UNITED ST ATES E N VI RON M E N T A L PR OTE C TION AGE NCY WASHING T O N D. C. 20460

OFFI CE OF THE A D M I N ISTR A T OR SCI E NCE A D VI SORY BO A RD July 11, 2008

EPA-COUNCIL-08-001

The Honorable Stephen L. Johnson Adm i nistra tor U.S. Environm ental Protection Agency 1200 Pennsylvania Avenue, N.W

. Washington, D.C. 20460

Subject: Benefits of Reducing Be nzene Em issions in Houston, 1990-2020

Dear Adm i nistrator Johnson:

The Advisory Council on Clean Air Comp liance Analysis (C ouncil) was asked to review a recent case study on the benefits of reducing be nzen e em issions. The “Air Toxics Case Study – Health Benefits of Benzene Reductions in Houston, 1990-2020” was conducted by EPA’s Office of Air and Radi ation (OAR) as part of its “812 Study,” an ongoing comp rehensive analysis of the total costs and tota l benefits of Clean Air Act Am end m ent (CAAA) program s. As recogni zed by OAR, the challenges for assessing progress in health improvem e nt as a result of reductions in em issi ons of hazardous air pollutants (HAPs) are daunting. Accordingly, EPA has been unable to adequately assess the econom ic benefits associated with hea lth improvem e nts from HAP reductions due to a lack of exposure-response functions, uncertainties in em issions inventories and background levels, the difficulty of extrapolat ing risk estim ates to low doses and the challenges of tracking health progress for diseas es, such as cancer, th at have long latency periods. Benzene, however, has a large epid em iological database which OAR used to estim ate the health benefits of benzene reductions due to CAAA controls. The Council was asked to consider whether this case study provides a basis for determ ining the value of such an exercise for HAP benefits characterization nationwide. The Council,

augm ented with m e m b ers of its Hea lth Ef f

ect s Subcomm ittee, provides detailed adv i ce in the enclosed Advisory with highlights summarized below.

The benzene case study estim ated the health benefits from an average reduction in benzene con centration s of 1 μg/ m 3 in the Houston area. Those health benefits were estim ated as 9 avoided leukem i a cases in th e Houston area during the study period (1990 through 202 0). Althoug h uncertainties and om itted factors co uld raise or lower this estim ate, we believe it is more likely to undere stim ate the health benefits. First, the study did not incorporate epidem iological data poi n ting to assoc i atio ns between benzene and other types of cancers. Second, because of the latency period associated with cancer, the health benefits of reduced benzene em issi ons through the 2020 term inus for the studywould extend beyond 2020. Third, the case stud y did not include the impact of the Mobile Source Air Toxics rule prom ulgated in 2006. Fourth, additi onal health benefits

accru i ng to individuals living in homes with attached garages were not in cluded in the base estim a te. (OAR acknowledged these lim itations in the case study.) Fifth, the estim ate did not incorporate an integrated approach for pr ojections of population change,

econom ic activity, or em issions. Nonethele ss, the Council lauds th e OAR’s air quality and exposure m odeling, the life table appro ach for estim a ting health benefits and supplem ental analyses of individuals in high-exposure environm ents. Given these achievem e nts, the case study offers a reasonable, if qualified, estim ate of health benefits.

For future studies, the Council urges gr eater attention to the discrepancies between em issions inv e n t ories and th e rea lity of monitored c oncentr ation s and to a r i c h er discuss i on o f the relative importance of sources o f uncertain ty . Recent air quality stud ies for southeast Texas indicate that industrial inventories underestim ate actual em issions by a factor of two or m o re for som e hydrocar bons. These inventory unde r-predictions have been attributed to m i ssing sources and under-p rediction of inventoried sources. Since there is a system ic under-predi ction of observed benzene con centrations in this case study, by a factor of two or m o re in som e loca tions, it is possible that the inventory used in this work significantly under-estim ated em issions. W e do not know whether reductions under CAAA would apply to th e m i ssing sources, but we urge greater attention to this issue, given that accurate em issions invento r ies are a critical n e ed for a plethora of policy purposes.

The benzene case study successfully synthe sized best practices and implem ented the standard dam a ge function approach to es tim ating the benefits of reduced benzene, however the Council is not optim istic that th e approach can be repeated on a national scale or extended to m a ny of the other 187 ai r toxics due to insufficient epidem iological data. W ith som e exceptions, it is no t lik ely th at the other 187 HAPs will have the quantitative exposure-response data needed for such anal ysis. Given EPA’s lim ited resources to evaluate a large num ber of HAPs individually, the Council urges EPA to consider alternative approaches to estim a te the be nef its of air toxics regulations. W e underscore the National Research Council’s call for m o re integrat ed m u lti-pollutant approaches (National Research Council, Air Quality Management in the United States, 2004). One example of s u ch an approach is OAR’s Risk and Technology Review (RTR)

program that seeks to evaluate air toxics risk by source category. Econom ies of scale

m a y also be found in the em erging 3D air qua lity m odeling work th at can now include individual air toxics so that HAPs need not be modeled separately.

iiDetailed recomm endations are in clud ed in the enclosed Advisory. On behalf of the entire C ouncil, we appreciate this opportun ity to provide tim ely advice to the Agency. We hope these comm ent s are helpful to the Office of Air and Radiation as it proceeds with this importan t work.

