: This evaluation assumes that human exposure 

behavior remains constant through time, and does not take into account innovations in sun 

protection technology (e.g., improved sunglasses and sunscreens), increased public awareness 

of the effects of overexposure to UV, and increased sensitization to the need for early treatment 

of suspicious lesions. 

• Improvements in medical care/increased longevity: Improvements in medical care and predictions 

of increased longevity for many population subgroups could affect estimates of future skin cancer 

incidence and mortality significantly. 

• Changes in socioeconomic profiles: Socioeconomic profiles can impact a variety of factors, 

ranging from demand for air travel to areas where high UV exposure is expected (i.e., the beach), 

to the types of skin cancer most commonly observed. 

• Changes in population composition and size: Population composition changes such as the 

expected increase in Hispanic populations, whose more pigmented skin is thought to decrease 

skin cancer risk, could have significant effects on future U.S. skin cancer rates. 

The above factors are either not easily quantified (e.g., human behavior), or they are not central to the 

analysis (e.g., improvements in medical care), and are therefore not addressed further in this evaluation. 

9.2.7 Accuracy of Baseline Information 

It is possible that error is introduced to the AHEF’s results through misreporting of skin cancer incidence 

and mortality data (i.e., the AHEF’s baseline estimates). With disease data, under-, over-, and misreporting are not uncommon. For example, a recent study revealed that the incidence of CMM has been 

systematically under-reported in the SEER data (Clegg et al. 2002).26 The original SEER data indicated 

that CMM rates in white males were relatively flat or even falling (ranging from -11.1 percent to 3.3 

percent annually after 1996). However, after adjusting for underreporting, CMM rates were actually found 

26 There is little reason to believe that the SEER CMM incidence under-reporting extends to the NCI-based CMM 

mortality input information. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 41 – 
Table 16. Major Sources of Quantified Uncertainty 

Source of Uncertainty Quantified Uncertainty 

Translating column ozone to ground-level UV 

TUV Model ≈ 5% 

Translating UV exposure to human health effects 

Uncertainty in the BAFs ≤ 30% 

• CMM mortality (6%) 

• NMSC mortality (5%) 

• NMSC incidence (30%) 

Uncertainty with choice of action spectrum ≈ 50% 

Early life exposure vs. whole life exposure ≈ 10% 

Total √(52 + 302 + 502 + 102) ≈ 60% 

to have increased between 3.8 to 4.4 percent annually since 1981 (Clegg et al. 2002). Underreporting of 

CMM incidence is largely attributable to diagnosis in doctors’ offices, as opposed to hospitals and other 

treatment centers with better reporting accuracy. However, the AHEF results are not significantly affected 

by this underreporting because CMM incidence estimates in the AHEF are not based directly on SEER 

incidence data. Rather, because the AHEF estimates CMM incidence based on the ratio of SEER 

incidence data to projected annual mortality estimates, and because underreporting would affect both 

baseline and scenario estimates, the effects on incremental changes in CMM incidence would be second 

order. 

9.3 Summary of Quantified and Unquantified Sources of Uncertainty 

Of the major sources of uncertainty associated with the AHEF, the total quantified uncertainty is roughly 

60 percent, as summarized in Table 16: 

In addition to the major quantified sources of uncertainty listed above, the atmospheric component of the 

AHEF (i.e., translation of ODS emissions into (a) EESC concentrations and (b) changes in column ozone 

concentrations) is also a source of uncertainty, though not quantitatively examined in this analysis. It 

should be noted, however, that this uncertainty associated with the atmospheric parameters used in the 

AHEF is inherent in all atmospheric models, including those used by WMO in its Scientific Assessment of 

Ozone Depletion reports (WMO 1990, 1992, 1995, 1999, 2003). 

Other unquantified sources of uncertainty discussed above relate to different parts of the AHEF that 

estimate changes in ozone, changes in UV radiation, and changes in health effects. Table 17 summarizes 

these unquantified uncertainties. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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Table 17. Factors with Unknown Contributions to Uncertainty 

Factor Parameter 

Change in Ozone 

Estimates 













Composition of Future Atmosphere 

Ability to Model Atmospheric Processes Accurately 

Response of Ozone Layer to Changing ODS Concentrations 

Effect of Climate Change on Ozone Depletion 

Global Compliance with Modeled Policy Scenarios 

Changes in Composition and Quantity of ODS Emissions 

Change in UV 

Radiation Estimates 

 Long-term Systematic Changes in Atmospheric Opacity (e.g., clouds, aerosols, 

other pollutants) 

Change in Health Effect 

Estimates 













Changes in Human UV Exposure Behavior 

Laboratory Techniques and Instrumentation for Deriving an Action Spectrum 

Improvements in Medical Care/Increased Longevity 

Changes in Socioeconomic Factors (e.g., demographics and human behavioral 

changes) 

Baseline Information (e.g., misreporting of skin cancer incidence and mortality 

data) 

Changes in Population Composition and Size 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 43 – 
10. Topics for Future Research 

Because research on the health effects of UV exposure is ongoing, no model or set of results quantifying 

health effects impacts can be considered final. Indeed, new information and research results on skin 

cancer and UV radiation continue to flow from the academic community. Many important issues such as 

the inclusion of data on UV responsiveness on darker-skinned populations, new information on UVinduced cataract incidence, incorporation of research on other human health endpoints (e.g., immune 

suppression), and investigation of the feasibility of predicting non-human endpoints must continue to be 

investigated. As significant new findings are incorporated into the AHEF, the accuracy of predictions and 

the implications for protecting stratospheric ozone will be enhanced. This section discusses some of the 

major topics where future research could improve the ability to accurately predict UV-related impacts. 

10.1 Prediction of Impacts on Different Skin Types 

The AHEF is not completely comprehensive in its assessment of UV effects in the population because it 

only accounts for light-skinned individuals in the United States. Although “light-skinned” individuals in the 

United States account for nearly 90 percent of the population,27 data are needed to account for additional 

population skin-types, in order to complete a more comprehensive assessment of health effects resulting 

from UV changes. 

10.2 Research on Dose-Response Relationship of Cataract Incidence and UV Exposure 

Although recent analysis of the dose-response relationship between UV exposure and cataract incidence 

in the United States were not able to establish a strong correlation, a more rigorous disaggregated 

analysis could be performed to ensure that this lack of correlation is not the result of data limitations. 

Similarly, if a dose-response relationship or BAF for cataract becomes available through independent 

research, this health effect should be reconsidered for inclusion in the AHEF. (For more information on 

modeling cataract incidence, see Appendix E.) 

10.3 Inclusion of Other Human Health Endpoints 

In addition to the health effects featured in the AHEF, UV-B has been associated with other human health 

consequences that are as yet unquantified, as discussed in Section 9.2. For example, UV-B exposure 

can produce immunosuppression in humans that may be associated with increases in the incidence and 

severity of some infectious diseases or result in an increased failure rate of vaccination programs 

(Longstreth et al. 1998). Numerous studies with animals have shown that UV-B exposure depresses 

immune responses to viruses and in microorganisms (principally those that enter via the skin), and results 

in more severe infections. Infectious diseases where such an effect has been shown using animal models 

include herpes, tuberculosis, leprosy, trichinosis, leishmaniasis, listeriosis, and Lyme disease (Longstreth 

et al. 1998). In addition, immunosuppression may be associated with increased cancer incidence, 

including UV-induced cancers. 

Although action spectra have been derived for immunosuppression (e.g., De Fabo and Noonan 1983), 

certain key data gaps make it difficult to develop estimates of health effects changes for this endpoint. For 

example, sufficient epidemiological data do not presently exist that would allow quantification of the 

number of affected individuals necessary to estimate baseline incidence. Further, the scientific community 

27 “Light skinned” populations include those defined in the U.S. Census (1990) as whites, Amer Indians, Asian or 

Pacific Islanders, and Other (including the majority of Hispanic populations). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 44 – 
is not in agreement as to which dose metric should be used to assess the consequences of 

immunosuppression. However, adding this endpoint could be reconsidered for inclusion in the AHEF if 

future studies fill these data gaps. 

In addition to the uncertainty surrounding the number of individuals affected by immunosuppression, the 

levels of immunosuppression experienced by cancer-afflicted individuals, and the dose metric, the relative 

strength of UV-B-induced immunosuppression compared to other cellular mutagenic effects remains 

unclear. UV-B simultaneously suppresses cellular immunity and damages specific DNA and anti-cancer 

pathways in cells that are important for mutagenesis. Much recent work has attempted to explain the 

differential effects of immunosuppression and other UV-B sensitive cellular changes using murine models 

(Noonan et al. 2003, Kannan et al. 2003, Moodycliffe et al. 2000). Furthermore, while additional 

information is needed to define the relationship between UV-B and immunosuppression, the role of UV-A 

in this relationship is also uncertain (de Fabo 2003). 

10.4 Inclusion of Impacts of Climate Variations on Ozone Depletion 

Future research may shed light on the relationship between global climate and stratospheric ozone 

depletion, which could eventually be modeled in the AHEF. 

10.5 Improved Ground-Level UV Measurements 

Ground level UV monitoring is essential for advancing human health effects research and should be 

linked to epidemiological observations to improve our understanding of UV-related health effects. While 

satellites provide an excellent means of measuring the UV radiation reflected from the atmosphere and 

estimating some atmospheric properties, such as clouds and total ozone column, they cannot directly 

measure ground level UV radiation. Additionally, satellites are insensitive to pollution in the lower 

atmosphere. Therefore, while satellite data compare fairly well to ground level UV measurements in clean 

locations, this is not so in polluted areas, such as cities. This produces a systematic effect such that 

ground level UV measurements are 10 to 20 percent lower than satellite measurements in urban areas. 

Because urban areas are where U.S. populations are concentrated, it is in these areas that monitoring is 

most important. But monitoring data must be robust in order to be useful to epidemiologists. In particular, 

long term monitoring is essential because of high inter-annual variability in UV radiation levels, and 

because many health effects are the result of long term accumulated exposure to UV radiation. With 

continued ground level UV monitoring, human health effects associated with changes in UV exposures 

can be better understood and, ultimately, incorporated into the AHEF. 

