# Chapter 1 0 Assessing Air Quali ty: Monitoring

Table of Contents

10.1 Introduction

............ ............ ............ ............ ............ ...... 1
10.2 Air Toxics Monitoring: R ecent Advances

............ ............ ............ ...... 2
10.3 Monitori

ng for Air Toxi cs Risk

Assess ments : Why Monitor?

............ ............ . . 4
10.4 Planning for Air Toxics Monitoring

............ ............ ............ ........... 9 10.4.1 General Planning Approach

............ ............ ............ .......... 10 10.4.2 Specific Planning I ssues
............ ............ ............ ............ 14
10.5 Implem

enting Air Toxics Monitoring

............ ............ ............ ......... 19 10.5.1 Locati ng Monit ors and Select ing Sample Size

............ ............ ....... 19 10.5.1.1 Locatin

g Moni tors

............ ............ ............ .... 19 10.5.1.2 Selec ting Sampl e Size

............ ............ ............ . 24 10.5.1.3 Setting Up a Mon itoring/Sampling

Program

............ ........ 25 10.5.2 Data Analysis and Reporting

............ ............ ............ ......... 28 10.5.3 The Use of Monitoring D ata to Calculate Exposure Concentrations

............ . . 29
10.6 Monitori

ng Met hods, Te chnolog

ies, and Costs

............ ............ ............ . 31 10.6.1 Ambient Air Mon itoring

............ ............ ............ ............ 33 10.6.2 Samp ling C osts

............ ............ ............ ............ ....... 35
10.7 Archiv

ing Air Toxics

Monitor ing D ata

............ ............ ............ ....... 35
10.8 Using Air Monitoring Data to Evaluate

Source Contribution

............ ............ . . 37 References

............ ............ ............ ............ ............ ............ 38April 2004 Page 1 0-1 10.1 Introduction In environmental investigations, the ter m “monitoring” describes the collection of actual sa mples of envir onmental media an d then subj ecting

those sam

ples (usu ally ) to chem ical ana lys is to determine the identity and c oncentration of the various pollutants in the sample. A distinction may also be made

between sampling (i.e., stack testing) a nd monitoring (i.e., for ambient concen trations) . In a

ir toxics risk assessment, this process c ommonly consists o f collecting air samples and either evaluating the samples at the monitoring station itself, or sending them to a laboratory f

or evaluation. For air toxics risk assessments, monitoring and analysis can help determine the concentration of both those pollutants in air and those that have migrated into other media, such as soil, water , sediments, and biota. This chapter discusses the use of monitoring to evaluate pollutants in air. Chapter 19 discusses the use of monitoring in media other than air. Many aspec

ts of a monitoring program will depend on the spatial scale of the assessment being supporte d by

the meas urement progra m:

• Micro- scale

– highly localized region s up to 100 me

ters in size ; these mig

ht refle ct city blocks or individua

l househol ds.

• Middle-sc ale – regions of seve ral blocks with size s of 100 to 5 00 meter s.

• Neighborho od-scale

– an extend

ed area with uniform land us e (and, hence, r elativel y homogeneous receptor population), extending up to sever al kilometers in size.

• Urban-sc ale – overall city or c ounty conditions, perhaps up to 50 km in size.

• Regional-

or national-scale

– a state, several states, or the entire nation. Air toxics risk assessments often examine exposure to relatively larg e numbers of people over relative ly la

rge g eogra

phic are as (e.g ., a neig

hborhood or urban area, c ounty , or larg er). I n these instances, the risk managers and ana lysts must carefully

use their planning and scoping activities to develo p the questions they

want to an swer and identify ing the t ype s of data

they will need to answer those questions. For some questions and data needs, monitoring is the pr eferred tool for estimating inhalation exposure concentrations for air toxics risk assessment, either as the primary way of de termining concentrations in air or as a way to test and normalize model results (and look for gaps in the emissions inventory ). This chapter provides an overview of monitoring, including recent advances by

EPA (Section 10.2); the reasons for monitoring (Section 10.3); how to plan a monitoring prog ram (Section 10.4); implementation (Section 10.5); available air monitoring methods (Section 10.6); archiving monitoring data (Section 10.7); and using monitoring data to eva luate source contribution (Section 10.8).April 2004 Page 1 0-2 EPA’s Ambient Monitoring T echnology Informat ion Center (AMTIC) AMTIC ( htt p:/ /www .epa .gov/ ttn /amt ic/ welc ome.h tml ) is centered around the exchange of am bient monitoring related inform

ation. Establi shed in

1991 a s an ele ctronic Bulletin Board System (BB S), AMTIC has e volv ed with changing tech

nology into a page on the World Wide Web. A MTIC i s opera

ted by EPA’s Office of Air Quality Planning

and Standards (OA QPS) through the Monitoring and Qua

lity Assurance Gro

up (MQAG). AMTIC contains information on all the Reference and Equiv alent m

ethods for the Criteria

polluta nts, th e toxic organics (TO) Methods for air toxics and other noncriteria pollutant methodologies, Fede ral Regulations pertaining to am

bient monitoring , ambient m onitoring quality assurance/quality control (QA/QC) related information, inform

ation on ambient monitoring re lated publications, ambie

nt monitoring

news, field and laboratory studies of in

terest, and updates on any new or developing E

PA Ambient Air standards. 10.2 Air Toxics Monitoring: Recent Advances EPA recently published a draft National Air Toxics Monitoring Strategythat describes the structure of the national air toxics monitoring progra m, includin

g its histo ry , status, and expected produ

cts. (1) At the start of the program, EPA’s focus was on “nationa lly p ervasiv e” prior

ity pollutants. In recent y

ears, EPA has initiated local scale monitoring studies to address potential air toxics problem areas. EPA’s air toxics monitoring is structured into four groups – national level, local scale, persistent bioaccumulative toxics (PBTs), and “other” EPA-specific progra ms.

• The National Air Toxics Trends Syst em (NATT S) prog

ram is a network of monitoring stations at 22 urban or rura l location s across

the count ry (see Exhib it 10-1). The foc us for the se sites is on seven “nationa lly p ervasiv e” prior

ity p ollutants ( formalde

hyd e, arse nic, chr omium, benzene, 1,3-butadiene, acrolein, and light absor bing carbon). All of the stations are expected to become operational in early 2004.

• Local scale monitoring studies a re designed to complement NATTS, but theyare shorterterm (less than 2 ye ars) and have more flexible study requirements to go bey

ond the scope of the NATTS. Local-level studies provide inf ormation of urban/local interest that is not achieva ble with a single monitoring

site at a c ity. For examp le, these studies ma

y a ddress specific source categor

ies or better characterize pollutant levels associated w ith different locations in a metropolitan area. EPA plans to implement 10 to 15 local scale monitoring project s that are

implement

ed by state, loc al, and tr

ibal (S/L /T) air p ollution control ag encies.

• Progra

ms that moni

tor atmospheric de

position of

PBTs incl

ude (1) t he Nation al Atmospheric Deposition Program – Mercury Deposition Network (NADP – MDN), a multi-agency program with approximately 90 monitoring sites; (2) the Integ rated Atmospheric Deposition Network (IADN) , a partnership between EPA and Canada, which is mea suring PBTs in the Great Lakes Reg

ion; and (3) the National Dioxin Air Monitoring Network (NDAMN), a 30- site research program.