S i n c e r e l y ,

/Signed/

Jam e s K. Hammitt, Chair Advisory Council on Clean Air Compliance A n a l y s i s

Enclosures

iii

### U.S. Environmental Protection Agency Advisory Council on Clean Air Compli ance Anal ysis Review of the Benzene Air T oxics Health Benefits Case Study

CHAIR

Dr. James K. Hammitt , Professor, Center for Risk Analysis, Harvard University, Boston, MA

COUNCIL MEMBERS

Dr. David T. Allen , Gertz Regents Professor in Che m ical Engineering, Departm e nt of Che m ical Engineering, University of Texas, Austin, TX

Dr. Dallas Burtraw , Senior Fellow

, Resources for the Future, W a shington, DC

Dr. Shelby Ger k ing , Professor of Econom ics, Departm e nt of Econom ics, College of Business Adm i nistration, University of Central Florida, Orlando, FL.

Dr. Wayne Gray , Professor, Departm e nt of Econom i cs, Clark University, W o rcester, MA.

Dr. F. Reed Johnson , Senior Fellow and Principal Econom ist, RTI Health Solutions, Research Triangle Institute, Research Triangle Park, NC.

Dr. Katherine Kie l , Associate Professor, Departm e nt of Econom i cs, College of the Holy Cross, W o rc ester, MA.

Dr. Virginia McConnell , Senior Fellow and Professor of Econom i cs, Resources for the Future, W a s h ington, DC.

Dr. Bart Ostro , Chief, Air Pollution Epidem iology Unit, Office of Environm ental Health Hazard Assessm ent, California Environm ental Protection Agency, Oakland, CA

Dr. David Popp , Associate Professor of Public Adm i nistration, Center for Policy Research, T h e Maxwell School, Syracuse University, Syracuse, NY

Dr. Chris Walcek , Senior Research Scientist, At m o spheric Sciences Research Center, State University of New York, Albany, NY

ivHEALTH EFFECTS SUBCOM MITTEE MEMBERS

Mr. J. Fintan Hurley , Research Directo r , Ins t itute of Occupational Medicine (IOM), Edinburgh, United Kingdom

, UK.

Dr. Patric k Kinney , Ass o ciate Professor, Departm e nt of Environm ental Health Sciences, Mailm an School of Public Health , Colum b ia University, New York, NY.

Dr. Michael T. Kleinman , Professor, Departm e nt of Community & Environm ental Medicine, University of Calif ornia, Irvine, Irvine, CA.

Dr. Morton Lippmann , Professor, Nelson Institute of Environm ental Medicine, New York University School of Medicine, Tuxedo, NY.

Dr. Rebecca Parkin , A ssociate Dean and Professor, Environm ental and Occupational Health, School of Public Health and Health Services, The George W a shington University Medical Center, W a shington, DC.

SCIENCE ADVISORY BOARD STAFF

Dr. Holly S t allw orth, Designated Federal Officer, Scien ce Advisory Board Staff Office, Environm ental Protection Agency, Washington, DC.

vNOTICE

This report has been written as part of the activ ities of the U.S. Environmental P r otection Agency’s Advisory Council on Clean Air Comp liance Analysis (Council), a federal advisory co mmittee ad m i nistrativ ely located un der the EPA Science Ad visory Board (SAB) Staff Office. The Council is chartered to provide extramural scientific inform ation and advice to the Adm i nistrator and other offici als of the EPA. The Council is structured to provide balanced, expert assessm ent of scie ntif ic m a tters r e lated to is su e and proble m s facing the A g ency. This report h a s n o t been reviewed for approval by the Agency and, hence, the contents of th is repo rt do not n ecess a rily repres ent the views and policies o f the EPA, nor of other agencies in the Execu tive Branch of the Federal governm e nt, nor does m e ntion of trade nam e s or comm ercial products constitute a recommendation for use. Council reports are pos ted on the SAB W e b site at: http://www.epa.gov/sab

.

vi

### Advisory Council on Clean Air Compli ance Anal ysis Advisory on Health Benefits of Benz e ne Reductions in Houston, 1990-2020

1. General Review . Please commen t o n the valid ity, re liability and utility of this ca se study and whether it achieved its p u rpos e in co ntributing to the science of estimating the benefits of reduced concentr ations of air toxics. Specifically, please

commen t on EPA’s data choices and charac terization of results given th ese data choices; EP A’s method ologica l cho i ces made for analyzing the data; an d implications this case study may have for fu ture analyses.

Overall, the Council concludes that this case st udy is of high quality and is in large part clearly docum ented. It provides a reasonabl y comp rehensive estim ate of the prim ary health benefits of reductions in benzene em i ssions in the Hou s ton area ass o ciated with regulations enacted pursuant to the 1990 Clean Air Act Am endm ents.

The case study uses a dam a ge-function appro ach that follows a linear cau sal chain: regulations induce reductions in benzene em issions that lead to lower am bient concentrations and reduced hum an exposure to benzene, which lead s to s m aller cancer risk to Houston-area residents, a welf are impr ovem e nt that can be quantified in m onetary

term s. A strength of this approach is that it follo ws a logical causal chain and that relevant data (notably em i ssion inventories and estim ates of the relationship between exposure and health effects based on hum a n-epidem iological data ) are available to characterize each link in the chain. (Indeed, benzene was selected for the case study because of the availability of relevan t data. ) This approach is more difficult to apply to pollutants f o r which exp o sure- r espo nse f unction s are less-we ll suited to e s tim ating th e probability of health effects, e ithe rb ecause the function m u st be extrapolated from anim al data (in which case the function m a y be intentionally biased upward so as to yield

an overes t im ate of risk) or becaus e there ar e only estim ates of reference concentratio ns (or other possible thresholds) th at p r o v ide little in f o rm ati on on how the probability of a health response changes with respect to changes in exposure.

