# H UMAN 

# H EALTH 

# B ENEFITS OF 

# S TRAT OSPHERIC 

# O ZONE 

# P ROTECTION

Peer Reviewed Report

Prepa red fo r: Global Programs Divi sion Office of Air and Ra diation U.S. Environmental Prote ction Agen cy Wa shin gton, DC 2 046 0

Prepa red b y

: ICF Co nsultin g 1725 Eye Street NW, Suit e 1000 Wa shin gton, DC 2 000 6

April 24, 2006

### Preface an d Peer ReviewSummary This report was prepa red by the U.S. Environm ental Protection Agency (EPA) with the support of its contracto r , ICF Con s ulting, Inc. (ICF). T h is re po rt describ es the a n a lytical and e m piri cal meth odolo g ies use d by the Atmosph e ri c Health Effects Frame w o r k (AHEF), a mo del used to predict chan ge s in huma n health effect s that result fro m chan ge s in the use an d relea s e of ozo ne-d epletin g sub s tan c e s(ODS). The autho rs of this repo rt con s ulte d wit h experts fro m governm en t, industry, an d aca demia i n the fields of atmosp he ric chemi s try an d dynamics, health effect s of ultraviolet radiatio n atm o sp heri cmo d e ling, and health effect s modeling (se e Ackn owl e d g ments se ction) . In Augu st and Septem ber of 200 3, the draft final document wa s pee r revie w ed for its tech nical conte n t by Dr. Edwa rd De Fab oof The Ge org e Wa shin gton University in Wa shin gton, DC, an d by Mr. Archi e Mc Culloch of Marbury Te chni cal Con s ulting in Che s hi re, United King d o m, and visiting re sea rch fellow at the Scho ol of Che m istry, University of Bristol. The pee r reviewers we re aske d to draw up on their expertise in ultraviolet (UV) radiatio n b i ologi cal effects asse ssm ent and atmo sph e ric sci en ce, respe ctively, to comm ent o n wheth e r themethods, too l s, and approa ch u s e d in the studyreflect sound scie n tifi c pra ctice a nd ad e quately add re ss the q u e s tions at hand. Written com m ents were receive d from peer revie w ers. In these co mments, the reviewe rs stated that the methodol ogy use d in this model repr esents a soun d, state-of -th e -art app roa c h to assessin g ozo ne-rel a ted health effect s. A number ofcomme nts id entified are a s for clarifi c ati on of spe c ific techni cal item s, all of whi c h have b een con s ide red by the authors. The reviewe r s stated that the repo rt provides soli d analysi s and di scussio n of results, g i ven t he scop e of the work and the un ce rtai nties that currently exist in the area s of ozon e depletio n a nd UV ra diati on health imp a cts e s timatio n
. Several area s we re hig h lig hted duri ng p eer review of this rep o rt. Dr. De Fabo hig h lighted the f a ct that one of the greate s t source s of u n ce rtainty in estimating UV radiation
- in duced he alth impact sis thelack of adeq uate exp e rime ntal dat a from whi c h a biologi cal a ction spe ctru m for cutan e o u s malig nant melano ma (CMM ) can b e develop ed. Due to this la ck of info rmat ion, the AHEF predi cts ca se s of malign ant melano ma ba sed o n the SCUP -h a ction spe ctrum fo r squ a mou sce ll carcino m a (SCC). Dr. DeFabo agre ed that the SCUP -h spectrum is th e most app ro pria te action s p ectrum available to model CMM at this time. He note d that the action sp ectrum for CMM still remain s to be determi ned, a nd that use ofthe SCUP-h in m odelin g CMM shoul d be re con s id ere d if futu re re se arch reve als th at the shap e of the action spectrum for CMM is not congruent with the SCUP -h action spectrum. EPA ac knowl edges that further sci entific re se arch in these and othe r are a s could com p lement an d signi fi cantly e nhan ce the in formation pres ented in thisreport. Dr. De F abo also a g re ed that the remova l of catara ct incid e n c e fro m the AHEF’s health effe cts modelin g reflect s a sou nd de cisi on, in light of rece nt anal yse sthat sug g e s t a wea kco rrelat ion betwe en UV exposure a n d catara ct in ciden ce in the United St ates. Dr. De Fab oalso affirme d that the pape r’s discu ssi on on immuno sup p r essio n accu rately reflects the curre n t state of the sci ence. Mr. McCullo ch sug g e s ted several revi si ons to the o r iginal text to remove ambi g u ity, and provided addition al informatio n on the method olo g ies a nd a s sumption s use d by WMO in their 199 9 an d 2003 repo rts, to all o w for a mo reaccurate a n d thoroug h co mpari s o n of the proj ecte d ozo ne con c e n tration s predi cted by WMO a nd by the AHEF. Mr. McCullo ch a l so comme nted on the ne e d to clearly ju stify the sele ction of 5 5 as the b rom ine efficien cy factor—or al pha facto r —for use in the A H EF inste ad of 45, whi c h is the va lue re co mm ende d by WMO (WMO 2 003). Th e sel e ction of an a l pha facto rof 55 is ba se d on the re sults of state-of-th e -a rt atmosp heri cmod e ls, and is al so the value u s e d in a recent report prep ared for the U.S. Depa rtmentof Def ense (Inde pe ndent Review Panel 2002, Wue bble s20 03). In gene ral, Mr. McCullo ch affirmed that theatmosph e ri c sci en ce mod u le of the pap er provide s cl ear descri p tion s of the method ology and mo del paramete rs u s ed, which allow the re ader to rea c h con c lu sion s about the way the methods have be en applie d and h o w theyrelat e to "mainstre a m" atmosp h e ric sci en ce (e.g., WMO Oz one Ass e ss ment s )

.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – i –All comment s of the reviewers we re con s ide r ed, an d the do cume nt wa s modified approp riately. EPA wishes to acknowledge everyone in volved in this report and thank re viewers for their ext ensive time, effort, and expert guid a n c e
. The involvement of peer reviewers an d other scie n tific conta cts g r eatly enhanced the techni cal soundness of this report. EPA accepts responsi b ility for all information presented and any erro rs co ntaine d in this do cum ent. Global Programs Divi sion (620 5J) Offic e of Atmos p hericPrograms U.S. Environmental Prote ction Agen cy Wa shin gton, DC 2 046 0

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – ii –

### TABLE OF CONTENTS Page Executive Summary.............................................................................................................. ................ES-1 1. Introduction...................................................................................................................
...................... 1 2. Model Overview.................................................................................................................
.................. 3 3. Baseline Incidence and Mo rtality Proj ections .................................................................................7 3.1 CMM Mortality Rates

............................................................................................................ ...... 7 3.2 CMM Incidence Rates.................................................................................................................8 3.3 NMSC Mortal ity Rates

........................................................................................................... ...10 3.4 NMSC Incide nce Ra tes........................................................................................................... ..10 3.5 U.S. Population Growth Rates ..................................................................................................1 3 4. Stratospheric Ozone De pletion M odeling ......................................................................................14 4.1 Approach for Stratospheric Ozone Concentrati on Modeling ....................................................14 4.2 Stratospheric Ozone Depleti on from ODS Em issions ..............................................................15 4.3 Ozone Protection Policy Scenario s and Projections of FutureOzone Concentrations ............16 5. Modeling Changes in UV Expos ure and Health Effects ................................................................18 5.1 The Tropospheric Ultraviolet-Vi sible Radiation (TUV) M odel ...................................................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 Inci dence/Mortality and NM SC Mortality ..............................21 5.3.2 Derivation of BAFs for BCC and SCC Incidence................................................................23 5.4 Resulting BAFs by Health Effect ...............................................................................................2 3 6. Projecting Future Health Effect s Under ODS Cont rol Policies ....................................................25 7. Modeling Re sults ............................................................................................................... ...............26 7.1 Results Presented by Policy Scena rio and by Heal th Effect ....................................................26 8. Sensitivity Analyses ........................................................................................................... ..............30 8.1 Action Spectra and Exposure/Dose Me trics Sensitivity Analyses ............................................30 8.2 CMM: Effects of Emphasizi ng Early Life Exposures.................................................................31 9. Uncertainty Analysis ........................................................................................................... .............33 9.1 Major Sources ofUncertainty

................................................................................................... .33 9.1.1 Translating ODS Emission s into EESC Conc entrations ....................................................34 9.1.2 Translating EESC Concentrations into Stratospheric Column Ozone ...............................35 9.1.3 Translating Column Ozone into Ground-Level UVRadiation .............................................36 9.1.4 Translating UV Exposures into Human Heal th Effects .......................................................36 9.2 Other Unquantified Source s of Uncert ainty ..............................................................................40 9.2.1 Composition of t he Future At mosphere..............................................................................40 9.2.2 Future Conditions of the Ozo ne Layer ...............................................................................40 9.2.3 Effect of Climate Vari ations on Ozone Depletion ...............................................................40 9.2.4 Global Compliance with Modeled Policy Scenari os ...........................................................41 9.2.5 Laboratory Techniques and Instrum entatio n......................................................................41 9.2.6 Demographic and Behavior Changes ................................................................................41 9.2.7 Accuracy of Ba seline Inform ation.......................................................................................4 1 9.3 Summary of Quantified and Unquantif ied Sources of Uncertai nty ...........................................42 *** Peer Reviewed Final Report (April 24, 2006) *** – i –10. Topics for FutureResearch
..................................................................................................... ........44 10.1 Prediction of Impacts on Different Sk in Types ..........................................................................44 10.2 Research on Dose-Response Relationship ofCataract Incidence and UV Exposure .............44 10.3 Inclusion of Other Hum an Health En dpoints.............................................................................44 10.4 Inclusion of Impacts of Climate Variations on Ozo ne Deplet ion...............................................45 10.5 Improved Ground-Level UV Measur ements .............................................................................45 10.6 Prediction of UV Impacts on Non-Human Endpoint s................................................................45 11. Refere nces..................................................................................................................... ....................47 12. Glossary....................................................................................................................... ......................54 Appendix A. Updates Made to the Origin al AHEF ................................................................................5 7 Appendix B: Supplementary Tables for Base line Incidence and Mort ality Projections ...................60 Appendix C. Supplementary Tables for St ratospheric Ozone De pletion Modeling ..........................63 Appendix D: Comparison of AHEF and WMO Predicted Ozone Concentrations..............................70 Appendix E: Modeling Cataract In cidence ....................................................................................... .....74

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – ii –

### Executive Summary Stratosp heri cozon e prote cts the biosph ere fro m pote n tially damagi ng do se s of ultraviolet (UV ) radiation. Depl etion of stratosp he ric o z on e, cau s e d by t he release of man-ma de ozone -de p l eting su bsta nce s (O DS)—su ch as chl o roflu o rocarbon s (CFCs), halo n s, methyl bromi de, and hydro c hlo rofluo ro c arbo ns (HCF Cs)—co uld lead to si gnifica nt in creases in UV radiation rea c hing t he Eart h's surfa c e, which coul d in turn lead to a d verse hum a n and anim a l health effect s, as well a se c o s ystem imp a cts. The Mont real Protocol o n Substan c e s T hat Depl et e the Ozone L ay er (Mont real Protocol ) is alandma r k internatio nal agre e me nt desig ned to protect the stra tosp heri co z o ne layer. The treaty wa s ori g inally sign ed in 19 8 7and sub s ta ntially amend ed in 199 0, 1992, and 1 9 9 7 . The Montreal Proto c ol stipulates pha seo u t sch edule sfor theprodu ction a nd co nsumpt i on of comp ou nds that de pl ete ozo ne in the st rat o sph e re
. The United States Environmental Protection Agency(EPA) uses it s Atmospheri cand Health Effects Frame w o r k (AHEF) to eva l uate ce rtain human h ealth impact sasso ciated with re duced emi ssi ons of O D S unde r the Mo ntreal Proto c ol and a s soci ated amen dm ents. Spe c ifically, the AHEF estimate s the pro babl e increa se s in skin can c er m o rtality and in cide nce in the United States that re sult from ODS em issi on scena rio srel a tive to the baseli ne. The baseline i sde fined as the h ealth effects t hat woul d have occurred if ozone con c entration s thatexisted in 1979-198 0 ha d been mai n tained throug h the time peri od model ed. The 197 9-19 80 co ncentrat i ons of o z on eare used a s the ba seline b e ca use at this date minim a l ozo ne depletio n had occurred. Diff eren ce s in h ealth effects can b e com p ared a c ro ss b road p o licy scenari o s to estimate pote n tial benefits of alternative ODS control s
. The accu ra cyof the AHEF’s pre d ictio n s depe nd s upo n contin ual u pdating of its inputs a nd m e thodol ogie s to reflect on -g oing scie ntific advances sin c e the AHEF’ screation in t he mid 198 0s. Significant new resea r ch re su lts that have been in co rpo rated into the revised versio n of the AHEF includ e the followin g :

 Re calib ration and refin e me nt of stratosp heri cozone concentratio n measurement s;

 Upd a ted O D S emissi on d a ta;

 Improved foreca sts of the impact of emi ssi on s of ODS on strato sp heri cozone concentratio n s;

 Ne w pre d ictio n s of the imp a ct of cha ngi ng ozone co n c entration s o n UV radi atio n intensity at the Earth' s su rface;

 Upd a ted information on thebiologi cal effects of UV ra diation of different wavele ngths (actio n spe ctra ) , and how a ge an d year of birth affect the indu ction of skin cancers and ot her hu man health effect s;

 Improved e s ti mation of proj ected ski n ca nce rmort alityrates, ba sed on more re ce nt and relia bl e epidemi o logi cal data;

 Revised he alth effects mo d e led by the AHEF, to moreaccurately p redict only tho s e he alth effects for whi c h an agre ed up on dose-re spo n se relation shi pis available; and

 Upd a ted po p u lation data. While e a ch of these mod e l update sha s affected the AH EF output to varying de gree s—eithe r slightly or signifi cantly increa sing o r decrea s in g total proje cted health effect s—ea ch ha s contribute d to more accurate mod e ling re sult s. In addition to these mo del update s , sev e ral othe r cha nge s have be en made to enha nce mod e l resolution and flexibility. Appendix A deta ils all of the model u p d a tes an d ch a nge s that

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – ES-1 –have bee n made to the AHEF since it s ince ption, and provide sexp l anat ion san d justification s for why each one wa s perfo rme d , and its impli c ation on mod e ling re sult s. De spite the s emodel upd ates, no mod e l or set of re sul t s quantifying health effect s impa cts can be con s id ere d final, given thatresea rch on the atmosp he rics of ozone depletio n and health effect s of UV exposure i s o ngoin g . Many important issues mu st con t inue to be in vestigated a n d , as sig n ifica n t new finding s are i n co rpo r ate d into the AHEF , the accu ra cy of predictio ns an d the implicatio ns for prote cting strato sph e ri c ozo ne will be enha nced. Fo r example:

 Additional research on the effects of UV radi atio n on d a rker-skin ned populatio ns woul d enabl e the AHEF to predi ct the in cre m ental h e a lth effects fo r all popul atio ns;

 Furthe r disag g reg a tion of catara ct incid e n ce d a ta by state, and the gene ration of a popul ationweig hted, ge ogra phi cally distrib u ted do se-re s p o n s e relation ship fo r cata ra ct inci den ce an d UV exposure wo uld allo w for approp riate modelin g ofcatara ct incid e n ce cha nge s in the AHEF;

 Additional sci entific re sea rch into the im pac t sof UV e x posu r e on i mmune sup p ression would allow for the i n clu s io n of this health e ndp oint into the model;

 Improved g ro und-l e vel UV monitori ng would allo w th eAHEF to incorpo r ate the effects of clo udcover a nd pol lution on UV radi ation at ground -level; a nd

 Additional research on the effects of UV radiatio n on n on-h u ma n en dpoint s (e.g., aquati c system s, agri c ulture) woul d allow the A H EF to pre d ict the broade r impact s asso ciated with O D S emission sce nario s. The AHEF is a living model , design ed wit h the ability to accept cha nge s in any model inp u t or assum p tion based on n e wscientific fi nding s, and/o rto inco rpo r at e any new in formation a si tbecome savailable. As the sci en ce o n strato sph e ri c ozone d epl etion and its as so ciated im pact sco ntinu e s to evolve, so too will the AHEF.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – ES-2 –

### 1. Introduction Stratosp heri cozon e prote cts the biosph ere fro m pote n tially damagi ng do se s of ultraviolet (UV ) radiation. Re cent de ple t ion of strato spheri co z on e, ca u s ed by th e relea s e of man-m ade o z one-depl eting sub s tan c e s(ODS)—such as chlorofluo roca rbo n s (CF C s), halon s, methyl bromi de, and hydro c hlo rofl uoro c a r b o n s (HCF Cs)—co uld lead to si gnifica nt increases in UVradiation rea c hing the Earth' s su rface, which co ul d in turn lead to incr e a sed adverse hu m an and a n ima l health effect s, in addition to po tential eco s ystem impact s
. UV radi ation-i ndu ced h ealt h effects a r e prima r ily relat ed to the ski n , eyes, and immune syste m . Skin effects in clud e erythema (sunbu rn ), agin g of the skin, and vario u s f o rm s of ski n can c e r—m el anoma, squ a mou sce ll carcino m a (SCC), and ba sal cell ca rc i n oma (BCC). Eye effects can incl ude cataract s, squ a mou sce ll can c er of th e cornea, con j unctiva, and other da mag e to the corne a(UNEP 199 8, Anduze 1993 ). Sunlig ht exposu r e a l so re du ce s immunol ogi ca l defenses, i m pedin g re si stan ce to infe ctiou s dise ases a n d skin tum o rs, and dimini shi ng the effect i v eness of vaccine s(CIESIN we bsite 2 0 03). Of the human h ealth effects from sun exp o sure , melanoma i sthe mo st lethal, cau s in g more tha n 7,000 de aths annu ally in the United States an d sh owi ng an al a r min g rate of incre a se worl dwi d e (Kann an et al. 2003). In addition to human a nd a n imal health effects, incr e a se d UV exp o su re can al so damag e ecologi cal and agri c ultu ral systems. Fo r e x ample, sunli ght is a ke y in gredi ent in the formation ofphotochemi c al smog, and can lo we r the immunityof vegetation to pest infe station, disru p t nutrient cy cle s , and cau s e bacte riopl an kton stre ss in water, which can le ad to t he redu ction ofamphibia n a nd fish po pul ations in aquati cecosys tem s . Furthe r, UV radiatio n can d a mag e some pl asti c materi als (T ang et al. 199 8, Cald well et al. 1998, Häder et al. 19 98, Andra d y et al. 1998). To prote ct stratosp heri co z one an d avoi d negative he alth and environmental im p a cts, the Mo n t real Protocol on Substa nces Th at Deplete th e Ozo ne Lay er (Mo n tre a l Protocol) was signe d in 19 87, a landma r k inte rnation a l ag re ement de sign ed to redu ce prod uctio n an d con s um ptio n of compo u n d s that deplete o z o n e in the strato sph e re. Th e Montre al Prot ocol, no w si g ned by more than 18 0 co un tries, was amend ed in 1 990, 199 2, an d 1997, furth e r tightenin g c ontrols, an d ultimately req u iring p h a s e out of ozo ne-depl eting su bsta nce s(O DS) li ste d by the Protocol. Howeve r, recovery of strato sph e ri c ozo ne remai n s u n ce rtain and d e p end s on conti nued inte rnat i onal commit m ent to limit ODS emi ssi o n s. The United States Environmental Protection Agency(EPA) uses it s Atmospheri cand Health Effects Frame w o r k (AHEF) to eva l uate ce rtain human h ealth impact s asso ciated with re duced emi ssi ons of O D S unde r the Mo ntreal Proto c ol and a s soci ated amen dm ents. Spe c ifically, the AHEF estimate s the pro babl e increa se s in skin can c er m o rtality and in cide nce in the United States that re sult from ODS em issi on scena rio s . Modelin g of cat a ra ct incid e n c e, althou gh i n clu ded in e a rlier versio ns of the AHEF, has b een remove d be cause of the unce rtain lin k betwe en UV expos ure a n d catara ct in ciden ce in the United States (se e text box on next page ). The incre a se in ski n ca ncer mortality and incide nce for all ODS emission scena rio sare e s timat ed relative to the baseli n e, which is de fined as the h ealth effect s t hat woul d have occurred ifozon e co nce n tration s that existed in 1979 -19 80 had be en mai n tained throu gh the time period m odele d . These differen c e sca n be com p a r ed across poli cy and control scen ario s to e s ti mate the a dditional be n e fits of sce na rios with increa singly stringent O D S controls. Th e major scena ri os are the fou rpre s e n ted in this pape r (see Section 4.3 ) . The AHEF ca n also b e use d to look at the effect of other emi ssi on scen ario s resu lting from potential future ODS contro l policie s. Thi s pap er revie w s the A H EF’ sinput s, assu mptions, an d comp utationa l methods fo r estimating fut u re hu man h ealth effects and re po rts the estimate d benefits of four poli cy/emissi on sce n a rio s : the Mo ntreal Pr oto c ol (198 7), theLondo n Ame ndment s (1 99 0), the

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 1 –Cop enh agen Amendme n ts (1992 ), and t he Montreal Adjustme nts (1997 ). 1 The re mainde r of the pape r is orga nized a sfollows: 2

 Section 2 p rovides a n overview of ho w the AHEF works;

 Section 3 ex plain s ho w the baseline e s timates of inci den ce an d mortality were obtaine d and proje cted intothe future for the health effects examined;

 Sections 4 p r esent the me thods u s e d for proje cting o z one de pletion under va riou s policy sce nari o s;

 Section 5 ex plain sthe me thods u s e d for estimating ch ange s in grou nd-level sola r UV irra dian ce a s so ciated with ozo ne de pleti on, as well a sthe re sulting cha n g e s in he alth e ffects, derived u s in g dose
- respon se rel a tion shi p s betwe en UV exposure a n d health effect incidence/mortality;

 Section 7p rese n ts the m odelin g re sult s for each of the four poli cy scen ario s examin ed;

 Section 8p rese n ts a sen s itivity analysis for action spe ctra and expo su re/do s e metri cs;

 Section 9 p rese n ts qu anti t ative and qu alitative analyses of the un certai nties in the AHE F ; and

 Section 10 sugge sts topi cs for future r e sear ch. Justi f icati on for Omission of Cataract Inci dence Repor ting in the AHEF M odel While cataract in cidence historica lly has been assessed in the AHEF model, this health effect is not currently modeled. This is a result of a recent analysis that has reveale d

a weak correlation between UV exposure and cataract incidence in the United Stat es. Specifically, in an effort to determine the statist i cal correlation between UV exposure and age on the development of cataract inciden ce, state-by-state average annual UV exposures were correlated wi th cataract inciden ce data reported by the National Eye Institute/Prevent Blindness America (NEI/PBA) (2002)—the mos t recent and comprehensive cataract incidence data a v

ailable—of pop ulations aged 40 and above. The results of this analysis suggest that cataract inciden ce is direct ly cor r elated with age. However, it was not possible to d e

velop a good correlation between the intensity of UV exposure and cataract inciden ce using this dat a. One reason for t he lack of correlation may be that the state-averaged UV values currently available ma y be too aggregated to discern differences in populationbased effects. It may be possib l e to develop a d o

se response curve using data that is further disaggregated. Therefore, if additional data or research becomes availab l e on t he dose-response relationship for c ataracts, this he alth effect will be reconsidered for inclusion in the AHEF. (See App endix E for more information on modeling cataract incidence.) Human behavior can complicate the development of a dose response curve for UV. Fo r example, many people in the United States wear protective sunglasse s when in the sun, or may engage in sun avoiding behaviors. These behaviors, while bene ficial to human health, tend to confound analysis of cat aract incidence versus UV exposure.

1 T he Vienn a A m endm ents (1 995) a nd the B e iji ng Ame ndm ents ( 199 9) are not present ed in this pa per b e caus e the ODS phase outschedu les ar e very sim ilar tothat of the Mont real Ad justmen t s. 2A glossarydef inin g terms use d in this pa per app ears at the end of this d o c u ment.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 2 –

### 2. Model Ove rview The AHEF ha s five main co mputational steps that l ead to estimated cha nge s in in cide nce and mortality for variou s UV-relate d hea lth effects for a given OD S emission sce nario. Th ese comp utationa l steps a r e as follows : 1. Proje cting ba selin e incid e n c e an d mortal ity of health effects; 2. Proje cting im pact sof future ODS emi ssi ons o n strato sph e ri c ozon e; 3. Modelin g the resulting cha nge s in gro u n d -level UV ra diation; 4. Derivin g do se -re sp on se rel a tionship sfor health effect incid e n c e an d mortality; and 5. Proje cting future he alth effects in cid e n c e and mo rtality. These step s are de scri bed in detail belo w
. Step 1. Projections o f ba seline incide nce and mor t alit y

are computed ba s

e

d on histori cal rate s assuming co lumn ozone conce n

tr

atio ns remained constant a t 1979 -1

980 le v

e

ls. The AHEF de fines the “b aseline” in cid e n c e an d/or mo rtality for skin can c er a swhat woul d be expecte d to occur in the f u ture if the co nce n tration ofstrato sphe ri c ozon e rem a i ned fixed at 1979-198 0 levels. Thi s baseline p rov ides a stand a rd agai nst wh ich to evaluat e increa se s in mortality an d/or inci den ce for these health effect s from future ODS emi ssi o n s an d ozonedepletion a n d , under m o stsce nari o s, fu ture recovery of the ozo ne layer to 1979-1 9 8 0levels. 3The followin g dataand cal c ul ations form the baseline estimate of current and fut u re in cide nceand mortality :

• Histo r ical dat a on skin can c er in cid e n c eand mort alitywere u s e d to derive rate s (per 10 0,000 peopl e) for UV-relate d hea lth effects in the U.S. popul ation. Rate s are ba se d on age, sex, and in some ca se s, birt h y ear.