• A variety of

EPA Regional air toxics monitoring activities that existed prior to NATTS continue.April 2004 Page 1 0-3

Exhibit 10-1. National Air Tox ics Trends Stations (N

ATTS) Sites

January 2003 Sta rtup January 2004 Sta rtup Pilot Programs Providence, RI Roxbur y, MA New York, NY Washington, DC Decatur (Atlan ta), GA Hazard, KY* Detroit , MI Deer Park (Hou ston

), TX St. Louis, MO Bountiful, UT Grand Juncti on, CO* San Jose, CA Seattle, WA Chit tenden C ount

y, V T* Rochester, NY Tampa, FL Chesterfiel d, SC* Chic ago, IL Mayville, WI Harrison Coun

ty, TX* Phoenix, AZ La Grande, OR* Barcelona/San Juan, PR Providence, RI Keeney Knob, W V* Tampa, FL Detroit , MI Rio Rancho, NM Ceda r Rap ids , IA San Jacinto, CA Grand Juncti on, CO* Seattle, WA

* rural site Source : EPA’s Latest F

indings on Nat ional A ir Qual

ity (2)

EPA has encouraged a sig nificant effort over the past few y ears to increase reporting of air toxics sampling results to EPA’s AirData database website ( http://www .epa.g ov/air/da ta ). For example, the Lake Michiga

n Air Directors Consortium (LADCO), the Nor theast States for Coordinated Air Use Management (NESCAUM), and the Califor nia Air Resources Board (CARB) “ mined” existing data from approximately 300 existing monitoring sites ac ross the U.S. to provide information about the spatial pattern, temporal profile, and ge neral characteristics of air toxics compounds. EPA collected additional data for this analysis from a yea r long monitoring study carrie d out in fou

r urban a reas an d six smaller

city /rural a reas. A number of

reports , newsletters, and related documents describing EPA’s air toxics monitoring eff orts are available at EPA’s Ambient Monitoring Technology Information Center website. (3)April 2004 Page 1 0-4 10.3 Monitoring for Air Toxics Risk Assessments: Why Monitor? Air toxics programs have long used monitoring to evaluate the concentration of chemicals in air. In gener al, monitoring (sampling and analy sis) results may help:

• Identify

and estimate current exposures to ambient concentrations of air toxics (outdoor and/or indoor) at a specific location of conce rn (e.g., a school or neighbor hood). As an example, EPA track s ozone con centra tions at nu

merous lo cations a

round the country , with results a vailable over the Inte rnet (http://www .epa.g ov/airnow/) for many location s, virtua

lly in real- time. As a

nother example, air

toxics monitoring ca

n be used to evalua te the impa

cts of a specific source on a near by rece ptor (“source-oriented” monitoring).

• Develop or refine values for specif ic parameters needed by

air dispersion models (for example, study-specific re lease data, meteorological conditions).

• Validate the predictions of a model in specified circumstance s (e.g. validate that the location of highe st exposure

predict ed by the model

is corre ct, which

increa ses conf

idence t hat a maximally exposed subpopulation has been identified – may be difficult to do without a very dense monitoring network).

• Track trends in air quality levels (e.g. to determine whether a ir pollution programs have generally been effective at reducing exposures).

• Ide ntify gaps in e missions inv entorie s (e.g. , monitorin g identi fies an a

irborne chemica l that is not reported in existing emissions inventories) or close gaps that might be pre sent in existing data (e.g., concentrations of spec ific air toxics in specific releases).

• De te rm in e c om pl ia nc e w it h a ir to xi cs le ga l r eq ui re me nt s ( e. g. , p er mi t l im it s a t a fa ct or y, emissions limitations on motor vehicles).

• Gather d

ata in sup port of e nforce ment acti ons. Ultimately, the choice of

whether to monitor or model (or both) depends on the goals of the assessment, the exposure setting, other specific project circ umstances (e.g., many

communities want monit

oring a s part of

a risk as sessment) , and the assessing entity . For exa

mple, to understa nd the expos

ure an a ctual ind ividual re ceives a s they

move abou

t their da ily a ctivities , personal monitoring is the best option because it reflects the patter n of this movement. However, such studies are rarely

done outside of research settings. As another example, compliance with a permitted release rate may also require monitoring as the preferr ed method of measurement. Exhi bit 1 0-2 pr ovid es a bri ef comp ariso n of mo delin g versu s mon itori ng.April 2004 Page 1 0-5

Exhibit 10-2. Comparison of Modeling an d Monitoring Approa ches for Estimating Ambient Air E xposure Conc entrations (ECs) Modeling Monitoring M ode ling is relative ly fast and in exp ensive com pare d to mon itoring. M any scre ening-le vel mo dels ca n be r un in spreadsheet formats and require rel atively simple input parameters . Many disper sion models , along with techni cal reference manuals and other support documents, are avail able f

or free download fr om EPA ’s Support Center for R egulato ry Air M ode ls (SC RA M ) web site ( htt p:// www.epa.g ov/tt

n/scr am /). Resources n ormally need to be e xpended t o enhan ce t he l ocal air toxi cs emi ssi on invent ories t o make air t oxics modeling more preci se. W ith monitori ng, it takes ti me to build data, and there are methodol ogical limi ts and l ogist ical iss ues. How expens ive monit orin g is depends on what you are try ing to do and how much you are willi ng to pay. Monitoring does not always requir e equipment pur chase, and some st ates a nd local areas alread y have equip ment. Som e less exp ensive mon itoring tec hniqu es are now a vailab le (e.g., pa ssive sam plers)

. Mo deling result s can estimate concentr ations over a large spati al area

(e.g. , a 50-km r adius f rom a source) and can pro vide a “big p icture” view o f the asses smen t area. M ode ling also allows fo r analysis o f EC at multip le po ints thr oughout

the ass essme nt a rea. The downs ide of modeling, however, is t hat t hese are predict ed concen trations. Monitoring r esult s provide ac tual measured concentr ations . Multi ple locat ions maybe requir ed to charac teri ze concentr ations over an are a, alt hough Geographi c Inf

ormat ion Sy ste ms (GIS) metho ds facilitate interpo lation b etwee n loca tions. The downsi de is tha t t

he moni tori ng maynot be represent ativ e of a l arge geogra phic area

. Screen ing -l evel models can pr ovide a predi cted est imat e of wheth er significa nt con centra tions are likely. A sim ple screening ana lysis may be sufficient to make a risk manage ment decis ion t hat no act ion i s re quir ed. Mo nitoring can be used to i dentify and measure exp osure s for sp ecific ind ividua ls at a spe cific locati on of concern

(e.g. , a sch ool). Thi s data can provide a quick screen to determine whether more exte nsi ve moni tori ng i s nee ded. Mo dels can be used to identif y areas where maximum concentrations are l ikely t o occur, and thus where to focus eff orts f or additi onal t iers of th e asses sment. Uncertai nti es in model parameters and the discrete divi sion of the wi nd field used in mod els (often with only eight wind directions) can r esul t i n in corre ct i denti fi cati on of t he l ocati ons of maximum concentration. Monitor ing can i denti fy areas and act ual leve ls of exp osure occu rring at the mon itoring sites