The case study relies on detailed, fine-scale m o deling to estim ate changes in am bient concentrations and hum a n exposure to benzene. This fine-scale m odeling is valuable for pollutants that exhibit sh arp spatial and temporal gradients, but is resource-intensive. It is not clear that a national-scale assessm e nt of the benefits of benzene regulation using this detailed m o deling would be cost-justified, m u ch less the national scale benefits of detailed m o deling for many of the other 187 air toxics identif ied by Congress in 1990. The Council encourages EPA to consider how to use a less intensive approach to obtain an alternative estim a te of the benefits of benzene regulation in th e Houston area and to

comp are the results of th e simp ler ap proach with the resu lts re ported in the curren t cas e study. (One possible approach w ould be to use a reduced-for m re-analysis using National Air Toxics Assessm ent concentratio n data, as su ggested in th e la st paragraph of the Executive S u mm ary.)

1The Council notes several lim itations of th e case study. First (unlike the m a in Section 812 analysis), the case study does not rely on an integrated projection of future population, econom ic activity, and em issions. In the case study, econom ic activity (and with it, benzene em issions in the wit hout-CAAA case) is projected to increase substantially, but the populat ion is held constant at its 2000 level and distribution. Sim ilarly, v e hicle m iles traveled (V MT) are p r o j ected to grow but with no change in road infrastructure. Moreover, there is no fee dback between regulation, air quality and behavior. For example, regulations that incr eas e vehicle costs coul d slow the rate of introduction of new vehicles (increasing em issions); improved air quality could alter residential patterns and the tim ing and lo cation of outdoor recreational and other activities (and hence exposure). M a ny of these effects are lik e ly to be qu antitatively unimportant, but the Council suggests that EP A consider which, if any, m a y be i mportant and discuss them

.

Second, the Council suggests it would be m o re useful to report the expected num ber and monetary value of averted fatal and non-fata l leuk em ia cases by the yea r in which they would have arisen, rather than reporting onl y th e cum u lative totals that g r ow over tim e simp ly because health b e nefits are incurre d each year (see, e.g., Table E S -1). Averted

cases are m o re approp riately con ceiv e d as a flow rather than a stock. From this perspective, benefits would be repo rted as “death s avoided per year o rper decade”. Th e fact that the expected nu mber of cases per year may be less than one poses no conceptual di ffi cul t y (t hi s fact appears t o be one reason EPA presents cu mulative rather than ann u al values). Moreover, the Council suggests that EPA include in its base-case results the cases averted by lower benzene exposures th rough 2020, even if these cases would have arisen after 2020 (i.e., the cases described in the subsection “Expected Total Benefits” on pp. 3-9 – 3-10). (The Council does not object to reporting totals over the period evaluated as a supplem ent to the annual values.)

This case w a s selected in large p a rt because of the availability of relevan t data on exposure-response relationships, em issions, and atm o spheric concentrations. The case study serves as proof-of-concep t that benefits of CAAA re gulations can be reasonably quantified f o r at least one air toxic and one location, but the appro ach clearly cannot be repeated with this level of detail for m a ny of the other 187 toxics, or even for benzene at national scale. The Council enc ourages EPA to consider what simp ler approaches could be used to estim ate the benefits of air-toxic regulation and to use th e results of this cas e study as one point of comparison for evalua ting the results of si mp ler approaches.

2. Emissions Estima tions . EPA developed a benz ene emissio n s inventory for the thr e e counties in the case stud y, based on EP A’s National Emissions Invento ry (NEI), MOBILE6.2 model, an d NONROAD 2004 m odel. Please comment on this approach to emiss i ons estima tion.

EPA used current best practi ces and approaches to estim ating em issions for this case study. The resulting inventory i ndicates that in the base year, industrial sources of benzene are substantial. As the with - and without-CAAA sce n arios are applied,

2substantial reductions in benzene em issions occu r in all source catego r ies, but the reductions are especially larg e in the industrial sector.

Because ind u strial em iss i ons are a significant fraction of both the base cas e invento r y and the projected em ission reductions, it is importa nt to characterize th e uncer tain ties in th ese

em issions. Recent air quality s t udies d one in south east Texas (the first and second Texas Air Quality Studies, Tex AQS I and II) ind i ca te th at for som e hydrocarbon em issions, industrial inventories underestim ate actual em issions by a factor of two or m o re (Ryerson, et al., 2003; K l eim a n, et al., 2002; Allen and Durrenberger, 2003). These inventory under-predictions have been attributed to m i ssing sources and underpredictions of inventoried sources.

Observed benzene concentrations are evidently u nder-predicted on averag e in th is cas e study, with predicted values less than half the observed value at a significant fraction of monitors (p. 3-7). This under-estim ation m a y reflect underestim ates of e m issions in the

em issions inventory. If so, th e effect of this underestim at e on estim ated benefits of regulation depends on whether the underest im ated em ission sources are af fected by CAAA regulations or not: if th ey are not affected, failing to in clude th em i n the m odel

m a y have little ef f

ect on the change in benzene c oncentr ation s and on hea lth benef i ts; if

em issions from these so urces are red u ced by th e CAAA regulations, be ne f its are like l y to be underestim ated.

Improve m e nts in the em ission inventory are lik ely to continue, and if future studies continue to employ best current em ission inventory practices, the quality of the estim ated concentration changes is likely to improve. At present, however, current inform ation and the m odel perform a nce as comp ared w ith observation suggest a system ic underprediction of benzene em issions. In the repo rt, a summary judgm e nt is offered that

m i ssing inform ation and general uncertainty is lik ely to lead to an unde r-representation of

em issions (p. 4-2).

The goals of the case study in clude identif ica tion of lim itatio ns and gaps in the da ta needed for analysis and provision of an estim a te of the uncerta inties. There is som e effort to discuss uncertainty, but it is not treat ed in an integrated fashion. The Council encourages a stronger narrative about the re lative importance of di fferent sources of uncertainty and som e discussion about how un certainty could be better addressed in future analy s is.