10.6 Prediction of UV Impacts on Non-Human Endpoints 

Many economic, ecological, and environmental effects are also associated with increased UV radiation. 

Economic impacts include damage to agricultural crops, effects on ecosystems, and degradation of 

polymeric materials. Each of these has far-reaching impacts on construction and manufacturing industries 

(Andrady et al. 1998). 

Enhanced UV radiation levels have varied repercussions on terrestrial ecosystems, ranging from 

decreased photosynthesis rates to changes in competitive balance and increased susceptibility to 

disease (Caldwell et al. 1998). 

Effects on aquatic ecosystems include decreased species diversity and reduced larval survival rates 

(Häder et al. 1998). Because UV radiation is able to penetrate tropical ocean waters to depths of more 

than 20 meters, many species of coral may also be affected by increased ozone depletion. For example, 

the death of organisms and the inhibition of skeletal growth and bleaching have been observed both in 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 45 – 
laboratory tests and in situ (e.g., Brown et al. 2000). In addition, UV radiation may indirectly damage coral 

reefs by causing the formation of oxidative radicals that can in turn damage organismal DNA, enzymes, 

lipids, and other molecules associated with cell function. Studies have shown that the fluorescent 

pigments in coral that regulate light for the zooxanthellae—symbiotic algae on which coral reefs depend 

for photosynthetic functions—may react to increased radiation from sunlight by scattering radiation of 

wavelengths not essential to photosynthesis (Salih et al. 2000). Although fluorescent pigments in coral 

may provide buffering action against increased UV radiation levels, studies have also indicated that 

increased UV radiation may play a role in coral bleaching by increasing DNA lesions (called thymine 

dimers) in corals and the zooxanthellae algae living within coral structures. Experiments showed that 

thymine dimers increased with increasing UV radiation, although the increase was not proportional 

(Anderson et al. 2001). 

Increased UV radiation intensity caused by ozone depletion may also cause negative impacts in 

amphibians, and may be responsible in part for the observed decreases in the size of amphibian 

populations. The egg stage is believed to be the period of life in which amphibians are most susceptible 

to UV radiation because eggs are often deposited in full sunlight. Researchers speculate that current 

levels of UV-B alone are not sufficient to explain the decrease in amphibian numbers. However, additional 

research is required to better understand the impact of UV radiation on susceptible species (Licht and 

Grant 1997). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 46 – 
11. References 

Anderson, Susan, Richard Zepp, Jana Machula, Debbie Santavy, Lara Hansen, and Erich Mueller 2001. 

“Indicators of UV exposure in corals and their relevance to global climate change and coral bleaching.” 

Hum. Ecol. Risk Assess. 7:1271-1282. 

Andrady, D.L., S.H. Hamid, X. Hu, and A. Torikai (1998), “Effects of increased solar ultraviolet radiation 

on materials,” Photochemistry and Photobiology B: Biology 46:93-103. 

Anduze, Alfred L., M. D. (1993), “Ultraviolet radiation and cataract development in the U.S. Virgin 

Islands,” J. Cataract Refract. Surg. 19:298-300. 

Autier, P., and J.F. Dore (1998), “Influence of sun exposures during childhood and during adulthood on 

melanoma risk,” Int. J. Cancer 77:533-537. 

Bais, A.F., S. Madronich, J. Crawford, S. R. Hall, B. Mayer, M. van Weele, J. Lenoble, J. G. Calvert, C. A. 

Cantrell, R. E. Shetter, A. Hofzumahaus, P. Koepke, P. S. Monks, G. Frost, R. McKenzie, N. Krotkov, A. 

Kylling, S. Lloyd, W. H. Swartz, G. Pfister, T. J. Martin, E. -P. Roeth, E. Griffioen, A. Ruggaber, M. Krol, A. 

Kraus, G. D. Edwards, M. Mueller, B. L. Lefer, P. Johnston, H. Schwander, D. Flittner, B. G. Gardiner, J. 

Barrick, R. Schmitt (2003), “International Photolysis Frequency Measurement and Modelling 

Intercomparison: Spectral Actinic Flux Measurements and Modelling,” Accepted in J. Geophys. Res. 

Bergfeld, W.F. (1997), “The aging skin,” International Journal of Fertility, 42:57-66. 

Brown, B.E., R.P. Dunne, M.S. Goodson, and A.E. Douglas 2000. “Marine ecology: bleaching patters in 

reef corals.” Nature 404:142-143. 

Caldwell M.M., L.O. Bjorn, J.F. Bornman, S.D. Flint, G. Kulandaivelu, A.H. Teramura, and M. Tevini 

(1998), “Effects of increased solar ultraviolet radiation on terrestrial ecosystems,” Photochemistry and 

Photobiology B: Biology 46:40-52. 

CIESIN (Consortium for International Earth Science Information Network) website. Accessed December 

2003. “Overview of Health Effects from Increased Ultraviolet-B Exposure due to Ozone Depletion.” 

Available at <http://www.ciesin.org/TG/HH/ozover.html>. 

Chin, Lynda, Jason Pomerantz, David Polsky, Mark Jacobson, Carlos Cohen, Carlos Cordon-Cardo, 

James W. Horner II, and Ronald A. DePinho (1997), “Cooperative effects of INK4a and ras in melanoma 

susceptibility in vivo,” Genes & Development 11:2822-2834. 

Clegg, Limin X., Eric J. Feuer, Douglas N. Midthune, Michael P. Fay, and Benjamin F. Hankey (2002), 

“Impact of reporting delay and reporting error on cancer incidence rates and trends,” J. Natl. Cancer Inst. 

94:1537-1545. 

Daniel, J. D., S. Solomon, R. W. Portmann, and R. R. Garcia (1999), “Stratospheric ozone 

destruction: the importance of bromine relative to chlorine,” J. Geophys. Res. 104:23871-23880, 1999. 

De Fabo, E.C. (2003), Peer Reviewer Comments on the Health Module of the Human Health Benefits of 

Stratospheric Ozone Protection, submitted to ICF Consulting in September 2003. 

De Fabo, E.C. (2001), Peer Reviewer Comments on Human Health Effects of Ozone Depletion from 

Stratospheric Aircraft, submitted to ICF Consulting on April 26. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 47 – 
De Fabo, E.C. and F.P. Noonan (1983), “Mechanism of immune suppression by ultraviolet irradiation in 

vivo. I. Evidence for the existence of a unique photoreceptor in skin and its role in photoimmunology,” 

J.Exp.Med. 158:84-98. 

de Gruijl, F.R. and P.D. Forbes (1995), "UV-induced skin cancer in a hairless mouse model," BioEssays 

17:651-660. 

de Gruijl, F.R., H.J.C.M. Sterenborg, P.D. Forbes, R.E. Davies, C. Cole, G. Kelfkens, H. van Weelden, H. 

Slaper, and J.C. van der Leun (1993), "Wavelength dependence of skin cancer induction by ultraviolet 

irradiation of albino hairless mice," Cancer Research 53:53-60. 

de Gruijl, F.R, and J.C. Van der Leun (1993), “Influence of ozone depletion on the incidence of skin 

cancer: quantitative prediction,” in Environmental UV Photobiology, A.R. Young, L.O. Bjorn, J. Moan, and 

W. Nultsch (eds.), Plenum Press, New York:1-39. 

De More, W. B., S. P. Sander, D. M. Golden, R. F. Hampson, M. J. Kurylo, C. J. Howard, A. R. 

Ravishankara, C. E. Kolb, and M. J. Molina (1997), Chemical Kinetics and Photochemical Data for Use in 

Stratospheric Modeling, JPL Publication 97-4, Jet Propulsion Laboratory, National Aeronautics and Space 

Administration, Pasadena. 

D’errico, M., A. Calcagnile, and E. Dogliotti (1996), “Genetic alterations in skin cancer,” Ann. Ist. Super 

Sanita 32:53-63. 

Elterman, L. (1968), “UV, visible, and IR attentuation for altitudes to 50 km,” Air Force Cambridge 

Research Laboratories (AFCRL) Report-68-0153, Bedford, MA. 

Fears, T.R., and J. Scotto (1983), "Estimating increases in skin cancer morbidity due to increases in 

ultraviolet radiation exposure," Cancer Investigation 1:119-126. 

Fidler, I.J. (1998), “Biology of melanoma metatasis,” in Cutaneous Melanoma, C.M. Balch, A.N. 

Houghton, A.J. Sober, and S-J. Soong (eds.), St. Louis: Quality Medical Publishing, Inc. 

Gao, W., J.R. Slusser, J.H. Gibson, G. Scott, D.S. Bigelow, J. Kerr and B. McArthur (2001), “Direct-sun 

column ozone retrieval by the ultraviolet multi-filter rotating shadow-band radiometer and comparison with 

those from Brewer and Dobson spectrophotometers,” Applied Optics 40:3149-3155. 

Geller, A.C., et. al. (1998), “Do pediatricians counsel families about sun protection?” Archives of Pediatric 

and Adolescent Medicine 152: 372-76. 

Häder, D.P., H.D. Kumar, R.C. Smith, and R.C. Worrest (1998), “Effects on aquatic ecosystems,” 

Photochemistry and Photobiology B: Biology 46:53-68. 

Harrison, S.L., R. MacLennan, R. Speare, and I. Wronski (1994), “Sun exposure and melanocytic naevi in 

young children in Townsville, Queensland,” Lancet 344:1529-1532. 

Hiller, R., R. Sperduto, and F. Ederer (1983), "Epidemologic associations with cataracts in the 1971-1972 

National Health and Nutrition Examination Survey," American Journal of Epidemiology 118:239-249. 