• Histo r ical U.S

. population e s timate s (up t o1990 ) we re obtaine d from the U.S. Cen s u s Bure au, and natio nal popul ation e s timates for 19 91-2 050 we rederived by a ge and sex groupin g s fro m U.S. Cen s us Burea u proj e ction s. Popul ation proj ecti ons by state, age, sex, and race—ba s e d on national p opu lation proj ecti ons for yea r2 050 an d statepopulatio n projectio n s thro ugh 20 25 4— were group ed by latitude-b a se d regi on s. (Popul atio n wa s assum e d to be con s ta nt from 2050 to 2100.)

• The num ber of individuals in each age a nd sex group wa s multiplie d by the appropriate hi sto r i c al incid e n c e an d/or mo rtality rate to pro d u c e an e s timat ed ba seline n u mbe rof future skin can c e r ca se s and d e a ths pe r year.

3 T he AHEFassumes that cha nges i n be havi o r that might confou nd the es tablis hment ofan accur ate ba selin e do not occur. F o r exa m ple, a po pu la tion that bec o m es less sun-s eeki ng co uld th eoretic all yh av ea lo we r bas el ine risk tha n the earl i er coh o rt that provid e d the bas eli ne data, an d an in crease i n clou d i ness or ra infal l could re duc e the num ber of hours spe n t outdo ors, there b y re duc ing ba selin e e x p o sur e s. 4State popu lati on proj ectio n s throu gh 2 025 w e re comp uted as t he sum of the totals for the states in eac h regi on, an d then reg i o nal p opu latio n s (b y age, se x, an d race) we r eproj ected to 20 50 base d on the n ation al Ce nsus projecti ons for 2050— un d e r the assumpt i on that the 2 0 25 reg i on al a g e , sex, an d rac eprop ortio n s o fthe total U.S. pop ulati on w i ll remain unch a n ged thro ug h 20 50. In this wa y , popu lati on esti mates for 199 0
- 202 5 we re ba sed on state p opu latio n proj ections, w h ile p opu lati on e s timates for 20 25-2 050 we re base d on n atio nal p o p u lati on proj ections. Se e Section 3. 5 for more details.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 3 –Becau s e ski n can c er a nd solar UV irra di ance vary by latitude, the baseli ne U.S. health effect s data were stratified into three latitude regio n s (i.e., 20 to 30° N, 30 to 40°N, an d 40 to 50° N), to corre sp on d with satellite data on ozone con c entration s. Beca use skin cancer in cide n c e an d mortal ity among darkerski nne d pop u l ations a r e no t well unde rst ood in term s of rates of re spon sivene ss to increa sed UV exposure s , these h ealth effects a r e only modele d for li ght-skin ned p opulatio ns. O n ce the requi red informatio n b e com e s avail able, data for dar ke r-ski nne d U.S. populat ions may be inclu ded. Step 2. Impacts of fu tur e

emissions of ODS on s t rato spheric ozon e conc e

n

tra t ions ar e

modeled. Since 1978, satellites have provided measurements of stratospheri c ozone concentrations usi n g a latitudinal gri d . Data from the first of these satellit es, the Nimbus-7, indicate that during the sat e llite’s lifespa n from 1978 to 19 93, ozon e co nce n tration s de cl ined in a man ner that corre s po nd s to an increa se in the con c e n t ration of strat o sp heri cchlo rine an d bro m ine rel e a s e d from the disso c iation of O D S molecule s. Using thi srelati onship, the AHEF ca n use estimated ODS emissi on s to predi ct future decrea s e s in strato sph e ri c ozo ne. First, the frame w o r k use sreg r e ssion co efficient s to quantify the relation shi p b e twee n pa st ODS emi ssi o n s an d pa stchang es in o z one con c entrations. The s eregressio n coeffici ents were de rived a sfollows:

• Histo r ical info rmation o n the con c e n trati ons ofst rato spheri co z on e by latitude an d month wa s obtained from satellite data.

• Estimates of emission s of ODS were o b t ained for pa st time period sthat could affect ozone d u ring the years for whi c h satellite data were available. These ODS em issions estimates were then combi ned with informatio n on ea ch O D S spe c ie s’ deg ree of disso c i a tion and rateof transp o rt to the strato sph e re. Using thi s inform ation, total ODS emissi on s we re conve rted to equivalent effective strat o spheri cchlorine (EESC) for each year and month for whi c h ozone measurements were availabl e from the Ni mbus-7 satell ite.

• Statistical linear regressions were performed usi ng the 1978-1993 annual EESC estimates and strato sph e ri c ozo ne con c e n tration s , as measured by the Nimbus-7, to es timate the impactof ODS on o z on e con c e n trati ons. Th ese re gre ssi on s we re estim a ted by month and by latitudinal band. 5

• Future cha n g e s in o z on e a s soci ated wit h proje cted e m issi on s for each ODS e m issi on sce n a rio were convert ed to EESC estimates whi c h were then multiplied by t he estimated regressi on coeffici ents topredi ct futureozon e co nce n tration sby month and lat i tude ban d. Step 3. Cha nges in grou nd-lev e

l UV r a

diation ar e estima t ed. Based o n proj ection s of stratosp heri co z one con c entrati on, UV radi ation inten s ities at the Eart h’s surfa c e were estim a ted by latitude , month, year, and time of day using the Trop osphe ric Ultraviolet
- Visible radiatio n mod e l (TUV, v3.9 a, as de scrib ed in Mad roni ch 19 93a, Ma droni ch 1 993 b). The T U V model gene rate s loo k -u p table s 6(see Section 5. 1 for more de tail) of wei ght ed sol a r UV irradia n ce at sea level as a function of sola r ze nith a ngle an d proj ected to tal co lumn ozone b a se d on the followin g assu mptions: obstructio n -free and clou d-free skie s; st anda rd p rof ile s of air den sity , temperature, and tropo spheri c ozone (USSA 1976); typical continental aerosol s (E lterman 1968); and 10 percent isotropi c ground reflectivity.

5A similar proc edur e has b e e n used i n W M O assessment s, w h ic h als ou s e the Nimb u s- 7 satell ite data (W MO 1995, W M O 1999). See App e n d i x D: Comparis o n of AHEFand W M O Predicted Ozone Co nc entratio n s for more informati on on ho w AHEFan d W M O column ozon e estimate s compare. 6 T he axes of these lo ok-up ta bles ar e solar z enith a n g l e an d colum n ozo n econce n tratio ns.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 4 –Once sol a r UV irradia n ce at the Earth’s surfa c e i s cal c ulate d , esti mates of UV exposure exp e rien ce d by human s can be com puted. Peak hou r or daily dose o n anyday of the year, or cu mulative doses for a set of months o rfor an enti r e year a r e exam ples ofpo ssib le dose metri cs. Th e AHE Festimate s UV exposure sfor both the entire day of June21st (i.e., pe ak day) a nd the cum u lative dose for theentire year (cal cul a ted a sthe do se on the 15 th day of each mo nth multiplied b y30 days per month sum m ed acro ss months) for selecte d actio n spe ctra. 7 Step 4. Dos e

-re s

pon se r e

lationship s

for skin ca n

cer incidenc e and mortal it

y

are selected. Determinin g the health effe cts cau s ed byUV exposu r efi rst requi re s informatio n o n the relative weig hts to be pla c ed o n each discreteUV wavelen g th to reflect the deg ree to whi c h ea ch wavelength ca use s biologi c dam age. Such a weig hting fun ction is calle d an action sp ectru m —a n e x perime n tally derived function that descri b e stherelative effectiveness of eac h UV wavel ength in the i ndu ction of skin can c ers. Action sp ect ra are no rmall ydeveloped by scie n tist s by exposin g a test animal to different UV wavele ngth sand then verify ing the effectiveness of each wavele ng th at inducin g a spe c ific he alth effect. For ea ch h eal th effect, an available a ction spe ctru m m u st be sele cted for u s e in the AHEF. Once the acti on sp ectrum for ea ch he alth effect is se l e cted, it is then po ssi ble to explore the relation shi p betwe en tho s e health effects and the int ensity of UV exposure. Th ese d o se-re s pon se rel a tio n shi p s a r e typically derived by correl ating mea s u r e m ents o resti mates of UV exposure re ceived for a sp ecific a ction spe ctru m and given health effect at vario u s lo cation s, and the level of incide nce or mort ality for that health effect at those same lo cati ons. For exampl e, the incid e n c eof SCC de creases with di stan ce from t he equ ator (i. e ., increa sin g latitude). It is also the ca se that UV irradian ce d e creases with di stan ce from t he equ ator. A dose
- respon se relation shi p can thus b e de rived statisti cally by correl ating the inci den ce of SCC mea s u r ed at various locatio n s at avariety of latitudes with the UV r adi atio n doses me a s ured o resti mated for tho s e same lo c a tion s , as s h ow n in F i gu r e 1

. Step 5. All inputs a r e co mbined to pr oject fu tur e

skin canc e

r incidenc e an d mortalit y

. The final stepin the modeli ng frame w o r k incorpo r ate sthe inputs fro m Steps 1-4 to proje ct future addition al skin can c e rs g e nerate d und e ra parti cula r emission sce nario com pared to 1979 -1 980 ba selin e ozo ne conditi ons. Thi sincl ude s two calculation sby t he AHEF. First , the AHEF project sfuture baseline ski n ca ncer in cide nce and mortality. The n the AHEF calcul ates the future ann ual percenta ge chang e in UV radi ation dose for a given actio n sp e ctrum a c ro ss the three latitudinal b and s of the United States for the spe c ific O D S emissio n scena rio. Mul t iplying t he percentag e ch ange in UV exposu r e in a future yea r by the appro p r iate do se -re spon se rel a tio n shi p yields t he perce ntag e cha nge in f u ture skin ca nce r incid e n c e/mo rtality attributable to the future chan ge in ozon e co nce n tration s . The s e pe rcentag es are then multiplied by the ba seline i n cid e n c e an d/or mortality fo r that health effect to com pute the ab so lute numbe r of additional f u ture cases o rdeath s attrib utable to ozo ne depl etion unde r vario u s ODS emissio n scena rio srel a tive to the 1979-198 0 ba seline o z one l e vels. 8

7It is important to note that thi sanal ys is doe s not inclu de a compre hens ive listi ng of al l pu blish ed acti on spectra that ma y b eap plic a b le to the pr edi ction of skin ca re and catar a ct s in huma n s. F o r exampl e, the der ivatio n o fne w action spectra for UV- m edi ated h ealt h effects not consid ered i n th is report (e.g., immunos uppr essi on) is an activ e field of researc h

. T he AHEF ’s modu l a r structure, de scribe d in det ai l bel o w , en abl e sne w actio n spectra or ne w i n formatio n on other UV-m edi ated h u man health e n d poi nts to be eas i l yincorp orate d into the mod e li ng frame wo r k. 8 T h is method o fmultipl y i ng th e chan ges i n U Vexpos ur e b y the BAFand th e und erl y i ng b a seli ne i n cid e n c e or mortalit y is the same as that u s ed b yoth e r re search ers to estimate chan ge s in hea lth effe cts based o n chan ges i n ozon e conc ent rations (e.g., Madro n ich a nd d e Gruijl 1 994, Pitcher an d Lo ngstreth 19 91).
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 5 –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: LnR ijkl = " ijkl + $ jk(U l ) + * ijkl (Assumes common slope)

50 40
30

20

10

100 120 140 160 180 200

The re maind e r of this pap er de scrib e s t he step s a b ov e in greate rdetail. Specifi c ally, Section 3 on Baselin e Inci den ce an d Mortality Projections ela bor ates on Step 1; Section 4 on Stratosp heri cOzon e Depl etion Mo deling p rovid es mo re information abo ut Step 2; Section 5 on Mod e ling Ch ange s in UV Exposu r e an d Health Effects furth e r ex plain sSteps 3 and 4; and Section 6 on Proje cting Fu ture He alth E ff e ct s Un der ODS Co nt rol P o licies describes Step 5 in more detail.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 6 –

### 3. Baseline Incidence and MortalityProjecti ons Baselin e inci den ce an d mortality rates f o r skin can c e rwe re e s timated usin g dat a on histo r ica l incide nce and/or m o rtali ty for these h ealth effects among the rel e vant U.S. population s . Th ese rates we re then applie d to an nual U.S. pop ulation e s timates to project future ski n cancer in cide n c e an d mortal ity in the absen ce of further strato sp heri cozone d epletion. A nu mber of different data so urce s we re u s e d to cal c ulate hi st orical inci den ce an d mortal ity rates for cutaneo us mal i gnant mela n o ma (CMM) a nd non
melano ma skin can c e r(NMSC). The section sbel ow re view the hi stori c al info rmation for ea ch of the health effect s rates u s ed a nd pre s e n t the data so urce s and meth od s for proje ctin g the U.S. population sat ris k

.
3.1 CMM Mort alit y Rates Baseline CM M mortality data for the years 1950 th rough 1984 were obtained from the EPA/National Cancer Institute (EPA/NCI ) data set, wh ich is descri bed in Scotto et al . (1991) and Pitche r and Longstreth (199 1). The data set u s ed in Scotto et al. ( 1991 ) rep o rts deat hs fro m CMM in in dividual s for 18 age grou ps (birth coh o rts), by sex and ra ce, coveri ng ev e ry county in the United States du ring the 35-yea r perio d. To gene rate CMM mo rtality rates pe r 10 0,000 pe ople per county, the CMM mo rta lity information wa s combi ned with popul ation data by cou n ty. Because th e raw m o rtality data from NCI cove r 5-ye ar, rathe r than 1-yea r , intervals, the f i ve-year mo rt ality total was then divided by the five-year pop ulation total. For each year in the five-year p e riod, this fra ction wa s mul t iplied by 100 ,000 to prod u c e the CMM mortality rate pe r 100, 000 pe ople p e r co unty (Pitche rand L o n g streth 1991 ). Once the mortality rate for each county and time pe ri od was calcul ated, the counties in the EPA/NCI CMM mo rtality data set we re agg re gate d into thr ee g eographi cal region s ba se d on latitude. The three geog rap h ical regio n s we re formed o u t of entire stat e s and ro ughly correspon d to latitude ban ds 20°N to 30° N, 30° N to 40°N, an d 40 °N to 50° N (e xcept for Calif ornia, which wa s split in h a lf and assig ned to the 30° N to 40° N and 20 °N to 3 0 °N g eog rap h ical regio n s). Table B-1 ofAppendix B lists the state sinclud ed in these g eog raphi cal regio n s. The resulting C MM mo rtality rates by age, birth co hort, sex, and geog rap h ical regio n acro ss the United Stat es are also pre s ente d in Appendix B. This regio nall y -agg reg a ted data set co ntains b a seli ne CMM mo rtality rates for 24 five-year birth coho rts (i.e., people b o rn withi n five years of ea ch other ) begi nning in 1 863and endi ng i n 1982 o b served over the 35-yea rhi story of the underlying data set(see T able s B-2 and B-3 and Appe ndixB). The sha d ed are a s in Table 1 and Table 2 repres ent actual info rmation from the original EPA/NCI data. In order for the AHEF to project fu ture baseli ne mortality rates, it wa snec e ss aryto es timate mortality rates fo r pop ulation salive at the time of colle ction of the NCI data as well as to estimate the f u ture mortality rates for those yet to be born. To proj ect future mortality rates fo r peopl e in the EPA/NCI data set, the morta lity rates for d i fferent age g roup s an d the i r years of birt h for ea ch of the three g e o g rap h ical regio n s we re examined u s i ng econom etric analys is 9to determi ne thequantitative influen ce of a ge and c o hort on thos e mortality rates

. In other words , thes e reg r e ssi on s correlate CM M mortality rate s with ag e and year of bi rth. It should be noted that, in the case o fCMM, morta lity at any given age a ppe a rs to have

9As it is used in this document, the term “econometric” refers to a statistical techn i qu e that ena bles ana l ys t s to determi ne to w hat degr ee o n e specific vari ab le (e.g., UV expos ur e) ma y b erespo n sib l e f o r a specific eff e ct (e.g., skin cancer) tho u g h tto be caused b y th e interacti on of severa l relate d varia b l e s. F o r examp l e , itis hypoth e si zed that ag e, birth year, an d UV expos ure a ll pla y a role i n the etiol o g yof CMM. Becaus e ODS control polic ies ca n on l y re duc e on e of these risk factors (i.e., the amou nt of UV-B reac hi ng the grou nd ca n be reduc ed b ya t h icker oz one l a yer), econ ometric es timation is us e d to isolate and quantify the co ntributi on ofU Vexposur e to CMM.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 7 –increa sed fro m the early 1 900 s to the mid-19 50 s, and then grad uall ydecline d . Beca use of this, agespe c ific mo rt ality rates de pend o n birth year, esp e ci a lly for people born b e twe e n approxim atel y the 1920s to the 1960 s. This effe ct, known as a ‘ c o hort effect,’ is includ ed in the reg r e s sion analyse s . Th e pre c ise rea s on s for th is phe nome n on are n o t kn own, but vari ous fa ctors, inclu d ing be h avior (e.g., use of sun s cre en, selectio n of clo t hing that expose s or cove rs skin) are b e lieved to play a role. 10 Finally, the actual an d pre d icted mo rtali ty rates fo r ea ch ag e gro up in the 1960, 1 965, and 1 9 7 0birth coh o rts we re averag ed for purp o ses of p roje cting futu re coh o rts’ m o rtality rates. T hese three co hort grou ps we re sele cted b e cause they rep r esent the mo st re cent birth coho rts with statistically ro bust mortality data

. Table 1 depi cts a po rtion of the bas eli n e mortality rat e s de rived for CMM usi ng the method s de scrib ed ab ove.
3.2 CMM Incide nce Rate s A limited set of data on CMM incid e n c e and mo rtality wa s extra ct ed from the S u rveillan c e, E p idemiol ogy, and End Results (SEER) Program, bas ed 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 dia gno se d betwee n the years 1 973 a nd 199 4 (Ri e s et al. 1999). This data setwas aggregate d in to eighteen a ge gro u p sby sex, ra ce (“ all race s,” lig ht-ski nne d, and darke r-ski nne d), and the three latitudin a l U.S. region s de scribe d in Secti on 3.1. Again, beca u s e of data lim itations, only lightski nne d pop u l ations were inclu ded in th e analysi s
. Bec a us e of the s m all s i z e of the SEER data s e t, a diffe rent method for projecti ng future inc i denc e was necessa ry. To do so, the ratio of the SEER-b ased CMM incid e n c e to mortality wa s cal c ul ate d and averag ed ove rten years, 1984 to 199 4, for ea ch of th ethree re gion s and for ea ch sex. (Result s of these cal c ulations are provided in Appendi x B.) Then, incidence-to-m o rtali ty ratios were applied to the EPA/NCI CMM mo rtality rates to gen erate comp re hen sive CMM incide nce rat e s by age, co hort, and sex, as repo rted in A ppen dix B. T he re sults of t h is process for sel e cte d cohort mid
- bi rth years a r e reporte d in Table 2 for lat i tudes 4 0 oN- 50oN. Age
- , sex-, regio n -, and coho rt- specifi cra te s do not ch ang e after 1990. The re sult s for addition al re gion s are rep o rted in App e ndix B. For very young ag e grou ps, in ci den ce an d mortality information we re quite sp arse and thu sitwas a s sumed t hat incid e n c ewas at lea s t equal to mortality. Hence, cohort s and age groups for which the EPA/NCI data set reported positi ve CMM mortality but for whi c h the SEER data set reported zero, the SEER data setwas adjusted to report an i dentical percenta ge ofCMM inci den ce. As discu s sed in Section 3.1, CMM inci d ence rate s exhibit a coh o rt effect, possibl y due to behavioral factors (e.g., use of sun s creen, sel e ctio n ofclothing t hat exposes or cove rs ski n ).

10CMM is the only health effect model ed i n the AHEFfor w h ic h there is a sta t istical ag e-li nk ed comp on ent, kno w n as a cohort effect.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 8 –Table 1.

o N to5 0 o C ohort Mi Ag e Group B i rt h Ye a W

hite M a l

e 18 65 0 - 4 5- 9 1 0 - 14 1 5 - 1 9 20- 2 4 25 - 2 9 3 0- 3 4 3 5 - 3 9 4 0 - 44 45 - 4 9 5 0- 5 4 55- 59 60 - 6 4 6 5- 6 9 7 0 - 7 4 7 5 - 79

18 70 18 75 4.7 1 3 18 80 3
. 51 9 4 .3 98 18 85 2. 88 2 3 .67 3 5.1 4 8 18 90 2.4 8 4 4
. 0 9 1 4
. 91 3 7 .7 29 18 95 1
. 7 2 5 2 .6 04 4. 95 4 5 .17 6 9.0 9 1 19 00 1
. 44 1 2 .3 32 3.2 0 6 4
. 4 8 7 6
. 02 9 9 .3 76 19 05 1. 683 2
. 21 2 2 .8 44 3.5 4 4 6
. 1 5 0 8
. 27 3 1 1.7 0 1 19 10 1.3 1 3 1
. 834 2
. 80 2 3 .4 51 5.1 4 8 7 . 0 9 2 9
. 59 8 11

. 4 0 0 1 9 1 5 1
. 19 0 1 .4 56 2. 153 3
. 40 2 4 .0 49 6.4 2 7 9
. 3 6 2 9

. 59 0 1 2.2 0 0 1 9 2 0 0. 93 7 1 .32 6 1.9 8 2 2
. 749 4
. 09 5 5 .5 49 7.3 4 2 8. 89 0 1 0

. 20 0 1 2.8 0 0 1 9 2 5 0.6 7 6 1
. 2 1 4 1
. 72 1 2 .6 64 3. 183 4
. 84 3 6 .9 16 7.3 2 0 9

. 4 4 0 10 .80 0 1 3 .4 00 1 9 3 0 0
. 4 0 6 0 .7 24 1. 35 0 1 .90 7 2.4 2 8 4
. 158 5
. 16 6 6

. 0 6 0 7 .4 20 9. 54 0 1 0

. 90 0 1 3.5 0 0 1 9 3 5 0
. 11 3 0 .3 18 0.8 0 5 1
. 1 9 1 1
. 61 1 2 .6 21 3. 898 4

. 90 0 5 .9 70 7.3 3 0 9

. 4 5 0 10 .80 0 1 3 .4 00 1 9 4 0 0. 038 0
. 10 3 0 .4 25 0.6 8 9 1
. 2 3 5 1
. 70 4 3 .3 09 3. 820 5

. 01 0 6 .0 80 7.4 4 0 9

. 5 6 0 10 .90 0 1 3 .5 00 1 9 4 5 0.0 0 0 0
. 020 0
. 12 2 0 .3 37 0.8 1 5 1
. 0 8 4 2
. 31 9 2.8 9 0 3

. 840 5

. 03 0 6 .1 10 7.4 6 0 9

. 5 8 0 10 .90 0 1 3 .5 00 1 9 5 0 0
. 01 8 0 .0 09 0. 000 0
. 16 6 0 .5 73 0.9 3 7 1
. 7 4 1 2

. 14 0 3 .0 00 3. 950 5

. 14 0 6 .2 20 7.5 8 0 9

. 7 0 0 11 .00 0 1 3 .6 00 1 9 5 5 0
. 00 0 0 .0 08 0. 000 0
. 12 6 0 .4 44 0.8 7 3 1. 42 0 2 .06 0 2.9 2 0 3

. 870 5

. 06 0 6 .1 40 7.5 0 0 9

. 6 2 0 10 .90 0 1 3 .5 00 1 9 6 0 0
. 00 8 0 .0 08 0. 024 0
. 14 2 0 .2 82 0.8 2 4 1