. Monitor ing can al so be us ed to i ndica te t he poi nt of maxima l ex posure if

the monit orin g is desi gned f or that purpose. The selection of t he monitoring locations is criti cal; if

placed in the wrong locations, monit ors can provide incorrect and mislea ding info rmatio n abo ut max imal ex pos ures. Models can

be use d to i denti fy the subs et of chemi cals of potenti al concer

n (COPCs) and exposure pathways/ routes that have t he gr eate st contr ibut ion t o ris k. Thi s canbe helpful i n focusing eff

orts for additi onal tiers of the assessment as we ll as determining app ropriate risk manage ment actions. Mo nitoring can be used to confi rm signif icant exposure pat

hways a nd routes . (Measured concentr ations can be compared to r isk- based scree ning lev els). It also c an be used to iden tify compounds tha t may

not h ave bee n sus pecte d and, henc e, were not inclu ded in mo dels (i.e., monitoring allows i dentifi cation of gaps in t he emissions invent ory). M ode ls allow “w hat if” sce nario s to be evalua ted (e .g., what if a permitted emission were doubled?). M onitoring can o nly evaluate current co nditions.
Mo re complex mod eling may all ow explicit predicti ons and esti mates of

varia bili ty i n exposure. A larg e num ber o f samp les gene rally is need ed to chara cterize variab ility; this may be pro hibitively expensiv e. Monitori ng, howeve r, provi des a dire ct and reliable mea ns to ch aracte rize va riability. M ode ls often us e simp lifying assum ption s and data in puts that ma y or m ay not b e rep resen tative of the spec ific assess ment area. This in troduces uncert aint y int o model predictions. Mo nitoring can be used to confi rm actual exposure levels, to investiga te assum ption s or ca librate models to s it e-s pecif

ic c ondit ions , an d to cl ose ga ps in data , redu cing un certain ties.April 2004 Page 1 0-6 Air toxics r

isk asses sments, however, tend to exa mine pote ntial exposu

res to ha zardous a ir pollutants (HAPs) a nd other a

ir toxics for a rela

tively large number of

people ov er rela

tively large geog raphic a reas (e .g., a neighborhood or u rban ar ea, cou nty , or larg er). I

n these in stances , the risk managers and analy sts must carefully use their pla nning and scoping activities to develop the quest ions they want to an swer an

d to identif

y the ty pes of da ta they will need to answe r those questions . For some

questions

and data needs, monitoring

is the pre ferre d tool. For others , modeling is better . In g eneral , most air t oxics risk a ssessmen

ts will benefit from some combination of both modeling and monitoring to provide the depth and breadth of inf ormation that will be

necessa ry to answe r the ass essment q

uestions ( see hy

pothetic al example

in Exhibit 10-3).

Exhibit 10-3. Hypothetical Example of a Combined Modeling and Monitoring Program

This fig ure illu strates a

hypo thetical

set of i sopleth s for an

nual av erage air conc entrations that a dispers ion m

odel pre dicted, assum

ing a single

source (black dot) ne ar the c enter of

the g eogra

phic region. Note th

at the model pred

icts the point of m

aximal exposure to

be somewhe

re within the area bounded by g

rid points 2, 4, S1 , and S3, based on the existing inform ation on release rate, win d direction, and effectiv

e releas e heig

ht. I n this h ypoth etical e xamp le, a m onitorin g stat ion was used to measure am

bient concentrations as a m eans of evaluating

the model pred

ictions. Note that the monitoring lo cation is not in the area of estimated hig hest concentration and, therefore, migh t not provide a better estim

ate of maxim

um exposure. Inde ed, most a

ir toxics risk assessments that e valuate exposures

to populat

ions rec eiving i

mpacts from one or more s ources should ge nerally consider using mod

eling a s their pr

imary tool to evaluate and characterize exposures and r isks. In certain instances, asse ssors may use monitoring as the primary tool to eva luate exposure concentrations for potentially exposed populations. The utility of modeling for neig hborhood and larger scale a nalyses is that it provides a better picture of the variation of exposure conditions over the assessment area domain (i.e ., modeling provides spatial resolution) and allows a more straightforwar d approach to source allocation (i.e., what portion of the risk is caused by each of the modeled sources).April 2004 Page 1 0-7 Monitoring, on the other hand, only provides estimates of concentrations at the point at which samples a re take n, and it is

often dif ficult to c learly

define t he spatia l covera ge that those measured concentrations represent. I n addition, it is often difficult to use monitoring data for source a llocation (espec ially

for che micals em

itted by numerous

sources ). Monitor

ing play s a crucial role in identify

ing important chemicals that the emissions inventories may not have capture d. In

rarer instance s, assess ors can u se monitor ing as the primar

y to ol to eval

uate exposures for potentially exposed populations; however, this method car ries a corresponding increa se in the u

ncerta inty of the re sults (see

Section 10 .4 on how to

use ambie

nt monitori

ng data to develo p estimate s of exposur

e conce ntration) . (Note that, in limite d circum stances , geostatistical techniques such as kriging are sometimes applied to estimate concentration variation betwee

n a set of monitors. T

his topic is

bey ond the sc ope of thi s refer ence ma nual; however , assessors are e

ncouraged to car

efully consider the unce rtaintie s associa

ted with th

is type of appr

oach and whether alternate tools, such as air dispersion modeling, would provide a bette r unde rstan ding o f conce ntrat ion gra dient s acros s the s tudy area. In addit ion, the av erage concentration of atmospheric pollutants across a study area is sometimes estimated by averaging the res ults of all the mo nito rs in t he area. Howev er, si nce po llut ant co ncent ratio n can ch ange rapidly acr

oss space and time, combining data across monitors may “average out” ver

y important information about exposure at a particular monitoring location. I t is for this reason that combining

data across monitors is not commonly perfor med and a ssessors

are enc ourag e to carefully consider the pros and cons of attempting such an ana lysis. I f monitors are combined, the results should, nevertheless, be reported alongside the results of each of the individual monitors.)
If a ssessors

make the choice t o implemen

t a monitor

ing prog ram, it is im

portant to caref ully design the sampling and analy sis approach to provide meaningful input into the risk manage ment decision . Beca

use sampl ing and

analy sis are r elativel y expensive a

nd time consuming, a welldesigned monitoring program c an ensure the efficient use of resour ces. Well designed and implemented monitoring programs quantify not only the concentra

tions but also information related to the associated data uncertainty . The study-spec ific conceptual model and analysis plan that assessors develop during the planning and sc oping phase help ensure a well-desig ned sampling and analy sis program that will yie ld results suitable for decision-making purposes. Monitoring programs are commonly designed to:

• Use a sa

mpling me

thodolog y that result s in scien

tifically defensi ble data and that meets regulatory

criteria or other concerns – it is important to utilize methodologies that are scientif ically defensi ble and a ccepta ble within

a regu latory context;