Holman, C.D.J., and B.K. Armstrong (1984), “Cutaneous malignant melanoma and indicators of total 

accumulated exposure to the sun: an analysis separating histogenetic types,” J. Nat. Cancer Inst. 73:75­

82. 

Hutchinson, Ezzie (2003), “Sunburn effects dissected,” Nature Reviews – Cancer: 151. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 48 – 
Independent Review Panel (2002), Suitability of CF3I to Replace Halon 1301 as the Inerting Agent in 

Wing Fuel Tanks on the F-16 Aircraft. Report prepared for the U.S. Department of Defense, Washington, 

D.C. 

IPCC (Intergovernmental Panel on Climate Change) 2001. Climate Change 2001: The Scientific Basis. 

Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on 

Climate Change. Edited by J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, 

K. Maskell, and C.A. Johnson. Cambridge, UK. 

Jiang, W., H.N. Ananthaswamy, H.K. Muller, and M.L. Kripke (1999), “p53 protects against skin cancer 

induction by UV-B radiation,” Oncogene 18:4247-53. 

Kannan, K., N.E. Sharpless, J. Xu, R. O’Hagan, M. Bosenberg, and L. Chin (2003), “Components of the 

Rb pathway are critical targets of UV mutagenesis in a murine melanoma model,” PNAS 100:1221-1225. 

Kirk, J. T. O., B. R. Hargreaves, D. P. Morris, R. Coffin, B. David, D. Fredrickson, D. Karentz, D. Lean, M. 

Lesser, S. Madronich, J. H. Morrow, N. Nelson, and N. Scully (1994), “Measurements of UV-B radiation in 

two freshwater lakes: an instrument intercomparison,” Archiv für Hydrobiologie 43:71-99. 

Ko, M.K.W., N.D. Sze, C. Scott, J.M. Rodríguez, and D.K. Weisenstein (1998), “Ozone depletion potential 

of CH3Br,” J. Geophys. Res. 103:28187-28195. 

Koepke, P., A. Bais, D. Balis, M. Buchwitz, H. De Backer, X. de Cabo, P. Eckert, P. Eriksen, D. Gillotay, 

A. Heikkila, T. Koskela, B. Lapeta, Z. Litynska, J. Lorente, B. Mayer, A. Renaud, A. Ruggaber, G. 

Schauberger, G. Seckmeyer, P. Seifert, A. Schmalwieser, H. Schwander, K. Vanicek, and M. Weber 

(1998), “Comparison of Models Used for UV Index Calculations,” Photochemistry and Photobiology 

(B:Photobiology) 67:657-662. 

Lantz, K. O., R. E. Shetter, C. A. Cantrell, S. J. Flocke, J. G. Calvert, and S. Madronich (1996), 

“Theoretical, actinometric, and radiometric determinations of the photolysis rate coefficient of NO2 during 

MLOPEX II,” J. Geophysical Research 101:14613-14629. 

Ley, R.D. (1997), “Ultraviolet radiation A-induced precursors of cutaneous melanoma in Monodelphis 

domestica,” Cancer Research 57:3682-3684. 

Licht, L.E. and K.P. Grant (1997), “The effects of ultraviolet radiation on the biology of amphibians,” 

American Zoologist 37:137-145. 

Longstreth J.D. (1998), “Melanoma genesis: putative causes and possible mechanisms,” in Cutaneous 

Melanoma, C.M. Balch, A.N. Houghton, A.J. Sober, and S-J. Soong (eds.), St. Louis: Quality Medical 

Publishing, Inc. 

Longstreth, J., F.R. de Gruijl, M.L. Kripke, S. Abseck, F. Arnold, H.I. Slaper, G. Velders, Y. Takizawa, and 

J.C. van der Leun (1998), “Health risks,” Journal of Photochemistry and Photobiology B: Biology 46:20­

39. 

Madronich, S. (1993a), “The atmosphere and UV-B radiation at ground level,” in Environmental UV 

Photobiology, A.R. Young, L.O. Bjorn, J. Moan, and W. Nultsch (eds.), Plenum Press, New York, 1-39. 

Madronich, S. (1993b), “UV radiation in the natural and perturbed atmosphere,” in UV-B Radiation and 

Ozone Depletion: Effects on Humans, Animals, Plants, Microorganisms, and Materials, M. Trevini (ed.), 

Lewis Publisher, Boca Raton, 17-69. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 49 – 
Madronich, S. (1992), “Implications of recent total atmospheric ozone measurements for biologically 

active ultraviolet radiation reaching the Earth's surface,” Geophysical Research Letters 19:37-40. 

Madronich, S. and F. R. de Gruijl (1994), “Stratospheric ozone depletion between 1979 and 1992: 

Implications for biologically-active ultraviolet-B radiation and non-melanoma skin cancer incidence,” 

Photochemistry and Photobiology 59:541-546. 

Madronich, S. and F. R. de Gruijl (1993), “Skin cancer and UV radiation,” Nature 366:23. 

Madronich, S., R.L. McKenzie, L.O. Bjorn, and M.M. Caldwell (1998), “Changes in ultraviolet radiation 

reaching the earth’s surface,” J. Photochem. Photobiol. B 46:5-19. 

Madronich, S., E. Weatherhead, and S. Flocke (1996), “Trends in UV radiation,” Int. J. Environ. Sci. 

51:183-198. 

McKenzie, R. L., K. J. Paulin, and S. Madronich (1998), “Effects of snow cover on UV irradiances and 

surface albedo: A case study,” Journal of Geophysical Research 103:28785-28792. 

McKinlay, A.F. and B.O. Diffey (1987), “A reference action spectrum for ultraviolet-induced erythema in 

human skin,” in Human Exposure to Ultraviolet Radiation: Risks and Regulations. W.R. Passchier and 

Bosnjakovic, B.F.M. (Eds); Elsevier, Amsterdam. 

McPeters, R.D., Pawan K. Bhartia, Jay R. Herman, and A. Oaks (1996a), “Nimbus-7 total ozone mapping 

spectrometer (TOMS) data products user's guide,” NASA Reference Publication. 

McPeters, R.D., S.M. Hollandsworth, L.E. Flynn, J.R. Herman, and C.J. Seftor (1996b), “Long-term ozone 

trends derived from 16-year combined Nimbus 7/Meteor 3 TOMS Version 7 Record," Geophysical 

Research Letters 23:3699-3702. 

Merriam, J. C., S. Lofgren, R. Michael, P. Soderberg, J. Dillon, L. Zheng, and M. Ayala (2000),” An action 

spectrum for UV-B radiation and the rat lens,” Invest. Opthalmol. Vis. Sci. 41:2642-2647. 

Moodycliffe, Angus, Dat Nghiem, Gavin Clydesdale, and Stephene Ullrich. 2000. “Immune suppression 

and skin cancer development: regulation by NKT cells.” Nature Immunology 1:521-525. 

NEI/BPA (National Eye Institute/Prevent Blindness America) 2002. Vision Problems in the U.S.: 

Prevalence of Adult Vision Impairment and Age-Related Eye Disease in America. Available at 

http://www.nei.nih.gov/eyedata/pdf/VPUS.pdf 

NASA/EPA 2001. Human Health Effects of Ozone Depletion From Stratospheric Aircraft. NASA/CR2001-211160, September 2001. 

NASA (National Aeronautics and Space Administration) 1999. Models and Measurements 

Intercomparison II, Langley Research Center, Hampton, VA, NASA/TM-1999-209554. 

Nesbit, M., V. Setaluri, and M. Herlyn (1998), “Biology of melanocytes and melanoma,” in Cutaneous 

Melanoma, C.M. Balch, A.N. Houghton, A.J. Sober, and S-J. Soong (eds.), St. Louis: Quality Medical 

Publishing, Inc. 

NOAA/National Weather Service (2002). “UV Index: Information,” Climate Prediction Center, web page 

last modified December 18, 2002. Available at 

http://www.cpc.ncep.noaa.gov/products/stratosphere/uv_index/uv_nature.html. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 50 – 
Noonan, Frances, Juan Recio, Hisashi Takayama, Paul Duray, Miriam Anvers, Walter Rush, Edward de 

Fabo, and Glenn Merlino. 2003. “Neonatal sunburn and melanoma in mice.” Nature 413:271-272. 

Oriowo, Olanreqju M., Anthony P. Cullen, B. Ralph Chou, and Jacob G. Sivak (2001), “Action spectrum 

recovery for in vitro UV-induced cataract using whole lenses,” Investigative Ophthalmology & Visual 

Science, October 42:2596- 2602. 

Pitcher, H.M., and J.D. Longstreth (1991), "Melanoma mortality and exposure to ultraviolet radiation: An 

empirical relationship," Environment International 17:7-21. 

Pitts, D.G., A.P. Cullen, and P.D. Hacker (1977), “Ocular effects of ultraviolet radiation from 295 to 365 

nm,” Invest. Ophthalmol. Visual Sci.16:932-939. 

Pfeifer, G.P. and M.F. Denissenko (1998), “Formation and repair of DNA lesions in the p53 gene: relation 

to cancer mutations?” Environ. Mol. Mutagen, 31:197-205. 

Ries, L.A.G., C.L. Kosary, B.F. Hankey, B.A. Miller, and B.K. Edwards (Eds.) (1999), SEER Cancer 

Statistics Review, 1973-1996, National Cancer Institute, Bethesda, MD. 

Salih, Anya, Anthony Larkum, Guy Cox, Michael Kühl, and Ove Hoegh-Guldberg 2000. “Fluorescent 

pigments in corals are photoprotective.” Nature 408:850-853. 

Scotto, J., H. Pitcher, and J.A.H. Lee (1991), "Indications of future decreasing trends in skin-melanoma 

mortality among whites in the United States," Int. J. Cancer 49:490-497. 

Scotto, J., T.R. Fears, and J.F. Fraumeni Jr. (1983), "Incidence of nonmelanoma skin cancer in the 

United States," U.S. Department of Health and Human Services, (NIH) 82-2433, Bethesda, MD. 