. 4 0 0 2

. 04 0 2 .8 90 3. 850 5

. 04 0 6 .1 10 7.4 7 0 9

. 5 9 0 10 .90 0 1 3 .5 00 1 9 6 5 0
. 00 9 0 .0 00 0. 027 0
. 08 0 0

. 4 1 5 0 .8 36 1. 41 0 2 .05 0 2.9 1 0 3

. 860 5

. 05 0 6 .1 30 7.4 8 0 9

. 6 0 0 10 .90 0 1 3 .5 00 1 9 7 0 0
. 00 9 0 .0 06 0. 024 0

. 12 4 0 .3 76 0.8 3 8 1

. 4 1 0 2

. 05 0 2 .9 10 3. 860 5

. 05 0 6 .1 30 7.4 8 0 9

. 6 0 0 10 .90 0 1 3 .5 00 19 75 0. 0 0 0 0
. 0 0 0 0. 024 0

. 12 4 0 .3 76 0.8 3 8 1

. 4 1 0 2

. 05 0 2 .9 10 3. 860 5

. 05 0 6 .1 30 7.4 8 0 9

. 6 0 0 10 .90 0 1 3 .5 00 19 80 0. 0 0 0 0.0 0 6 0

. 024 0

. 12 4 0 .3 76 0.8 3 8 1

. 4 1 0 2

. 05 0 2 .9 10 3. 860 5

. 05 0 6 .1 30 7.4 8 0 9

. 6 0 0 10 .90 0 1 3 .5 00 h it

e Fema 18 65 18 70 18 75 2.8 0 0 18 80 3
. 29 4 3 .5 89 18 85 1. 89 0 2 .94 7 3.7 5 0 18 90 1.9 5 0 2
. 3 6 7 3
. 71 8 4 .2 44 18 95 1
. 4 9 2 2 .6 09 3. 24 8 3 .95 7 4.8 8 4 19 00 1
. 08 7 1 .8 00 2.3 4 9 2
. 8 1 4 3
. 71 3 5 .9 87 19 05 1. 036 1
. 45 4 1 .9 57 2.4 1 0 3
. 6 7 3 5
. 19 5 5 .8 40 19 10 0.9 9 9 1
. 688 1
. 65 8 2 .1 60 2.9 6 5 5
. 0 4 1 5
. 50 5 6.3 2 0 1 9 1 5 0
. 82 6 1 .1 76 1. 840 2
. 10 9 2 .6 78 3.4 9 8 4
. 0 2 8 5

. 54 0 6 .5 00 1 9 2 0 0. 70 9 0 .94 4 1.4 7 1 1
. 683 2
. 53 0 2 .8 63 4.0 7 8 4. 70 0 5 .85 0 6.8 0 0 1 9 2 5 0.5 4 8 0
. 9 2 9 1
. 34 2 2 .0 10 2. 006 2
. 83 3 3 .1 13 4.1 2 0 5

. 0 2 0 6

. 16 0 7 .1 20 1 9 3 0 0
. 3 4 4 0 .6 17 1. 12 9 1 .21 9 2.0 4 2 2
. 622 3
. 23 4 3

. 4 3 0 4 .3 30 5. 23 0 6 .37 0 7.3 3 0 1 9 3 5 0
. 02 8 0 .3 10 0.5 9 1 0
. 9 5 5 1
. 25 6 1 .8 71 2. 552 2

. 87 0 3 .3 20 4.2 2 0 5

. 1 1 0 6

. 26 0 7 .2 20 1 9 4 0 0. 000 0
. 05 0 0 .2 08 0.4 9 8 0
. 7 9 4 1
. 46 9 2 .1 24 2. 400 2

. 88 0 3 .3 20 4.2 2 0 5

. 1 2 0 6

. 26 0 7 .2 20 1 9 4 5 0.0 0 0 0
. 011 0
. 13 1 0 .2 53 0.5 5 5 0
. 9 4 8 1
. 37 3 1.9 8 0 2

. 420 2

. 90 0 3 .3 40 4.2 4 0 5

. 1 4 0 6

. 28 0 7 .2 40 1 9 5 0 0
. 01 9 0 .0 38 0. 018 0
. 09 2 0 .2 50 0.6 3 4 1
. 1 8 4 1

. 44 0 2 .0 30 2. 470 2

. 95 0 3 .3 90 4.3 0 0 5

. 1 9 0 6

. 34 0 7 .2 90 1 9 5 5 0
. 01 7 0 .0 09 0. 017 0
. 09 4 0 .3 76 0.6 6 3 1. 08 0 1 .44 0 2.0 4 0 2

. 480 2

. 95 0 3 .4 00 4.3 0 0 5

. 1 9 0 6

. 34 0 7 .3 00 1 9 6 0 0
. 01 7 0 .0 08 0. 008 0
. 07 7 0 .3 29 0.6 1 9 1

. 0 6 0 1

. 42 0 2 .0 20 2. 460 2

. 93 0 3 .3 80 4.2 8 0 5

. 1 7 0 6

. 32 0 7 .2 80 1 9 6 5 0
. 00 0 0 .0 00 0. 009 0
. 07 3 0

. 2 9 2 0 .6 07 1. 04 0 1 .41 0 2.0 0 0 2

. 450 2

. 92 0 3 .3 60 4.2 7 0 5

. 1 6 0 6

. 31 0 7 .2 60 1 9 7 0 0
. 00 6 0 .0 03 0. 009 0

. 07 8 0 .3 06 0.6 1 2 1

. 0 5 0 1

. 42 0 2 .0 10 2. 450 2

. 93 0 3 .3 70 4.2 7 0 5

. 1 7 0 6

. 31 0 7 .2 70 19 75 0. 0 2 3 0.0 0 3 0

. 009 0

. 07 8 0 .3 06 0.6 1 2 1

. 0 5 0 1

. 42 0 2 .0 10 2. 450 2

. 93 0 3 .3 70 4.2 7 0 5

. 1 7 0 6

. 31 0 7 .2 70 19 80 0. 0 1 1 0.0 0 3 0

. 009 0

. 07 8 0 .3 06 0.6 1 2 1

. 0 5 0 1

. 42 0 2 .0 10 2. 450 2

. 93 0 3 .3 70 4.2 7 0 5

. 1 7 0 6

. 31 0 7 .2 70 Ta bl e1. CMM Mo rt al i tyBa sel i n e Coh o rt T a b l esby S ex ( p e r1 00, 000)

Note: Theshaded areasrepre s ent actualinf ormati on f ro m the ori g in alEPA/NCI data. Unshade dareas represent proj ectedmortality rates. Thi s tab l e can b e readin two ways . First, th e in crease in mortality for t h e coh ort bornina gi ven y ear (e. g ., 19 6 0 ) canberead hori zo ntallyas th e individu als inth at co hort ag e. For exam ple, a female born in 1 950 w ou l d h av ea b a selin e mortali ty rate o f 0.018 in 1962 ( 1 2years o l d) an d a 0

. 0 9 2 mortali ty rate in 1 969 ( 1 9 years o l d ) . Seco nd , mortality rates for a sin g le ag e group can b etracke dacro ss b irthcoh orts b yreadingv ertically from the selected ag e group (e.g., 20-2 4). B a selin e mortali ty rates for males ag e d65-69 can be observe d to increase fro m 2

. 88 2 to 9

. 6 0 0 for in dividu als bornin 1 885 an d 198 0, resp ectiv ely. It is im p ortant tono te th at th e ro ws for these tables arepresente d in term s of “b irthc o h ort mi d-y ear.” For exam pl e, “1 9 80" repre s ent s birth y e ar s1 9 7 8 , 1 979 , 19 80 , 198 1, an d 198 2. Si milarly, “1 98 5 " represents b irth year s 19 83 , 19 84 , 198 5, 1 986 , and 1 987

.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 9 –Table 2 Tab l e 2: Estimated CMM In cid e nce Rates fo r Selected Years b yRegi on, Sex, Coho rt, and Ag e Gro up (per 1 00, 000) 40 o N t o5 0 oN Coho rt Mid Ag eG r o u p Birt h 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 W

h it

e 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 Whit e 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 Mo rta lit yRates The ba selin e mortality databy county for basal cell carcinom a (BCC) and squa mo us cell ca rci n oma (SCC), both f o rm s of non-melanoma skin cancer (NMSC), were obtained from the EPA/NCI data set followin g the same regi ona l aggre gation and re gressi o n extrapolatio n pro c ed ures descri bed in Section 3.1 for CMM mortality. Because t he und erlying data set does n o t prov ide disagg regated info rm ation by tumor type (i. e ., BCC is no t reporte d se parately from SCC), Table 3 repo rts all NMSC fataliti e s. Unli ke melano ma m o rtality rates, no co hort
- by-coh ort histo r i c al tabul ation is necessa ry for cal c ulatin g NMSC mortality because NMS C mortality datado not displ ay a cohort tre nd. NMSC m o rtality rates f o r the thre e latitudinal re g i ons by ag e, sex, and birth coho rt are re ported in Ap p endix B. It is possibl e that the numbe r of deaths in cl uded in thi sd a ta set is so mewh at unce rtain, due to a m biguitie sin the rep o rtin g and recording of i n formatio n on death ce rtificates. It is expected that de aths would g enerally be u nderrepo rted, for example, if complication s resulting fr om non-m e lan o ma skin ca ncer ho spitali z at ions (e.g., infection s , pn eumoni a) a r eerro neo usly l i sted a s t he cause of death on death cert ificates. Neve rthele ss, this data set is far mo re co mpre hen sive than any ot he r for non -mel anoma m o rtal ity and therefore is u s e d in the AHEF.

3.4 NMSC Incide nce Rate s The AHEF u s es ba seli ne BCC a nd SCC incid e n c e rat e s de rived fro m incide nce data for the three latitud e band s of interest (e.g., 20° N to 30° N, 30 °N to 40° N, a nd 40° N to 50 °N). In ciden ce rates fo r lig ht-skinn ed males and females, by region and age group, as reported in U.S. EPA (1987) and Fears and Scotto (198 3), are shown in Tabl e 4 and Tabl e5. These e s ti ma tes were o r iginally de rived from Scotto et al. (198 3) a nd are based on a nation-wid e survey in eight cities a c ro ss the United States du ring th e one-ye ar perio d from June 1, 197 7, throu gh May 3 1 , 1978. As the data in thetables indi cat e , the incide n c e of both SCC an d BCC is ske w e d towa rd the old e r age g ro u p s , and a st ron g latitude gra d ient is ap parent.
Unli ke mela n o ma inci den ce rates, n oco hort-by-coho rt histori c al tabulation i sne ce ssary for NMSC incid e n c e be cause non -mel anoma ski n cancers do notdisplay a coh o rt trend. Hen c e, the age -specifi c incid e n c e rat e s for NMSC by sex and re gion, as sho w n in Table 4 a nd Table 5, were ap plied toall coh o rt s.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 10 –Tab l e 3: NMS CM o rtal i ty Base l i n eCoh o rtTa bl e sb yS ex(p er 100 , 000 )

o N to5 0 o C ohort Mi Ag e Group B i rt h Ye a W

hite M a l

e 18 65 0 - 4 5- 9 1 0 - 14 15 - 1 9 2 0- 2 4 25- 2 9 30 - 3 4 3 5- 3 9 4 0 - 4 4 4 5 - 49 50 - 5 4 5 5- 5 9 60- 6 4 65 - 6 9 7 0- 7 4 7 5 - 7 9

18 70 18 75 13
. 6 9 8 18 80 7 . 76 6 9 .9 50 18 85 4. 81 3 6 .01 1 1 0 .6 95 18 90 3
. 68 4 4 .7 73 5. 9 1 5 8
. 1 5 3 18 95 1
. 9 0 1 3 .0 48 3. 24 0 5 .58 7 4.7 0 4 19 00 1
. 12 5 1 .7 62 2.8 9 3 3
. 2 2 2 3
. 23 2 6 .3 68 19 05 0. 626 1
. 23 7 1 .6 73 2.5 0 8 2
. 1 0 4 4
. 49 4 6 .3 71 19 10 0.2 4 9 0
. 697 0
. 91 4 1 .5 09 1.8 9 9 2
. 6 1 3 4
. 47 9 5.8 6 0 1 9 1 5 0
. 15 7 0 .4 04 0. 411 0
. 91 2 1 .1 21 1.8 8 2 3
. 5 9 6 3

. 99 0 5 .8 50 1 9 2 0 0. 05 2 0 .12 9 0.2 9 3 0
. 458 0
. 66 1 1 .2 84 2.4 3 6 2. 77 0 3 .95 0 5.8 1 0 1 9 2 5 0.0 9 1 0
. 0 9 2 0
. 07 9 0 .2 83 0. 278 0
. 91 3 1 .7 56 2

. 30 0 2 .8 40 4. 0 2 0 5

. 8 8 0 19 30 0. 00 0 0
. 0 5 7 0
. 0 8 1 0
. 2 28 0
. 16 7 0 .4 4 3 1. 2 6 8 1

. 5 2 0 2 .3 50 2. 88 0 4 .06 0 5.9 2 0 1 9 3 5 0
. 01 4 0 .0 30 0.0 7 1 0
. 0 4 3 0
. 05 8 0 .2 25 0. 822 1

. 02 0 1 .5 30 2.3 5 0 2

. 8 9 0 4

. 07 0 5 .9 30 1 9 4 0 0. 025 0
. 01 3 0 .0 00 0.0 0 0 0
. 0 4 0 0
. 13 3 0 .4 87 0. 570 1

. 02 0 1 .5 30 2.3 6 0 2

. 8 9 0 4

. 07 0 5 .9 40 19 45 0
. 02 1 0 .0 2 0 0. 0 1 1 0
. 0 2 3 0
. 02 3 0 .0 22 0. 4 8 9 0.3 9 5 0

. 612 1

. 07 0 1 .5 70 2.4 0 0 2

. 9 3 0 4

. 11 0 5 .9 80 1 9 5 0 0
. 07 1 0 .0 09 0. 000 0
. 02 8 0 .0 10 0.0 4 9 0
. 2 1 8 0

. 26 7 0 .3 98 0. 614 1

. 07 0 1 .5 80 2.4 0 0 2

. 9 4 0 4

. 12 0 5 .9 80 1 9 5 5 0
. 01 6 0 .0 16 0. 008 0
. 01 7 0 .0 18 0.1 5 6 0. 13 7 0 .26 4 0.3 9 5 0

. 611 1

. 06 0 1 .5 70 2.4 0 0 2

. 9 3 0 4

. 11 0 5 .9 80 1 9 6 0 0
. 00 8 0 .0 08 0. 008 0
. 02 5 0 .0 26 0.0 8 5 0

. 1 2 5 0

. 25 2 0 .3 83 0. 599 1

. 05 0 1 .5 60 2.3 9 0 2

. 9 2 0 4

. 10 0 5 .9 70 1 9 6 5 0
. 00 0 0 .0 08 0. 000 0
. 00 0 0

. 0 1 1 0 .0 74 0. 11 4 0 .24 1 0.3 7 2 0

. 589 1

. 04 0 1 .5 50 2.3 8 0 2

. 9 1 0 4

. 09 0 5 .9 50 1 9 7 0 0
. 00 3 0 .0 05 0. 003 0

. 01 1 0 .0 16 0.0 7 8 0

. 1 1 8 0

. 24 5 0 .3 76 0. 592 1

. 05 0 1 .5 50 2.3 8 0 2

. 9 1 0 4

. 09 0 5 .9 60 19 75 0. 0 0 0 0
. 0 0 0 0. 003 0

. 01 1 0 .0 16 0.0 7 8 0

. 1 1 8 0

. 24 5 0 .3 76 0. 592 1

. 05 0 1 .5 50 2.3 8 0 2

. 9 1 0 4

. 09 0 5 .9 60 19 80 0. 0 0 0 0.0 0 5 0

. 003 0

. 01 1 0 .0 16 0.0 7 8 0

. 1 1 8 0

. 24 5 0 .3 76 0. 592 1

. 05 0 1 .5 50 2.3 8 0 2

. 9 1 0 4

. 09 0 5 .9 60 h it

e Fema 18 65 18 70 18 75 7.9 2 4 18 80 3
. 46 6 4 .9 35 18 85 1. 96 4 3 .03 4 3.9 0 4 18 90 1
. 36 9 1 .3 72 2. 6 2 6 3
. 7 7 2 18 95 0
. 7 9 9 1 .2 47 1. 66 5 1 .90 6 2.1 5 4 19 00 0
. 47 2 0 .7 81 0.9 3 3 1
. 7 3 6 1
. 44 9 2 .2 21 19 05 0. 355 0
. 59 1 0 .7 54 1.1 5 4 1
. 0 8 5 2
. 16 8 1 .9 64 19 10 0.3 1 1 0
. 235 0
. 50 5 0 .8 25 0.8 1 0 1
. 2 6 5 1
. 49 2 2.2 6 0 1 9 1 5 0
. 11 3 0 .1 94 0. 263 0
. 61 2 0 .6 16 1.0 1 5 1
. 4 1 0 1

. 69 0 2 .3 00 1 9 2 0 0. 08 7 0 .11 0 0.2 4 1 0
. 306 0
. 38 8 0 .6 56 1.2 3 4 1. 24 0 1 .68 0 2.2 9 0 1 9 2 5 0.0 7 5 0
. 0 6 4 0
. 11 3 0 .2 32 0. 249 0
. 47 0 0 .8 13 1

. 00 0 1 .2 30 1. 6 7 0 2

. 2 7 0 19 30 0. 05 3 0
. 0 4 1 0
. 0 3 9 0
. 1 10 0
. 08 1 0 .2 0 4 0. 2 7 8 0

. 6 5 1 0 .9 21 1. 15 0 1 .59 0 2.1 9 0 1 9 3 5 0
. 01 4 0 .0 28 0.0 0 0 0
. 0 0 0 0
. 12 6 0 .0 73 0. 278 0

. 40 6 0 .6 63 0.9 3 2 1

. 1 6 0 1

. 60 0 2 .2 10 1 9 4 0 0. 013 0
. 02 5 0 .0 13 0.0 2 5 0
. 0 1 3 0
. 03 9 0 .0 69 0. 192 0

. 38 9 0 .6 46 0.9 1 5 1

. 1 4 0 1

. 58 0 2 .1 90 19 45 0
. 01 1 0 .0 2 1 0. 0 2 2 0
. 0 0 0 0
. 01 1 0 .0 54 0. 0 5 6 0.1 3 1 0

. 205 0

. 40 2 0 .6 59 0.9 2 8 1

. 1 5 0 1

. 60 0 2 .2 00 1 9 5 0 0
. 05 6 0 .0 00 0. 009 0
. 00 0 0 .0 19 0.0 2 0 0
. 0 6 6 0

. 06 2 0 .1 32 0. 206 0

. 40 4 0 .6 61 0.9 3 0 1

. 1 6 0 1

. 60 0 2 .2 00 1 9 5 5 0
. 05 0 0 .0 00 0. 000 0
. 00 9 0 .0 26 0.0 1 7 0. 02 5 0 .05 6 0.1 2 6 0

. 200 0

. 39 8 0 .6 55 0.9 2 4 1

. 1 5 0 1

. 59 0 2 .2 00 1 9 6 0 0
. 00 8 0 .0 17 0. 000 0
. 00 9 0 .0 00 0.0 0 4 0

. 0 1 4 0

. 04 5 0 .1 16 0. 190 0

. 38 7 0 .6 44 0.9 1 4 1

. 1 4 0 1

. 58 0 2 .1 90 1 9 6 5 0
. 00 9 0 .0 09 0. 009 0
. 01 8 0

. 0 1 4 0 .0 10 0. 01 9 0 .05 1 0.1 2 1 0

. 195 0

. 39 2 0 .6 50 0.9 1 9 1

. 1 4 0 1

. 59 0 2 .1 90 1 9 7 0 0
. 02 3 0 .0 09 0. 003 0

. 01 4 0 .0 11 0.0 0 9 0

. 0 1 9 0

. 05 0 0 .1 21 0. 195 0

. 39 2 0 .6 49 0.9 1 9 1

. 1 4 0 1

. 59 0 2 .1 90 19 75 0. 0 1 1 0
. 0 0 0 0. 003 0

. 01 4 0 .0 11 0.0 0 9 0

. 0 1 9 0

. 05 0 0 .1 21 0. 195 0

. 39 2 0 .6 49 0.9 1 9 1

. 1 4 0 1

. 59 0 2 .1 90 19 80 0. 0 2 3 0.0 0 9 0

. 003 0

. 01 4 0 .0 11 0.0 0 9 0

. 0 1 9 0

. 05 0 0 .1 21 0. 195 0

. 39 2 0 .6 49 0.9 1 9 1

. 1 4 0 1

. 59 0 2 .1 90 Note: Theshaded areasrepre s ent actualinf ormati on f ro m the ori g in alEPA/NCI data. Unshade dareas represent proj ectedmortality rates. Thi s tab l e can b e readin two ways . First, th e in crease in mortality for t h e coh ort bornina gi ven y ear (e. g ., 19 6 0 ) canberead hori zo ntallyas th e individu als inth at co hort ag e. For exam ple, a male born in 1 965 w ou l d h av ea b a selin e mortali ty rate o f 0.008 in 1972 (7 years o l d) an d a 0

. 0 7 4mortality rate in 1 992 (27 years o l d)

. Second , mortali ty rates f ora sing le ag e groupcan b et rackedacro ss b irth cohort s by readin g v ertically f ro m th e selectedag e gro up (e.g ., 2 0 -24 ) . Baselin e mortality rates for females ag ed 4 0 -4 4 can b eob serv edto de crease fro m 0. 31 1 to 0. 12 1 for indi vi dual s bornin1 9 1 0an d1 9 8 0 , respec ti v ely . Thi s i sco nsi s tent withth edeclin e inC MM mortali ty rates ob s erv e d af ter th e 195 0s. It is i m p ortant to no te that t h e rows for these ta bles arepresented in term s o f

“b irt hco hort mi d

- year .” Forex am p l e, “1 98 0 " represents b irth year s 19 78 , 19 79 , 198 0, 1 981 , and 1 982

. Similarly, “1 985 " represents b irth year s 1 983 , 19 84 , 198 5, 1 986 , and1 987

.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 11 –Table 4: Estimated BCC Incidence Rates byRegion, Sex, and Age Group (per 100,000)AgeGroups0-45-910-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8585+Male40oN to 50oN0.10.10.12.92.922.122.191.191.1259.0259.0466.0466.0761.0761.01160.01160.01310.030oN to 40oN0.80.80.82.62.629.429.4120.0120.0297.0297.0557.0557.0872.0872.01150.01150.01140.020oNto30oN0.30.30.33.83.849.449.4236.0236.0596.0596.01080.01080.01790.01790.02330.02330.02300.0Female40oN to 50oN0.50.50.55.65.622.222.291.091.0202.0202.0287.0287.0466.0466.0638.0638.0754.030oN to 40oN0.30.30.35.05.033.833.895.895.8198.0198.0310.0310.0454.0454.0630.0630.0608.020oN to 30oN0.50.50.55.85.849.849.8175.0175.0365.0365.0561.0561.0815.0815.01130.01130.01190.0Source: U.S. EPA (1987)and Fears and Scotto (1983).Table 5: Estimated SCC Incidence Rates byRegion, Sex, and Age Group (per 100,000)AgeGroups0-45-910-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8585+Male40oN to 50oN0.20.20.20.30.31.61.67.47.432.632.687.487.4147.0147.0350.0350.0432.030oN to 40oN0.10.10.10.80.83.33.322.222.267.767.7170.0170.0295.0295.0490.0490.0624.020oN to 30oN0.00.00.00.30.311.211.243.043.0169.0169.0377.0377.0640.0640.0966.0966.0696.0Female40oN to 50oN0.00.00.00.10.11.41.44.14.110.510.527.527.554.854.8113.0113.0168.030oN to 40oN0.10.10.10.10.12.42.47.17.119.519.548.048.091.691.6173.0173.0284.020oN to 30oN0.00.00.00.10.44.34.315.315.355.755.7121.0121.0254.0254.0383.0383.0537.0Source: U.S. EPA (1987)and Fears and Scotto (1983).*** Peer ReviewedFinal 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 Webs ite 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. Howe ver, 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 regi ons 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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 13 –

### 4. Stratospheric OzoneDepletion Modeling This se ction first revie w s the approa ch u s ed to mod e l future stratospheri co z on e con c e n tration sba sed o n past, cu rrent, and proje cted future ODS e m issi on s. Ne xt, it describe sthe ODS p o licy control scenari o s unde rlying th e simulatio n s pre s ente d in this re po rt. The se ction con c lud e s by pre s entin g proj e ction s of future o z one con c e n tration sthrou gh 21 0 0