It is not clear exactly what requirem ents of the CAAA o f 199 0 are included in the analys is. In each subsection of the em issions appendix, thes e could be s p ecified. For the mobile source section, for example, it is not cl ear which regulations affect benzene levels and why. The I/M rules, ATP program s, and fuels program s appear to reduce benzene, but the details of how they do so are not clear.

In addition to these general observations, the Council offers som e specific comments:

31. Temp erature and hum idity play a role in em issions from som e sources. By 2020, clim ate m a y be expected to cause g r ea ter em issions on a given projection of

econom ic activity. Is this inco rporated in the analysis? 2. A hierarchy of sources for em issions da ta for the base years 1990 and 2002 is given. In som e cases inform ation from th ese various sources is contrasted, but there is no system atic evaluation of the (dis)harm ony of inform ation across these

sources. 3. The personal transportation m odel accounts for turnover of the vehicle fleet. Does this include technological changes that would affect evaporative em issions? 4. It appears that a factor accounts for the entry of ne w nonroad vehicles into the

m a rket by accounting for sales. Does th is account for changes in use and retirem e nt of existing vehicles, and for techno log i cal d i fferences ? 5. Have industrial leak detection and repair reductions, that are part of the T e xas State Implem entation Plan for attaini ng the ozone NAAQS, been incorporated into the with-CAAA e m i ssion scenarios? 6. Appendix A p. 41: “The differences in the distribution am ong the top categories could be the result of the [sic] including facilities as point so urces in the 1996 NTI inventory that were aggregated and re ported as nonpoint s ources in the 2002 NEI.” This is easily verified. Does the sum of point and non-point em issions from each data so urce ag ree? 7. Appendix A pp. 66-68: Are the VMT figures for 2002 and 2009 linked to projections of energy prices in the D O E study used earlier in the appendix? That is, are the a s sump tions interna lly co nsisten t ? If energy prices incre a se, V M T should respond. Note that the CAAA m a y a ffect gasoline prices (e.g., by requiring reform ulated gasoline). Do these differen ces in g a solin e price s lead to hig h er VMT in the without-CAAA scenarios? 8. Estim ating em issions bottom

- up from a pl ant-level inventory seem s reasonable, and the detailed discussion of the process in the Pechan repo rt showed th at the inventory was carefully constructed. Th e projections to 2020 based on estim a tes of industry growth seem less convin cing. Given that a few industries are responsible for the bulk of the em issions , it m i ght be worthwhile to pay extra

atten tion to modeling th eir gr owth, or considering the im pact of different growth

assump tions.

3. Air Quality Modeling a n d Exposure Modeling. EPA used the America n Meteorological Society/US EP A Re gulatory Model (AERMOD) to estimate changes in ambien t concentrations and the Ha zardous Air Pollutant Exposure Model (HAPEM6) to estimate individual exposures to benzene levels. Please comment on this approach.

The use of AERMOD to estim ate changes in co ncentration s seem s appropria te. W h i l e more sophisticated m o dels could be utili zed, there are good reasons to use plum e-scale models that can resolve i ndividual plum es under conditions when s m all-scale local sources are important in de fining areas of m a xi mum i mpact. The HAPEM6 m odel was exercised appropriately and the results appear sound.

4However, the relatively large discrepancy between m odel calculat ions and monitored benzene concentrations at specific sites sugge sts som e serious defici encies in either the

em issions inventory (discussed in response to question 2) or the AERMOD plum e m o del configuration. The addition of a somewhat arbitrary “background” to the AERMOD calculations using am biguous m e thods alleviat es only a portion of the discrepancy. T h ere

m a y be a bias introduced in AERMOD by th e tre a tm ent of calcula tions d u ring ca lm periods, and a suggestion is m a de below to address this shortcom ing. However, the Council suspects that calculated differenc es in concentration due to the C AAA regulations are prob ably estim ated more reas onably than absolute concentrations (i.e., sim ilar errors in estim a ting absolute concentr atio ns are likely to occur in the with- and without-regulation cases and to roughly offset one another when calculating the difference).

Background concentrations : The m e thod by which “background” concentrations are specified is am biguous. It appears that som e percentile of the cum u lative monitored benzene concentrations is used. If backgr ound concentrations are added to calculated benzene con centration s when comp aring m on itor e d and calcu lated c oncentrations, they should be set using m onitored concentrations com i ng from s p ecific wind directions. For

example, “background” concentrations probabl y occur m o re frequently when winds are

com i ng from a southerly direction (winds bl owing from the Gulf of Mexico towards land). Furtherm ore, the “background” should possibly be wind-direct ion dependent. That is, when winds are out of the north, monitore d concentrations in the northern portion of the dom ain should be used as a background. Sim ilar procedures could be used for easterly or westerly winds.

Calm periods : There is a probable shortcom ing re lated to using AERMOD during calm periods, which is when high concentrations occur. A significant reason for the serious under-calculation of benzene c oncentrations m a y pertain to results from the assum e d “steady state” plum es inhere nt in the AERMOD form ulati on, coupled with the error of ignoring calm periods. Ignori ng calm periods will s e riously underweight periods when the greatest concentrations occur, thus re sulting in poten tia lly la rge underestim a tes of calcu lated concentration s . Therefore, concen trations m u st be estim ated during calm periods. In earlier EPA plum e m odel s , calm winds were set to a m i ni m u m value (e.g., 1

m

/ s), and directions were random ly set or se t to the last or next reported non-calm wind direction. T h ese m e thods of treating calm s s hould be imple m ented in this study. If 1 m / s winds were assum e d during calm periods, signif icant impacts would be calculated in the vicin ity of point sources, especially f o r su rface so urces (as op posed to stack em issions) that a r e pr es um ably sign if icant in the be nzene inventory for the Houston area. During calm s , e m issions build up at the source lo cation and subsequently affect downwind

recep tors w h en the win d resum e s. Hence the C ouncil reco mmends that calm perio d s be modeled by setting wind speed to a m i ni m u m va lue (e.g., 1 m / s, 1.5 m / s ) and wind direc tion be set to the next reported wind direction. Thus if m e asurable winds com e from the north for one hour, followed by three hours of calm

, then m easurable winds from the west, the calm should be m odeled as a west erly wind at the selected m i nimum speed.