Serafino, G. and J. Frederick (1987), “Global modeling of the ultraviolet solar flux incident on the 

biosphere,” in Assessing the Risks of Trace Gases that Can Modify the Stratosphere, Vol. VII, U.S. 

Environmental Protection Agency, Washington, D.C. 

Setlow, R.B. (1974), “The wavelengths in sunlight effective in producing skin cancer: A theoretical 

analysis,” Proc. Natl. Acad. Sci. USA 71:3363-3366. 

Setlow, R.B., E. Grist, K. Thompson, and A.D. Woodhead (1993), "Wavelengths effective in induction of 

malignant melanoma," Proc. Natl. Acad. Sci. USA 90:6666-6670. 

Shetter, R.E., C.A. Cantrell, K.O. Lantz, S.J. Flocke, J.J. Orlando, G.S. Tyndall, T.M. Gilpin, S. 

Madronich, and J. G. Calvert (1996), “Measurements and calculations of the photodissociation rate 

coefficient for O3 + hν → O(1

D) + O2 during the MLOPEX II field campaign,” J. Geophys. Res. 101:14631­

14641. 

Shetter, R. E., A. H. McDaniel, C. A. Cantrell, S. Madronich, and J. G. Calvert (1992), “Actinometer and 

Eppley radiometer measurements of the NO2 photolysis rate coefficient during MLOPEX,” Journal of 

Geophysical Research 97:10349-10359. 

Stamnes, K., S. Tsay, W. Wiscombe, and K. Jayaweera (1988), “A numerically stable algorithm for 

discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media,” Appl. Opt. 

27:2502-2509. 

Tang, X., S. Madronich, T. Wallington, and D. Calamari (1998), “Changes in tropospheric composition 

and air quality,” Journal of Photochemistry and Photobiology B: Biology 46:83-95. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 51 – 
Taylor HR, West SK, Rosenthal FS, Munoz B, Newland HS, Abbey H, Emmett EA. ” Effect of ultraviolet 

radiation on cataract formation.” N Engl J Med. 1988 Dec 1;319(22):1429-33. 

UNEP (United Nations Environment Programme) (1989, 1991, 1994, 1998), “Environmental effects of 

ozone depletion,” UNEP Environmental Effects Panel Report, United Nations Environmental Programme, 

Nairobi, Kenya. 

U.S. Census Bureau Website (2000a), “(NP-D1-B) Quarterly Projections of the Resident Population by 

Age, Sex, Race, and Hispanic Origin: Middle Series, January 1, 1999 to January 1, 2101,” 

http://www.census.gov/population/www/projections/natdet-D1B.html, accessed 2/24/2000. 

U.S. Census Bureau Website (2000b), “Detailed State Projections by Single Year of Age, Sex, Race, and 

Hispanic Origin: 1995 to 2025,” http://www.census.gov/population/www/projections/stproj.html, accessed 

2/24/2000. 

U.S. Census Bureau Website (1990), “1990 Census of Population and Housing,” available at 

<http://factfinder.census.gov/servlet/DTTable?_ts=89647172958>. 

U.S. EPA (2001), Human Health Effects of Ozone Depletion from Stratospheric Aircraft, In Publication, 

United States Environmental Protection Agency, Washington, D.C. 

U.S. EPA (1999). Class I Ozone Depleting Substances and Class II Ozone Depleting Substances. EPA 

website. Available at http://www.epa.gov/ozone/ods.html and http://www.epa.gov/ozone/ods2.html. 

U.S. EPA (1987), Regulatory Impact Analysis: Protection of Stratospheric Ozone, United States 

Environmental Protection Agency, Washington, D.C. 

USGCRP (US Global Change Research Program) 1998. “Depletion and Recovery of the Ozone Layer: 

An Update of the Scientific Understanding.” USGCRP Seminar, 23 September 1998. Available at 

<http://www.usgcrp.gov/usgcrp/seminars/980916FO.html> Accessed December 2003. 

USSA (1976), “U.S. Standard Atmosphere,” National Oceanic and Atmospheric Administration (NOAA), 

National Aeronautics and Space Administration (NASA), United States Air Force, Washington, D.C. 

Weinstock, M. A. (1993), “Ultraviolet radiation and skin cancer: epidemiological data from the United 

States and Canada,” in Environmental UV Photobiology, A.R. Young, L.O. Bjorn, J. Moan, and W. 

Nultsch (eds.), Plenum Press, New York, 1-39. 

WMO (1990), Scientific Assessment of Ozone Depletion: 1989. World Meteorological Organization Global 

Ozone Research and Monitoring Project -- Report No. 20. 

WMO (1992), Scientific Assessment of Ozone Depletion: 1991. World Meteorological Organization Global 

Ozone Research and Monitoring Project -- Report No. 25. 

WMO (1995), Scientific Assessment of Ozone Depletion: 1994. World Meteorological Organization Global 

Ozone Research and Monitoring Project -- Report No. 37. 

WMO (1999), Scientific Assessment of Ozone Depletion: 1998. World Meteorological Organization Global 

Ozone Research and Monitoring Project -- Report No. 44. 

WMO (2003). Scientific Assessment of Ozone Depletion: 2002. WMO Global Ozone Research and 

Monitoring Project - Report No. 47, Geneva, 2002. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 52 – 
Wuebbles, D.J., R. Kotamarthi, and K.O. Patten (1999), “Updated evaluation of Ozone Depletion 

Potentials for Chlorobromomethane (CH2ClBr) and 1-bromo-propane (CH2BrCH2CH3),” Atmospheric 

Environment 33:1641-1643. 

Wuebbles (2003), Personal Communication with Dr. Don Wuebbles, Department of Atmospheric 

Sciences, University of Illinois, Urbana, IL, March 6, 2003. 

Zanetti, R., S. Franceschi, S. Rosso, S. Colonna, and E. Bidoli (1992), Eur. J. Cancer 28A:1172-1176. 

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12. Glossary 

Action spectrum — Experimentally-derived plots describing the relative effectiveness of each 

wavelength of UV-A and UV-B radiation in the induction of a specific health effect (e.g., skin cancer, 

sunburn). Action spectra are used as weighting functions in order to estimate the potential of a particular 

UV exposure to induce adverse health effects. 

BAF (biological amplification factor) — BAFs are equal to slope of the dose-response curve (see 

“dose-response relationship”). 

Basal cell carcinoma (BCC) — BCC is the most common type of skin cancer. It is caused primarily by 

exposure to ultraviolet radiation, and occurs most frequently among light-skinned persons over age 45. 

Although BCC has a very high cure rate, in rare instances it can be lethal. 

Baseline — The AHEF defines the “baseline” incidence and/or mortality for skin cancer as what would be 

expected to occur in the future if the concentration of stratospheric ozone remained fixed at 1979-1980 

levels. 

Birth cohorts — Individuals assigned by year of birth into groups for further study. The AHEF uses the 

results of these birth cohort studies to create and project a baseline estimate of skin cancer incidence 

and/or mortality. 

Column ozone — The amount of ozone (measured in Dobson units) contained in a vertical column of air 

extending from the Earth’s surface to an orbiting satellite designed to measure ozone concentrations. 

Roughly 90 to 95% of column ozone is in the stratosphere with small amounts (5-10%) in the 

troposphere. 

Cutaneous malignant melanoma (CMM) — CMM is the most serious type of skin cancer. It occurs most 

frequently in light-skinned persons over age 40 with light complexions and hair color. 

Dobson unit — A measure of the thickness of the ozone. For a vertical column of ozone compressed at 

0 degrees Celsius and 1 atmosphere pressure, a Dobson unit is defined to be 0.01 millimeter thick. 

Dose metrics — Measures used to express the amount of UV radiation received over a specific time 

period (i.e., the dose). Examples are peak hour dose, daily dose, or cumulative doses for a month or for 

an entire year. 

Dose-response relationship — The relationship between an effect (e.g., skin cancer) and the exposure 

(e.g., UV radiation) producing that effect. If plotted on a log-log scale, BAFs are equal to the slope of the 

dose-response curve. 

Econometric — A statistical technique that enables analysts to determine to what degree one specific 

variable (e.g., UV exposure) may be responsible for a specific effect (e.g., skin cancer) thought to be 

caused by the interaction of several related variables. For example, it is hypothesized that age, birth year, 

and UV exposure all play a role in the etiology of CMM. Because ODS control policies can only reduce 

one of these risk factors (i.e., the amount of UV-B reaching the ground can be reduced by a thicker ozone 

layer), econometric estimation is used to isolate and quantify the contribution of UV exposure to CMM 

incidence and mortality. 

Equivalent effective stratospheric chlorine (EESC) — An expression of the ozone-depleting capability 

of a substance and its degradation products, expressed in terms of chlorine molecules. The EESC of 

substances that contain bromine instead of chlorine can be calculated by using a bromine efficiency 

factor (“alpha factor”) that defines the number of chlorine molecules needed to deplete one molecule of 

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ozone as efficiently as one bromine molecule. The AHEF assumes that 55 chlorine molecules are needed 

to deplete a molecule of ozone as efficiently as one bromine molecule. 

Incidence — For the purposes of this paper, the incidence is defined as the number of new cases of a 

given health effect that develop each year. 

Isotropic ground reflectivity — The assumption that all wavebands of UV radiation striking the Earth’s 

surface are reflected equally in all directions. 

Latency — The length of time between the exposure to a stressor (e.g., UV-B radiation) and the 

response to that stressor (e.g., skin cancer). 

No further ozone depletion — The total column ozone observed in the 1979-1980 timeframe is defined 

as the baseline against which the impacts of future ozone changes are measured. These column ozone 

levels are assumed in the AHEF to remain constant in the baseline projections. 

Ozone-depleting substances (ODS) — A group of chemicals containing chlorine and/or bromine atoms 

that destroy ozone molecules in the stratosphere. Emissions of these chemicals are the chief cause of 

anthropogenic stratospheric ozone depletion and have been targeted for phaseout by the Montreal 

Protocol and its amendments. 