• Ide ntify and quan tify air toxics ( or their breakdow n produc ts) of inte

rest with respect to contribution to risk in all media of interest (including, in some cases, non-air media; see Chapter 19);

• Attain quantitation requirements (e.g., quantitation limits) sufficient to compare to doseresponse values ( e.g., t

he sensit ivity should be s

ufficie nt to allow

reliable measure ments below concentrations anticipated to produce adver se health effects);

• Demonstrate acceptable confidenc e in the data set to be used for decision-making ba sed on qu al it y as su ra nc e b en ch ma rk s i nc lu di ng be nc hm ar ks fo r p re ci si on , a cc ur ac y, representativeness, completeness, and compar ability; andApril 2004 Page 1 0-8 Quality Assurance Project Plan (QA PP) and Data Quality Objectives (D QO) Process As Chapter 6 introduc ed, a QAPP is part of the overall risk assessmen t analysis plan that e

nsures the quality of data used

in decisions. Gene

rally included in the data quality prog ram is the DQ O process, which e stablishes the criteria t hat m ust be m et if dat a are to meet the nee ds of a decisio n-m aker ( e.g. it establishes the error bo

unds on data, w hich are related in turn to the u ncertain ties a d ecision -maker, can tolerate in reaching

a defensible decision). Assessors can accom plish this goal throug

h the following sev en steps: (a)
1. Stat e the prob lem. 2. Identify the decision to be m ade. 3. Iden tify in puts to the dec ision (i .e., w hich da ta are n eeded). 4. Define the stud

y bou ndaries (i.e., what fa

ctors, s cenario s, etc. , will b e includ ed in the study

to produce these d ata). 5. Devel op a de cision r

ule (i.e ., how

the dat a will r elate to a speci fic deci sion to be ma de). 6. Specify

limit s on de cision e

rrors (i. e., ho w mu

ch unce rtainty can exi st and s till allow a def ensible decisio n to be made ). 7. Optimize the

design of the stu

dy to ensure the data quality m eets the decision rule. The QAPP specifies precisely how to collect and analyze the d ata to meet the g

oals established by the DQO process. The QAPP establishes spec ific procedures that assessors fo llow to meet D QOs. These DQOs include proced

ures for identifying

reliable methods, choosing sam ple locations and frequencies, h andling

samp les, cal ibration

of equi pmen t, recor ding a nd arch iving of data , and a

nalysi s of the data. The DQO goal is to ensure that all members of the project te am understand, and foll ow, procedures that will ensure the results of the study meet the data q

uality needs of a d

ecision. Once these D QOs ha

ve bee n establ ished, it is nec essary to develop a p lan as t

o how t he parti cipants

will meet the m in pract ice whi le col lecti ng the dat a for th e study.

(a) U.S. Environ

mental Protection A

gency (EPA

). 1994. Guidance for the Da ta Quality Objectives Process , EPA QA/G-

4, Office of Research and Developm ent, EPA/600/R- 96/055; available at http://ww.epa.gov

/swerust1/cat/epaqag4.p df

. • Provide f

or a cle ar and un

ambiguo us data va

lidation a nd repor ting meth odology so monitoring

results c an be tra

cked, ve rified, a nd valida ted when they are use d in decis ions. The desig n of a moni

toring progra

m that mee

ts data qu ality o bjective s (DQO ) and quality assurance project p lan (QA

PP) require

ments dep ends on the

answer s to four q

uestions:
1. What is the

risk mana gemen t decisio n to be m ade, and how will as sessors us e monitoring results in that decision? Monitoring programs ty pically are

a component of risk assessments that support risk management decisions; these decisions normally focus on how best to reduce risks from exposure to air toxics through reducing or otherwise limiting emissions.a A sen sitivity analysis sho ws the re lative effec t of unce rtainty in ea ch asp ect of a n assess men t on the o verall

uncert aint

y in that asses sment. Ideall y the data qual ity objecti ves wi ll be more stri ngent f or those measurement s that play a l arger r ole inthe f inal decisi on, si nce narrowi ng the u ncerta inty in these measurement s si gnif icant ly r educes uncertainty

associated with the decision. April 2004 Page 1 0-9 2. How accura

tely m ust the re sults be to be useful in the se decisi ons? The reliability of monitoring program results must be adequate for the needs of the risk management dec ision. For examp le, risk a ssessors need to qu

antify air conc entrations and/or exposures

within some bounds of accuracy

and/or precision. It is important to meet these cr iteria of accuracy

and precisio n, but not ne cessar

ily t o exceed t

hem. As no ted in App endix H, the data qua lity objectives must provide results that allow reliable decision-making. Howeve r, resources that participants devote to one aspect of a monitoring prog ram, such as choosing a large r number of sampling sites, will draw resources away from another aspect of the progr am, such as sampling for a larger number of air toxics. This is why it is essential to understand fully the decision that the given set o f results will suppor

t, other r esults tha t assessors will ne ed to support that same d

ecision, and how pa

rticipan ts can ba lance monitoring

results a cross the se different data needs to reduce the le vels of uncertainty

to acceptable levels. Assessors can achieve this goal by

conducting a sensitivit

y analysis (a) , which determines what aspects of a full monitoring pr

ogram w ill requir e the gr eatest a ttention a nd resour ces; monit

oring r esults that play the most significa nt role in a decision may r equire the greatest allocation of resourc es. 3. What methodologies are available to monitor at a particular level of quality? The choice of monitoring method depends on the specific air toxic(s) to be analyzed, the objective of the monitoring (as the DQOs specified), the time over which a result is to apply, and availab le resou

rces. I t is importa

nt to note here tha

t there d o not curr ently exist valid methods ( either f

ield, lab , or both)for a la

rge nu mber of c hemicals that may be of inte

rest; for methods that do exist, the achievable sensitivity may not match the DQOs (this is another reason that modeling is often used as the primary decision making tool since these issues are irrelevant to models).
4. What r esour ces are avai labl e for t he moni torin g prog ram? The choice of a monitoring strategy often depends primarily

on available resources (e.g ., time, money). These f actors are of particular concern in air toxics monitoring because most studies of chronic exposure gener ally require a minimum of

one full y

ear of d ata to characte

rize chronic exposur

e. I t is not uncommon to have a lag time of two y ears or more from the beginning of a monitoring st ud y to a f

in al re po rt wh en on e c on si de rs th e t im e i t t ak es to pl an th e m on it or in g s tu dy, obtain ac cess to la

nd, build the monitori

ng struc tures, r un the stud

y, a naly ze the samp les, validate the resu lts, and wr

ite the da ta repor t.
10.4 Planning for

Air Toxic s Monitor

ing As noted above, planning is a critical part of any air toxics monitoring program. The discussion of planning below first describes a rec ommended general approac h (Section 10.4.1) and then outlines several specific planning issues (Section 10.4.2). EPA has deve loped resources that provide a dditional d

etails on o

peratin g proc edures, with discu ssions of d ata qual ity i ssues, definitions, and a

pplications to spec ific methodologie s. (4)April 2004 Page 10-10 Examples of Study-Sp ecific Questions

• What is the maxim

um plausible v

alue of EC for the population in a geographic region, taking into acc ount spatial and tem poral variab ilit y and unce rtain ty?