.
4.1 Appro ach fo r Strato sphe ric Ozon e Concen trat ion Modeling ODS such as chlorofluo ro carbo n s, hal on s, ca rbon tetr achl orid e, me thyl chlorofo rm, methyl bro m ide, and HCF Cs are u s ed in a vari e ty of applications in clu d i ng refrig eratio n and air-con ditioning, foam blowi ng, aero s ol s, fire sup p re ssion and explo s io n prote ction, solvent cl eani ng, sterila nt s, tobacco exp ansi on, adhe sive s, co atings, in ks, a nd agri c ultu ra l use s
. Some of these se ctors im mediately em it the ODS us ed (e.g., fumigation an d ae rosols). Othe rs emit the chemi c al a s it performs a specifi cfunctio n over the co urse of a num ber of years. For exampl e, the lifetime of some refrig eration e quip m ent can b e up to 25 year s, som e foam s ca n co ntinu e emitting their blowi ng agent for up t o45 years, and fire su pp re ssi on ag ents c an remai n in a total flooding syste m for years until discha rge d . As a re sult, ne w equi pment prod uctio n ea ch year g ene rates a sto c k of each chem ical in those se ctors with multi-ye ar emi ssi on p rofiles. Thi s create sa delaybetween init ial use of so me ODS an d their su bsequ ent emission i n to the atmosphere. Becau s e of th is time lag be tween con s u m ption and e m issi on s, it is nece s sary tomodel emi ssi ons in two sep a rate step s. First, the a nnual am ountof ODS con s umed in ea ch of the end uses mu st be d e termin ed. Then, this co nsum ption m u st be tra n slat ed into emis sion s into theatmosph e re over su bsequ ent years. These two steps a r e exam ined individu a lly below. Step 1. Estimate Global Cons umptio n of ODS Annual con s u m ption of ODS is base d on histori c al e s timates of O D S use, indu stry growth rate s, and the allowable p ro ductio n of ODS for each re gion. Co nsu m pt ion for 19 89 is con s ide r ed the ba se year for ea ch ODS in ea ch end u s e for th e United States, the re stof the develope d worl d, and the develo p in g worl d. Base yea rco nsum ption is then allo wed to grow fo r ea ch O D S by end use usi ng market gro wt h rate assumptio n s to approxim ate a no-cont rol s scen ario (i.e ., if consum ption gre wa c cording to de mand in the absen ce of re gulation o rinternatio nal ag reement ). Co n s umptio n is then re du ced f rom the ba se year by the spe c ified pha se-out pe rce n tage s sp ecific to ea ch ODS and regi on for ea ch year to mod e l the poli cy scena rio (se eAppendix C for more detail on these p e rcenta g e s ). Step 2. Estimate Global Emissions of ODS Once co nsu m ption ha s b een proje cted for all years, emission s ca n be estimate d usin g a se ri es of emission prof iles for each ODS sector, generated by EPA’s Vintaging Model. The Vintaging M odel is a tool develop e d for estimati ng the ann ual chemi c al u s eand emissio n s from in du st rial se ctors that have histori c ally u s ed ODS in th eir produ cts. In the context of the AHEF, the Vintaging Model p rovid es emission p rof iles that tran slate con s um p t ion estima te s into ann ual emission p rof iles for future years based on the lifetime and leak rates of e a ch type of e quipme n t. To track this i n formatio n, the Vintaging M odel follo ws o ne “vintage ” o fnew equi pm ent throug h its lifetime and mod e ls e m issi on s at each stage of the equi pmentlifecycle (i.e., manufactu rin g , lifetime, maintena nce, and dispo s al ). Equipment types are divi ded into two cla s ses, eithe rse rvice able or non servi c ea ble. For no n-se rvicea ble eq uip m ent, emissi ons a r e a s su med to take p l ace at di spo s al and can be
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 14 –either “i nsta ntaneo us” emi s sion s in the year of man u fa cture (fo rae rosols a nd so lvents) o ra “p rog r e ssive disp osal” (fo rfoams) that allows for emi s sion s to extend over a nu mber ofyears. For se rvice a ble equipm ent, such a sref r ige r ation, air-con d itioning, an d fire-extingui shing eq uipme n t, the Vintaging Model applie s information on an nual lea krate s, se rvicing e m i ssi on s, and dispo s al emi ssi on s for ea ch en d use to model the lag betwee n co n s umptio n of a chemi c al an d its eventual emissi on to the atmosphe re. By aggreg ating the emissi on output s, the Vintaging M odel create s annu al profile s of emission s for ea ch end u s e. The s e profiles a re then appli e d by end use to the United States and th e rest of glob al con s um ption for each poli cy scen a rio to develop glo b a l ODS emi s sion proj ectio n s . Once emi s sion s are estimated, th e next step in the methodol ogy is to pr ed ict the effect that these emi ssi on s will have on colum n ozon e.

4.2 Stratosph e ric Ozon e De p l etion from ODS Emissions The rel a tion ship between strato sph e ri c ozo ne de pleti on and relea s es of ODS i smodele d follo wing the method impl e m ented at theUniversity of Illinois at Urb ana-Ch ampai gn (UI U C) by Dr. Do n Wue bble s et al. (WM O 199 5). This app ro ach use satmo spheri cmo del s and semi -e mpiri c al meth ods to be st q uantify the temporal evol ution of ozo n e impact s associate d with key ODS over spe c ific time hori z on s relat i ve to the referen c e ga s, CFC-11. To measure the ozo ne-depl eting ca pa city of a ce rtain co nce n tration ofODS while accou n ting fo r the time del ay betwee n consumption a nd emissio n of the ODS, the equivale nteffective s t ratos p hericc h lorine (EESC) c o nc ent rat i on is es timated. 11 In computing EESC, an adjustment i s required to quantify the greater efficiency ofbromine in depleting ozo ne rel a tive to chlori ne. AHEF uses t he UIUC met hod, whi c h se ts the bromi n e efficien cy factor, alph a ( α ), at 55. Usi ng α = 5 5 me ans that on e bromi ne atom can de plete as mu ch o z o ne as 5 5 chl o rine atom s (Wuebbl es 2 003). Th e re sults that led to an alph a of 55 are fou nd i n a study pub lishe d by the Indepe nde nt Revie wPanel (2002 ) in its asse ssm ent of CH 3 I for the U.S. Depa rtment of Defe nse. 12 In addition, the AHEF assum e s a 3-ye ar la g betwe en tro posphe ric e m issi on s of ODS and their m i gration to the stratosphere (percol a tion) where they c ause ozone depletion (i.e ., contribute to EESC). Usi ng the results of the Vintaging Model, modeled gl obal values ofannual EESC for the period 1978 to 1993 were co mpared to an nually- an d m onthly-ave r ag ed strat o sp he ric o z on e co n c entration s at different latitudes, as measured by NASA's Nim b us-7 Total Ozone Mapping Spectrometer (TOMS), reported in McPeters et al. (1996a, 1996b). The em pirical rel a tionshi p between EESC and ozone was then estimated usin g linea r regre s sion s fo r the peri od 1 978 to 199 3 to derive colu mn ozo ne e s timates for e a c h month of the year and f o r the followi ng latitudinal regio n s: 20 °N to 30°N, 30° N to 40° N, 40 °N to 50° N, 5 0 °N to 60° N, and 60 °N to 70° N. 13

11 Atmospheric l i f etimes (ALT ) of ODS and the latitud i n a l loc ati on of the em issions are criti c al factors use d in der ivin g EESC valu es. For exampl e, short-live d ODS species em i tted in hi gh er latit udes w i ll h ave a lo we r impact on EESC than w i ll the sa me speci e s em itted near th e e quator, w h ere a sODS species w i th l o n ger at mosph e ric lifeti m es w i ll eventu a ll y re ac h the stratosph e re an d hav e the same EES C r egar dless ofthe latitud eof rele ase (W ueb bles 2 0 0 3 ). 12Earlier wo rk b yW u e bbl es et al. (199 9) an d Ko et al . (199 8 ) used sli ghtl y different al ph a valu es—of 60 and 5 8 , respectiv e l y— base d on the i r respectiv eatmosph e ric mod e l s . Although th e latest ozon e a ssessment (W MO 2003) recomme nds a n alp ha va lue o f45 (Dani el at al. 199 9) for lo ng-liv ed g a ses, and hi gh er val ues of 58 a nd 60 for shortlived gases, it i sbeli e ved th at an al ph a valu e of 55 is mo st defensi b le and a ppro p riate for u s e in the AHEFmodel. T h is decision i sbased o n the results ofstate-of-the art atmosph e ric mod e l s , and is also t he val ue us ed i n a recent report pre pare d for the U.S. Dep a rt ment of Defens e (Inde pen de nt Revi e w Pa ne l 20 02, W uebb les 2 0 0 3 ). 13 T he AHEFonl y us es ozon e c once n tratio ns from the three l atitude b a n d s that cover the U n ited States (i. e ., 20oN to 30oN, 30oN to 40oN, an d 40o N to 50oN).
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 15 –The re sult s of these regression s are the n use d to pre d ict ch ang es i n colum n ozo ne re sulting from changes in future EESC by month for the different latitude bands. OD S emi ssi on projections for the perio d 198 5 to 2100 a r e b a se d on pa st ODS u s e and various futu re ODS contro l sce nari o s, combine d with end u s e - spe c ific emi s sion rates d e v eloped u s in g the Vintagin g Model. Futu re ozone con c entration s are then cal c ulated based on proj ected EESC and the month- and la titude-specific regressi on para m eters. These cal c ul ations p rod uce estimate s o ffuture colum n ozo ne con c entration s (m easure d in Dob s o n units) for ea ch latitude ba nd an d each mo nth offuture years for a giv en ODS control scena rio. Each O D S co ntrol scen ari ois com pared to a bas eli ne ozo ne level that assume s no furthe r ozone depletio n after 1980. Usin g the satellite measurement s of atmosph e ric o z o ne, e s timate s of baseli ne monthly ozo n e levels for e a ch latitud e b and are equ al to the averag e of 1979 an d 1980 o z on e levels, whi c h a c coun ts for the ob served natu r al biann ual vari ability of ozon e levels (WM O 1992 ).

4.3 Ozon e Prote c tion Polic y Scenarios a nd Projectio ns of Future Ozon e Con c entr ations Since the o r ig inal Montreal Protocol wa s sign ed in 19 8 7 , amendm en ts to expand and a c celerate the controls o n O D S have bee n approved b ythe nations thatare Parti e s to the Prot ocol. The h e a l th effects impact s of four poli cy scen ario s—the o r i g inal M ont rea l Protocol, theLondo n Ame ndment s of 1990, the Cop enh agen Amendme n ts of 1992, and the Montre al Adjustme nts of 1997—are addresse d in this analysi s
. 14These poli c ie s are briefly described b e lo w. More d e tailed information o n the spe c ific rest rictio ns fo r all ODS for each poli cy scen ario i spre s ente d in App endix C. The ori g inal Montre al Prot ocol, si gne d in 1987, was t he first ste p in internatio na l efforts to pro t ect strato sph e ri c ozo ne. Un der this agre e me nt, devel oped countri es we re re quired tobegin ph asi n g out CFCs in 19 93and achieve a 50% red u cti on relative to 1986 con s um ption levels b y1998. Und e rthis agre e me nt, CFCs we re theonly ODS ad dre s sed. Skin can c er in cid ence and mo rtality modeled unde r this policy refle ct decrea s e s in CFC e m issio n s ba se d on control mea s ure sado pted by signato r ie s for develop ed an d developin g (Article 5 ) co untrie s
. The Lon don Amendme n ts chan ged the ODS emi ssi o n sc hed ule b yrequiri ng th e compl e te p haseout of CFCs, halo n s, and ca rbon t e trachlo r ide b y2000 in dev elope d co untries, and by 2 010 in devel o p ing cou n trie s. Me thyl chlorofo rm was also a dded to the li st of controlle d ODS, with pha seo u t in develope d cou n trie s targ eted in 200 5, and in 20 15 for develo p ing countri es. Th us, relative tothe original Montre al Protocol, this policy sce nari odecrea s e d U.S. skin can c er in cid e n c eand mortality

. The Co pen ha gen Amen dm ents si gnifica ntly accel e rat ed the pha se out of ODS a nd incorp orated an HCF C pha seo u t for develop ed co untrie s , begin n ing in 20 04. Und e r this a g reem ent, CF Cs, hal on s, carbo n tetrachl ori de, and methyl chlorofo rm were targeted fo r complete p h a se out in 199 6 in develop e d cou n trie s. The re sult s show furth e r redu ction sin heal th effect s related to ozone de pletion

. Finally, the Montreal Adj u st ments, the m o st stri ngent control poli cy acc epte d by the Partie s thus far, inclu d e sthe pha seo u t of HCF Cs in de veloping cou n tries, a swell as the pha se out of methyl bromi de in develop ed an d developin g cou n trie s in 2 005 an d 201 5 , respe ctively. Proje cted col u mn ozone concentratio n s for the fourp o licy co ntrol scen ario s exa m ined in this pape r relative to ozone level sprevalent in 19 79-1 980 (“b a s e lin e”) are p r esented in Fi gure 2 fo r the30°N to 40 °N degree latitud e band. The fi gure sho w s that the initial internatio nal a g ree m ent s to curtail ODS e m issi on s

14 T he modul ar n ature of the AH EFenabl es the model to be e a sil ya dapt ed to pred ict chan ges in ski n can c er incid enc e an d mortalit y , as w e ll as catar a ct incid ence, re s u l t ing from almo st an y sce nari oinvolvi ng a ch ang e in oz one conce n tratio ns. F o r examp l e, the AHEF wa s recentl y us ed i n a peer-r evie w e d ass e ssme n t to predict the huma n hea lth impacts resulti ng from a prop ose d fle e t of superso ni c aircraft (U.S. EPA 2001).
*** Peer Revi ewe d Final Report (Ap r il 2 4, 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 origi nal Proto c ol a nd the Lon do n Amendme n t s) did mu ch t oredu ce the risk of future UV radiatio n-in du ced h ealth effects. Th e more recent poli cy scen a rio s—t he Cop enh a gen Amen dm ents an d the Montre al Adjustme nts—provid e ad ditional ri sk redu ction s . Un der the s e poli c ie s, atmosp heri cchl o ri ne and bromine concentrations (express ed as EESC) are predi cted to return to 1979-1980 levels i n the mid2040 s an d to maintain the s e levels ind e finitely in the abse n ce of ad ditional O D S regul ation—a s suming full complia nce with these mandate d re striction s
. 15 Figure 2. Average Annu a l Ozone Con centr ation by ODS Scenario fo r the 30o N to 4 0oN Latitud e Band

While n o t add resse d in this analysi s , the Beijing Amen dment (2 000 ) introdu ce s further O D S produ ction and con s ump t ion controls beyond tho s eof the Mont real Adjustm e nts. Specifi c a lly, the Beijin g Amendme n t call s for a fre e ze o n HCFC produ ction, b ans the imp o rt or export of HCF Cs between no nParties to theCope nha gen Amendment s, and p rohibi ts the non -essential p rod u ction an d co n s umptio n of brom ochloro m ethane. Ho wever, be ca u s e the s e ad di tional poli cy requireme nts repre s e n t sma ll increme n tal chang es whe n compa r e d to the overall O D S emissio n scena rio (relat ive to those of the Montre al Adjustment s), thi s poli cy scen ario is n o t ad dre s sed furth e r in this repo rt or in the AHEF.

15 T he model ing proj ections ass u me no im pact sfrom change s in non-ODS atmosph e ric p e rturbati ons (e. g ., chang es in gl oba l climat e).
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 17 –F( λ , x, t) = F toa ( λ ) T( λ , x, t; θ o , O 3 , clouds, aerosols, ...) 

### 5. Modeling Changes in UV Ex po sure and Health Effects This se ction descri b e sthemethod for calcul ating future g rou nd-l e vel UV exposure sand relat i ng these values to cha nge s in huma n health effects. Gene rally , this is don e by first using the predi cted future ozo ne con c e n tration sund er vario u s p o l i cy scena rio s as di scusse d in the previou sse ction to model the cha nge in UV radiation rea c hin g the gro und in t he three latitude ba nds a c ross th e United States. Incre m ental chang es in h e a lth effect inci den ce an d/or mortality are t hen mod e led based on tho s e predi cted cha nge s in gro u n d -level UV int ensity.

5.1 The Tropo sp heric Ultr av iolet-Visible Radiat ion (T UV) Mod e l The amo unt of UV radiatio n rea c hin g the Earth’s surf ace i scom p u t ed usin g the TUV ra diation model (Mad roni ch 1 992, 199 3b; Madroni ch an d de Gruijl 19 93; Madroni ch et al. 1996, 1998 ). The T U V model provide s“l oo k-u p table s ” o fbiologically effective UVradiation a sa function of th e total ozon e colum n and sola r ze nith a ngle. The s e table sare the n use d to cal c ulate cumul a tive UV radi ation (me a sured in Watts/m 2 ) by latitude, time of day, day of month, and a m ount of ozo ne mea s u r ed in Dob s o n uni ts. The AHEF ha s many different look-up tabl es, one for ea ch different wayof weighting the entire UV spe ctru m rea c hin g the grou nd. The rationale for al ternative ways to weig ht the UV spe ctru m is discu s se d fully in Section 5.2. The TUV mo del is wi dely regarded a sa n accurate tool for cal c ulati ng su rface UV radiation le vels and h a s been u pdate d many times to reflect curre n t scie n ce . The accu ra cy of the TUV model ha s be e n demon strated in a numbe r of compa r i s o n s to dire ct m easure m ent s of UV at the Earth’s surfa c e (e.g., Shetter et al. 1992, 19 96; Kirk et al. 199 4; Lantz et al. 1996; Gao etal. 2001; Bais et al. 2003). Since 1989, the TUV model ha s been u s e d in the sci entif ic evaluation so fozone d eple t ion and rel a ted environ menta l con s eq uen ces a smand ated und er the Montre al Prot ocol (WM O 1 990, 199 2, 19 95, 1999; UNEP 19 89, 1991, 19 94, 1 999). The TUV mo del com pute sthe radiation levels at anyheight in the atmosp he re, inclu d ing the Earth’s surfa c e. Th e spe ctral i rra di ance inci dentat the Earth’ s su rfa c e (i.e. , the radiat ion at the Earth’s su rfa c e measured a c ross the UV spectrum), defi ned a sF( λ , x, t), at time t, locatio n x, and wavele ngth λ , may be rep r e s ente d symbolically a sthe produ ct of the solar spectral irradia n ce at t he topof the atmosphere, F toa ( λ ), an d a n atmosp he ri c tran smi ssi o n factor, T.

Equation 1
Values of F toa ( λ ) are kno w n from dire ct o b se rvation so f the Sun by satellite-, ballo on-, and g ro u nd-b a sed instru ment s. The value s of T depend o n param eters such a sthe so lar ze nith ang le ( θ o ), atmo spheri c optical p rop erties—s pe cifically UV radiat ion ab sorp tio n by ozon e a nd po ssi bly certain poll u ta nt gase s (e.g., NO 2 , SO 2 )—a nd UV scattering by air mole cul e s (kno wn a sRayleigh scatte ring ). Atmosp heri c particl es (e.g. , cloud s and aero s ol s) ca n contrib u te to both ab sorption (e.g., so ot) and scatte ri ng (e.g., water mi st) (mathemati c al ly describe d by the Mie t heory) a nd may be used a s inputs to the TUV. Surface reflectio n s ca n contri bute substa ntially to the radiat ion incid ent at the surfa c e a n d are also incl uded a s inputs to the TUV (see, for example, McKenzie et al. 1998 ). Many of these facto rs a r e fun ctio n s of locatio n (i.e., latitude, surfa c e elevatio n, altit ude above the su rface ) and time (i.e., day, year, and long
term trends), s othat F( λ , x, t) is also a strong fun ction of time and geographi c sit e . The TUV model cal c ulate s T by first sub d ividing the atm o sp here into vertical laye rs (typically 1 km thick), each having an
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 18 –assume d unif o rm compo s it ion. The mod e l use svertic al profile s of air den sity , temperature, a nd ozone from the Unit ed States Standard Atmosphere 16(USSA 1976). The propagation of radiation through the atmosp he re i sthen compu t ed usin g a four-stre a m di screte o rdi nate smethod (Stamne s et al. 1988 )—a n approa ch thatis widely a c cepted in the radiative tran sfer com m unityas being a c curate to with in a few percenta ge p o ints or b e tter. The four-stream metho d is su peri o r to most othe r m odel s cu rrentl yin operation, wh ich u s e a two
- stream di screte ordin a tes method.

5.2 Action Spectra An action spe ctrum d e scrib es the relative effectivene ss of en ergy at different UV wavelength sin prod uci ng a p a rticul ar bi olo g ical respon se, such as d e v elopment of CMM, BCC, or SCC. The AHEF and the TUV rely on actio n sp e ctra for e a ch health e ffect becau se a ction sp ectra provide inform at ion rega rdi ng whi c h wavelen g ths of the total UV spe ctru m are mo st effective at cau s i ng the parti cu lar health effect. For example, UV-B wavele ngth s(280 -3 20 nm ) are kn own to cau s e erythe ma, as well as the developm ent of skin can c e r , cataract s, and su ppres si on of comp on ents of the im mune system . UV-A radiatio n (3 20 -400 n m ) is n o t as re adily absorb ed by ozo ne (NOAA/National Weather Se rvice 2002 ), and is not as p o te nt as UV-B in the etiology of UVdamag e-related h eal th effects. To ensure that th e wavele ngth sare ap propri ately weighted whe n pre d icti ng ea ch he alth effect, it is necessa ry to measure the extent to whi c h wavele ngths cau s e a particula r h ealth effect. Prior to devel oping in cide n c e an d mortal ity estimates for health effe cts, an a ction spe ctrum m u st be s e lect ed for eac h health effect. Obtaining action s p ectr ar e qu ir eshe avy in ve s t ment, in terms of c o s t, labor, an d time. Moreove r , becau se it is unethi cal to e x pose h u man subje cts to h a rmful UV ra diation to test for the in ductio n of ski n can c e rs, a ction spe ctra f o r hum an he alth effects a re only availab l e for gene rali zed DNA da mag eand a variety of animal su bject expe rim ents. Thu s , the identificatio n of an approp riate a ction spe ctru m for each he alth effect mo deled by the AHEF req u ire d careful sel e ction from the limited available a ction spe ctra. Th e followin g se cti on de scribe s the action sp ectra con s id e r ed an d sele cted for e a ch h ealth effect model ed i n the AHEF, and explain s the rea s oni ng behind the ul timate sele ct ion.
5.2.1 Selection ofAc tion Spectra for Mod e

ling Skin Can cer For mo deling melano ma an d NMSC in cid ence and mo rtality, three a ction spe ctra were co nsi d e r ed:

 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. 17This may be a good selection for predicting skin cancer incidence and mortality in humans, as theoretical analyses suggest that skin cancers resu lt 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 portio n 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 derive d on the basis of erythema (sunburn) induction in human volunteers (McKinlay and Diffey 1987). It has been observed in several

16 T he US Stand ard Atmosph e r e(USSA) provi des aver ag e valu es for a nu mber of atmos pher ic param eters at different altitu d e s. 17Absorbi ng mol e cul e s in the c e ll la ye rs of hu man skin se le ct ivel y fi lter out some wav e le n g ths of UV light , reducin g the effective en erg y t hat reac h e s huma n DNA . Microbial org anisms ma y n o thave the sam edegr ee of pro t ection from UV and thus ar e e x pos ed to agreateramo u n tof radiation.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 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. epidemi o logi c studie sthat CMM ri sk i sa s soci ated wi t h intermittent intense UV e x posu r e such as that acqui red throug h su nb urnin g
.

 SCUP- h (19 9 3 ): T he SCUP spect r um was de rived on t he basi s of the indu ction of SCC in hairle s s mice (den oted a sSCUP-m). Be cau s e mo useskin a nd hu man skin have different absorptio n sp ectra fo r UV li ght, the actio n spe ctru m was corre cted for hum an ski n transmissio n by making a dj ustment s to accou n t for differences in e p idermal thickne s s and th e numbe r of hair follicle s per u n it area. Thi s adju s ted a ction sp ectr um i sden oted a sSCUP-h (d e Gruijl et al. 1993). Figure 3 gra p h ically presen ts the three a ction spe ctra descri bed a b o ve. As can b e see n , all of these action spe ctra sho wst ron g sen s itivity at UV-B wavel ength s(he n ce the sen s itivity of skin can c er to o z on e c h anges ) , and muc h less so to UV-A (whic h has a m u ch lo we r se nsitivity of skin can c e rto ozon e cha nge s).