Prevalence — Refers to the total number of existing cases of a given health effect, at a specific time, as 

opposed to new cases (“incidence”). 

Solar UV irradiance — UV radiation from the sun reaching the Earth’s surface. 

Solar zenith angle — The solar zenith angle is the angle of the sun’s direction with respect to the local 

upward vertical, measured in degrees from 0o

 (overhead sun) to 90o (sun at the horizon). 

Spectral irradiance – The radiation at the Earth’s surface measured across the full UV spectrum. 

Squamous cell carcinoma (SCC) — SCC is the second most common form of NMSC in humans. It is 

thought to be primarily caused by exposure to ultraviolet radiation. SCC may grow quickly and spread to 

other parts of the body, making it a dangerous form of skin cancer. 

Total Ozone Mapping Spectrometer (TOMS) — A satellite-based instrument to measure the total 

vertical column of ozone in the atmosphere. The method is based on detecting UV radiation 

backscattered by the lower atmosphere after it passes through stratospheric ozone. The amount of ozone 

is determined by comparing the backscattered radiation at several pairs of wavelengths, selected for 

different sensitivity to absorption by ozone. The TOMS instrument aboard the Nimbus-7 satellite provided 

nearly continuous global ozone data from late 1978 to early 1993. Shorter data records from more recent 

satellites (Meteor-3, Earth Probe) are also available. 

Ultraviolet (UV) radiation — Ultraviolet radiation is a portion of the electromagnetic spectrum with 

wavelengths shorter than visible light. The sun produces UV radiation, which is commonly split into three 

arbitrarily-defined bands: UV-A, UV-B, and UV-C. Because the AHEF relies on action spectra equations 

to estimate health effects, it is not necessary to define the exact wavelengths that make up each band. 

UV-A is not absorbed by ozone. UV-B is mostly absorbed by ozone, although some reaches the Earth. 

UV-C is completely absorbed by ozone and normal oxygen. The AHEF uses the percentage change in 

UV exposure multiplied by the appropriate BAF and the age-specific baseline incidence or mortality rate 

to predict future changes in human health effects. Although the AHEF considers only solar UV radiation, 

UV radiation from artificial sources (e.g., tanning beds, welding, mercury lamps) are also associated with 

adverse health effects. 

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UV-A — A band of ultraviolet radiation with wavelengths from 320-400 nanometers produced by the sun. 

UV-A is not absorbed by ozone. This band of radiation has wavelengths just shorter than visible violet 

light. 

UV-B — A band of ultraviolet radiation with wavelengths from 280-320 nanometers produced by the sun. 

UV-B has been associated with human health impacts and is particularly effective at damaging DNA. 

UV-B has been identified as a cause of melanoma and other types of skin cancer as well as cataract and 

suppression of the immune system. It has also been linked to damage to some materials, crops, and 

marine organisms. The ozone layer protects the Earth against most solar UV-B radiation. 

UV-C — A band of ultraviolet radiation with wavelengths shorter than 280 nanometers. UV-C is extremely 

dangerous, but it is completely absorbed by ozone (at wavelengths between 240 and 280 nm) and normal 

oxygen (O2) (at wavelengths between 200 and 280 nm), and hence does not reach the Earth’s surface. 

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Appendix A. Updates Made to the Original AHEF 

Since its inception in the early 1980s, the AHEF has undergone a series of updates to ensure that it 

reflects the current state of knowledge about the relationships between ozone depletion, UV exposure, 

and human health endpoints. Thirteen revisions or updates have been performed since 1985. Of these, 

four involve significant changes to the results predicted by the model: 

• The bromine alpha factor was changed from 40 to 55 to account for recent findings that bromine 

emissions are more potent in depleting ozone than previously thought. 

3⁄4 Impact: Medium-sized increase in results (roughly 30%). 

• For projecting CMM mortality and incidence, BAFs were developed using the SCUP-h action 

spectrum in place of the DNA-h action spectrum. Since action spectra specific to CMM have not 

yet been developed, the SCUP-h was also chosen for modeling CMM, as it is the only action 

spectrum derived based on the induction of skin cancer. Peer reviewed articles and studies 

support this. 

3⁄4 Impact: Large decreases in estimated health effects. CMM incidence and mortality rates 

estimated using the SCUP-h action spectrum are approximately 40% lower than those 

developed using the DNA-h action spectrum. 

• NMSC mortality rates were revised based on NCI NMSC mortality data and well-established 

statistical techniques for estimating BAFs. Previous versions of the AHEF used a 1 percent 

incidence-to-mortality assumption to predict mortality for NMSC because mortality data for NMSC 

was lacking. Also, the BAF for modeling MNSC mortality is now based on the SCUP-h action 

spectrum, and not the DNA-h action spectrum that was used in previous versions of the AHEF. 

Developments in the skin cancer literature suggest that SCUP-h is the most accurate action 

spectrum available for NMSC, because it covers a broader spectral range and is more 

appropriate than use of the DNA-h action spectrum. 

3⁄4 Impact: The combined impact of these two changes is a large decrease in NMSC 

mortality estimates (by approximately 80%). These lower NMSC mortality estimates 

reflect more accurate data on NMSC mortality instead of relying on unfounded 

assumptions regarding the correlation of NMSC incidence and mortality. 

• Cataract incidence was removed as a health effect assessed by the AHEF. This decision was 

based on recent analyses that showed weak dose-response relationship between cataract 

incidence and UV exposure in the U.S. population based on readily available data. 

3⁄4 Impact: Small decrease in projected health effects. When cataract incidence was 

included in the AHEF projections, cataracts accounted for only about 5% of the 

monetized benefits. 

A number of minor updates also ensure that the AHEF uses the most recent data and reflects state-ofthe-art knowledge about issues related to stratospheric ozone depletion and the prediction of UVmediated human health effects. These updates include the following: 

• The BAF used to project SCC incidence was updated from 2.5 to 2.6, based on new research 

conducted by de Gruijl and Forbes (1995). The new BAF is derived based on the SCUP-h action 

spectrum instead of the SCUP-m action spectrum, which was used in previous studies (e.g., Van 

der Leun and De Gruijl 1994, de Gruijl and Van der Leun 1994). The SCUP-m spectrum was 

derived on the basis of the induction of SCC in hairless mice. The SCUP-h spectrum is an 

adjusted spectrum, corrected for human skin transmission to account for differences in epidermal 

thickness and the number of hair follicles per unit area. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 57 – 
3⁄4 Impact: Small increase in estimated NMSC incidence and mortality. Projected SCC 

incidence rates estimated using the SCUP-h action spectrum are approximately 4% 

higher than those developed using the SCUP-m action spectrum. 

• Methyl bromide baseline usage estimates were updated, as current and future methyl bromide 

uses are lower than had been originally projected in the older version of the AHEF (due to both a 

decline in baseline use and to future controls). 

3⁄4 Impact: Small to very small decrease in total cancer incidence and mortality. 

• Population projections through 2050 were updated for the United States by state, race, and sex to 

reflect more recent information (i.e., 1990 data and 1998 mid-level projections from the U.S. 

Census Bureau). 

3⁄4 Impact: Negligible change in results. 

• The NCI CMM mortality data were tied to the SEER CMM incidence and mortality data instead of 

relying on data from the 1970s, to produce more consistent CMM incidence estimates. 

3⁄4 Impact: Negligible change in results. 

• Satellite measurements of stratospheric ozone were updated, based on new data provided by 

NASA. 

3⁄4 Impact: Negligible change in results. 

• The AHEF’s TUV inputs (which provide ground-level irradiance through time and by latitude) were 

updated to reflect updates and refinements made directly to the TUV model, all of which have 

been published in peer reviewed journals (Madronich 1992, 1993; Madronich and de Gruijl 1993, 

Madronich et al. 1996, 1998). In addition, the accuracy of the TUV has been demonstrated in a 

number of comparisons to direct measurements of UV radiation at the Earth’s surface (e.g., 

Shetter et al. 1992, 1996; Kirk et al. 1994; Lantz et al. 1996). In addition, since 1989, the TUV 

model has been used in the scientific evaluations of ozone depletion (WMO 1990, 1992, 1995, 

1999) and related environmental consequences (UNEP 1989, 1991, 1994, 1998) as mandated 

under the Montreal Protocol. 

3⁄4 Impact: Two to four percent change in absolute UV irradiance used to estimate health 

effects. Because these changes apply to both the baseline and policy scenarios, there 

are only second order (i.e., negligible) impacts on incremental health effects estimates. 

• The capability to use age-weighted UV exposures was added to the AHEF, to place a greater 

emphasis on exposures received early in an individual’s life. This weighting changes the total 

number of predicted cases or deaths by up to 11 percent. This improvement allows an evaluation 

of the manner in which cases or deaths are distributed across age cohorts and generations, for 

comparison purposes. This is particularly useful for exploring the hypothesis that early life and 

intense UV exposures are disproportionately responsible for CMM incidence and mortality. 

3⁄4 Impact: Adds flexibility to the model; only changes results if age-weighting is being 

evaluated (i.e., for whole-life exposure, results are unchanged). 

• The modular structure and design of the AHEF’s input data files and routines were refined, to 

efficiently handle future enhancements and additions. For example, adding a new action 

spectrum has been greatly simplified. Similarly, when relationships between UV exposure and 

other benefit categories—human and non-human—are available, these can be easily added to 

the new AHEF once the appropriate data become available. Possible enhancements and 

additions to the new AHEF include human immune system impairment, and effects on 

agricultural, terrestrial, and aquatic ecosystems. 

3⁄4 Impact: Increases model flexibility by allowing new impacts to be evaluated; no impacts 

on current results. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 58 – 
• The design of the AHEF’s output data files and routines were refined to produce more detailed 

outputs that easily summarize results from a variety of perspectives, such as total health effects, 

incremental effects over time, at finer levels of resolution (resolution increased from a 100 cases 

level to a one case level). 