Reference meteorology : In a coas tal setting such as Houston where land -sea breezes dom i nate local circulations, the physical loca tio n of a m e teor ological m onitor will

5strongly influence AERMOD calcu lations. It is noted that only two surface m onitors (IAH and HOU airports) are read ily available and the IAH monitor shows fewer m i ssing or calm reports. Missing reports should be us ed as a prim ary gauge of m o nitor quality and m onitors or years with the fewest m i ssing observations would be pref erred. The Council suggests that there ar e probably a large num b er of wind and m e teorological observations available in the greater Houston m e tropolitan area that should be evaluated for use in this study.

The existing study contains a m i nor ambiguity relating to the choice of the year for which

m e teorology observations are used as input to the different emission scenarios considered by AERMOD. Different years’ m e teorology wi ll yield different concentration changes resulting from e m ission changes. Changes in concentrations between 1990 and 2000 that result only f r om differences in m e teorology are not attributable to CAAA regulations, and should n o t be high lig hted. An addition a l uncertainty aris es since 201 0 and 2020

m e teorology is not available for sim u lating co ncentrations in thos e years. T h e Council feels that it is desirable to use as m u ch m e teorological inform ation as computationally feasible. Mu ltip le years of m e teorology from mu ltip le m onitors shou ld be used for all the

em ission scenarios. Ann u al average concentrat ions could then be either averaged or appropriately statistically m e rged to obtai n single values for each receptor. At a mi n i mu m, both the 1990 and 2000 meteorology should be used for all em ission scenarios and then m e rged. The ca lcula tion of concentratio n changes f o r m u ltiple ye ars’ of

m e teorology could potentially pr ovide a basis for describi ng uncertainty and bounds for sensitivity studies that might be con s idered in the future.

The analysis should attempt to estimate the expected value (and distribution) of changes in benzene c oncentr ation s rathe r th an the re alized value in an y pa rticular year, which depends on realized m e teorological conditions. The years referred to in the study concern the em issions during that y ear, not the m e teorology. For comparing calculated and monitored concentrations, it is appropriate to use m e teorological data corresponding to the m onitored tim e period.

4. Life table approach for health benefits . Please commen t on EPA’s life table approach for estimating health benefits, specifically addressi ng the following:

• EPA’s selection of leukemia as the primary health endpoint;

• EPA’s use of weighted, cumulative expos ure measures in the life table risk model to account for th e cessation lag in the re alization of benefits fo llowing benzene exp o sure reductions;

• EPA’s inter p retation of the litera tu re on latency and cessation lag for benzene-ind u ced leukemia;

• EPA’s choice of a linear dose-response function;

• EPA’s sensitiv ity analys es of the pr imary benef i ts estima te ( i .e., choic e of epidemiological cohort study, the hea lth endpoints of all leukemia versus acute myelogenous leukemia, the lag le ngth, and the exposure values u s ed); and

• EPA’s choice not to apply an adjustment for ex posure to benzene in ear l y life.

6The Council commends EPA’s use of a life-ta ble approach in this case study. This approach has the potential to m o re accurate ly represent the eff ects of environm ental health risk s on m o rtality than the co mmonly used attributable-death approach that provides little inform ation on the tim i ng of deaths and does not reflect competing mortality risks. The case study shows that it is possible to perfor m an evidence-based analysis of the health benefits of reduced benzene em issions. The Council suggests that it would be helpful to have a short description of the life-table approach th at is accessib l e to non-specialist readers before repor ting details of the analysis.

Leukem i a as prim ary health endpoint: The Council supports the use of all leukem i as as the pr im ary health endp oint and s e n s itiv ity an alyses using A M L. W e recomm end that the criteria used to judge the wei ght of the evidence be m o re clearly indicated, perhaps in a table.

Noting that EPA’s assessm e nt of the health literature finished in 2005, we suggest that

more recen t evidence on non-Hodgkins Lympho m a (NHL) and benzene exposure (S m ith et al., 2007; Steinm aus et al., 2008 ) be assessed and, if appropria te, used to calculate risks and benefits that would supplem ent those for leukem ia.

Som e studies suggest other health ef fects, but the evidence is weak at this tim e. Two topics for possible consideration are:

(i) A Danish study of children’s traffic-rela ted exposures, Raaschou-Ni elsen et al. (2001), found a near-significant 25% in creased risk of lymphom a s (p for trend = 0.06) for a doubling of the concentration of benzene during pregnancy.

(ii) A study suggesting that benzene is car diotoxic and arrhythm ogenic. Kotseva and Popov (1998) found that systolic and diastolic blood pressure s and the prevalence of arterial hypertension were signi ficantly higher in workers exposed to benzene than in control groups. There was a si gn ificant correlation between the length of service and increased systolic and diastolic blood pressu re, after controlling fo r m a jor cardiov ascu lar risk factors. Note that hype rtens i on m a y predict cardiac m o rtal ity, as accepted for lead in the Section 812 retrospective analysis.