• What is the location of this m aximal value within the geog

raphic region?

• Which air toxics are found a t the highest concentrations with respect totheir doserespo nse valu es (e.g. , which a ir tox ics have the gr eatest p otential

to prod uce a ha zard quotien t above one)?

• Do monitoring

results generally agree or disagree with the v

alue of air concentrations identified by existing

models? 10.4.1 General Planning Approach Planning an air to xics monitor

ing prog ram involves a step -wise int egrat ion of sam

pling pr otocols with data quality criteri a and da

ta analy sis proce sses that a

re consi stent with

the study -specif ic concep tual mode l (CM), QA PP, and DQO

process es. Althou

gh pre sented st

ep-wise , the proc ess is actually iterative, a

nd decisions at one step may r equire verification or modification of assumption s or deci sions made at previ ous steps.
1. Understand the problem . As noted above, a ssessors

may design m onitoring progra ms to support a number of

differe nt ty pes of ma nageme nt decisions. For r

isk asses sments, the

CM can focus participants’ understanding of both the scope and the breadth of the problem that the sampling and analy sis are to address. The most important qu estions to answer immediately are: whe

ther assessors will use monitoring results to characterize exposure and risk, whether they

will use results to evaluate air quality model performance and look for gaps in t he emissions inventory , or whet her they will use r

esults for both rea sons. This i s a critical question for participants to answer , becau se the da ta needs can be d rastica lly different, depending on how the assessors will use the monitoring data.
2. Identify existing data . Sampling and analysis for risk a ssessment may not be necessary if the information to be developed is already

available from other sources and meet s the qual ity requirements for decision making. The data sources discussed in Chapter 4 may provide s ufficie nt informa

tion for the risk management decision.
3. Itemi ze data ne eds . Where existing da ta are insufficient to answer the study-specific que stions, it will be necess ary to obt ain ne w data throu gh monitoring . Potentia

l data ne eds inclu

de: filling gaps in emissions inventory data; providing input data for models and validatin g modeli ng resu lts; generating

new data to more fu

lly c haract erize expo sures in areas, populations, or pathways; establishing trends over time; or supplementing a body of data to increase their quality for the risk management decision. The proc ess for itemizing data needs includes articulating critical de cision criteria (which may drive data quality ne eds and/or selection of specific methods), apply ing these criteria to determine are as where existing data are insufficient, and identify ing the manner in which new data can supple ment existing data to meet the decision criteria. I n many way

s, the identification and enumeration of data needs acts a bridg e betwe en the conceptua l model an

d the DQOprocess
. 4. Define data

quality nee ds . The reliability (e.g ., accuracy and precision) of monitoring results must be adequate to meet the needs of the risk manag ement decision. However, given finite resources, even well-designe d studies may not be able to ac hieve all quality criter

ia. That limitation makes it important to determine which criteria are essential for addr essing theApril 2004 Page 10-11 study -specif ic decis ion proble

m and for focusing resourc es on mee

ting (a nd not nec essarily exceeding) those criteria. The DQO p

rocess d etermine s gene

ral data quality objectiv es to meet specifi c needs. This process can be informed b oth by

a well spe cified d

ecision s tatement

and by a sensitiv

ity analy sis to dete rmine whi ch aspec

ts of a fu ll monitorin

g prog ram will r equire t he gre atest attention and reso urces to support that decis ion. I dentific ation of d ata qual ity n

eeds at t his level is targeted on the specific problem identified in Step 1, but is independent of the specific methods to be applied. It is important to base data quality criteria a

t this step on what is re quired to

answer the probl em identif

ied in Step

1, not on impressions

of best available analy

tical methods, approaches used in the past, or consideration of questions that might be of general scientific inter est but are not of direct use in the decision problem. A common ap

proach i s to consid

er all as pects of sample a nd data ha

ndling f rom colle ction to data re port w riti ng, as t hese af fect t he con fiden ce wit h whic h deci sion s can b e made t hrough th e i nt ro du ct io n o f r an do m o r s yst em ic er ro rs . A nu mb er of fa ct or s a ff

ec t d at a q ua li ty, including bias related to sampling error (e .g., taking only a single sample at one location, which may or may

not be representative of actual ambient conc entrations) and relative precisio n relate d to analy

sis methods

. 5. Select m onitoring m ethods to m eet data quality nee ds . The choice of monitoring method depends on the scale of the assessment, specific conta minant(s) to be analyzed, the sa mpling time over which the

result is d

erived ( e.g., a

sample c ollected

over 15 mi

nutes versus a sample collected over 24 hours), the decision criteria or other reporting limit needs, and the resources available (see Section 10.3). Me thodologies include the sampling methods and techniques, sampling program desig n (i.e, sampling frequency, coverage, and density ), as well as a naly tical met hods. The

data qua lity needs ide ntified in Step 4 represent the total da ta quality requireme

nts of all aspects of the sampling and analy sis process necessary

to support risk-ba sed deci sion-mak

ing. The refore , evalua

tion of all aspects of samplin

g and a naly sis with respect to data quality needs is necessary

for proper method selection. The QAPP process involves balancing decisions for method selec tion to meet data and quality needs. Selection of the methods for both sampling and data analy sis defines the approach and defines what is termed the measurement quality objectives . Althoug h there i s a natura l tendenc y to select sa mpling a nd analy sis methods

based on previous data, it is important that the benefit of consistency and likely improved compar ability are not outweighed by

data gaps that Step 3 identified. For example, in a risk assessment for chlorina ted volat ile solven ts, the pr esence of fluor inated vo latile sol

vents may

cause assessors to overestimate chlorinated concentrations due to ana lytical interfer

ences. The method selection generally takes into account the known or suspected presence of other chemicals having similar toxic effects, sy mptoms, and mechanisms, and/or that which otherwise may a

ffect sampling and analy

sis results. To take this into account, the study may require adding chemicals to the targ et analyte list, selecting a method where these compounds are not potential interferents, or limiting the scope of the study with stated assumptions about contributions from these undefined factors (e.g ., stating only that the mea sured concentration is the sum of a defined set of analy tes and not applicable to any one analyte

in the mixture).April 2004 Page 10-12

Detection Limits and L imits of Quantitation The detectio n limit

is the minim

um concentration that an analyst can reliably expected to fin d (i.e., detect) in a sam

ple, if it is pre sent. F or any giv en me thod (e. g., t

he me thod to analy ze for volat ile organ ic com pounds [VOCs] in air), this lim

it is est ablishe d in each lab for each i nstrum ent and is called the method detectio n limit or MDL . An MD L of 1μ g/m 3 , indicates that a field sam ple that contain s 1 μg