Based o n theavailable a ction sp ectra, the SCUP-h act i on sp ectrum wa s sel e cte d for modeli ng all types of ski n ca ncers. Ration ale s for sele cting thi s actio n sp ect r um for e a ch type of skin cancer a r e a sfollows:

 SCC: The scientific community is largely in agreemen t that the SCUP-h is the appropriate action spectrum for SCC. Because there is sufficient scie ntific evidence to indicate that SCUP-h is the correct choice to predict SCC, the SCUP-h acti on 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 ofthis 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 modeli ng 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 ac tion spectrum for BCC reveal that it is not congruent with the SCUP-h action spectrum for SCC.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 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 Dos e -Res po nse Relation ships Once the app rop r iate a ctio n spe ctru m is sele cted for each health e ffect and the UV dose of biologi cally active radi ation and mo rbi d ity or mortali ty acro ss la tit ude s are id en tified, statistical reg r e ssi on analyses are u s ed to e s timate the d o se -respon serelation ship, kno w n in te ch nical terms a sthe biolo g ical amplificatio n factor (BAF), for ea ch he alth effect. The dose re spo n se relation shi p , or BAF, measu r e s the degree to whi c h chan ge s in UV expos ure weig hted b ythe approp ri ate action sp ectru m (a s m easure d in Watts/m 2 ) ca use in creme n t al chan ge s in health effects (in c iden ce or mort ality), estimated aft e r acco unting for the influen ce of birth ye ar and a ge, a sne ce ssary. BAFs are d e fined a sthe pe rce n t cha nge in a health effect re sulting f rom a on e-p e r ce nt cha nge in the intensity of UV radiation (weighted by th e cho s e n ac ti on sp ectrum). For ea ch he alth effect, the AHEF applie s the BAF to predi ct future inci den ce an d mortal ity. Estimated groun d-l e vel effective UV irra dian ce from the TUVmodel is co mbined with a sele cted B A F to translat e cha nge s in UV exposure to a percenta ge chang e in exp e cted h ealth effects. The re maind e r of this sect ion provid es a descri p ti on of how the available data a nd existing studie s are utilized to develop BAFs for each h ealth endp oint, as well a sthe un certai nties th ese e s timate d BAFs introduc e to the AHEF’s res u lts

. 5.3.1 Deriv a

tion of BAF s for CMM Incidenc e/Mortality and NMSC M o

rtalit y BAFs for CM M incide nce were not esti mated se pa rately from the BAFs for CM M mortality beca u se, as descri bed in Section 3.2, CMM in cide n c e rate s do n o t vary indep ende ntly of CMM mortality rates in the model. The i sdue to the fact that CMM incide nce rates were derive d based on the EPA/NCI CMM mortality dataset and the ratio of incide nce to mo rt ali ty for CMM. Therefo r e, BAFs u s ed to predict the incid e n c e of CMM are the same a s tho s e use d to pre d ict CMM m o rtality (i.e., th e percenta ge cha nge in CMM in cide n c e is the sam e as for CMM mortality). The gen eral method for e s timating BAFs for CM M in cide nce and mortality and NMSC mo rtal ity 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 lo cations across the United States. (Because the EPA/NCI data for NMSC do not prov ide 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 det ermine 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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 21 –determi ned, to accou n t for the coh o rt effect. 18The fun ctional form of the reg r e s sion equation use d is expre s sed in Equat ion 2, belo w : 

# [B 0 

### + B Ci

### C i 

### + B A B ln 

# ( ) UV 

# ] R

ij 

# = e 

# ∑ ∑ A j j 

### + UV ij 

# + ε ij Equation 2 where: R ij = mortality rate (CMM or NMSC) for the i th cohort and the j th age group 19 UV ij = percent change in UV radiation exposure for the i th cohort and the j th age group C iand A j = cohort and age group categorical variables 20 B Ci and B Aj = coefficients that control for the influence of cohort and age factors on CMM and NMSC mortality 21 B 0 = regression coefficient (intercept) B UV = BAF for either CMM or NMSC
ε ij = 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 e x , where x is the power. This is obtained by deriving a best-fit equat ion that describes the appropriate dose-response curve, where the dose is the yearly amount of weighted UV radiati on, 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 associ ated 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 (g iven 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

18More specific a l l y , UV e x pos ur e estimates we re combi ned w i th historica l mo rtalit y d ata for both CMM an d NMMSC mortalit y , as pr ovided in the EPA/NCI data set. T he resulting data set cont ain ed CMM and NMSC mortalit y rates per 100,0 00 p e o p l eb y s e x, age, and co hort birt h year for a lar ge num ber of s i tes coveri ng th e Unite d State s , as we l l as estimated UV expos ures at e a ch loc atio n
. 19As exp l ai ne d i n Section 5. 3, BAF s for CMM mortalit y a nd i n cid ence and NMSC mortalit y we r eestimat ed dir e ctl y from primary d ata sources. B A F sfor NMSC and catar a ctin cide nce we r ee s timated b yoth e r researc hers usin g methods sim ila r to those used to calc ulate B A F sfor skin cancer mortal it y
. 20Of the healt h effects of concern, cohort effe cts are onl y sig n ifica n t for CMM (i.e., statistics on inci de nce and mortalit y dem o n strate an a ge- linke d comp on ent). 21 T h is regressi o n is run usi ng the ma xim u m li kelih oo d no n- li near Po isso n method b e ca u s e the error ter m is no lon ger normal l y distri b u ted after the n atural l og is a p p lie d to both si des of the eq u ation.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 22 –indu ction re sulting from UV-B radiatio n (D’e rri co et al . 1996). 22Certa i n portion s of this gen e app ear to be more su scept ible to UV-B-i ndu ced mutat i on than othe rs . The s e “m utation hotspot s” a r e ea sily affected by UV radi ation, as evide n ced by the more than 28 0 of the 393 amin o acid s sequ en ced o n this g ene that have bee n found to be mut a ted in tumors (Pfeifer a n d Deni ssen ko 1998 ). Un certai nty associ ated wit h the derived BAFs for CM M incide nce and mo rtality and NMS C m o rtality are discu s sed in detail in Secti on 9.1.4.
5.3.2 Deriv a

tion of BAF s for BCC a nd SCC Incidence The BAFs for both BCC an d SCC in cide nce a r e ba se d on analys e sco ndu cted by de Gruijl a nd Forbe s (199 5)—the most up -to-d a te estimate s provided in t he literatu r e, derived u s in g the SCUP-h action spe ctru m . In cal c ulatin g BCC a nd SCC incid e n c e, DeGruijl and F o rbe sassu me d that human s are steadily expo sed to UV rad i ation throu g h out their lifetimes, and thatvarious ski n sites of hu ma ns are exposed to specifi cfractio n s of ann ual ambient UV e x posu r e. The uncertainty a s soci ated wit h the derived BAFs for BCC and SCC in cide nce is di scu s sed in Se ction 9.1.4.

5.4 Res u lting BAFs b y Health Effect The re sultin g BAFs for all h ealth effects are p r e s ente d in Table 6, as well as thekey inputs u s ed to derive each one. Fo r CMM in cide nce a nd mo rtality and NM SC mortality, BAFs for light -skinn ed mal e s an d females a r e shown for both annual exp o s ures with ty pical cl oud con d itions a nd fo r a pea k cle a r day exposure (i.e., June 21 ). G enerally, pea k cle a r day B A Fs are su bstantially great er than an nu al dose BAFs becau se the annu al value is ba sed o n e x posu r e a c ro ss the e n tire year, while th e pea k day is only for exposure o n the su nnie s t d ay of the year. Becau s e theva riation in UV radiation a c ross latitude s is greate r on an an nual basi sthan it is on June 2 1 s t (when all latit udes a c ro ss the Unite d States are in the summ er sea s o n and UV radi ation i srelatively hi gh everywhe re ), unde rlying variations in UV radi ation are mu ch less for pea k clea r days th an for cumulative annual e x posu r e. The r efore, BAFs are la rge ron pea k cle a r days, as theymust acco un t for the unde rlying fixed va riation in can c er a nd mo rtality acro ss latitudes. In both ca se s, the re sults will be simila r in magnitud e : if the pea k day UV exposure metric i suse d , a relatively sma ller variatio n in UV exposure across latitude s will be multiplied by a relatively higher BAF, while the reverse is tru e if the cumul a tive annu al UV exposure m e tric is u s e d . Beca use ann u a l dose BAFs better refle ct unde rlying UV radiation va riation sac ro ss latitude s, they are co nsi d ered to be m o re rep r e s entative of actual ex posure san d are, ther efore , used in the AHEF. It should be note d , however, that the AHEF is ca pable of using p e a k clear d ay exposure sa swell. For mo st heal th effects, BAFs are high er for males th a n for female s, implying that skin can c e rincid e n c e and mo rtality in males a r e ty pically more affected by a given ch ang ein the intensi ty of UV radiation. This may be due to differen c e s in UV exposu r e patterns b e twee n men and women.

22Mice lacki ng o ne functio n a l cop yof ge ne p5 3 we re sh o w n t obe more sus c eptib le to skin cancer in ducti on than those w i th t wo function al co pi es. Mice w i t h o u t a function al cop yof this ge ne we r eeve n more susceptible to tumor ind u ction on bo th the skin an d the e y es (Jia ng et al. 1999).

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 23 –Table 6. Summaryof Calculated BAF s and KeyIn puts Health E ffec t Data Sources a Selected Action Spectrum BAF: Use d

in A H EF (An nual Exposures) BAF: Not Used in AHE F (Peak Clear Da y Exposures ) Males Females Males Females CMM Incidence/ Mortality Incidence: Ratio s from SEER data set (see Section 3.2) Mortality : EPA/ NCI data set (see Section 3.1 ) BAF : Developed using econometric analysis (described in Se ction 5.3.1) SCUP-h (1993) 0.5846 0.5047 1.444 1.310

BCC Incidence Incidence: Base d 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: Base d 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 Se ction 5.3.1) SCUP-h (1993) 0.7094 0.4574 2.068 1.565

NA = Not available. a For all health e ffects, the Serafino and Frederic k model (1987) was used by ind ependent researchers to derive the BAFs used in the AHEF. Th e Serafino and Frederick model is a radiative transfer model that translates ozone measurements from the Nimbus-7 satellit e into ground-level solar UV irradiance, simi lar to the TUV model used in the AHEF.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 24 –

### 6. Projecting Future Health Effect s Under ODS Control Policies The final stepin the modeli ng frame w o r k integrate s ba selin e estimat e s of health e ffects, estimates of future chang e sin ozone co nce n tration, a nd the asso ci ated ch ang es in solar UV irradia n ce, app rop r iate UV spe ctrum weig hting fun ction s, and BAFs to proj ect future additional skin can c er in cid e n c eand mortality for a n y ODS emission control scen ario. Scen ario e s timate s are th en co mpared to the incid e n c e and mo rtality estimate s expecte d und er 1979 -19 80 o z one conditio n s
. For exampl e, sup p o s e that it is nece s sary to estima te the absolute i n crea se in B CC in cid e n c efor lightski nne d male s age d 50-54 living in t he southern latitude ban d of the Un ited States. The BAF for lightski nne d maleBCC inci den ce is 1.4 (seeSection 5.3. 2 ) and the futu re pe rcentag e cha nge in cumulative lifetime SCUP-h-weig h ted UV exposu r eof the age 50
- 54 g rou p is 1 percent (a hy pothetical esti mate). Multiplying these two num bers togethe r indicate s th atthe increa sein BCC inci d ence amon g this popul ation group is e s timated to be 1.4 percent for a 1 percent cha nge in weight ed UV expo sure. When this perce nta ge ch ang e in incid e n c e is multiplied bythe ba seline li ght-skin ned male BCC inciden ce for the 50-5 4ag e grou p (sup pose this is 1 00,000 ca se s), t he result is an estimate of the absol ute numbe r of addition al future can c e rs o fthis type among this p opu lation gro up attributable to a 1 percent chang e in UV radi ation dose. These cal c ulatio ns a r e su mma rize d in Equation 3:

(Cumulative Percentage Increase in UV Exposure) x (BAF for BCC) x (Baseline Incidence of BCC for the Population Group) = Abso lute Increase in BCC Incidence: (0.01) x (1.4) x (100,000) = 1,400 cases Equation 3
Performi ng th ese cal c ulatio ns for e a ch h ealth effect a nd for ea ch fu ture pop ulatio n grou p prod uce s predi ction s ofthe additiona l health effect s for a pa rticu l ar ODS e m ission scena riorelative to those prevale n t duri ng the ba seli ne (19 7 9
- 19 8 0 ).

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 25 –Table 7. Summaryof In cremen tal Skin Canc erIncidenc e/Mo rtalit y for ODS Polic y Scenarios Relativeto Baseline Cohort Gro u p / Scen ario CMM Inc i de nc e (Cas es) CMM Mortality (Deaths) BCC Incidence (Cas es) SCC In cid e n ce (Cas es) NMSC Mortality (Deaths) Montre a l Protoc ol 189 0-19 80 301,6 87 44,58 2 8, 814,8 35 5,050,8 75 30,85 9 198 5-20 10 794,1 21 109,3 52 21,25 0,45 0 11,51 7,06 6 66,82 9 201 5-20 50 2,042,3 58 265,7 59 50, 97 8,56 9 26,62 7,76 5 147,5 54 205 5-21 00 3,228,5 17 409,8 76 78, 70 8,57 4 40,79 3,03 7 220,4 52 Lon don A m e ndme n ts 189 0-19 80 101,5 23 13,77 4 2,785,7 32 1,514,6 57 7,960 198 5-20 10 113,8 85 13,85 4 2,688,7 89 1,375,3 22 6,926 201 5-20 50 80,37 9 9,527 1,830,8 67 924,5 16 4,602 205 5-21 00 31,56 9 3,831 734,6 34 377,3 81 1,946 Cope nha ge n A m endme n ts 189 0-19 80 76,04 8 10,11 8 2,047,3 91 1,096,1 53 5,593 198 5-20 10 66,92 2 7,815 1,495,2 78 743,6 82 3,634 201 5-20 50 18,02 6 2, 023 379,2 85 186,0 09 906 205 5-21 00 0 0 0 0 0 Mo n t real Ad ju stmen t s 189 0-19 80 68,81 6 9,076 1,834,1 42 974,8 27 4,923 198 5-20 10 54,94 0 6,356 1,210,0 46 599,4 67 2,925 201 5-20 50 10,30 8 1, 155 216,2 45 105,9 93 517 205 5-21 00 0 0 0 0 0 Note: The numb e rs presented a b o ve indicate the number of ca ses in excess of the baseli ne (1979-1 980) foreach scenario.



### 7. Modeling Results This se ction pre s ent s the proje cted cha nge s in inci de nce a nd/or m o rtality for ea ch of the heal th effects and poli cy scenari o s exa m ined.

7.1 Res u lts Pres ente d b yPolic yScenario and b yHealth Effect Table 7 p r e s e n ts the incre m ental num b e r of skin can c er ca se s/de aths in excess of the basel ine (i.e., those a s so ciat ed with ch an ges in colum n ozo ne con c entration s fro m levels ob se rved in 197 9-1980 ) that are p roje cted to occur un de r each O D S control sce n a r i o . D e cr e a s i ng in c i de nc es/mo rtalities that result as more stringent control sc enarios are impl emented illu strates the benefits of each f u rther amendment and/or a d ju stment to the Montreal Proto c ol. Table 8 p r esents the a v oid ed he alth effects re alized in moving from one O D S poli cy scena rio to the next (e.g., from the Montre al Prot ocol to the Lo ndon Amendme n ts). Figure 4 illustrate s that a sODS contro ls are tighte n ed, addition al incide nce an d mortality estimate s for each health e ffect relative to baseline m o ve clo s er to zero on the y-axis (i.e., clo s er to the incid e n c e an d mortality that would b e e x pected if 19 79-1 980 o z o n e con c e n trati ons h ad be en maintained throug hout th e time perio d modele d ).

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 26 –Table 8. Incremental Nu mber of Avoided Skin Cancer Incidence/Mortalit y Under ODS Polic y Scenarios w i th Increa sin g lyStringent Contr o ls
Cohort Group/Sc e n a r io CMM Inc i de nc e (Cas es) CMM Mortality (Deaths) BCC Incidence (Cas es) SCC In cid e n ce (Cas es) NMSC Mortality (Deaths)
In cremen tal N u mb er o f Avo i d e d Cases/D e ath s : F r o m Mo n t real Pro t o c o l to Lo n d o n A m e n d m en ts

189 0-19 80 200,1 64 30,80 9 6 , 029,1 0 3 3 ,536,2 17 22,89 8 198 5-20 10 680,2 36 95,49 8 18,56 1,66 1 10,14 1,74 5 59,90 3 201 5-20 50 1,961,9 79 256,2 3 2 49, 14 7,70 3 25,70 3,24 9 142,9 5 2 205 5-21 00 3,196,9 48 406,0 4 5 77, 97 3,94 0 40,41 5,65 6 218,5 0 6 T o t al 6,039,3 27 788,5 8 4 151,7 12,4 0 6 79,79 6,86 6 444,2 5 8 In cremen tal N u mb er o f Avo i d e d Cases/D e ath s : F r o m L o n d o n A m en d m en ts toCo pen h a g e n A m e n d m en ts

189 0-19 80 25,47 5 3,655 738,3 4 2 418,5 04 2,368 198 5-20 10 46,96 3 6,038 1,193,5 1 1 631,6 40 3,292 201 5-20 50 62,35 3 7,504 1,451,5 8 2 738,5 07 3,697 205 5-21 00 31,56 9 3,831 734,6 3 4 377,3 81 1,946 T o t al 166,3 60 21,02 8 4 , 118,0 6 8 2 ,166,0 33 11,30 3 In cremen tal N u mb er o f Avo i d e d Cases/D e ath s : F r o m Co p e n h a g e n A m en d m en ts to Mo n t real Ad ju stmen t

189 0-19 80 7,232 1,042 213,2 4 9 121,3 26 670 198 5-20 10 11,98 2 1, 459 285,2 3 2 144,2 15 708 201 5-20 50 7,718 868 163,0 4 0 80,01 7 389 205 5-21 00 0 0 0 00 T o t al 26,93 2 3,369 661,5 2 0 345,5 57 1,767 Note: The numb e rs presented a b o ve indicate the number ofavoid ed cases from on e policy scenario to another

. Based o n dat a pre s ente d in Table 7, Fig u re 4 throug h Figure 7 g r a phically pre s e n t the incre m ental health benefits for succe ssively more string en t scen a rio s fo r CMM in cide nce, CMM m o rtality, NMSC inci den ce, and NMS C m o rtality. 23As sh own in Fig u re4, the curve repre s e n ting i m pact sa sso ciated with theMontreal Adjustme nts most cl osely approa che sthe ba seline (1979 -19 80) o z on e co ncent ration (at zero on the yaxis) after a n u mbe rof years, but there remain si gnifi cant oppo rtunit i es for furth e r redu cing h e a l th effects. Becau s e the recovery of o z on e (i.e., the return to p r e-depletio n levels prevalent in the 1979 -1 980 timeframe ) is proje cted to o c cur a rou nd 2 050, no ex po sures attri but able to ozonedepletion will accrue fo r peopl e born after this re cov e ry date. Incremental UVe x posu r e sfor peopl e born b e fore 20 50, h o weve r, will co ntinue t oresult in he alth effects in cide nce and mortality after 2050, albeit at a lower rat e than in earlie r years. By approximat ely 2150, itis predi cted thatthere will beno living pop ulation that e x perien c e d increme n tal e x posu r e a s so ciated with de pleted o z on e levels, and h ence, no addi tional health effects incid e n c e or mortality above those exp e cted to o c cu r unde r “n orm a l” co ndition s (i.e., 1979-1 980 o z on e levels).

23 T hese estimat e s do not i n clu de effects on o z one from c lim ate variati on a nd other factor s. Ho w cl imate ma y ultimate l y affect the recovery ofstratospheri cozone is u n cl ear an d be yo n d the scop eof the AHEF

. *** Peer Revi ewe d Final Report (Ap r il 2 4, 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 19 9 5 2 0 05 2015 20 2 5 20 35 2045 20 5 5 2 0 65 2075 20 8 5 20 9 5
YearExcess 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 t he 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

1 9 85 1995 2 0 05 2 0 15 2025 2 0 35 2 0 45 2055 2 0 65 2 0 75 2085 2 0 95
YearExcess 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 Revi ewe d Final Report (Ap r il 2 4, 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

1 98

5 19 9 5 20 0 5 20 1 5 2025 2035 2045 2 05

5 2 06

5 20 7 5 20 8 5 20 9 5 YearExcess 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 1 995 2005 2015 2025 2035 204 5 2055 20 65 2075 2 085 2095
YearExcess 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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 29 –Table 9. Summaryof Results fo r Selecte d He alth Effect s b y Action Spectr um Under th e Montreal Adjus t mentsScenario Ac tion Cohort Gro u p Health Effect Spectrum CMM CMM NMSC In cid e n ce Mortality Mortality

(Cas es) (Deaths) (Deaths) 189 0-19 80 98,91 4 13,02 5 7,342 198 5-20 10 78,92 2 9,106 4,354 DNA-h 201 5-20 50 14,65 8 1,636 760 205 5-21 00 0 0 0 189 0-19 80 68,28 9 8,983 4,865 198 5-20 10 54,65 2 6,295 2,892 Eryt hema 201 5-20 50 10,25 8 1,143 510 205 5-21 00 0 0 0 189 0-19 80 68,81 6 9,076 4,923 198 5-20 10 54,94 0 6,356 2,925 SCUP-h 201 5-20 50 10,30 8 1,155 517 205 5-21 00 0 0 0 

### 8. Sensitivity Anal ys es This se ction descri b e stheresult s of se nsitivity anal yse sco ndu cte d con c e r nin g the sele ction of action spe ctra fo r CMM incid e n c e and CM M a nd NMS C mo rta lity and the effects of mo deling e a rly life exposure on CMM mort ality.

8.1 Action Spectra and Expo sure/Dose M etrics Sen s itiv it yAnal ys e s Becau s e the re are un ce rtai nties a s soci ated with sele cting an ap pro p r iate UV a ctio n spe ctru m a nd do se metric fo r ind u ction of the UV radi ation -indu ced h ealt h effects di scussed in thi spape r, it is important to explore the i m pact sof alternative a ctio n spe ctra a n d dose met r ics on these e s ti mates. Tabl e 9 pre s ent s results of sim u lation s perfo rmed u nde r the Montreal Adjustme nts policy sce nari oto estimate CMM incid e n c e an d mortality an d NMSC mo rt ality using th ree different a ction spe ctra (i.e., erythem a, DNA-h, and SCUP-h ). The increm en tal health effects a r e comp arabl e for a ll action spe ctra, with the DNA-h a ction spectrum having some what hig her va lue s for the sele cted h eal th effect end-points. As di scu s sed in Se ction 5.2.1, the DNA -h action s p ectrum is tightly foc u s ed on the UV -B portion ofsolar UV irra dian ce. In co ntrast, the other a ction spectra give m o re weight to the UV-A po rt ion of sola r UV irradia n ce, whi c h is n o t altered by ozo ne de pleti on. Thu s , BAFs gen erated from the DNA-h actio n sp ectru m are li kely to be different from those g ene rat ed by the more UV-A-fo c u s ed actio n s pectra, res u lting in diffe rence sin estimate s of predi cted ski n can c e rca ses. For rea s o n s outlin ed in Section 5.2.1 , selectio n of the SCUP-h action spe ctru m to model CMM and NMS C is the most sci entif ically valid choi ce. It also yield sesti mated health benefits that are lo we r tha n woul d be m odele d usin g t he DNA -h a ction spe ctru m , for a more conservative modelin g app roa c h.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 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 YearExcess 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 : Effe cts of Emphas i zing EarlyL i fe Exposure s Another a r ea of unce rtainty for whi c h se nsitivity was modele d invo lves the wei g ht that is given to UV exposure sat different point s in an individ ual’s life. Cu mulative UV exposure ove ra lifetime is thought to be the most i m porta nt risk factor for NM SC. Howe ver, early life exposure h a s b een ide n tified as a potentially important ri sk fact or associated with increased suscepti b ility to CMM (Harri son et al. 1994, Zanetti et al. 1992 ). It has been e s timat ed that peopl e receive 50 percent of the i r cum u lative lifetime UV exposure b e fore the ag e o f18 (Bergfeld 1997). Becau s e the re is a long lat ency pe riod a s soci ated wi t h the onset of CMM, di se ase manifestati on for exposure to UV may not be see n for de cad e s (Gelle r et al. 1998). While the current sim u latio n for CMM doe s not mod e l latency, it does offe r the option ofwei ghting UV ex posure sby a ge and by type of exposure (e.g ., peak day e x posu r e an d annu al expo sure ) sum m ed over the lifetime of the individual. Figure 8 pre s ents the resul t s of these si mulation s for CMM mo rtality using an nu al and pe ak d ay exposure s , e a ch comp ute d either by weighting all ex posure s eq ua lly over a person’ s lifetime, or by weig hting onl y the exposures re ceive d b e twee n age o ne and a ge twenty. More spe c ifically, the followi ng approa ch wa s used for e s t i mating wh ole
- life versus e a rly-life expo sures:

 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 t he ages of one and 20 are considered (i.e., later life exposures do not contribute to the results).