Interp re tatio n of the liter atur e on latency and cessation lag: We agree with EPA that th e evidence m a inly concerns latency, not cessati on lag, but that “infor m a tion about latency can also help inform our estim ate for a cessation lag.” The C ouncil suggests that EPA provide a clearer explanati on about how knowledge of late ncy can inform judgm e nt about cess a tion lag, i.e., better expos iti on of the rationale and model.

The review of evidence on latency, and on the relevant tim e -window of e xposure (summ a rized in Appendix C, Exhibit 4), appears to be comp rehensive. In particular, it is useful to see evidence th at the h i ghes t ri sk s were associa t ed w ith r e la tive l y recen t exposures and that, in general, “distant” exposur es had little identifi able effect. W e agree with the view expressed in Appendix C of the case study (p.17) that the evidence as

7reviewed “p oints to a lag structure where a ne w steady-state risk leve l is reached within 15 years following a regulatory change” and th a t it is approp riate to incorp orate this inte rpre tatio n into th e be nef its ana l ys is. W e note an additional paper that provides general support for this evaluation: Richardson (2008) found the greatest risks in the 10 years imm e diately after exposure, a sm aller increase in the period 10-19 years after exposure, and no evidence of increased risk at 20 years or more after exposure.

Use of weighted, cum u lative exposu r e m easures to accoun t for cessation lag: W e believe this m e thod is appropriate for application with the linear dose-response m odel. However the implications of its use with nonlinear concentration-response m odels require evaluation.

Choice of a linear dose-response function: W e support using a linear exposure-response (E-R) function for the prim ary analyses and encourage EPA to provide a more comp l e te discussion of the m e rits of alternative E-R f unctions and their implications for estim ated benefits.

Based on th e Pliofilm cohort analys es, a linear (multiplicativ e) exposure-response function, as used, is reasonable and argua bly best. However Crump (1994, 1996) found suggestiv e though not comp elling (0.05 < p < 0.1) eviden ce to support an intens ity

dependent quadratic m o del when using the Pa ustenbach et al. exposure estim ates which Crump (1996) evaluated as the best available at that tim e . Questions have been raised about the Paustenbach et al. (1992) reanalys is of the Pliofilm cohort data; e.g., the exposure estim a tes, concurrent exposures, in consistent assumptions and calculations, estim ates of derm al absorption, and lack of control for confounding (e.g., Utterback and Rinksy, 1995). Addition a lly, Crump’s convers ion of the Pliofilm data to all age groups added uncertainty to his estim ates.

While m a intaining a linear (Pliofilm cohor t) or supralinear (Chinese cohort) multiplicativ e m odel as its first cho i ce, EPA should consider u s ing the qu adratic intensity-dependent results fo r sensitivity analyses – for m e thodological reasons, because the non-linear m odel would be m o re difficult to implem e nt; and for substantive reasons, because Cru m p (1996, Table 6) sugg ests it would lead to m u ch lower estim ates of the benefits of reducing benzene em issions; i.e., the choice of E-R m odel is likely to be one of

the m o st inf l uentia l steps in th e be nef its ana l ys is.

As noted in EPA’s benefits analysis, (Appendix C, pp. 12-13), it is unclear whether lack of a clear effect at low exposures reflects neg ligible risk or lack of study power (or both). In this context, the use of a no-threshold m odel for the prim ary analyses is reasonable and consistent w ith EPA’s Guidelines for Carc inogen Risk Assessm e nt, which favor linear extrapo l atio n to low dos es in th e face of un certainty becaus e it is h ealth-p rotective. Nevertheless, for this analysis, where a be st estim ate is the goal, EPA should review evidence of a possible th reshold and decide whether sensitivit y analyses using a threshold model are w a rran t ed. Fo r example, recent detail ed re-ex a m i nation by M i ller at al. (20 05) of three nested case-control studies noted that the case-control study of Glass et al. (2003)

8showed clear evidence of incr eased risks above 16 ppm

- y ears, but not otherwise and found that the other two studies were consistent with this finding.

Sensitiv ity a n alyses of th e prim ary benef its es tim ate: W e appreciate tha t se nsitiv ity analyses were done on several issues where m o re than one good analytical option was available. T h ese were us eful in iden tifying which issues in practice m a ke a difference to the final estim a te of avoi ded cases.

Choice not to apply an adjustm e nt fo r exposure to benzene in early life: The Council supports EP A’s decision not to apply an adjust m e nt for exposure to benzene in early life. A search of recen t literature by one m e mber did not find any new evidence to requ ire inclusion of exposure to benzene in early life.

5. Valuation . Please comment on EPA’s approach to assigning economic value to avoided cases of leukemia, both fatal and non- fatal, with s p ecif i c re fere nce to:

• EPA’s use of a “pre-mortality morbid ity” supplement to VSL for fata l leukemias;

• EPA’s development of a unit value for a no n-fatal case of leukemia bas ed on current literature and previous SAB advice; and

• EPA’s choice not to include a “cancer premiu m,” consis te nt with the SAB Environmental Economics Advisory Committee (EEAC) p a nel in 2001

Overall, EP A has m a de good choices in valui ng the reduced risks of leukem ia, both fatal and non-fatal, in the case study.

“Pre-m ortality m o rbidity” supplem ent: The Council approves of EPA’s us e of a “premortality morbidity” supplem ent to VSL for fatal leukem i as although it notes that this supplem ent m a y be justified for reasons ot her than the one given. Based on prior SA B guidance, E P A adds an estim ate of the m e dical costs asso ciated with cancer prio r to death. This value is interpreted as a cost-of- illn e ss m easure of the patien t ’ s lost wellbeing during the period while he/she is su ffering from cancer. However, the m e dical costs are clearly part of the social cost of can cer and so should be incl uded as part of the social value of preventing leukem i a, even if there was no private loss in w e ll-being from

cancer m o rbidity. In principle, the component of m e dical co sts born by the individual is incorporated in conventional estim ates of VS L, but given the m a gnit ude of m e dical costs for cancer (estim ated as $110,000 in this case), m o st individuals could not pay them and a large part of these costs m u st be paid by others (through public or private insurance

program s).