/m 3 or below of contam

inant will probably not be detected by the instrument in question. The limit of qu antitat

ion (LOQ ) , on the other hand, is the minim um concentration for which the analyst can reliably

say that the substance is present in the sam ple and at a specific con

centration within some pre- established lim its of p recision

and acc uracy . If the lim it of quantitati on is 2 μg/m 3 , then m easurem ent resu lts abov e 2 μg /m 3 may be

reported as not only

indicating the presen

ce of the subst ance in the sampl e, but a s indic ating t he spec ific c oncent ratio n measure d (i.e. , posit ive identification, certain concentration). Measurements between the MDL and the LOQ , indicate the presence of the substance in the sample, but analysts can on ly mak e an estimate of the concentration (i.e., certain identifi cation, uncerta in conc entration). NO TE: It is com mon ( but inco rrect) to refer to the quantitation limit a s the detection limit. The LOQ, practical q uantitation limit (PQL), estimated quantitation limit (EQ L), and samp le quantitation limit (SQ L; see below) are all lim its of quantitation, not dete ction. Thus, w

hen one says “benz ene was

not det ected at a detec tion lim

it of 5 μ g/m

3 ,” this most likely

actuall y means “b enzene was not detected; th e limi t of qua ntitation was 5 μg/m 3 .” Likew ise, when a lab

reports a me asurem

ent as “ <5 μg

/m 3 ,” this most lik

ely means “not detected; the limit of quan

titation was 5 μ g/m

3 .” Ther e is m uch confusion o

n this p oint and

analy sts m

ust clar ify with th e labor atory ex actly wh at the y mean in the ir lab report s (and wh at the analyst needs t o have reported to them for the ir risk

assessm ent acti vities ). For

air tox ics risk

assessm ents, the MDL is largely irrelevant for purposes of estim ating exposure and

the limit of quan

titation is the critical inform ation that need s to be reported (see Ch apter 7)

. In e stablish

ing li mits o f detect

ion and quantit ation, it is necessary to give the confidence le vel associated with the detection limit and the limit of quantitation. In this fig ure, the confidence level is 9 9 percent. The Limit of Detection (LOD)is then the minim

um concentration that has a 99 percent probability of producin

g a result above backgroun d noise (backgro und is s hown in t he figur e as a hor izonta l bar) using a specific method. The LOD includes two considerations: an instrum ent detection li

mit , accounting for variation in the instrument when it is presented with repeated samples at the same concentration, and additional variatio n caused by the need to sample, hand

le the sample, e

tc. (which can cause variations in the relationship between the concentration in the environmental medium and the concentration presen ted to the instrument). The LO D is the horizontal line in the bar m arked A. N ote that the range o f variation of resul ts fro m a concen trati on at th e LOD (sh own as th e bar marke d A), and the low er end o f this range just barely avoids m

oving into the range of back ground variab ility.April 2004 Page 10-13

Detection Limits and L imits of Quantitation (continued) The LOQ assum

es best practice in perform ing the measurements. I

t also is of interest to ask what the LOQ would be using more commo n, routine practice. The Practic al Quan titationLimit (PQL) is the minimum concentration that has a 99 percent probability of producing a res ult above the LO D under routine lab conditions (show n as the bar m

arked C). U

nder these conditions, the variation will be larger than under ide al conditions, and so the PQL is hig her than the LOD. Each lab m ust establish these parameters for each method on each analytical instrum

ent. When actual env

ironmental sam

ples are evaluated on an

instrument, the actual PQL reported for any given sample m ay vary (for example, if a sample is hig

hly concentrated and

needs dilution before ana lysis, the resulting

PQL for that sample will be e levated by an amount proportional to the dilution). I t is for this reason that PQ Ls reported for actual sam

ples are referred to as a sample q uantita tion limi ts or SQLs . When using analytical mon

itoring data for air toxic s risk assessm ent purposes, the MDL is irrelevant . The SQ L is the ke y fact or in de velop ing ex

posure concent

rations (see Chapter 7)

. Having estab lished these terms, some system then is needed to “flag

” results as being e ither usable or unus abl e for the p urpo ses of de cis ion-ma king. For ex ample , in t he Su perf und p rogr am, (a) results

are flagged “ R”if the data are unusable for some reason and “J”if the data fall between the SQL and the MDL. A more t horo ugh de scr ipt ion o f dat a qua lif ier s if p res ent ed in Appe ndix I.

(a) U.S. Environ

mental Protection A

gency. 19 92. Guidance for Data U sability in Risk Assessmen t (Part A) . Office of Emergency and Remedial Respons e, Washington, D.C. EPA Public ation 9285.7-09A; availa ble at http://w

ww.ep a.gov

/superfu nd/prog rams/ risk/d atause/p arta.ht

m
.

6. Develop systems to ensure that data me et decision requirements . Setting the objectives and sele cting sa

mpling a nd methods

capable of meetin

g the DQ Os are the prelud e to determining whether and to what deg ree the data may

support risk management decisions. Having collected and analy zed the data, it will be necessary to determine whether decisions can now be made with the desired confidence . For example, the actual data collected must be assesse d for qua

lity and compa

red ag ainst any

decision criteri a such as

toxicity dose-re sponse values. Where the

quality is insuffi cient to su

pport the decision (e.g. , insuffic ient to determine whether the benchmark is or is not exceeded) , the previous steps may ne ed to be re-assessed.
It is a lso importa

nt to eval uate the contribu tion to unc ertainty

that is re lated to sa

mple collection and sample program design a s well as analytica l method uncertainty. Sampling uncertainty is dec

reased when sampling density increases, however resource limits often constrain sample density. Ty pically, err

ors in the collection of field samples are much gre ater than errors introduced by preparation, handling, and data ana lysis; y et, most sampling studies have devoted resources to assessing a nd mitigating laboratory

errors. Ultimately, the

proper use of a QAPP that c onsi ders t he ent ire pr ocess (samp le col lecti on th rough l ab dat a repor ting) allows for evaluation of and reduction in uncertainty across all the activities of the monitoring

progra m, focusi ng reso urces on those asp ects con

tributing most sign

ificantl y to uncert ainty affe cting d ecisi on-ma king.April 2004 Page 10-14 7. Develop documentation . The QAPP a nd other p lanning

document s must rec ord the r esults of the environmental data collection design proce ss. Information to be documented includes the assumptions, findings, outliers, biases, data confidences, and other f actors that are critical to implementation, as well as evaluation and eventual interpretation of the data c ollected. Data co llected a

nd analy zed is ofte

n reviewed thorou

ghly to ensure they are ade quate to support decisions; sufficient documentation allows such a review.
10.4.2 Specific Planning Issues The desig n of the monitoring

progra m also rai ses some s

pecific issues:

• Select appropriate monitoring or sampling methods for the chemical(s) to be measured

. In gener al, it is important that the methods selected have the sensitivity ne eded to monitor at concentrations likely to be of health and/or regulatory concern. At a minimum, the PQL or SQL should be be low any releva nt health benchma rks (e.g ., the huma

n health d ose-re sponse values discussed in Chapter 12). For some chemicals, the limit of the curre nt technology

may not allow for a PQL or SQL that is below a health benchmark (or, that level may be reached, but at a hi gher c ost). I

n such inst

ances, t he planni ng and s coping

team must d

ecide how best to balance resources to support data quality needs.