3⁄4 Impact: Increased model resolution (i.e., allows smaller impacts to be evaluated); no 

impacts on results. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 59 – 
Table B-1. Regional Definitions for Mortality Data Set 

40o

 N to 50o N 30o N to 40o N 20o N to 30o N 

Alaska California (North)* Alabama 

Connecticut Colorado Arizona 

Idaho Delaware Arkansas 

Maine District of Columbia California (South)* 

Massachusetts Illinois Florida 

Michigan Indiana Georgia 

Minnesota Iowa Hawaii 

Montana Kansas Louisiana 

New Hampshire Kentucky Mississippi 

New York Maryland New Mexico 

North Dakota Missouri South Carolina 

Oregon Nebraska Texas 

Rhode Island Nevada 

South Dakota New Jersey 

Vermont North Carolina 

Washington Ohio 

Wisconsin Oklahoma 

 Pennsylvania

 Tennessee 

Utah 

Virginia 

 West Virginia

 Wyoming 

* Counties in California were segregated into either the South or Middle region. The population 

was split based upon county population data from the Demographic Research Unit of the 

California Department of Finance. 

Appendix B: Supplementary Tables for Baseline Incidence and Mortality 

Projections 

This appendix presents supplementary information for the determination of baseline incidence and 

mortality projections for both cutaneous malignant melanoma (CMM) and non-melanoma skin cancer 

NMSC, as discussed in Section 3 of the report. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 60 – 
Table B-2. CMM Mortality Rates (per 100,000) for White Males

 by Cohort and Year of Death, U.S. Totals 

Cohort Year of Death 

Mid Birth 

Year 

1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 

1865 6.324 

1870 5.802 8.006 

1875 4.879 7.079 10.273 

1880 3.861 5.833 7.344 10.522 

1885 3.379 4.408 5.968 8.917 12.992 

1890 3.144 3.921 5.560 8.264 9.861 15.229 

1895 2.434 3.311 4.511 6.078 9.050 12.475 17.863 

1900 1.895 2.618 3.745 5.218 6.784 10.513 14.613 

1905 1.705 2.583 3.491 4.389 6.530 9.297 12.934 

1910 1.347 2.111 3.051 3.959 5.424 7.793 11.424 

1915 1.359 1.771 2.496 3.684 4.667 7.047 9.659 

1920 0.987 1.472 2.277 3.243 4.243 6.137 7.537 

1925 0.780 1.227 1.867 2.658 3.759 5.354 6.996 

1930 0.419 0.779 1.555 2.294 2.672 4.435 5.547 

1935 0.117 0.377 0.883 1.482 2.047 2.995 4.062 

1940 0.015 0.131 0.526 0.886 1.509 2.423 3.608 

1945 0.003 0.018 0.159 0.433 0.998 1.690 2.546 

1950 0.013 0.013 0.020 0.175 0.598 1.151 1.866 

1955 0.007 0.007 0.009 0.150 0.475 0.987 

1960 0.012 0.009 0.025 0.138 0.382 

1965 0.005 0.007 0.013 0.071 

1970 0.006 0.008 0.008 

1975 0.009 0.009 

1980 0.003 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 61 – 
Table B-3. CMM Mortality Rates (per 100,000) for White Females 

 by Cohort and Year of Death, U.S. Totals 

Cohort Year of Death 

Mid Birth 

Year 

1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 

1865 5.528 

1870 3.856 6.281 

1875 3.853 4.838 7.106 

1880 3.077 4.257 5.126 7.226 

1885 2.200 3.223 4.374 6.111 7.322 

1890 2.244 2.934 3.949 5.107 6.480 9.065 

1895 1.822 2.435 3.417 4.051 5.707 7.735 10.521 

1900 1.490 2.020 2.637 3.425 4.491 5.999 8.973 

1905 1.294 1.811 2.270 2.917 3.908 5.559 6.565 

1910 1.183 1.770 1.917 2.686 3.264 4.653 5.763 

1915 1.005 1.370 1.901 2.335 2.944 3.700 4.413 

1920 0.858 1.286 1.665 1.991 2.650 3.225 4.120 

1925 0.611 0.971 1.469 1.987 2.382 2.979 3.482 

1930 0.404 0.732 1.109 1.486 2.167 2.794 3.171 

1935 0.098 0.330 0.719 1.116 1.435 2.082 2.721 

1940 0.015 0.092 0.267 0.646 0.998 1.625 2.156 

1945 0.006 0.018 0.118 0.336 0.725 1.051 1.502 

1950 0.014 0.011 0.016 0.107 0.304 0.730 1.155 

1955 0.017 0.005 0.019 0.101 0.387 0.648 

1960 0.007 0.002 0.010 0.097 0.294 

1965 0.005 0.000 0.011 0.079 

1970 0.003 0.000 0.006 

1975 0.009 0.003 

1980 0.012 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 62 – 
Table C-1. Original Montreal Protocol (1987) 

Controls for Non-Article 5 Nations Controls for Article 5 Nations 

CFCs Halons CFCs Halons 

Base Year: 1986 1986 Base Year: 1996 1996 

Controls Controls 

Year Percent of Percent of Year Percent of Base Percent of 

Base Year Base Year Year Base Year 

July 1, 1989 100% --- July 1, 1999 100% --- 

1990 100% --- 2000 100% --- 

1991 100% --- 2001 100% --- 

1992 100% 100% 2002 100% 100% 

1993 80% 100% 2003 80% 100% 

1994 80% 100% 2004 80% 100% 

1995 80% 100% 2005 80% 100% 

1996 80% 100% 2006 80% 100% 

1997 80% 100% 2007 80% 100% 

1998 50% 100% 2008 50% 100% 

Appendix C. Supplementary Tables for Stratospheric Ozone Depletion Modeling 

Tables C-1 through C-4b of this appendix present the ODS controls for both non-Article 5 and Article 5 

countries under each of the policy scenarios examined (i.e., the original Montreal Protocol, the London 

Amendments of 1990, the Copenhagen Amendments of 1992, and the Montreal Adjustments of 1997) as 

discussed in Section 4.3 of the report. Table C-5 lists the types of CFCs and HCFCs that are included in 

the phaseout schedules. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 63 – 
Table C-2. London Amendments (1990) 

Controls for Non-Article 5 Nations Controls for Article 5 Nations 

CFCs Halons Carbon 

Tet. 

Methyl

Chlor. 

CFCs Halons Carbon 

Tet. 

Methyl

Chlor. 

(CCl4) (CH3CCl3) (CCl4) (CH3CCl3) 

Base Year: 1986 1986 1989 1989 Base Year: 1996 1996 1999 1999 

Controls Controls 

Year Percent Percent Percent Percent Year Percent Percent Percent Percent 

of Base of Base of Base of Base of Base of Base of Base of Base 

Year Year Year Year Year Year Year Year 

July 1, 1989 100% --- --- --- July 1, 1999 100% --- --- 

-- 

1990 100% --- --- --- 2000 100% --- --- --- 

1991 100% --- --- --- 2001 100% --- --- --- 

1992 100% 100% --- --- 2002 100% 100% --- --- 

1993 100% 100% --- 100% 2003 100% 100% --- 100% 

1994 100% 100% --- 100% 2004 100% 100% --- 100% 

1995 50% 50% 15% 70% 2005 50% 50% 15% 70% 

1996 50% 50% 15% 70% 2006 50% 50% 15% 70% 

1997 15% 50% 15% 70% 2007 15% 50% 15% 70% 

1998 15% 50% 15% 70% 2008 15% 50% 15% 70% 

1999 15% 50% 15% 70% 2009 15% 50% 15% 70% 

2000 0% 0% 0% 30% 2010 0% 0% 0% 30% 

2001 0% 0% 0% 30% 2011 0% 0% 0% 30% 

2002 0% 0% 0% 30% 2012 0% 0% 0% 30% 

2003 0% 0% 0% 30% 2013 0% 0% 0% 30% 

2004 0% 0% 0% 30% 2014 0% 0% 0% 30% 

2005 0% 0% 0% 0% 2015 0% 0% 0% 0% 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 64 – 
Table C-3a. Copenhagen Amendments (1992) – Controls for Non-Article 5 Nations 

CFCs Halons Carbon 

Tetrachloride 

Methyl 

Chloroform 

HCFCs* Methyl 

Bromide 

(CCl4) (CH3CCl3) (CH3Br)** 

Base Year: 1986 1986 1989 1989 1989 (3.1%) 1991 

Controls 

Year Percent of Percent of Percent of Base Percent of Percent of Percent of 

Base Year Base Year Year Base Year Base Year Base Year 

July 1, 1989 

1990 

100% 

100% 

--- 

--- 

--- 

--- 

--- 

--- 

--- 

--- 

--- 

--- 

1991 100% --- --- --- --- --- 

1992 100% 100% --- --- --- --- 

1993 80% 100% --- 100% --- --- 

1994 25% 0% --- 50% --- --- 

1995 25% 0% 15% 50% --- 100% 

1996 0% 0% 0% 0% 100% 100% 

1997 0% 0% 0% 0% 100% 100% 

1998 0% 0% 0% 0% 100% 100% 

1999 0% 0% 0% 0% 100% 100% 

2000 0% 0% 0% 0% 100% 100% 

2001 0% 0% 0% 0% 100% 100% 

2002 0% 0% 0% 0% 100% 100% 

2003 0% 0% 0% 0% 100% 100% 

2004 0% 0% 0% 0% 65% 100% 

2005 0% 0% 0% 0% 65% 100% 

2010 0% 0% 0% 0% 35% 100% 

2015 0% 0% 0% 0% 10% 100% 

2020 0% 0% 0% 0% 0.5% 100% 

2030 0% 0% 0% 0% 0% 100% 

* The baseline HCFC consumption, or HCFC cap, was calculated as the total ODP-weighted HCFC consumption in 1989 plus 

3.1 percent of the ODP-weighted CFC consumption in 1989. 