Note also that if a large component of m e dical costs ar e paid by public sources, the rationale for claim i ng that these costs undere stim ate the loss in we ll-being experienced by the ind i vidu al collapse s. The tota l lo ss su ffered by an individual includes his private financial costs (e.g., m e dical expenses and lo st incom e ) and the di rect utility loss associated w ith dis ease. The private cost of il lnes s clearly und erestim a tes this total, as it excludes the utility loss. However, there is no necessary relationship betw een the value of

9publicly paid m e dical costs and pr iva t e utility los s – the va lu e of the utility loss to the individual could be m u c h larg e ror much sm alle r than the va lue of the pu blic ly paid

m e dical costs.

This argum ent implies th at it is app r o p ria t e to include m e dical costs in v a lu ing f a tal leukem i as, but doing so is unlikely to ade quately incorporate the loss in w e ll-being associated w ith pre-m o rtality m o rbidity. Im proved m easures of the value of the premortality morbidity req u ire add i tion al prim ary research.

Unit value f o r non-fatal leukem i a: The empirical literature on valuation of non-fatal health effects, and specif i cally non-fatal can cers, is extrem ely lim ited. In the absence of

more recen t estim ates using current-practice m e thods, the Magat et al. (1 996) study o f risk-risk tradeoffs between non-fatal lym phom a and autom o bile-acciden t fatality is cited as a basis for the value of non-fatal lymphom a

. One of the lim itations of this study is that the la tency a ssocia t ed with lymphoma was not specified and so it is uncertain what

assump tions respondents m a de about the ti m i ng of lymphom a . In addition to known lim itations of the original study, we are conc erned about how transf erable the value for non-fatal ly mphom a is for leukem i a. The use of a value for chronic bron chitis as an estim ate of the value of non-fatal le u k em ia clearly lacks face valid ity.

The Council suggests that EPA provide m o re inform ation on the duration and severity of non-fatal leukem ia, non-fatal lymphom a , a nd chronic bronchitis for evaluating the possibility of transferring benefit estim a tes from these other conditions. Severity could be described by symp tom s , or perhaps using stan d a r d health -re la ted quality o f lif e (HRQL) indexes suc h as the Eur o QoL EQ-5D, Short Form SF-6D, or the He alth Utilitie s Ind e x (HUI). (Some Council mem b ers are skeptical of the ability of these generic m easures to

accura tely m easure severity, noting that rela tiv e severity of different co nditions can depend on the index used for m easurem ent.) If the severity and duration of both non-fatal lymphom a a nd chronic bronchitis are sim ilar to those of non-fatal leukem i a, the use of the m onetary values for these other conditions w ould be supported. Sim i larly, if either non-fatal ly mphom a or chronic b r on chitis is m o re sim ilar to the targ et co ndition (non

fatal leukemia), m o re weight could be put on the value of the corresponding health condition. In addition, the Council recommends that the EPA exam ine the valuation literature for other illn ess e s with sim i lar se verity and duration. A lthough th e EPA appears to believe non-fatal leukem ia is a chroni c condition, studies of leukem i a survivors m a y indicate th at transfers of high-quality acute-condi tion values are m o re appropriate.

Cancer prem ium : VSL estim ates derived from wage-ri sk studies m a y provide a biased estim ate of population willingn ess to pay for reduc ing the risk of fatal cancers b e cause of differences in the nature of the risks being valu ed and characteristics of the affected population. The possibility of environm entally induced cance r m a y evoke special fears and be seen as a m o re involuntary, u n cont ro llab l e hazard than workplace acciden t s. Environm entally induced cancers may have a long latency period wher eas accident risks

are m o re imm e diate. In its 2000 guidance, the SA B Environmental Econom i cs Advisory Comm ittee concluded that the empi rical literatu re was insuf f icient to support the use of an alternative VSL for e nvironm entally i nduced cancer. The Council does not disagree

10with this conclusion, but notes that the relevant literature is growing and encourages EPA to review this issue. Recentrelevant studies in clude: Van Houtven, Sullivan, and Dockins ( 2008), who find that WTP to reduce cancer risk with a five year latency period is three tim es larger than W T P to reduce cu rrent au tom obile -acc ident risks (the c a n cer pr em ium falls when longer latency periods are cons idered ); Hamm itt and Liu (20 04), who find weakly significant evidence that W T P to reduce cancer risk is about one -third larger than WTP to reduce risk of an otherwise sim ilar non-cancer disease in a contingent valuation study in Taiwan; and Tsuge, Kishimoto, and Takeuchi (2005), who identif y a sm all but significant preference for avoi ding cancer risks in Japan. In addition, Sunstein (2005) argues that VSL figures should be m a de risk-specific and sugge sts that the va lue of a life saved from cancer is at least twice th e valu e of a life saved from a workplace acciden t.

Studies that have elicited ri sk-risk and benefit-risk trad eoffs have quantified relative tolerances f o r different mortality risks. In a stud y of hor m o ne-rep l acem ent therapy, Johnson et al. (2007) found that women were 2 1⁄2 tim es m o re tolerant of heart-attack mortality ris k than of breast-cancer mortality risk. In a general-popul ation survey of older adults, Haub er et al. (in p r ess) found that respond ents were willing to accep t a risk of dying from s t roke as high as 30% in return for a treatm e nt that preven ted Alzheim e r’s disease from progressing beyond the m ild stage.