Table 10 tab u l ates excess CMM mo rtality using the SCUP -h a ction spe ctrum fo r both wh ole lifetime exposure a n d early life exposu r e a s sum p tions for the Montre al Adjustment s poli cy scena rio, assumin g pea k and a n n ual do se exp o su re sce n a r i o s. The table indicates thatCMM mortali ty change s b yup to 11 percent when the age-weig hting expo sure assumptio n s are cha nge d.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 31 –a Table 10. Inc r emental CM M Mortalit y Under Different Dose an d Exposure Weigh t ing Assumption s (SCUP-h Action Spectru m) CMM MORTALI T Y Cohort G r ou p Annual Dose Peak Dose Whole Life Exp o

sure Early Life Exp o

sure Whole Life Exp o

sure Early Life Exp o

sure 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%

aResults are rou nded to the n ear est 100. The timing of the health effects cha nge s, however, is more d r am atic. Usi ng the a ge 1-2 0cu mu lative exposure sce nario shifts th e increme n tal skin can c e rris k s u bs tantially forward in time from c u rrent gene ration s to future one s. Therefo r e, u n ce rtainty co nce r nin g the approp riate e x posu r e do semanifest s itself less in the total incre m ental ri sks predi cted, th a n in whe n tho s e in cre m ent al effects a r e predi cted to occur a nd, thus, wh o will b ear them (i.e., this shifts the risk to child ren bo rn after 1980). Mo re spe c ifically, for the whol e life exposu r e assumptio n , the risks of ozone de pletion are bo rne p r i m arily by the pre s ent popul ation of adults who wi ll experien c e these h ealth effects a s the yage. It is children a nd futu re gene ration s that will experi ence increa sed early life UV exposu r e sand the a s so ciated in crem ental health effects late r in their lives. It should b e n o ted that th is shift of health risks d oes n o t reflect a formal modelin g of CMM laten cy, which wo uld involve an ela borate m e tho d for assigni n g different we ights to exposure sin curre d at different age s o r some othe r yet-to-b e-d e velo ped ap pro a ch

.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 32 –

### 9. Uncertainty Analysis The AHEF, like any complex modeling framework, us es 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 uncertain ty involved. This section uses a combination of empirical analyses and theoretical reasoning to ro ughly 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 majo r 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 unquant ified sources of uncertainty that affect the AHEF’s results, but that are not consid ered 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 es timate stratospheric column ozone changes. These column ozone changes then are used to compute ch anges in ground-level UV radiation, from which estimated changes in human health effects can be calc ulated. 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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 33 –Each of the linka ge s identi f ied in the figure is the sou r ce of some d egre e of unce rtainty. Although som e might attempt to combine t hese differentsource s u s in g statistical techni que s, it is best to con s i der ea ch sou r ce se pa rately for two reason s. First, the quantitat ive estimates of the levels of unce rtainty of the AHEF's m anyinputs an d modelin g com p onent s we re deriv ed u s in g different tech nique s of varying levels of preci s io n. Secon d , and perh a p s more importa nt, is that uncerta inties con c erning some of the inputs and computat ions mig h t beinversely rel a ted. For ex a m ple, if the TUV's e s timate d grou nd -leve l UV radiatio n is bi ase d up ward, so that variat ions in UV exposure sa r e too high, then the estimated BAFs (whi ch are derived based on correl ation with gro und-l evel UV radi ation variation) will be biased downward. From a purel ystatistical st andpoint, the largest source of uncertainty in the AH EF is the EESC-to-column ozo ne compo nent, with sta ndard erro rs arou nd t he m ean effect s ra nging fro m ab out 25 to over 100 percent. However, as will be discussed i n Section 9.1. 2, this is a product of t he limited data available for the reg r e ssi o n analysi s an d likely doe s not reflect thetrue un certai nty that would be reveal ed with s u bs tantially more data. By contra st, the TUV' s e s timates of cha nge s in gro u n d -level UV ra diation du e to chan ge s in column ozo ne impa rt statistical un certainty of up to 10 percent

. Similarly, the choi ce of acti on sp ectrum for ea ch health en dpoi nt yields very small vari atio ns in the he al th effects re sults, with the exceptio n of the DNA-h action spe ctrum (which is not use d in the AHEF), as explained in more d e tail in Section 9.1.4

. The last two so urce s of quantified uncerta inty—t he age-wei g hted expo sure scena rio a s sumptio n and the estimated BA Fs—a lso int rodu ce rel a tively modest vari ation in theestimated he alth effects, o fabout 11 percent an d u p to 30 perce nt, resp ective ly. Thus, a s is true of any co mplex modeli ng frame w o r k with multiple inputs a nd co mputational p rocedu re s, the AHEF do es contain u n c ertai n ties. P e rha p s ove rtime, these ca n be red u ced as ad ditional data and resea r ch be come availabl e. At present, howeve r , t he AHEF embo dies the b e st inputs, a s su mptions, an d comp utationa l procedu re s that are kno w n. The rem a inder of this section di scu s se s the five major a r ea s o f uncertainty in greate rdetail

. 9.1.1 Translating ODS Emissions into EESC Concentrations One source o funcert a inty in the AHEF methodol ogy is that the ma gnitude of o z one de pletion and recovery ba sed on O D S emissi on s co ul d be different from those predicte d und er t he internatio nal co ntrol s in place no w or sche dule d for the future. This c ould o c cur be ca useODS use might be less th an allo wed unde r the vari ous curre n t and future ph a s eo ut requi re ment s, or O D S use could b e highe r in the future if ODS u s e exceed s allo wabl e amount s du e to non-com p lian c e with t he pha se out target s. Ho we ver, to date, cou n trie s have re po rtedly tended t oover-com pl y wi th Montre al Protocol ob ligation s(i.e., they have gene rally und ertaken O D S pha seo u t efforts befo r e thelim its impose d by the Protocol ta ke effe ct), as descri bed in WMO (200 3). For example, in 1999, rep o rts of CF C produ ction indi cated that pro ductio n of CFCs was 2 0 , 000 ODP
- ton s less than all o wa ble con s u m ption in that year (WMO 2003 ). 24Thus , the scena rio of total complia nce use d in the AHEF ma y potentially rep rese n t the maximum ODS emission s scena rio. Similarly, the parameters that c h aracteriz e the pr ocessof how ODS emis s i onstrans l ate into EESC are also ta ken to be given, de spite the fact that the rea ction kin e tics of these transiti ons a nd the compo s ition

24Althoug h scie n tific measur e m ents of actual CF C-11 an d C F C-12 emissi o n s have i n d i cat ed that mi xin g ratios we r e 5 to 10 perc enthigh er than rat i os that wo u l d have b e e n e x p e cted if prod uction lev e ls we r eide n tical to th ose rep o rted, the discre p a n cyb e t wee n mea s ureme n ts and reported va l u e scould b erel ated to differe nc es in meas ure d and reporte d val u e sthat have occ u rred th ro ug ho ut the entire m easur ement p e r i od for CF C-1 1 and CF C- 12, rather than as a result of u nder-r eporti ng i n 199 9. Supp o rting this h y p o thesis, meas ure m ent and pr od uction va lu es h ave be en closer i n recent ye ars (W MO 2 003).
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 34 –Table 11. Means and Standard Erro rs of EESC to Column Ozone Co efficients for Select Months and Lati tudes (Change in Dobson Uni t s for a 1 ppb Change in EESC) Month 3 0 ° N-2 0 ° N LatitudeBa nd 4 0 ° N-3 0 ° N LatitudeBa nd 5 0 ° N-4 0 ° N LatitudeBa nd Mean Stan d a rd Erro r Mean Stan d a rd Error Mean Standard Error Janu ary -0.016 0 0.010 4 (65%) -0. 034 4 0.012 4 (36 % ) -0.043 1 0.012 2 (28 % )

April Jul y October -0.014 2 -0.003 2 -0.007 7 0.009 6 (68%) 0.005 5 (17 2 %) 0.005 3 (69%) -0.026 8 -0.008 0 -0. 007 6 0.010 8 (40 % ) 0.006 0 (75 % ) 0.004 5 (59 % ) -0.040 0 -0.010 3 -0.012 2 0.010 7 (27 % ) 0.007 4 (72 % ) 0.004 5 (37 % ) of the future atmosp he re a r e also su bje ct to unce rtai n ty (as di scu ssed in mo re d e tail in Sectio n 9.2). These input s are a s up -to-date as the av ailable co mp lex atmosph e ric mo del s ca n provide. Mo reove r , unde rtaki ng asen s itivity analysis for all o fthe relev ant para m eters that tran sl ate ODS emi ssi o n s at the grou nd into E ESC woul d b e prohi bitively reso urce inte nsive. He nce, the unce rtain ties in ODS use/emi s sions to EESC portion ofthe AHEF’s structure are noted, but not quanti t atively exami ned.
9.1.2 Translating EESC Concentrations in to Stratospheric Column Oz one From a statist i cal sta ndp oin t, the largest sou r ce of uncertainty in the AHEF is introdu ced by th e limited data point s available for us e in predict ing c h anges in c o lumn ozone res u lting from c h anges in EESC. The rea s on the s e factors are st atistically un certain is that t hey are e s tim a ted from a very limited data set of satellite-measured st ratospheri c ozone concentrations and estimat ed EESC for the years that s t ratos p hericoz one data are available from NASA's Nimbus-7 Total Oz one Mapping Spect rometer (TOMS ) (i.e., 1978 to 19 93 ). Relatively few ob se rvations in a d a ta set ca n lead t olarge stand ard e rro rs in any statistical analysi s
. Furthe rmo r e, UV radi ation cha nge s re su lting from ozo ne depl etion and groun d le vel pollution (i .e., tropo sph e ri c ozo ne ge nerat ion) are more accurate ly measured by spe ctra lly resolved gro und -based monitors than by satellite measurement s on whi c h the AH EF current ly relies (thi s i sdiscussed f u rther in Section 10.5). Satellite data are not as accurate for m easuring ozone concentrat i ons at ground-level as they are at higher altitud e s beca u se of the co upli ng b e twee n UV a b so rption by ozo ne an d UV scatteri ng by aero s ol s a nd parti culatematter. These con s id er ati ons a r e imp o rtant when theozon e pertu rbation s occur in the l o we r-to
- mid d l e tropo sph e re, where so ot and other a e rosols a r e p r e v alent. When ozo ne pertu rbatio ns occur in the stratosp he re (i .e., well above the regio n whe r e scatte ring occu rs), a b so rption pred omin ates. Thus, the altitude at whi c h ozon e pertu r bation soccu r can affect UV radiation atthe groun d level. These effects a r e no t well captu r e d by sate llite data and h e n c e, gro und le vel UV monitoring d a ta coul d help to improve mo d e ling e s timates, parti cula rl y in urban a r e a s. Table 11 presents the estimated mean impact s of EESC on col u m n ozone, al ong with the standard errors, for fou rdifferent mo nths an d for e a ch of the three latitude ba nds mo dele d by the AHEF. Becau s e the AHEF est i mates EESC by year and then estimates col u mn ozone by m onth and latitude based on reg r e ssi on an alyse s usi ng TOMS data, the variati on i n the AHEF’s predi cted o z one by month and latitude is attributabl e in large part to the data source and not the regressions that estimat e the impact of EESC on col u mn ozone. EESC is measured in parts per billi on and colum n ozo ne is m easured in Dobson unit s
. Table 11 illustrates that an EESC in crease of 1,000 parts per billion (ppb ) result s in an estimated redu ction of 1 6Dob s o n Uni t s of colum n ozo ne in Jan uary in the 30 °N to 20° N lat i tude ban d.

Standard erro rs of ro ughly 25 to over 15 0 percent indi cate large stat is tical un ce rtainty of the column ozo ne coeffici ents. Until ad ditional data on col u mn o z one from sate llite or gro und -level mea s u rements a r e obtaine d to re fine these e s t i mates, such uncertain ty cannot be red u ce d. For ad ditional di scu ssi on on the uncertainty a s soci ated wit h the AHEF’s column o z o n e estimate s, see App endixD, which co mpares AHE F and WMO (1 999) p r e d icte d ozo ne con c entration s.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 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 contri buted 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-le vel 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 etal. 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 relevantwavelength 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 Eart h’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 ofphotons by stratospheric ozone occurs at altitudes far above those of clouds and air pollut ants. Any future systematic changes in cloud cover (e.g., related to climate change) or air pol lutants 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 chan ge in estimated UV exposure by the BAF for a particular action spectrum, exposure scenario (discuss ed 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 exposu re 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 es timates 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 appr opriate 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 u nder the Montreal Adjustments ODS control scenario, ­

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 36 –Table 12. Inc r emental CM M Incidence and Mortality and NMSC Mo rta lit yfor ThreeAction Spe ct ra Ac tion Spectrum/ A l l Cohorts CMM Incidenc e CMM Mortality NMSC Mortality Exce ss In cid e n ce Difference from SCUP-h Exce ss Mortality Difference from SCUP-h Exce ss Mortality Difference from SCUP-h DNA-h 192,4 94 41.5% 23,76 7 41.3% 12,45 7 79.8% Eryt h e ma 133,1 99 -1.2% 16,42 1 -1.4% 826 7 -2.7% SCUP-h 134,0 64
- 16,58 7
- 8,365
- there is a ran ge of expecte d incid e n c e a nd mortality e s timate s for CMM an d NMSC ba sed o n whi c h action spe ctrum is selecte d . Both the SCUP -h an d erythema spe ct r um have g o o d correl ation (within a few perce nta ge point s) for the examined health effect s, while the DNA-h spe ctru m has wide r variability. T h is d i ver g enc e is be ca us e th e D N A -h actio n sp ectr um is more tightly focuse d on the UV-B portion of the spe ctru m . Fu rtherm o re, there is a p o o runde rsta ndin g of the corre ction facto rs need ed to adj ust between viral/bacte rial DNA (fo rwhi c h the spe ctrum wa s ori g i nally develop ed) an d hum an DNA (i.e., DNA -h).

As addition al data be come available on t he do se-r e s p onse relatio n s hip for CMM and NMS C , use of the SCUP-h actio n spe ctru m m ay be re-eval uated. 9.1.4.2 Unc e

rtaint y regarding e x

posure p e

rio d Another sou rce of un certai nty in the AHEF’s health effects e s timat e s is a s so ciat ed with the e x posu r e perio d over a person’ s lifetime that is mo st likely to be the cau s e of UV-related h ealth effects. This is esp e ci ally rel e vant for CM M, sinc e it has bee n hypot hesi z e d that CMM is la rgel y the produ ct of intense exposure sea rly in life (e.g., through ag e20) rath er th an cum u lative lifetime exposu r e. As di scu s sed on page 3 2 (see Table 10 ), CMM mortality cha nge s by ±11 perce nt when the expo sure assu mpt i ons a r e cha nge d, with uncertainty con c e r nin g the approp riate exposu r e do se manife stin g itself less in the total increme n tal ri sks p r edi cted , than in when those in cre m ental effect s are p r e d icte d to occur, an d who will bear the m (i.e., shifting the risk to child ren bo rn after 1980). 9.1.4.3 Unc e

rtaint y

in the BAFs Un certai nty in the BAFs is asso ciated wi th (1) the a c cura cy of the BAFs themse lves, as mea s ured by the uncertainty ra nge s, and (2 ) whethe r or n o t the BAF can be app rop r i a tely calculat ed for the he alth effect of con c e r n, whi c h depe nd s on the sele ction of the action spe ctru m . As descri bed in detail belo w , the uncertainty in the AHEF’s predi cted ex cess UV-rel ate d human h eal th effects is 6percent for CMM mortality, 5 percent for NM SC mortality, and 30 p e rc e n t for NMSC i n cid e n c e. Th ese u n certain ty ranges are small an d not significan tcompa r ed tothe levels of uncertainty that are commo n in health effects asse ssm ents for other h a zard s. CMM Inci dence /Mortality The BAFs u s ed by the AHEF for CMM i n cid e n c e an d mortality were estimated e c on ometri call y by correl ating data on latitudin a l variation s in UV exposur e and skin ca nce rmortality

. As with any statistical estimate, the s e e s timated BAFs have st anda rd e rro rs. The estimat ed BAFs for CMM mo rtality and their stand ard e r ro rs for the S C UP-h UV acti on sp ectr um usin g the cu mulative lifetime UV expo sure assumptio n a r e sh own in T able 13. At a 95 perce nt co nfiden ce interval, the BAF for light-skinn ed male s based on a n n ual expo sure s ran g e s from 0.55 to 0.62. This yield san unce rtainty range of ap proximately ±6 pe rcent around the cent ral value (m e d ian).

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 37 –Table 13. Estimated Mea n , Standard E rrors, and Confiden ce In tervals for th e BAF s for CMM Mortalityfor the SCUP-h Actio n Spectrum and Exp o sure Sce n a r ios, b ySex Annual With Cl ouds Peak Clear Day (June 21) Lig ht

- Skinne d Males Lig ht

- Skinne d Females Lig ht

- Skinne d Males Lig ht

- Skinne d Females
Mean 0.5846 0.5047 1.444 1.310 Standar d

Error 0.02 0.02 0.05 0.06 95% Confi denc e Limi t Upper Bou nd Lo wer Bou nd Upper Bound Lo wer Bound Upper Bound Lo wer Bound Upper Bound Lo wer Bound 0.62 0.55 0.55 0.46 1.55 1.34 1.43 1.19 97.5% Confide nce Limi t Upper Bou nd

0.63 Lo wer Bou nd

0.54 Upper Bound

0.56 Lo wer Bound

0.45 Upper Bound

1.56 Lo wer Bound 1.33 Upper Bound

1.45 Lo wer Bound

1.17 Although researche rs’ und ersta ndin g of the biology a nd patho gen e s is of CMM tumors ha s imp roved in recent years (Ne s bit et al. 1998, Fidle r19 98), un ce rtain ty remains a b out the etiolo gy and mech anism (s) of inductio n o fthese tumors (Lo n g s treth 1998 ). While most re se archers ag ree th at the primary environ menta l risk fa ctor fo r CMM i sexp o su re to su nli ght, there is u n ce rtainty ab out three imp o rtant as pect sof thisrelationship:

• Effects of Early Life Exposure. Some studies indicate that exposur es early in life could increase the risk of adult cases of CMM, although pr eliminary results suggest that high childhood exposures are only important in the co ntext 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 rate s 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 C MM 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 susp ected (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 reconsid ered 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 tu mor 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, re search indicates that UV light targets the retinoblastoma (RB) pathway of the p21 genetic locu s, 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 t he effects of UV radiation on DNA and the p21 gene, there is some uncertainty regarding the dose-response relationship derived from incidence and
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 38 –Table 14. Estimated Mea n , Standard E rrors, and Confiden ce In tervals for th e BAF s for NMSC Mortalityfor the SCUP-h Actio n Spectrum and Exp o sure Sce n a r ios, b ySex Annual With Cl ouds Peak Clear Day (June 21) Lig ht

- Skinne d Males Lig ht

- Skinne d Females Lig ht

- Skinne d Males Lig ht

- Skinne d Females
Mean 0.7094 0.4574 Standar d

Error 0.03 0.03 0.07 0.09 95% Confi denc e Limi t Upper Bou nd Lo wer Bou nd Upper Bound Lo wer Bound Upper Bound Lo wer Bound Upper Bound Lo wer Bound 0.77 0.65 0.52 0.40 2.21 1.93 1.74 1.39 97.5% Confide nce Limi t Upper Bou nd

0.78 Lo wer Bou nd

0.64 Upper Bound

0.53 Lo wer Bound

0.39 Upper Bound

2.22 Lo wer Bound 1.91 Upper Bound

1.77 Lo wer 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 cancermortality. The estimated BAFs for NMSC mortality and their standard errors for the SCUP-h UV action sp ectrum 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 an d BCC Incid e n ce Table 15 p r e s ents the mea n BAF values and asso ci at ed stan dard e rro rs fo r SCC and BCC in ci den ce, whi c h were derived by de Gruijl an d Forbes (1995) using similar statistical te chni ques. S ources of uncertainty a s soci ated wit h the BAFs calcul ated by de Gruijl a nd Forb es in clu d e confo undi n g factors, su ch a s migration, patient repo rting d e lay, high early life exposu r e, and pote n tial exposure to o t her carcinogens. Relative error for ca rcinogenicity caused by waveleng ths over 340 nm was still very sub s tantial in 1995. In addit i on, the mode l was u nabl e to accou n t for epide rmal thi c kenin g and pigmentatio n that alter spe ctral sen s itivity of the skin, althoug h co rrection s for thi c ker hu man e p idermises coul d be ap pl ied. Also, differen c e s between mice and human s (e.g. , better adapt ation of huma n s to increa se s in UV exposure) may have influen ced the result s of appl ying the hairl ess mou s e m odel to human s. Thi syields an up per un ce rtainty range ofap proximately 3 0percent for the BCC an d SCC inc i denc e AHEF es timates

. Table 15. BAFs and Stan dard Errors for BCC and SCC Inciden ce SCC BCC U.S. Males U.S. F emales U.S. Males U.S. F emales 2.6 ± 0.7 2.6 ± 0.8 1. 5 ± 0.5 1.3 ± 0.4 Source: de Gru ijl an d Forbes 199 5.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 39 –9.2 Othe r Unqu a n tified Sourc es of Uncertaint y There are a n u mbe rof other so urce s of uncertainty in the AHEF’s health effect s predi ction s . Some of these sou r ce s of uncertain ty are pos sibl e to quantify, but are not ce ntra l to the structure of the AHEF. Others canno t be quantifie d becau se an y assu mption s ore s timate s wo uld be si mply spe c ulat ive. These other sou r ces of unce rtainty 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. Accu rate p r e d iction of futu re ch ang es in human he al th effects woul d requi re con s ide r ation of the net effect of all of the factors d e scrib ed ab ov e. Alt hough this challe nge is beyon d theability of the curre n t state of atmo sph e ri c and e p idemiol ogi ca l scie n ce, thes e un ce rtainti e s are de scri bed qu alitatively in more detail belo w . This se ction concl ude s with a summa ry o fthese un cert ainties.
9.2.1 Composi t ion of the Fu tur e

Atmo spher e The exact co mpositio n of the future atm o sp here as a result of com p lian c e with d i fferent ODS pha seo u t polici e s i sun kno w n. As le vels of atmospheri cchlo rin e are redu ce d, the impact of ozon e depl etion from chlo rine a nd bromi ne ra dical spe c ie s ge nerate d fr om ODS would chang e. In add ition, long-te rm system atic ch ange s in atm o sp heri cop a c ity (e.g., clo uds, ae ro sol s , other polluta nts) will also i m pact the AHEF’s ability to model ch ange s in o z o ne. Like wi se, future chang e s in climate could re sult in cha nge s in the atmosp he ric circulatio n pattern s and t herefo r e coul d cha nge clo ud cove r. Theimpact sof such cha nge s on t he pre d icte d recove ry of the ozo ne laye r are un kno w n
. All of these uncertaintie s coul d influen ce the AHEF’s ability to model atmosp he ric p roce s se s accu rately.
9.2.2 Futur e Cond itions of th e Ozon e La y

e

r Un certai nties also can b e contribute d by assumptio n s rega rdi ng thefuture co nditions of the o z one layer in respon se to the pha se out of ODS. Some comp uter m odel s pre d ict that the phaseout of ODS will slo wand eventually sto p the rate of ozo ne de pleti on, and s ugg est that natural ozo ne-ma king pro c e s se s will en able strato sph e ri c ozo ne to retu rn to 1979 -1 9 80 ozone con d it ions. The s e model s also predi ct that the recovery will eventuall yresult in increa sed con c e n tration sbeyond 19 79-19 80 levels 25(se e Cha p ter 12 in WMO 1999 for m o re detail). Because there i s incom p lete knowl edge a b out the behav ior of ozo ne p r ior to the satellite measurem ents taken in 1979- 1980, the AHEFimposes a li mit on future ozone recovery to the con d ition s ob serve d in 197 9-19 80.
9.2.3 Effe ct of Climate Varia t ions on Ozon e Deple t ion The effect s of global clim ate variation so n strato sph e ri c tempe r ature and, in turn, on ozo ne de pletion, are not well un de rstoo d , and h ave therefo r enot been mo deled in the A H EF. While t h is effect is n o t inco rpo r ate d into any other internatio nal model s used to assess future glo bal o z one de pletion , it does rep r e s ent a modelin g co n s traint that sh ould be n o ted

.

25W hether this recoverysce nar io, call ed “ozo n esuper ab und a n ce,” is like l y t ooccur is op en to debate, part i cular l y beca u se of the potentia l for compl e x interact ions b e t we en g l ob al clim ate chan ge a nd stra tospher ic ozo n ed y nam ics. Mode l comput ations h ave pr edicte d both h i gher a nd l o we ramounts of oz one i n the futur e
. *** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 40 –9.2.4 Global Compliance with Modeled Policy Scenarios This analysis assumes global compliance with each ofthe modeled policy scenarios. To the extent that these limitations are not adhered to, future ODS em issions could be different in both composition and quantity.
9.2.5 Laboratory Techniques and Instrumentation Additional uncertainty can be contributed by the l aboratory techniques and instrumentation used for deriving the action spectra used to weight UV expos ure. Discrepancies between the wavelengths of UV radiation intended to be administered and the wavelengt hs 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 notfully simulate the polychromatic light received directly from the sun.
9.2.6 Demographic and Behavior Changes Future demographic and behavior changes that coul d 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 an d sunscreens), increased public awareness of the effects of overexposure to UV, and incr eased 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 popul ation subgroups could affect estimates of future skin cancer incidence and mortality significantly.