• Select appropriate monitoring sites, sample collect ion frequency, and length of sampling time for the spatial and temporal variation of the scale being assessed and for the objective of the air toxics monitoring being conducted . The way monitoring captures this variation depends on the particular measure(s) ne eded to support the risk management decision. For example, the monitoring goal might be to estimate the a verage long-term exposure to people spread over a large geographic reg

ion (e.g., the averag e urban exposure for a typica

l resident in a town). In this case, mea surements spaced on a grid throug hout that region, or selected with a spatial density proportional to population density, may be appropriate. On the other hand, if the goa l is to identify or verify

the maximum modeled exposure or to perform a screening-le vel assessment in a population living down-wind from an industrial source, sampling should be performed at the loca tion likely to represent the highest exposure, or in seve

ral diff erent r egions t o identify the site r eprese nting the highest exposure. Again, issues such as atmospheric photochemistry and differential settling of metals ar e importa nt conside

rations. Assessors often make similar decisions when considering tempora l variation. For example, samples may vary

over time due to fluctuations (e.g., emission rates from a fa cility may fluctua te over t ime) or a sys tematic t

emporal trend (e .g., a f

acility might change it s producti on methods

or produc

ts over time). I

n the for mer cas e, it is nec essary

to obtain enough samples spread over a lar ge interval of time to estimate the mean over the measurement interval. In the latter case, the samples must be spaced in time so as to capture the trend (i.e., a time-trend stud y must be performed). In a ddition, the objective of a study may be to ca

pture high short-term spikes in chemical concentra tions. In this case, samples collected over a 24-hour period may “dilute out” these spikes, and frequent shorter term samples (e.g., collected over 15 minutes) may be required.April 2004 Page 10-15 A recent evaluation of many of the issues regarding var iability was rece

ntly published using data fr om a wide r

ange o f monitori

ng sites through out the Un

ited State s (see a

lso Exhibit 10-4). (5) These results support the conclusions that: (1) environmental variability is a more important source o f uncer

tainty than ana lyti cal unce

rtainty , emphasi zing the need to ca refully select the location and timing of monitor

ing; (2) tempora l variab ility

dominate s data variabi lity , emphasi zing the need to not only caref ully select the timing o f monitori ng, but to ensure that results are properly averaged over re levant exposure periods; and, (3) analy tical uncertainty be comes a more significant contributor to overall unce rtainty as ambient concen trations a pproach backg round lev els.

• Most ofte n, the m onitoring e fforts addre ss the four m ain source s of variabili ty in measurements . These four sources are:

– Analytical . The same sample a naly zed repe atedly

yie lds diffe

rent con centra tions.

– Sampling . Duplicate samples collected using two identical monitoring devic es from the same loc ation and

time y ield diff erent c oncentr

ations. Th

is ty pe of dup licate sa

mpling is often performed to determine the precision of the me thod. In gener

al, a minimum of 10 percent of the measurements in a monitoring prog ram should be co-located to collect duplicat e sample s.

– Temporal . Repeated samples at different times at the same location y ield different concen trations.

– Spatial . Samples f

rom diffe rent loc ations at t

he same t ime y ield diff erent c oncentr

ations. Ideally, assessors allocate monitoring resources in a manne r that is consistent with the relative contribution of these four sources to uncerta inty. However, unc ertainty may not be evident prior to establishing the sampling progra m. Some insights on the relative co nt ri bu ti on s c an be ob ta in ed fr om th e r ec en t s tu dy o f m on it or in g v ar ia bi li ty, (5) but it generally will be necessary

to perform an analy sis of the analytical unc ertainty, the pre cision, and the degree of spatial and tempor al variability befor

e a firm judgment of the relative contributions can be made. As noted previousl

y, a mbient air

monitoring

data may not provid

e a compl etely

accura te picture of exposure. There are sever al reasons for this limitation. First, air toxics monitors usually are phy sically located to provide

an estima

te of air concen tration a t a speci fic location. The assessor must then determine how representative the results are to populations in the geographic are a around the monitor. For some chemicals, monitoring results ca n be reasonably

representative, especially if the concentration does not show high levels of spatial variability. For othe

r chemicals, results may

not be very r

epresentative at all, especially at some dista

nce from the monitor. In addition, because

people move aroun

d outside, their exposures are an a verag e of the a

mbient air

concen trations over the g eogra

phic reg ions in which they move; this exposure may not correspond to the average a t any particular monitoring

location . People a

lso rece ive prote ction from the ambie nt environment, ei

ther in vehicles or by moving

indoors or through filters. Thus, ambient air concentrations mea sured through monitoring and analy sis can be taken as an indication of the potential for exposure at a given location.April 2004 Page 10-16 Exhibit 10-4. Temporal and Sp atial Sources of Variability in Formaldehyde Sampling

The four graph s in this

exhibit summ arize t he resu lts of B ortnick

and Stetzer, (4) obtained by sequentially rem

oving sources

of variability. Note that the analy tical variability is the

smallest source of var iability in this case, f

ollowed by sa mplin g va riability and tem poral/sp atial variabili

ty. C learly

inApril 2004 Page 10-17 Exhibit 10-4 (co ntinued)

this case, a choice of s ampling focu sed on temporal and spatial contributions to v ariability is needed; since temporal v

ariability domina

ted, primary

attention would focus first on that compo nent in each sampled ge ographic region

.b “Drift” ref ers to the f

act that monit oring syst ems that are cali brated generally change t heir electr onic and other chara cteristics in tim e, so the calibra tion facto r also c hang es in time

. April 2004 Page 10-18 • Follow and define standard operating procedures . Risk assessors follow and define standard operating procedure s both in the field (during sample collection and transport to the laboratory)

and in the laboratory

(during sample analysis). Procedures include those related to sample c ollection , sample tr ansport, sample storage ( including prevent ion of sam

ple degradation), and chain of custody procedures, as well as sample analy sis, validation, and data re porting . Proced

ures to id entify potentia l problems

are put i n place. Periodic

audits (both field and lab) are commonly performed to ensure procedures a re being followed and th at me as ur em en t a nd an al yti ca l d ev ic es ar e w or ki ng pr op er ly.

• Determine quantitation and compare limits . A common approach is to determine quantita tion limits a

nd compar

e them ag ainst rel evant de cision ne eds, incl uding he alth benchmarks and likely

environmental levels. These quantitation limits should be below the health benchmarks and environmental levels to provide data of use in risk-based decisions.

• Properly calibrate measure ment processes . One way to ensure the accuracy

of the method is to properly calibra

te measurement processes. To accomplish this, assessors perf orm calibra tion on a time schedu

le shorte r than the time need

ed for the equipme

nt to “drif t” (b) further than is permitted under the criteria of ac curacy and precision. It is for this reason that it is essential that systems be re-c alibrated periodically , on a schedule that is related to the data quality objectives. I

n addition, it is desirable to cross-calibrate measurement methods by compari ng resu lts from se

veral individuals

and labs. In a

n inter-l aboratory

compari son, split and duplicate samples are submitted to several labs simultaneously, the results are collected, and variation between labs are assessed. I deally, sample ana

lysis in a monitoring study would be conducted at a laboratory that has participated in such an inter-laboratory compari son and ha s been ce rtified t o produc e results

within ac ceptabl e data qu ality limits.