The possibility of a VSL cancer prem ium is worth additional study and the benzene case study could be improved by incorporating m o re di scussion of this issue in light of recent research. The Council agrees that although these studies point to eventual VSL adjustm e nts for cancer and other kinds of hea lth risks, there is no scaling factor that policy m a kers can use today. Such adjustm e nts would presum ably vary with latency, type of health condition, and possibl y other factors. Differences in the duration and the pain and suf f

e ring rela ted to the illness an d trea tm ent m a y partly explain differences in riskspecific VSL. A cancer prem ium pr esum ably w ould include such m o rbidity values, since they are inseparable from the m o rtality risk a nd should in principle b e incorporated in the willingn ess- to-pay da ta used to cons truc t the VS L.

The Council recomm ends that the EP A evalua te the implicit cancer prem ium assum e d by includ ing a pre-m o rtality m o rbidity value. The report could indicate, or at least speculate on, how large the cancer prem ium m i ght be base d on estim ates provided in the literature. Clearly this recognizes an a dditional source of uncertainty in the valuation, and that uncertainty should be clearly discussed.

6. Analyses of Individuals in High-Exposure Environments . We conducted three supplemental analyses of CAAA- related impacts to Houston residents anticipated to have higher than average benzene exposur es due to their location:1) residents living in cen sus tracts with high modele d exposu r es; 2) residents living n e ar roadways; and 3) residents living in homes with attached garages. Please comment on the data and methodologica l cho i ces fo r thes e analyses w ith spec if ic r e ferenc e to:

11• EPA’s choices regarding the most useful high exposure scenarios to evaluate; and

• EPA’s overall approach to valuing risk reductions using VSL, which does not account specifically for individuals who may have a higher than average baseline mortality risk due to high expos ures to multiple HAPs and (as stated above in the question on a possible cancer premium) does not apply adjustmen t s to account for the characteri stics o f

the HAP risks being reduced.

The case s t u dy dem onstrates that it is feasible to e s tim ate redu ctions in hea lth r i sk to subpopulations having higher-than-average be nzene exposure, though the significance of the heterogeneity of effects is not well explained. Using a linear exposure-response function, the total risk is unaffected by the distribution of exposure changes in the population. The report would benefit from disc ussion of what “high exposure” m eans in the context of this case study, where the indivi dual lifetim e risk is less than about one-inten thousand.

The Council notes that the proportional diffe rences in risk reduction for populations in high- and m e dian-exposure environm ents app ear rather m odest: the estim a ted risk reduction for residents o f the two census tracts with the h i g h est exposu r e in each of three counties range between 72 and 98 percent, a fa ctor of 1.1 to 1.5 larger than the average risk reduction across the three counties of 65 percent. Near-road effects are som e what larger, averaging a factor of 1.5 larger than in the absence of near-r oad effects for the ten

census tracts with the h i g h est on-road -rela ted b e n zene concen trations and reach ing a factor of two for one tract. Effects of att ached garages m a y be m u ch larger: accounting for these m i ght increase estim ated benefits of the CAAA regulations by 20 to 100 percent. The Council suggests that improved data on the exposure effects of attached

garages m a y be relatively easy to collect (though beyond the scope of the case study) and

m a y have a significan t effect on the estim ated regulatory benefits of benzene and perhaps other air toxics with significant in-g arage sources. Council mem b ers found the

assump tion that each v e h i cle idles for five m i nutes in the g a rage before an d after each trip surprisingly large (p. E- 4). If this assump tion significantly affects the exposure estim ates, it should be better supported and perhaps m odi fied.

The Council notes that the risk calculat ions for the analysis of high-exposure subpopulations are qualitatively different from the calculations for the main analysis. While the main analysis uses a life-table approach in which risks depend on exposure over a defined tim e period on the order of ten years, the high-exposur e analysis calculates lifetim e cancer risk using an attributable-deaths approach assum i ng lifetim e exposure at a constant level and that lifetim e risk is pr oportional to lifetim e exposure. It would be worth highlighting this distin ction an d comp aring estim ates from the two approaches for the total population.

The Council suggests that indivi duals living near sources that have relatively frequent upset conditions (i.e., transient high-em ission episodes due to equipm ent failure or other causes) m a y constitute another high-exposur e subpopulation. W h ile the total em issions

m a y be only a sm all percentage of the total population-wide exposure, the local residents

12m a y be at much higher risk. The Council encourages EPA to consider whether a subpopulation of this type can be identified, and whether its risk is likely to substantially exceed th at of the general populatio n.

Another source of high exposure that is not considered in the case study includes indoor sources such as cigarette sm oking and em issi ons from consum er products. These sources often cause greater benzene e xposures than those from outdoor sources, as discussed in the reports of the RIOPA studies, includi ng one conducted in Houston. While the CAAA imposed no controls on such indoor sources, the increased proporti on of the residual benzene exposure risk attributable to them should be included in the report’s discussion section to provide an overa ll public health context.

W ith regard to VSL, questions con c e r ning wheth e r and how the valua tion of m o rtality risk should be adjusted to account for char acteristics of the risk (e.g., cancer comp ared with workplace acciden t; degree of voluntarin es s and contro llability) are d i scussed in response to question 5. W ith regard to the highe r baseline risk for individuals exposed to multiple HA Ps, the Council no tes th at in theo ry VSL should be larg er for otherwise sim ilar indiv i duals with higher tota l mortalit y risk (the so-called “dead-anyway” effect; Pratt and Zeckhauser, 1996). However, this effect depends on the m a gnitude of total mortality risk, and the difference in total risk attributable to HAP exposure is unlikely to be quantitatively important (e.g., annual risks for highly exposed indi viduals in the case study are on the order of one per m illion while total annual mortality risk is on the o r d e r of one per thousand or more, depending on age).

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