** The methyl bromide freeze did not apply to post-harvesting uses (i.e., quarantine and pre-shipment). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 65 – 
Table C-3b. Copenhagen Amendments (1992) - Controls for Article 5 Nations 

CFCs Halons Carbon 

Tetrachloride (CCl4) 

Methyl Chloroform 

(CH3CCl3) 

Base Year: 1996 1996 1999 1999 

Controls 

Year Percent of Base 

Year 

Percent of Base 

Year 

Percent of Base 

Year 

Percent of Base 

Year 

July 1, 1999 100% --- --- --- 

2000 100% --- --- --- 

2001 100% --- --- --- 

2002 100% 100% --- --- 

2003 80% 100% --- 100% 

2004 80% 100% --- 100% 

2005 50% 50% 15% 70% 

2006 50% 50% 15% 70% 

2007 15% 50% 15% 70% 

2008 15% 50% 15% 70% 

2009 15% 50% 15% 70% 

2010 0% 0% 0% 30% 

2011 0% 0% 0% 30% 

2012 0% 0% 0% 30% 

2013 0% 0% 0% 30% 

2014 0% 0% 0% 30% 

2015 0% 0% 0% 0% 

Note: The Copenhagen Amendments did not change the Article 5 control schedule. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 66 – 
Table C-4a. Montreal Adjustment (1997) - Controls for Non-Article 5 Nations 

CFCs Halons Carbon 

Tetrachloride 

Methyl 

Chloroform 

HCFCs* Methyl 

Bromide 

(CCl4) (CH3CCl3) (CH3Br)** 

Base Year: 1986 1986 1989 1989 1989 (2.8%) 1991 

Controls 

Year Percent of Percent of Percent of Percent of Percent of Percent of 

Base Year Base Year Base Year Base Year Base Year Base Year 

July 1, 1989 100% --- --- --- --- --- 

1990 100% --- --- --- --- --- 

1991 100% --- --- --- --- --- 

1992 100% 100% --- --- --- --- 

1993 80% 100% --- 100% --- --- 

1994 25% 0% --- 50% --- --- 

1995 25% 0% 15% 50% --- 100% 

1996 0% 0% 0% 0% 100% 100% 

1997 0% 0% 0% 0% 100% 100% 

1998 0% 0% 0% 0% 100% 100% 

1999 0% 0% 0% 0% 100% 75% 

2000 0% 0% 0% 0% 100% 75% 

2001 0% 0% 0% 0% 100% 50% 

2002 0% 0% 0% 0% 100% 50% 

2003 0% 0% 0% 0% 100% 30% 

2004 0% 0% 0% 0% 65% 30% 

2005 0% 0% 0% 0% 65% 0% 

2010 0% 0% 0% 0% 35% 0% 

2015 0% 0% 0% 0% 10% 0% 

2020 0% 0% 0% 0% 0.5% 0% 

2030 0% 0% 0% 0% 0% 0% 

* The baseline HCFC consumption, or HCFC cap, is calculated as the total ODP-weighted HCFC consumption in 1989 plus 2.8 percent of the 

ODP-weighted CFC consumption in 1989. 

** The methyl bromide freeze does not apply to post-harvesting uses (i.e., quarantine and pre-shipment). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 67 – 
Table C-4b. Montreal Adjustment (1997) - Controls for Article 5 Nations 

CFCs Halons Carbon 

Tetrachloride 

Methyl 

Chloroform 

HCFCs Methyl 

Bromide 

(CCl4) (CH3CCl3) (CH3Br)* 

Base Year: 1996 1996 1999 1999 2015 Average of 

1995-1998 

Controls 

Year Percent of Percent of Percent of Percent of Percent of Percent of 

Base Year Base Year Base Year Base Year Base Year Base Year 

July 1, 1999 100% --- --- --- --- --- 

2000 100% --- --- --- --- --- 

2001 100% --- --- --- --- --- 

2002 100% 100% --- --- --- 100% 

2003 80% 100% --- 100% --- 100% 

2004 80% 100% --- 100% --- 100% 

2005 50% 50% 15% 70% --- 80% 

2006 50% 50% 15% 70% --- 80% 

2007 15% 50% 15% 70% --- 80% 

2008 15% 50% 15% 70% --- 80% 

2009 15% 50% 15% 70% --- 80% 

2010 0% 0% 0% 30% --- 80% 

2011 0% 0% 0% 30% --- 80% 

2012 0% 0% 0% 30% --- 80% 

2013 0% 0% 0% 30% --- 80% 

2014 0% 0% 0% 30% --- 80% 

2015 0% 0% 0% 0% --- 0% 

2016 0% 0% 0% 0% 100% 0% 

2040 0% 0% 0% 0% 0% 0% 

* The methyl bromide freeze does not apply to post harvesting uses (i.e., quarantine and pre-shipment). 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 68 – 
Table C-5. Class I and Class II Ozone-Depleting Substances 

Class I 

Group I (CFCs)* 

CFC-11 CFC-12 CFC-113 CFC-114 CFC-115 

Group II (halons)* 

halon-1211 halon-1301 halon-2402 

Group III (other CFCs)* 

CFC-13 CFC-111 CFC-112 CFC-211 CFC-212 CFC-213 

CFC-214 CFC-215 CFC-216 CFC-217 

Group IV 

carbon tetrachloride 

Group V 

methyl chloroform 

Group VI 

methyl bromide 

Group VII 

C3H2F3Br3 C3H3FBr4 C2H4Br C3H3F3Br2 C2HF3Br2 C3H2F5Br 

C2H2F3Br C3H4FBr3 C3HFBr6 C3H4F3Br C3H3F2Br3 C2HFBr4 

C3H4F2Br2 C3H5F2Br C3HF2Br5 CH2FBr C2H3F2Br C2H2FBr3 

C2H2F2Br2 C2HF2Br3 C2H3FBr2 C2HF4Br C3H6FBr C3HF3Br4 

C3HF4Br3 CHFBr2 C3HF6Br C3HF5Br2 C3H2FBr5 

C3H2F2Br4 CHF2 Br 

(HBFC-22B1) 

C3H2F4Br2 C3H5FBr2 C3H3F4Br 

Class II (HCFCs) 

HCFC-21 HCFC-22 HCFC-31 HCFC-121 HCFC-122 HCFC-123 

HCFC-124 HCFC-131 HCFC-132 HCFC-133 HCFC-141 HCFC-142 

HCFC-221 HCFC-222 HCFC-223 HCFC-224 HCFC-225 HCFC-226 

HCFC-231 HCFC-232 HCFC-233 HCFC-234 HCFC-235 HCFC-241 

HCFC-242 HCFC-243 HCFC-244 HCFC-251 HCFC-252 HCFC-253 

HCFC-261 HCFC-262 HCFC-271 

Isomers of the listed chemicals are also

Source: U.S. EPA Website 1999. 

 included in the ODS phaseout. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 69 – 
28 The WMO (2003) report does provide updated estimates of projected EESC levels (Figure 1-23), but does not 

provide updated ozone depletion estimates associated with these EESC projections. Therefore, the 2003 WMO 

projections are insufficient for use in conducting a comparative analysis with the AHEF. Further, the 2003 

assessment incorporated revised assumptions regarding HCFC production levels. Thus, the WMO (2003) EESC 

projections are lower than those projected in WMO (1999). Without the revised ozone concentrations projections 

associated with this lower EESC scenario, it is unclear how the latest WMO projections compare to the AHEF in 

terms of ozone concentration predictions—and/or how these changes in ozone concentrations would impact changes 

in incremental health effects. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 70 – 

Dobson Units 

Figure D- 1. Stratospheric Ozone Column Levels at 40-50°N: AHEF Predictions, Montreal 

Adjustments 

360 

355 

350 

345 

340 

335 

330 

325 

320 

315 

9

7 

85

1

9

27

2

69

2

9

9

992

19

20

20

6

13

20

034

2041

20 8

55

62

20

076

2083

2090

1

20

20

0

20

4

20

20

Year 

Appendix D: Comparison of AHEF and WMO Predicted Ozone Concentrations 

To validate the AHEF’s projections of stratospheric ozone concentrations, the AHEF ozone level 

predictions under the Montreal Adjustments (see Figure D- 1) were compared to measurements and 

modeling reported in Figure 11-14 of the Scientific Assessment of Ozone Depletion, 1998 by the World 

Meteorological Organization’s (WMO 1999) Global Ozone Research and Monitoring Project (see Figure 

D- 2). The WMO graph of ozone levels also applied the Montreal Adjustments policy scenario in 

predicting chlorine and bromine levels in the atmosphere and resulting stratospheric ozone column levels. 

Both the AHEF and WMO projections are based on annually- and monthly-averaged stratospheric ozone 

concentrations for different latitudes as measured by NASA's Nimbus-7 Total Ozone Mapping 

Spectrometer (TOMS) satellite. The most recent ozone assessment conducted by the WMO (2003) does 

not provide similar predictions of column ozone concentration that are comparable to AHEF output (in 

terms of format and type of data reported), therefore, direct comparison with the AHEF was not 

possible.28 
Figure D- 2. Stratospheric Ozone Column Levels at 45°N: WMO Measurements/Predictions 

Source: World Meteorological Organization 1999. 

As illustrated by Figures D-1 and D-2, the AHEF and WMO projections agree that year of minimum 

stratospheric ozone concentration would be in the late 1990s to early 2000s. This finding agrees with the 

U.S. Global Change Research Program estimate, which projected that concentrations of ODS in the 

atmosphere would peak before the year 2000 (USGCRP 1998). Figure D- 1 and Figure D- 2 also show 

that AHEF and the 1999 WMO assessment are in agreement regarding the speed of ozone recovery, 

both projecting full recovery around 2045. 

The major difference between the two models is the absolute minimum predicted ozone concentration. 

The AHEF predicts that the minimum value reaches approximately 320 Dobson Units (DU) for the 40-50 

degree latitude band, while WMO estimates indicate an absolute minimum of approximately 335 DU at 45 

degrees of latitude. 