• Changes in socioeconomic profiles : Socioeconomic profiles can impa ct 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 e ffects on future U.S. skin cancer rates. The above factors are either not ea sily quantified (e.g., human behavior), or they are not central to the analysis (e.g., improvements in medical care), and ar e 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 estima tes). With disease data, under-, over-, and misreporting are not uncommon. For exam ple, a recent study revealed t hat the incidence of CMM has been systematically under-report ed in the SEER data (Clegg et al. 2002). 26The original SEER data indicated that CMM rates in white males were relatively flator even falling (ranging from -11.1 percent to 3.3 percent annually after 1996). However, after adjusti ng for underreporting, CMM rates were actually found

26 T here is little r eason to believ ethat the SEER CMM incidence under-reporting exte nds to the NCI-bas ed CMM mortalit y input information.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 41 –Table 16. Major Sources of Quan tifie d Unce rtain ty Source of Uncertaint y Quan tified Uncertaint y Transla t ing column ozon e to ground -level UV TUV Model ≈5% Transla t ing UV expos ure to human h ealth e ffects Un certai nty in the BAFs ≤30%

• CMM mortality (6%)
• NMSC mo rtal ity(5% )
• NMS C in cid e n ce ( 30% ) Un certai nty with choi ce of action spe ctrum ≈50% Early life exposu r e vs. wh ole life exposure ≈10% Total √(5 2+ 3 0 2+ 5 0 2+ 10 2 ) ≈60 % to have incre a se d betwee n 3.8 to 4. 4 percent ann ual ly since 1 981 (Cle gg et al. 2002 ). Und e rrepo rting of CMM in cide n c e is la rgely attributable to diagn osi sin d o ctors’ office s, as oppo se d to hospital s a nd other treatment cen t ers with better re porti ng ac curacy . However, the AHEF re sults are not signifi ca ntly affected by this underreporting beca use CMM inci dence estimates in the AHEF are not based di rectly on SEER incid e n c e dat a. Rather, be cau s e the AHEF estimate s CMM in cide n c e ba se d on the ratio of SEER incid e n c e dat a to proje cted annual mo rta lity estimates, and be cau s eunderre porti ng wo uld affe ct both baseline an d scena rio e s timates, the effects o n increm ental chang es in CM M in cide nce wo uld be se co nd orde r. 9.3 Summaryof Quan tified a nd Unqu anti fied Source sof Unc e rtaint y Of the major sou r ces of un certai nty associate d with the AHEF, thetotal quantified un certai nty is roughly 60 perce nt, as su mma rize d in Table 16:

In addition to the major q u a n tified sou r ce s of uncerta in ty listed above, the atmo sp heri cco mpo n ent of the AHEF (i.e., trans lation of ODS emis s i ons into (a) EESC c o nc entrations and (b) c h anges in column oz one con c e n tration s ) i s also a so urce of uncert a inty, though not quantitati v ely examined in this analys is. It sho u ld be n o ted, however, that this unce rtainty as soci ated with the atmosp he ric para m eters u s ed in the AHEF is inhe rent in all atm o sp heri cmo d e ls, incl uding those u s e d b yWMO in its Scientific Assessment of Ozo ne Deplet ion rep o rt s (WMO 1 990, 1992, 19 95, 1 999, 200 3). Other u nqua n t ified source s of unce rtainty discu ssed a bove relate todifferent part sof the AHEF that estimate cha nge s in ozon e, chan ge s in UV radiatio n, and ch ang es in health effects. Tabl e 17 summ ari z e s these u nqu an tified unce rtai n ties.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 42 –Table 17. Fa ctors w i th Unknow n Con t ribution sto Unce rtain t y Factor Para mete r

Cha nge i n

Oz o ne Estima tes 









 Comp ositio n of F u ture Atmospher e Abilit y to Mod e l Atmospheric P rocesses Accu ratel y Resp onse of Ozone L a y er to Cha ngi ng ODS Conce n tratio n s Effect of Climate Cha n g eon Ozone D epl etio n Globa l Comp li ance w i t h Mod e le d Policy Sc enar ios Cha nges i n Co mpositi on an d Quantit y of ODS Emissions Cha nge i n

UV Radi ation Esti ma tes  Lon g-term S yst ematic Ch ang e sin Atmos pher ic Opacit y ( e .g., clouds, aeros ols, other po llut ant s)

Cha nge i n

He a lth Effect Estima tes 









 Cha nges i n Hu man UV E x pos ure Beh avi or Lab oratory T e chni ques and In strumentatio n for Derivi ng a n Action Spectru m Improveme n ts in Med i cal C a r e/Increase d Lo ngev it y Cha nges i n So cioec onom ic F a ctors (e.g., demogra phics a n d huma n be havioral chan ges) Basel i ne Infor m ation (e.g., misrep orting of s k in canc er inci denc e an d mort alit y data) Cha nges i n Po pul ation C o mp ositio n an d Siz e

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 43 –

### 10. Topics for Future Research Becau s e re se arch on the h ealth effects of UV expos u r e is on goin g , no model o rset of re sults quantifying health effect s impact scan be co nsi dere d final. Indee d, new inform ation and research results on skin can c e rand UV radiation co ntinue to flow from the ac a demic comm unity. Many importa nt issu es such as the inclu s io n of data on UVresp on siven e ss on da rk er-skinn ed po p u lation s, new informatio n o n UVindu ced cataract inci den ce, incorpo r ation of rese arch o n other hu ma n health en dp oints (e.g., im mune sup p re ssion ) , and investig ation of the feasibility ofpre d icting n on-h u man en dpoi nts mu st cont inue to be investigate d . As signifi cantnew findin g s are in co rpo r at ed into the AHEF, the accura cy of predi ction s and the implications for protecting strat o spheric ozone will be enh anced. This section discusses some of the major topi cs where future research coul d improve the ability to accura tely predi ct UV-related impact s
.
10.1 Prediction of Impacts on Differen t Skin Ty pes The AHEF is not compl e tel ycomprehe n s ive in its a ssessment of UV effects in the popul ation becau se it only accou n ts for light-skin ned individu al s in the Unit e d States. Although “li ght-skinned ” individ uals in the United States account for n early90 pe rcent of the pop ulation, 27data are ne ede d to accou n t for addition al popul ation ski n -types, in o rder to comple te a more co mpre hen sive asse ssm ent of health effects resulting from UV chan ges.
10.2 Res ear ch on Dose -Respo n se Relation ship of Catar act Inciden c e and UV Ex posure Although recent analysi so fthe dose -re spon se rel a ti o n shi p betwee n UV exposure and catara ct inciden ce in the United States we re n o t able to est ablish a st ro n g correl ation, a more rigo ro us di sag g re g a ted analysi s coul d be perfo rm ed to ensure t hat this lack o fcorrelation i snot the re su lt of data limitations. Similarly, if a dose-re spo n se relation shi por BAFfor catara ct beco m es availa blethrough in de pend ent resea r ch, this health effect sho u ld be reconsi dered fo r inclu s io n in the AHEF. (For more inform ation on modelin g cat a ra ct incid e n c e, se e Appe ndix E.)

10.3 Inclusion of Othe r Huma n Health Endpoints In addition to the health effects featu r ed in the AHEF, UV-B ha s be en asso ciate d with other h u man he alth con s e que nce sthat are a s yet unquantifi ed, as di scus sed in Se ctio n 9.2. For example, UV-B exposure can p rod uce immuno su ppression in h u m ans that may be associ ated with in cre a se s in the in cide nce and sev e rityofso me inf e ct iou sdisea s e sor re sult in an in crea sed failu rerate of vacci nation prog ra ms (Lon gst r eth e tal. 1998). Numerou sstud ies with a n im als have sho w n that UV-Bexposu r e de pre s ses immune resp onses to viru se s and in mi cro o rgani sm s (prin c ip ally those that enter via the skin), and results in more severe infection s . Infectiou s di se ase swhere such a n effect has b een sho w n u s ing a n i m al model s inclu de he rpe s , tuberculo si s, lepro s y, trichin o si s, leishmania s i s , listeriosi s , and Lyme dise ase (Lon gst r eth et al. 1998). In addition, im muno sup p ression may be asso ciated wi th incre a sed can c e rinci de nce, inclu d ing UV -indu ced can c ers. Although a ction sp ectra have been de rived for immun o su ppressio n (e.g., De Fab oand Noon a n 1983 ), certai n key d a ta gap s make it difficult to develop e s ti mates of heal th effects ch a nge s for this endp oint. For example, suff icient epi demi o logi cal data do not pre s e n t ly exist that woul d allo w q uantificatio n of the numbe r of affected in dividu als ne ce ssaryto estimate b a seli ne in cide nce. Fu rthe r, the sci entific comm unity

27“Light skin n e d ”popu latio n s in clud e those d e fine d in the U.S

. Census (19 9 0 ) as wh it es , Amer In dia n s , Asian or Pacific Islander s , and Other (inclu din g the m ajorit yof His pa nic po pul atio ns)

. *** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 44 –is not in agre e ment as to whi c h do se m e tric shoul d b e use d to assess the co nseque nces of immuno su ppression. Howe ver, adding th is end point coul d be recon s ide r ed fo r in clu s ion in the AHEF if future studies fill these data gaps. In addition to the uncertaintysurroun ding the numbe r of individuals affected byimmuno su ppression, the levels of imm uno sup p re ssi on experi e n c ed by can c e r -a fflicted indivi dual s, and th e dose metri c , the relative stren g th of UV-B-ind uced i mmuno su ppression comp ared to othe r cellul a r muta geni c effects remai n s uncl ear. UV -B simultane o u sly su ppresses cellula r im munity and d a mage s spe c ific DNA an d anti-can c e r pathways in cells that are i m porta nt for mutagen esi s
. Much recent work ha s atte mpted to explain the differential effects of immu nosupp re ssi o n and othe r UV -B sen s itive cellul a r chan ges u s in g mu rine mo del s (No ona n et al. 2003, Kann an et al. 2003 , Moodycliffe et al. 2000). Furthe rmo r e, while a ddition al informatio n is neede d to de fine the relati onship betwe en UV-B an d immuno su ppressi on, the ro le of UV-A in this relatio n shi p is al so uncertain (de Fabo 20 03 ).

10.4 Inclusion of Impacts ofClimat e Variations on Ozo n e Deplet ion Future re sea rch may shed light on the re lations hip betwee n global climate and st ratosp heri co z one depletio n, whi c h could eve n tually be mo deled in the A H EF.

10.5 Impro ved Ground-L e ve l UV Meas ure m ents Grou nd level UV monitori n g is esse ntial for advan cing human he alth effects rese arch and sho u ld be linke d to epid e miologi cal o b se rvation s to improve o u r understan din g of UV-related health effe cts. Whil e satellite s prov ide an excelle nt means of measuri ng th e UV radi atio n reflecte d fro m the atmosp here a nd estimating so me atmosphe ric p rop ertie s , such as cl ou ds an d total ozon e col u mn, they cannot dire ctly measure gro und level UV radiatio n. Additionally, sa t e llites are insensitive to pol lution in the lowe r atmosp he re. Therefore, while satellite d a ta comp are fairly well to ground level UV measu r em ents in cl ean loc a tions , this is not s oin polluted areas , s u ch as c i ties . Thisproduc es a s y s t ematiceffects u ch that grou nd level UV mea s ure m ents a r e 10 to 20 percent lowe r than satellite measu rements in u r b an are a s. Becau s e u r b an are a s a r e whe r e U.S. p opulatio ns a re con c e n trate d , it is in these area s that monitori ng is most impo rta n t. But monitoring d a ta mu st be ro bust i n or de r to be useful to epi d e miologi sts. In particula r, long term mo nitoring i s essential be cau s e of high inter-ann ual varia b ility in UV radiation levels, and becau se man yhealth effects are the result of long te rm accumul a ted expo sure to UV radi atio n. With contin ued g ro und level UV monitori ng, h u man he alth effects a s soci ated with cha nge s in UV e x posu r e s can b e better unde rsto od a nd, ultimately, incorp orated into the AHEF.

10.6 Prediction of UV Impacts on Non - Hum a n Endpoints Many eco n o m ic, ecologi cal, and enviro n mental effects are al so a s so ciated with increa sed UVradiation. Econo mic im pact s incl ude damag e to ag ricultu r al cro p s , effects on eco s ystem s , and de gra dati on of polymeri cmaterials. Ea ch of these ha s far-rea c hi ng i m pact son co nstru ction a n d manufa cturi ng indu strie s (Andrady et al. 1998). Enhan ced UV radiation le vels have vari ed rep e rcu s si ons o n terrest r ial ecosys te ms, ran g ing from decrea s e d ph otosynthe s i srates to chan ges in comp e t itive balance and in cre a se d su sceptibility to dise ase (Cald w ell et al. 199 8). Effects on aq uatic e c o s yst e ms in clud e decrea s e d sp ecie s diversity and redu ce d larval su rvival rates (Hä d e ret al. 1998 ). Beca u s e UV radiati on is abl e to penetrate tro p ical o c e an waters to d epth s of more than 20 mete rs, many spe c ie s of co ral may also be a ffected by in cre a sed o z on e depletio n. For exampl e, the death of o r gani sm s an d the inhibition of skeleta l g rowth an d ble a chi ng have been o b serve d both in
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 45 –laboratory tests and in situ (e.g., Brown et al. 2000). In addition, UV radiation m ay indire ctly damage co ral reefs by cau s ing the format ion of ox idative radi cal sthatcan in turn d a mage o r g ani smal DNA, e n zyme s, lipids, an d other mol e cule s asso ciate d with cell fu n ct i on. Studies h ave sho w n th at the fluorescent pigment s in coral that re gul ate light for the zoox a n thell ae—symbioti c alga e on which coral re e f s depe nd for photo s ynthetic fun ction s—may rea ct to increa se d r adiatio n from sunlig ht by scatterin g ra di ation of wavele ngth snot essential to photosynth esi s(Salih et al . 2000). Althoug h fluore s cent pigm ent s in co ral may provide buffering a cti on agai nst in cre a sed UV radi ation level s , studie shave also in dicated that increa sed UVradiation m ay play a role in coral bl ea ching by incre a sin g DNA lesion s (calle d thymine dimers) in co rals an d the zooxanthella e algae living within coral structures. Expe riment s sh owed that thymine dime rs in crea sed with increa sin g UV r adi atio n, although th e increa se was not p ropo rtional (Ande rson et al. 2001). Incre a sed UVradiation inte nsity cau s e d by ozon e dep letion may also cau s e n egative impacts i n amphibi an s, and may be resp on sible in part for t he o b se rved de creases in the size of amphi bian popul ations. The egg stag e is believe d to be the peri o d oflife in which amp h ibia n sare m o st su sceptible to UV radiatio n becau se eg gs are often d epo sited in fu ll sunlig ht. Re sea r che rs sp ecul ate that current levels of UV-B alone are n o t sufficient to explain t he decrea s e in a m phibia n nu mbers. Ho we ver, additiona l resea r ch is re quire d to better und erstan d the impact of UV radiatio n on su scepti b le sp eci e s (Licht an d Grant 1 997 ).

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 46 –

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*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 51 –Taylor HR, West SK, Rose nthal FS, Munoz B, Ne wla nd HS, Abbe y H, Emmett EA. ” Effect o fultraviolet radiatio n on catara ct format ion.” N Engl J Med. 198 8 De c 1;319 (22 ) :1429 -3 3. UNEP (Unite d Nation s En vironme n t Progra mme ) (1 989, 199 1, 19 94, 1998 ), “Environm ental effects of ozo ne de pleti on,” UNEP Environm ental Effects Panel Repo rt , United Natio n s E n vironm ental Programme, Nairobi, Kenya. U.S. Cen s us Burea u We bsite (2000 a), “(NP-D1-B ) Qu arterly Proj ections of the Resid ent Popul ation by Age, Sex, Race, and Hispa n ic O r igin: Mi ddle Seri es, Janua ry 1, 199 9 to Janu ary 1, 2101,” http://www.c e nsu s .gov /po p u lation/www /proje ctio n s/n atdet-D 1B.html, acc e s s ed 2/24/200 0. U.S. Cen s us Burea u We bsite (2000 b), “Detaile d State Proje ction s by Single Year of Age, Sex, Race, and Hispani c Ori g in: 1995 to 2025,” http://www.census.gov/population/www/projections/stproj.html, accessed 2/24/200 0. U.S. Cen s us Burea u We bsite (1990 ), “1 990 Cen s u sof Population and Hou s ing, ” available at <http://factfinder.census.gov/se rvlet/DTT able?_t s=89647172958>. U.S. EPA (2001), Human Health Effect s of Ozone Depletion from Stratospheri c

Aircraft , In Publication, United States Environment al Protectio n Agency, Washington, D. C. U.S. EPA (1999). Class I Ozone De pleting Substances and Class II Ozone Deplet ing Substances. EPA website. Available at http://www.epa.gov /ozone/ods.html and http://www.epa .gov /ozone/ods2. html. U.S. EPA (1987), Regulato r y Im pact Analysis: Prote c tion of Stratosph e ri c Ozon e , United States Environme n tal Protectio n Agency, Washington, D. C. USGCRP (US Global Ch a nge Research Program) 1 998. “Depleti on and Re covery of the Ozon e Layer: An Update of the Scientific Und e rstandi n g .” USGCRPSeminar, 23 Septembe r 1 998. Availabl e at <http://www.usgcrp.gov/usgc rp/seminars/980916F O.html> Accessed Decem b er 2003. USSA (1976), “U.S. Standard Atmosphe re,” National Oceanic and Atmo spheric Administration (NOAA), National Aeronautics and Space Admini stration (NASA), United Stat es Air Force, Wa shington, D.C. Wein sto ck, M

. A. (1993), “Ultraviolet ra diation an d skin can c er: e p idemiol ogi ca l data from the United States and Canad a,” in En vironm ental UV Photobiol ogy , A.R. Young, L.O. Bjorn, J. Moan, a nd W. Nultsch (ed s .), Plenum Pre ss, New Yo rk, 1-39. WMO (199 0), Scientific Assessm ent of Ozone Deplet ion: 1989 . World Mete orol ogical Org ani zation Gl obal Ozo ne Resea r ch a nd Mo nitoring Proje ct -- Repo rt No. 20. WMO (199 2), Scientific Assessm ent of Ozone Deplet ion: 1991 . World Mete orol ogical Org ani zation Gl obal Ozo ne Resea r ch a nd Mo nitoring Proje ct -- Repo rt No. 25. WMO (199 5), Scientific Assessm ent of Ozone Deplet ion: 1994 . World Mete orol ogical Org ani zation Gl obal Ozo ne Resea r ch a nd Mo nitoring Proje ct -- Repo rt No. 37. WMO (199 9), Scientific Assessm ent of Ozone Deplet ion: 1998 . World Mete orol ogical Org ani zation Gl obal Ozo ne Resea r ch a nd Mo nitoring Proje ct -- Repo rt No. 44. WMO (200 3). Scientific Assessm ent of Ozone Deplet ion: 2002 . WMO Glob al O z on e Re se arch an d Monitori ng Project - Repo rtNo. 47, Gen e va, 2002.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 52 –Wue bble s , D. J., R. Kotamarthi, and K.O. Patten (1999 ), “Up dated e v aluation of Ozo ne Deplet ion Potentials for Chlo rob rom o m ethane (CH2ClBr) and 1
- brom o-p ro p a ne (CH2B r CH2 C H3),” Atm osphe ric Environm ent 33:164 1-1 6 4 3

. Wue bble s (2 003), Perso n a l Comm uni cation with Dr . Don Wue bble s , Dep a rtme n tof Atmospheric Scien c e s , Uni v ersity of Illinois, Urban a, IL, March 6, 2003. Zanetti, R., S. Fran ce schi, S. Ross o, S. Colo nna, an d E. Bidoli (1992), Eur. J. Cancer 28A:1 1 72-1 176.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 53 –

### 12. Glossary Actio n spectrum — Experimentally-d eri v ed plots de scribi ng the rel a tive effectiveness of ea ch wavele ngth o fUV-A and UV-B radiatio n in the indu ct ion of a spe c if ic health effe ct (e.g., ski n can c e r , sun burn). Act i on sp ectra are use d as wei ghting fun ctio n s in orde r to estimate the potential of a particula r UV exposure to induce adverse health effects. BAF (biologi cal amplification factor)— BAFs are equal to sl op e of the dose
- respon se cu rve (see “do s e -re sp on se rel a tion shi p ”). Bas a l cell carcinoma (BCC) — BCC is the most com m on type of sk in can c er. It is ca used pri m arily by exposure to u l traviolet radi ation, and o c curs mo st fre quently amon g light-skin n e d person sov er age 4 5

. Although BCC ha s a very high cure rate , in rare in sta n ce s it can b e lethal. Baseline — The AHEF de fines the “b aseline” in cid e n c e an d/or mo rtality for skin can c er a swhat woul d be expecte d to occur in the fut u re if the con c entrati on of strato sph e ri c ozo ne re main ed fixed at 1979-1 980 levels. Birth co horts — Individua ls assig ned b yyear of birth into group s for furthe r stu d y. The AHEF use sthe results of the s e birth coh o rt studies to create and p r oj ect a ba selin e estimate of ski n ca ncer in cide nce and/or m o rtali ty. Column ozone— The am ount of ozo n e(mea sured in Dob s on u n its) contai ned in a vertical col u mn of air extending fro m the Earth’s surfa c e to an orbiting satel lite desig ned to measu r e o z on e co ncent ration s. Rou ghly 90 to 95% of colu mn ozo ne is i n the strato sp here with sm all amount s (5-10% ) in the tropos p here. Cutaneo u s malignant melanoma (CMM)— CM M is the most seriou s type of skin can c e r . It occu rs mo st freque ntly in light-skin ned person sove rage 40 with light compl e xions a nd hai r colo r. Dobs on unit — A measu r eof the thickn ess of the ozone. Fo r a ve rtical colum n of ozon e com p re ssed at 0 degree s Ce lsiu s and 1 at mosp he re pressure, a Do bs o n unit is d e fined to be 0

. 01 millimeter thick. Dose metric s — Mea s ure sused to exp r ess the am o unt of UVradi ation re ceive d over a sp ecific time perio d (i.e., the dose). Examples a r e p e a k ho ur do se, da ily dose, o rcum u lative doses for a m onth or for an entire yea r
. Dose -re s pon se relation s h ip— The re lationship betwee n an effe ct (e.g., ski n can c e r) a nd the expo sure (e.g., UV radi ation) p rod uci ng that effect. If plotted on a log-lo g scal e, BAFs are e qual to the sl ope of the dose-re spo n se curve. Econometric— A statistical techni que t hat enabl es a nalysts to det ermin e to wh at degre e on e spe c ific variable (e.g., UV exposu r e ) may be re sp onsi b le for a spe c ific effe ct (e.g., skin ca nce r) tho ughtto be cau s e d by the intera ction of several rel a ted variabl e s
. For exam pl e, it is hypothes i z ed that a ge, birth year, and UV expo sure all play a role in the e t iology ofCMM. Becau s e ODS control polici e s can o n ly redu ce one of these risk fa ctors (i. e ., the amoun t of UV-B rea c hin g the gro und can be re duced by a thicker o z on e layer), e c on o m etric e s timation is used to isolate a nd quantify the contributio n of UV exposure to CMM incid e n c e an d mortality. Equi va lent effective stra tospheric chlorine (EESC)— An expression of the o z one-depl eting capa bility of a sub s tan c e and its de gradation p rod u cts, expresse d in terms of chlo rine m o le cule s. The E ESC of sub s tan c e s that contai n bromine in stea d of chlori ne can b e cal c ul ated by usin g a bromin e efficien cy factor (“alp h afactor”) that define s the n u mbe rofchlo rine mol e cule s nee ded to d eplete on e molecule of
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 54 –ozo ne a sefficiently as o n ebromin e mol e cul e . The AHEF assu me s that 55 chl o rine mol e cule s are n eed ed to deplete a molecule of o z on e as effici ently as one bromi ne mole cule. Incidence— For the pu rp ose s of this p aper, the in ci den ce is d e fined a s the nu mber of ne w ca se s of a given health effect that develop ea ch year. Isotropic gro und refle c tiv i t y— The assumptio n that all waveba nd s of UV radi ation stri king th e Earth’s surfa c e a r e reflected e qua lly in all direct ions. Late nc y — T he length of time betwe en the expo sure to a stre ssor (e.g., UV-B ra diation) a nd the respon se to that stre ssor (e.g., skin can c er). No furt he r ozone d e pleti o n — The total colum n ozone ob se rved in the 1979-1 980 timefram e is define d as the ba seli ne agai nst which the imp a cts of future o z on e ch ang e sare m e a s ured. The s e col u mn ozone levels are assumed in the AHEF to rem a in co nsta nt in the baselin e proje ction s. Ozon e-d e plet ing subs tan ces (O DS)