This primary reason for this difference is believed to be variations between the models’ estimated 

equivalent effective stratospheric chlorine (EESC), particularly in the earlier years modeled (Figure D- 3). 

Factors that influence EESC estimates include the estimated ODS emissions, the degree of dissociation 

of each ODS species, and the rate of transport to the stratosphere. In the estimation of ODS emissions 

alone, for example, there exists significant opportunity for variation. For example, the AHEF estimates 

annual ODS emissions by generating annual emission profiles for each ODS end use, by chemical, for all 

ODS-consuming countries. This is performed through a complex, bottom-up model that estimates the 

stock and growth rates of all types of ODS-containing equipment, as well as their average lifetimes, 

annual leak rates, service rates, and disposal rates. Conversely, WMO estimates are based on measured 

values of EESC extrapolated into the future by compound. WMO projections are based on emission 

functions acting on the banks of material yet-to-be emitted from end-use categories with similar emissions 

patterns. The WMO assumes that the banks by end-use categories are replenished by sales, where sales 

are based on future production and consumption estimates. Thus, given the difference in modeling 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 71 – 
Figure D- 3. Comparison of AHEF and WMO (1999) EESC Estimates 

0 

500 

1000 

1500 

2000 

2500 

3000 

3500 

4000 

1980

1990

2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100 

Year 

EESC (pptv)

WMO 

AHEF 

approaches, and the uncertainty associated with the assumptions used by both approaches, variation 

across models is to be expected. 

However, in a peer reviewed report titled Human Health Effects of Ozone Depletion from Stratospheric 

Aircraft (2001), it was found that the difference between the AHEF and WMO model predictions of the 

maximum ozone depletion is well within the acceptable range of variation. This report, co-authored by 

NASA and EPA, compared and evaluated six different atmospheric models capable of projecting ozone 

concentrations. These six models generated ozone estimates that varied by a factor of five, as presented 

in Figure D- 4. Indeed, the estimated ozone levels of the six models had an uncertainty range of +0.9% to 

-2.1% around the central value of -0.6% in 2050. If this range is taken to define the uncertainty arising 

wholly from variability within the models, the AHEF and WMO predictions can be considered to be 

statistically similar. 

Similarly, the 2003 WMO Scientific Assessment supports the assertion that the AHEF and WMO column 

ozone estimates are within an acceptable range of variation. As shown in Figure D- 5, the differences in 

column ozone predictions from different computer models used for the GHG scenario MA229 in the 2003 

WMO report are roughly the same order of magnitude as those between the AHEF and WMO (1999) 

estimates. 

Thus, the general agreement of the decline and recovery estimates, the shapes of the curves, and the 

use of identical data sets indicate that the similarities between the AHEF and WMO approaches outweigh 

29 WMO GHG scenario MA2 is taken from IPCC (2001) scenario A2. In this scenario, by 2050, atmospheric levels of 

CO2 concentrations reach 532 ppmv, CH4 concentrations reach 2,562 ppbv, and N2O concentrations reach 373 ppbv. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 72 – 
the differences—which are themselves within an acceptable range of variation for atmospheric models of 

this kind. 

Figure D- 4. Sensitivity and Uncertainty Envelopes Used in HSCT Analysis 

Source: NASA/EPA 2001. 

Figure D- 5. Variations in Column Ozone Projections from Different Atmospheric Models* 

*Predicted future evolution of column ozone is shown for WMO GHG scenario MA2 

from eight 2-D models. The plot also includes observations of past trends (red lines) 

prior to 2000. 

Source: WMO 2003. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

– 73 – 
Table E-1. Baseline Cataract Incidence Rates by Age Category 

(Used in Previous Versions of the AHEF) 

Age 

Category 

Incidence Rates Assuming 1979-1980 Ozone Levels (per 

100,000) 

55-64 450 

65-70 1,350 

75-84 2,750 

85+ 0a 

a

 Because the prevalence (existing cases) of cataract in the 85+ group is the same for 

the age 75-84 category, incidence (new cases) for the 85+ group is assumed to be 

zero 

Appendix E: Modeling Cataract Incidence 

In previous versions of the AHEF, cataract incidence attributable to increased UV exposure has been 

modeled. This health effect was handled in much the same way as the other health effects, with baseline 

cataract incidence rates estimated using cataract incidence data reported in U.S. EPA (1987). These 

cataract incidence rates were derived from prevalence data presented in Hiller et al. (1983), which in turn 

were based on a subset of the National Health and Nutrition Examination Study (NHANES) data.30 The 

subset consisted of 2,225 subjects between the ages of 45 and 74 at 35 different locations across the 

United States. Incidence rates were stratified by location, based on the three latitudinal bands (i.e., 20°N 

to 30°N, 30°N to 40°N, and 40°N to 50°N). Future cataract incidence for particular ozone depletion 

scenarios was estimated in the same way that skin cancer incidence and mortality are computed—i.e., by 

multiplying incidence rates by population projections by age, sex, and latitude band. Table E-1 reports 

baseline cataract incidence rates developed by age group. 

In deriving the dose-response relationship for cataracts, BAFs were developed based on a study 

conducted by Taylor et al. (1988), which looked at exposure histories for a sample population in a single 

location (i.e., the Chesapeake Bay in Maryland). The resulting BAF was 0.225 for males and females. 

However, while the Taylor study was able to show that high cumulative levels of UV-B exposure 

significantly increased the risk of cortical cataract in a controlled population, the study was not appropriate 

for deriving a BAF for cataract incidence because it is based on only one location in the United States, as 

opposed to latitudinal averages, like the other health effects that are modeled. 

Thus, a recent effort was made to revise the BAFs for cataract incidence based on available action 

spectra and the latest data on U.S. cataract incidence. All available action spectra were reviewed, 

including: 

 Erythema (1987): This is an action spectrum derived on the basis of erythema (sunburn) 

induction in human volunteers (McKinlay and Diffey 1987). This action spectrum was used to 

predict cataract incidence in the original AHEF developed in the 1980s. 

 Merriam et al. (2000): This action spectrum was generated based on induction of cataracts in 

rats. The action spectrum spans the UV spectrum from 295 nm to 315 nm, covering only the UV-B 

spectrum. 

 Pitts et al. (1977): This action spectrum was generated as an average of action spectra values 

based on the ocular effects of UV radiation on the corneas of humans, primates, and rabbits, as 

well as human conjunctiva and rabbit uveitis. 

30 NHANES analyzed all three forms of cataract, but only cortical cataract is clearly associated with UV exposure, 

while much uncertainty exists with regard to the role of UV-B and the other forms of cataract. Thus, in terms of using 

the NHANES data in developing baseline cataract incidence in the AHEF, cataract incidence may be overestimated. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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Figure E-1. Action Spectra Considered to Model Cataracts 

0.0001 

0.001 

0.01 

0.1 

1 

10 

280 300 320 340 360 380 400 

Oriowo et al. 

Pitts et al. (average) 

Merriam et al. 

Erythema 

Note: The Response (plotted on the Y-axis) is a log scale. At the normalizing wavelength, every 1 percent 

change in UV radiation causes a 1 percent change in the health effect. 

 Oriowo et al. (2001): This action spectrum was generated based on induction of cataracts in pigs 

using whole cultured lenses. The action spectrum spans from 270 nm to 370 nm, thus extending 

into the UV-A spectrum. 

Figure E-1 graphically presents the identified action spectra for cataract induction. As can be seen, these 

action spectra show strong sensitivity at UV-B wavelengths (hence the sensitivity of cataracts to ozone 

changes), and much less to UV-A (which is not significantly affected by ozone changes). 

While the shapes of these four action spectra are very similar, the Oriowo action spectrum was selected 

for use in the AHEF because it is believed to be the most appropriate for modeling cataracts in humans 

due to the following: 

 The Oriowo action spectrum is based on the induction of cataracts in pigs, which is more 

appropriate for modeling cataract incidence than the Erythema action spectrum, which is based 

on the induction of erythema (sunburn) in humans. 

 Of all action spectra developed based on the induction of cataracts in animals, the pig lens is 

believed to be most physiologically similar to the human eye—as opposed to the rat or rabbit 

lenses used in the Merriam et al. (2000) and Pitts et al. (1977) action spectra. 

 The Oriowo action spectrum is not an averaged action spectrum, like the Pitts et al. action 

spectrum, and is therefore less likely to be complicated by inconsistencies arising from attempts 

to combine data from different studies. 

 The Oriowo action spectrum appeared in a peer-reviewed journal (Investigative Ophthalmology & 

Visual Science), and is the most recently developed action spectrum of the three action spectra 

considered. 

Then, in an attempt to calculate the BAFs, estimated average UV exposures by state were correlated with 

cataract incidence data reported by the National Eye Institute/Prevent Blindness America (NEI/PBA) 

(2002)—the most recent and comprehensive cataract incidence data available—of populations aged 40 

and above. The results of this analysis suggested that cataract incidence is directly correlated with age. 

However, it was not possible to develop a good correlation between the intensity of UV exposure and 

cataract incidence. Therefore, it was not possible to predict changes in cataract incidence based on 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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changes in ODS emission scenarios with a high level of confidence. Consequently, this health effect was 

omitted from the AHEF. 

The lack of correlation between UV radiation intensity and cataract incidence may be due to confounding 

factors that prevent accurate assessment of UV exposure within the population. Such confounding factors 

include changes in behavior (i.e., wearing protective sunglasses or actively avoiding the sun) or changes 

in demographics (i.e., travel or relocation). For example, the actual cumulative UV exposure of someone 

who has moved to a new state may not be well represented by the averaged UV radiation values in their 

new location. 

Despite this, it may be possible to discern differences in population-based effects if state UV radiation 

intensity values become available in a more disaggregated form. If additional research or data become 

available, this health effect can be reconsidered for development of a dose-response relationship and 

inclusion in the AHEF. 

*** Peer Reviewed Final Report (April 24, 2006) *** 

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