— A group of chemicals cont aini ng chlo rin e and/or b ro m ine atom s that destroy ozon e mole cul e s in t he st rat o sp here. Emissi on s of these chemi c al s a r e the chief cause of anthro pog eni c stratosphe ri c ozone d epl etion and h ave been target ed for pha se o u t by the Montreal Protocol and i t s amend men t s. Prevalence — Refe rs to the total numb e r of existing ca se s ofa given health effe ct, at a specifi ctime, as oppo se d to new cases (“in cide nce”). Solar UV irradiance— UVradiation fro m the sun rea c hin g the Earth’s su rfa c e. Solar zenith angle— Thesolar zenith angle i sthe a ngle of the su n’s directio n with re spe ct to the local upward ve rtical, measu r e d in degree s fro m 0 o(overh e ad su n) to 90 o(sun at the hori z on ). Spectr a l irradiance – Th eradiation at the Earth’ s su rface m e a s ured acro ss thefull UV spect r um. Squamous c e ll carcinoma (SCC) — S CC i sthe se cond mo st co mmon form o fNMSC in hu mans. It is thought to be prima r ily cau s ed by expo sure to ultrav io let radiation. SCC may gro wqui ckly an d spre ad to other pa rts ofthe body, making it a dan g e rou s form ofskin can c e r
. Total O z one Mapping Sp ectrom ete r (TOMS) — A satellite-base d instrument to measure the total vertical colum n of ozon e in the atmosp he re. The meth od is ba se d o n detectin g UV radiation backscattere d by the lowe r atmosphe reafter it passe s throu gh st ratosph e ri c ozo ne. The amo unt of ozon e is determine d by compa r in g the backscattered radiat i on at several pairs of wavel ength s , sele cted for different se nsitivity to absorption by ozo n e . The TOMSinstrum ent a boar d the Ni mbus-7 satell ite provided nearly contin uou s glob al o z on e data fro m late 1978 to ear ly 199 3. Shorter d a ta reco rd s from more recent satellites (Meteor-3, Earth Pr obe) are al so available. Ultrav iolet (UV) radiat io n — Ultraviol e t radiation i sa portion of the ele ctrom a gnetic spe ctrum with wavele ngth s sho rter tha n visible light. Th e sun p rod uces UV radiati on, whi c h is commonly split into three arbitrarily-defi ned ba nd s: UV-A, UV-B, and UV-C. Because the AHEF relies o n action spe ctra equatio ns to estimate h ealth effects, it is not nece s sary to definethe exact wavelength sthatmake up ea ch ba nd. UV-A is not a b so rbe d by ozon e. UV-B is mostlyabso r bed by ozone , although so me rea c h e s t he Earth. UV-C is com p letely absorbed by ozoneand normal o xygen. The AHEF uses thepercentag e chang e in UV exposure multiplied by the app ro p r iat e BAF and the age-sp ecifi cba selin e inciden ce or m o rtality rate to predi ct future ch ang es in human he alth effects. Although the AHEF con s ide rs only sola r UVradiation, UV radi ation from artifi cial sou r ces (e.g., tanning be ds , welding, me rcu ry lamp s) are al so a s so ciated with adverse he alth effects.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 55 –UV- A— A ba nd of ultraviol e t radiation with wavelen g ths from 3 20-400 na nomet ers p ro d u c ed by the sun. UV-A is not a b so rbe d by ozon e. This ba nd of radiat io n has wavele ngths ju st sh orter tha n visi ble violet light. UV- B — A ba nd of ultraviol e t radiation with wavelen g ths from 2 80-320 na nomet ers p ro d u c ed by the sun. UV-B ha s be en asso ciate d with huma n health impa ct s and is p a rti c ula r ly effective at damagi ng DNA. UV-B ha s be en identified as a cau s e ofmelanom a a nd ot her type s of ski n ca ncer as well a scataract an d sup p re ssion of the immun e system. It has al so be en linke d to dam age to som e material s, cro p s, and marin e org ani sm s. The ozo ne layer p rote cts t he Earth again s t most sola r UV-B ra diation. UV- C — A ba nd of ultraviol e t radiation with wavelen g th s shorte r tha n 280 na nom eters. UV-C is extremely dang ero u s, b u t it is comple tely absorbed by oz one (at wavele ngth s betwe en 24 0 and 28 0 nm) and no rmal oxygen (O 2 ) (at wavelen g ths betwee n 2 00 and 2 80 n m ), and he nce doe s not re ach the Ea rth ’ s su rfa c e.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 56 –

### Appendix A. Updates Madeto 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 u pdates 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 mort ality 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 thatwas used in previous versions of the AHEF. Developments in the skin cancer literature sugges t that SCUP-h is the most accurate action spectrum available for NMSC, because it co vers a broader spectral range and is more appropriate than use of t he 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 do se-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 strato spheric 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 Forb es (1995). The new BAF is der ived 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 V an 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 hai r follicles per unit area.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 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 ozo ne were updated, based on new data provided by NASA. 3⁄4 Impact: Negligible change in results.

• The AHEF’s TUV inputs (which provide ground-leve l 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 (Madro nich 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; Lant z et al. 1996). In addition, since 1989, the TUV model has been used in the scientific evaluatio ns 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 dist ributed 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 c hanges 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 allowi ng new impacts to be evaluated; no impacts on current results.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 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 ofresolution (resolution increased from a 100 cases level to a one case level). 3⁄4 Impact: Increased model resolution (i.e., a llows smaller impacts to be evaluated); no impacts on results.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 59 –Table B-1. Regional Definitions for M o rtalit y Data Set 40 oN to 5 0 oN 30 oN to 4 0 oN 20 oN to 3 0 oN Alaska California (No rth)* Alabama C o nn ectic u t C o lor a do Ar iz on a Idaho Delawa re Arka nsas Maine Distri ct of Col u mbia California (So u th)* Massa c hu sett s Illinois Florid a Michig an Indiana Georgia Minne sota Iowa Hawaii Montana Kansa s Loui siana Ne w Ham p sh ire Kentucky Missi ssippi Ne w Yor k Mary lan d Ne w Mex i co North Da kota Missou ri South Ca rolin a Oreg on Neb r a s ka Texas Rho de Isla nd Nevad a South Da kota Ne w Je rsey Vermont North Ca rolin a Wa shin gton Ohio Wisco n si n Okla homa Pennsylvani a Tenne ssee Utah Virginia We st Virginia Wyoming

*Cou n ties in C a lifor nia we re s egre gate d into either the S out h or Midd le reg i on. T he popu l ation w a s split b a se d upo n cou n t y pop ulati on d ata from the De mogra phic R e s earch U n it of the Califor ni a Dep a rtment of F i na nce. 

### Appendix B: SupplementaryT a bles fo r Baseline Incidence and Mortality Projections This ap pen di x prese n ts su ppleme n tary i n formatio n for the determination of ba seline in ciden ce and mortality proj ection s for bo th cutane ou s maligna nt me lanoma (CM M ) and n on-melano ma skin can c e r NMSC, a sdiscu s sed in Se ction 3 of the repo rt.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 60 –Table B-2. CMM MortalityRate s (pe r1 00,000 ) for White M a les b yCohort a nd 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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 61 –Table B-3. CMM MortalityRate s (pe r1 00,000 ) for White F e males b yCohort a nd 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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 62 –Table C-1. O r iginal Montreal Protocol (198 7) Controls for Non-Ar ticle 5 Nati ons Controls for Ar ticle 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 St ratospheric 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 examin ed (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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 63 –Table C-2. London Ame n dments (199 0) Controls for Non-Ar ticle 5 Nati ons Controls for Ar ticle 5 Nations CFCs Halons Carbon Tet. Methyl Chlor. CFCs Halons Carbon Tet. Methyl Chlor. (CCl 4 ) (CH 3 CC l 3 ) (CCl 4 ) (CH 3 CC l 3 ) 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% 3 0

% 2010 0% 0% 0% 3 0

% 2001 0% 0% 0% 3 0

% 2011 0% 0% 0% 3 0

% 2002 0% 0% 0% 3 0

% 2012 0% 0% 0% 3 0

% 2003 0% 0% 0% 3 0

% 2013 0% 0% 0% 3 0

% 2004 0% 0% 0% 3 0

% 2014 0% 0% 0% 3 0

% 2005 0% 0% 0% 0% 2015 0% 0% 0% 0%

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 64 –Table C-3a. Cope nhag e n Amendmen ts (199 2)– Controls for Non-Article 5 Natio n s CFCs Halons Carbon Tetrachl oride Methyl Chlor o

form HCFCs* Methyl Bromi de (CCl 4 ) (CH 3 CC l 3 ) (CH 3 Br

)** Base Year: 1986 1986 1989 1989 1989 (3.1%) 1991 Controls Year Percent of Percent of Percent of Bas e 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 calculat ed 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 Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 65 –Table C-3b. Cope nhag e n Amendmen ts (199 2) - Co ntrols fo r Article 5 Nation s CFCs Halons Carbon Tetrachl oride ( CCl 4 ) Methyl Chloroform (CH 3 CC l 3 ) Base Year: 1996 1996 1999 1999 Controls Year Percent of Bas e Year Percent of Bas e Year Percent of Bas e Year Percent of Bas e 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: T he Copenh agen Am en

dmen t s did n o

t c

h ang e t

he Art i c

l e 5 c

ontro ls

c hedul e.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 66 –Table C-4a. Montreal Adj u stmen t(1 9 97) - Con t rol sfor No n- Article 5 Natio n s CFCs Halons Carbon Tetrachl oride Methyl Chlor o

form HCFCs* Methyl Bromi de (CCl 4 ) (CH 3 CC l 3 ) (CH 3 Br

)** 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 bas el ine HCFC c ons umpt io

n, or HCFC c ap, is c

alc

ulat ed

as

th

e t

o

tal ODP-weighte d HCFC c ons umpti o n in 19 89 plus 2.8 perc ent

of the ODP-weighte d CF C c ons umpt ion

in 198 9. ** The m e

thy lbro m

id

e fre e

ze

d

oes not app ly

to pos t-h

arv es

ting us es

(i.e., qu aran ti ne and pre-s hip

ment).

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 67 –Table C-4b. Montreal Adj u stmen t(1 9 97) - Con t rol sfor Article 5 Natio n s CFCs Halons Carbon Tetrachl oride Methyl Chlor o

form HCFCs Methyl Bromi de (CCl 4 ) (CH 3 CC l 3 ) (CH 3 Br

)* 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 %

*T he meth y l br omid e freeze d oes not a ppl y t opos t harvesti ng uses (i.e., q uara n tine and pre-shi p me nt).

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 68 –Table C-5. Cl ass I and Cla ss II Ozone -Depleting Substa nces Class I Group I (CF Cs) * CFC-11 CFC-12 CFC-113 CFC-114 CFC-115 Group II (hal ons) * 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 CF C-216 CFC-217 Group IV carbon tetrachloride Group V methyl chloroform Group VI methyl bromide Group VII C3

H2

F 3

Br

3 C3 H 3

FBr 4 C2 H4

Br C3 H 3

F 3

Br

2 C2 HF 3 Br

2 C3 H2

F 5

Br C2

H2

F 3

Br C3 H4

FBr 3 C3 HFBr 6 C3 H4

F 3

Br C3 H 3

F 2

Br

3 C2 HFBr 4 C3

H4

F 2

Br

2 C3 H5

F 2

Br C3 HF 2 Br

5 CH2 FBr C2 H 3

F 2

Br C2 H2

FBr 3 C2

H2

F 2

Br

2 C2 HF 2 Br

3 C2 H 3

FBr 2 C2 HF 4 Br C3 H6

FBr C3 HF 3 Br

4 C3

HF 4 Br

3 CHFBr 2 C3 HF 6 Br C3 HF 5 Br

2 C3 H2

FBr 5 C3

H2

F 2

Br

4 CHF 2 Br (HBFC-22B1) C3 H2

F 4

Br

2 C3 H5

FBr 2 C3 H 3

F 4

Br
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 Is

omers of the lis

t ed c h em ic

als are als o Source: U.S. EPA Website 1999. i n c

l ud

ed in t h e ODS ph as

eout

.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 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 co mparative 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 ho w the latest WMO projections compare to the AHEF in terms of ozone concentration predictions—and/or how thes e changes in ozone concentrations would impact changes in incremental health effects. *** Peer Reviewed Final Report (April 24, 2006) *** – 70 –Dobson Units Figure D- 1. Strato sph e ric Ozon e Col u mn Leve ls at 40 -50 ° N: AHEF Pre d ictions, Montreal A d ju st me n t s
360 355 350 345 340 335 330 325 320 315 9 7

85 1 9 27 2 69 2 9 9 9 92 19 20 20 6 13 20 0 34 20 41 20 8 5 5 62 20 0 76 20 83 20 90 20 1 20 0 20 4 20 20 Y

ear 

### Appendix D: Comparison of AHEF and WMO Predicted Ozone Concentrations To validate the AHEF’s projections of strato spheric ozone concentrations, the AHEF ozone level predictions under the Montreal Adjustments (see Fi gure 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 recentozone assessment conducted by the WMO (2003) does not provide similar predictions of column ozone co ncentration that are comparable to AHEF output (in terms of format and type of data r eported), therefore, direct co mparison with the AHEF was not possible. 28Figure 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, whic h 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 ag reement regarding the s peed 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), pa rticularly in the earlier y ears modeled (Figure D- 3). Factors that influence EESC estimates include the estimated ODS emis sions, the degree of dissociation of each ODS species, and the rate of transport to t he stratosphere. In the estimation of ODS emissions alone, for example, there exists significant opportunityfor 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 thro ugh a complex, bottom-up model that estimates the stock and growth rates of all types of ODS-cont aining equipment, as well as their average lifetimes, annual leak rates, service rates, and disposal ra tes. Conversely, WMO estimates are based on measured values of EESC extrapolated into the future bycompound. WMO projecti ons 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 esti mates. Thus, given the difference in modeling

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 71 –Figure D- 3. Comparison of AHEFand WMO (1999) EESC Estimates



### 0 500 1000 1500 2000 2500 3000 3500 4000

1980 1 9

9

0 2000 2010 2020 2030 2040 2050 2060 2 0

7

0 2080 2 0

9

0 2 1 0

0


### YearEESC (pptv) 

### WMO AHEF approa che s , and the un ce rtainty asso ciat ed with the a s sumption s u s ed by both a ppro a che s , variation across mo del s is to be exp e cted.

Ho wever, in apeer reviewe d repo rt titled Human Health Effects of Ozon e De pletion from Stratosph e ri c Aircraft (200 1 ) , it was foun d that the differen ce b e tw e en the AHEF and WMO m odel predi ctio ns of the maximum ozone de pletion is well withi n the accepta b l e rang e of variation. Thi sreport, co-a uthore d by NASA and EPA, compared and evaluated six different atmospheri c model s capabl e of proj ecting ozone con c e n tration s . The s e six model s gen erated ozone e s timate s that varied by a factor of five, as pre s e n ted in Figure D- 4

. Indeed, the estimated o z one level sof the six model s had a n un certainty ran g eof +0.9% to -2.1% aro und the central value of -0.6%in 2050. If th is ran ge is ta ken to define the un certai nty arising wholly from va riability withi n the model s, the AHEF an d WMO p r edi ction sca n becon s ide r ed t obe statistically si milar. Similarly, the 2003 WMO S c ientific Asse ssment supp orts the a s serti on that the AHEF and WMO col u mn ozo ne e s timates are within an accepta b l e rang e of variat ion. As sh own in Fig u reD- 5, the differen c e s in colum n ozon e predi ction s from differentcom pute rmo dels u s e d for the GHG sce nario MA2 29in the 2003 WMO repo rt are roug hly the same o rd e r of magni tudeas those betwee n the AHEF and WM O(1999 ) es timates

. Thus, the g e n e ral ag re eme n t of the decli ne and recove ry estimate s, the shape s of the curve s , and the use of ide n tical data set s indicate that the sim ila ritie sbetwee n the AHEF and WMO app roa c h e s outweig h

29W M O GHG scenar io MA2 is taken from IPC C(200 1) scen a ri o A2. In this scenar io, b y 20 50, atmosph e ri c levels of CO 2concentrat i ons re ach 5 3 2 ppmv, CH 4concentrati ons re ach 2,56 2 pp b v , and N 2 O concentratio n s rea c h 373 p pbv.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 72 –the differen c e s —w hi ch are themselve s within an acce ptable ra nge of variation for atmosphe ric models of thisk i nd. Figure D- 4. Sensitivity and Uncertainty Envelopes Used in HSCT Analysis

Source: NASA/EPA 2001.
Figure D- 5. Variations in Column Ozone Proj ections 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 in cludes observations of past trends (red lines) prior to 2000. Source: WMO 2003.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 73 –Table E-1. Baseline Cata ract Incide n ce Rates by Age Category (Us e d in Prev i ous Versions of th e AHEF) Ag e Cate gory In cid e n ce Rates As su min g 197 9-19 80 Oz o n eL e ve ls (p er 100,0 00) 55-6 4450 65-7 01,350 75-8 42,750 85+ 0 a
aBecause the pr evalence (existing cases) of catar a ct in the 85+ gr oup is the same for

the age 75 -84 ca tegory, incidence (ne wcases) for t he 85+ grou p is assumed to be zero 

### Appendix E: Modeling Cataract Incidence In previou sversi o n s of the AHEF , cataract inci den ce attributable toincre a sed UV exposu r e h a s be en modele d . Thi s health effe ct was ha ndle d in much thesa me way as the other he alth effects, with baseli ne cataract incidence rates estimated using cataract incidence data reported in U.S. EPA (1987). These cataract in cid ence rate s were de rived from preval en ce data pre s e n t ed in Hiller etal. (1983 ), which in turn were ba sed o n a sub s et of the Nation al Health a nd Nutrition Examination Study (NHANES) d a ta. 30The sub s et con s isted of 2,225 subje cts betwe en the age s o f45 and 74 at35 different locatio n s a c ro ss the United States. Inciden ce rates we re strat i fied by lo cati on, based on the three latitudinal b and s (i.e., 20°N to 30°N, 30 °N to 40°N, and 40° N to 50° N). Future cata ract in cide nce for parti cula r ozo ne de ple t ion scena rio s wa s estim a ted in the same way that skin c ancer in cide n c e an d mortal ity are comp u t ed—i.e., by multiplying in cide nce rate s by populatio n proje ction s by age, sex, and latitude b and. Table E-1 repo rts baseline cata ract in cide nce rates devel oped by ag e grou p.

In deriving th e dose-re spo n se relation ship fo r cata ra cts, BAFs we re develo ped based on a study con d u cted byTaylor et al. (1988 ), whi c h looked at exp o su re hi stori e s for a sampl e popul ation i n a singl e locatio n (i.e., the Che s a p e a ke Bay in M a ryland ). Theresultin g BAF wa s 0.225 f o r male s an d females. Ho wever, whi l e the Taylor study wa s abl e to sho w thathigh cumul ative levels of UV-B expo su re signifi cantly increa sed the risk of co rtical catara ct in a controlled p o pulation, the study wa s notappro p ri ate for derivin g a BAF for cataract inci den ce becau se it is based on o n l yone locatio n in the Unite d States, as oppo se d to latitudinal average s, like the other health effects that are modele d

. Thus, a recen teffort was m ade to revise the BAFs for cataract in cid ence ba sed o n available a ction spe ctra a nd the latest dataon U.S. cataract inci den ce. All available action spe ctra were revie w ed, including:

 Eryt hema (1987): Thisis an action s pectrum derive d on the basi s of erythema (sun burn) indu ction in h u man volunte e rs (M cKinlayand Diffey 1987). Thi s a ction sp ectrum wa s used to predi ct cata ra ct incid e n c e i n the origin al AHEF develo ped in the 19 80s.

 Merriam et al

. (2000) : Thi s actio n sp ect r um was g e n e rated b a sed on indu ction of cataract s in rats. The a cti on sp ectrum spa n s the UV spe ctrum fro m 295 nm to 315 nm, cove ring only the UV-B s p ectr um.

 Pitts et al

. (1 977) : Thi sa ction sp ectrum wa s gen erate d as an ave r a ge of action spectra value s based on the ocul ar effect s of UV radiati on on t he cornea s of huma n s, prim ates, and ra bbits, a s well a s huma n conj un ctiva and ra bbit uveitis.

30NHANES an al yze d al l three forms of catara ct, but onl y cort ical catar a ct is clearl y associ a t ed w i th UV e x posur e, w h il e much u n c ertaint ye x ists w i th re gar d to the role of UV
- B and the oth e rforms of cataract. T hus, in terms of using the NHANES d ata in dev el opi ng bas eli ne cat a ract inci denc e in the AHEF , cataract inci de n c e ma y be ove r estimated.
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 74 –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.  Oriow o et al

. (2001 ) : This action spe ctrum wa s gen e r ated ba se d o n indu ction ofcatara ct s in pigs usin g wh ole cultured le nse s . The a ction spe ctru m sp a n s from 2 70 n m to 370 nm, thus extendi n g into the UV-As p ectrum. Figure E-1 graphi cally pre s ents the ide n tified action sp ectra fo r cata ract ind u ctio n
. As can be seen, these action spe ctra sho wst ron g sen s itivity at UV-B wavel ength s (he n ce the sen s itivity of cataract s to ozo ne cha nge s), an d much le ss to UV-A (whi ch is not sig n ificantly affect e d by ozon e chang es).

While the sha pes of the s e four a ction sp ectra a r e verysimilar, the Orio wo a ction spe ctrum wa s sel e cte d for use in the AHEF be cau s e it is believ ed to be the most app ro pri a te for modeli ng cata ra cts i n human s due to the followin g :

 The Ori o wo a ction spe ctru m is ba sed o n the i ndu ctio n of cataract s in pigs, whi c h is more approp riate for mod e ling catara ct incid e n ce tha n the Erythema acti on sp ectrum, whi c h is b a se d on the indu cti on of erythem a (su nbu rn ) in human s.

 Of all action spectra develo ped ba se d on the inductio n of catara cts i n animal s , the pig len s is believed to b e most physiologi cally sim ilar to the hu man eye—a soppo sed to the rat or rabb it lenses u s e d in the Merria m et al. (2000 ) and Pitts et al. (1977 ) a ction sp ectra.

 The Ori o wo a ction spe ctru m is not an av erage d action s p ectrum, lik e the Pittset al. action spe ctru m , an d is therefo r e less likely to be com p licated by inco nsi s ten c ie s ari s i ng from atte mpts to c o mbine data from different s t udies

.  The Ori o wo a ction spe ctru m appea re d in a peer-reviewe d journal ( Investigativ e

Ophthalm ology & Vis ual Science), and is the most re ce ntly develope d a cti on spe ctru m of the three action spe ctra c o ns ide r ed

. Then, in an attempt to calculate the BAFs, estimate d averag e UV e x posu r e sby state we re co rrel a ted with cataract incidence data reported bythe National Eye Institute/Prev ent Blindness America (NE I/PBA) (200 2)—the most re ce nt and com p rehe nsive catara ct inciden ce d a ta available

—of popul ations a ged 4 0 and ab ove. The re sults of t h is an alysi s sugge sted that catara ct in ciden ce is di re ctly correlate d with age. Ho wever, it was not po ssibl e to develop a good corr el ation betwee n the intensity of UV exposure and cataract in cid ence. Therefore, it wa s no t possi ble to predi ct ch ang es in catara ctincide nce ba sed o n
*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 75 –cha nge s in O D S emissio n scena rio swit h a high level of confiden ce. Con s eq ue ntly, this health effect wa s omitted from the AHEF. The lack of correlation b e twee n UV ra di ation inte n s ity and cata ra ct incid e n c e may be due to confoun ding factors that prevent accura te asse ssm en t of UV exposure withi n th epopulatio n. Such confo und ing factors inclu de chan ges in b ehavi o r (i.e., wea r i ng prote ctive su n g la sses o ractively avoiding the sun) or ch ang es in demog ra ph ics (i.e., travel or relo catio n ) . For exam pl e, the actual cumul a tive UV exposu r e o fsomeon e who h a s mov ed to a new state may not be well rep r e s ent e d by the average d UVradiation val ues in thei r new loc a tion. De spite this, i tmay be possible to di scern differen c e sin popul ation-based effect s if state UV radiation intensity valu es be com e av ailable in a more di sa ggregated form. If additional rese arch o rdata becom e available, this health effect can b e re con s ide r ed fo r de velopment of a dose-re spo n se relation ship and inclusion in the AHEF.

*** Peer Revi ewe d Final Report (Ap r il 2 4, 2006) ** * – 76 –