• Adequately record and archive results . The bes t monitorin g prog ram can fail due to improper record-keeping. A pe riodic, random check of the archived re cords (e.g., computer files) is commonly made against “hard copies” to ensure the integr ity of the proce ss of recording the data. The re cording of all results, including a description of the QA/QC and Data Qua lity Indic ators, is e ssential b ecause risk mana

gers will use the

results in their decision s.

• Match measureme nt intervals to the relevant modeling assumptions or health endpoints . Different health effects requir e varying averaging time-periods. Canc er and other chronic effects ge

nerally re quire averages over r elatively long

periods such as a y

ear or more (up to a lifet ime). I n this cas e, sample s may

be taken randomly

or sy stematic ally through out the y ear, wit h the cri terion of obtaining

an accu rate es timate of

the mean . Acute effects, however, require a n understanding of the temporal variability over short periods of time. For example, monitors ne ed to mea sure ben zene conc entrations within shorter time intervals (e.g., 15 minute, one-hour, 24-hour) for comparison with a health benchmark reflective of the same time period.April 2004 Page 10-19 • Ensure that temporal sampling reflects diurnal (time-of-day) and seasonal variability

. It is important to recognize that source terms and meteorological conditions can vary sys tematica

lly b oth over a

day and throu

ghout the seasons. Monitoring

progra ms commonly reflect this patte

rn, provi ding pro

per ave rages through out a day (by sampling at selected time points in a day) and be

tween the seasons (by

sampling in the different seasons). In g eneral , most monitoring schemes tha t are de signed t

o attenua te and va lidate a m odel will collect samples and analy ze a relatively short list of “indica tor compounds.” If attenuation and validation are the primary motivation for sample collection, it may not be nec essary to mea sure every compound

being mo deled, a s long as it can be assumed t

hat unmode led compo

unds would be expected to behave similarly . However, the amount and ty pe of data collected in the monitoring program designed to validate predicted model results should match the assumptions of the modeling program. For e xample, if the goal of the modeling progra m is to estimate long term (usu ally annual a verag e) conc entrations, then monitoring data must a

lso be col lected in suf fic ien t q uan ti ty to dev elo p an ann ual ave rag e va lu e to com par e to th e mo del res ul ts . (In gener al, monitor

ing samp les colle cted eve ry six day s for a y

ear ar e requir ed to deve lop a stab le estimate of annual average.) (2)
10.5 Implem enting Air Toxics Mo nitoring Imple menting a monitori

ng prog ram rais es two issu

es in addi tion to the i

tems abov e that re late to planning for a monitoring study . These include selecting the actual loca tion of monitors and selecting methods for data analy sis and reporting. Each is discussed in a separate subsection below.
10.5.1 Locating Monitors and Selecting Sample Size Determining the location of an air toxics monitor depends on a number of fa ctors, including the specific purpose of the monitoring (e.g ., confirm modeled concentrations at a specific loca tion, estimate background concentrations), mete orological and terrain constraints, and the re lative magnitude and location of the source(s) of c oncern versus other emissions sources that might contribute to measured air concentrations. For e xample, locations too close to a source may underestimate exposure concentrations if the plume has not y et reached ground level whe re people c an come i

nto conta ct with the

contaminants. L ocations too far f rom the so

urce ma y a lso underestimate exposure concentrations for larg e groups of people due to the dispersion that takes place b etween the po int o f touc h-dow n of th e plum e and t he poi nt of m onit oring.
10.5.1.1 Locating Monitors
EPA’s Quality Assurance Handbook for Air Pollution Measurement Systems (6) provides a set of consiste nt QA pra

ctices that will impr

ove the qu

ality of the na tion’s amb ient air q

uality monitoring

data and ensure c omparab ility

among si tes acr oss the na tion. Altho

ugh the se practices were developed spec ifically for c riteria air pollutants, they pr ovide useful guidance for air toxics risk assessments. Exhibit 10-5 summarizes some of the Handbook’s guidance on the relationship between topography, air flow, and the location of monitoring locations. The following factor s are usu

ally consider ed when s iting mon

itors:April 2004 Page 10-20 • Perform m easurements at locations that are repre sentative of exposure . Determining the location will depe nd on whet

her the goal is to qu

antify exposures

in gene ral, or e xposures to the maximally exposed individual. In the latter case, locations too close to a source may underestimate expo sure if the plume ha s not y et reac hed gr ound leve l where p eople ca n come into conta ct with the

contaminant. L ocations

too far f rom the so

urce ma y a lso under estimate exposure to large groups of people due to the dispersion that takes place between the point of touch-down of the plume and the point of monitoring. Exhibit 10-3 above presented an example of this issue. In that hy pothetical example, the area of maximum concentrations predict ed by the air q uality model fa lls somewhere within the area bounded by grid points 2, 4, S1, and S3 . If the goa

l of monitoring is to verify these maximum concentrations, then theideal location for the monitor would be on the plume centerline at the exact point of touchdown of the plume. However, if the goal of monitoring is to verify maximum concentrations at the point of actual exposures, location at the site indicated in Exhibit 10-3 may be more appropriate (measurements at the point of plume touch-down may overestimate maximum actual exposure if there are no individuals within that area). I t is essential to determine whether monitoring will estimate exposures to existing individuals or to hypothetical individua ls who mig

ht move into

curren tly u noccupie d areas

.
Exhibit 10-5. Relationships of Topog raphy, Air Flow, and Monitor ing Site Selection Station Category Characterization A (gro und lev el) Heavy

polluta nt conc entrations, hig

h poten tial for polluta

nt build up. A site 3-5 m (10-16 ft) from

a major traffic artery that has local terrain features restricting ventilation. A sampler probe that is 3- 6 m (10- 20 ft) above g round. B (gro und lev el) Heavy po llutant concentrations, m inimal potential for a pollutant buildup. A site 3-14 m

(15-50 ft) from

a major traffic artery, with good na tural ventilation. A sa

mpler probe that is 3-6 m (10-20 ft) ab ove ground

. C (gro und lev el) Moderate pollutant concentrations. A site 15- 60m (5- 200 ft) from a m ajor traffic artery. A sam

pler probe that is 3- 6 m (10- 20 ft) above g round. D (gro und lev el) Low pollutant concentration s. A site > 60 m ( > 200 ft) from a traffic artery.

A sampler prob e that is 3-6 m (10- 20 ft) above g round. E (air mass) A sampler prob e that is 6-45 m (20- 150 ft) above g round. Two subclasses: (1) good exposu

re from all side s (e.g ., on t

op of a buildin

g), o r (2) dir

ectiona lly biased e xposure (probe extend ed from a wind ow). F (source-oriented

) A sam

pler tha t is adjac ent to a

point s ource. Monitor

ing that yie lds data directly relatable to the e missions source. Source: Table 6.5 of E

PA’s Quality Assuran ce Handbook for Air Pol lution Measur ement Systems (6) When source location is the goal of monitoring, the siting of a monitor de pends on the meteorological conditions and the spatial locations of suspected source s. Again, the hypothetical example in Exhibit 10-3 provides some insights. I f the source is suspected to be at the center of the geog

raphic area, and if the wind direction is predominantly towards the east (as it is in that example), the monitor or sampler would be located to the east of the source and operated both at times when the wind blows towards the e ast and when the wind blows in the opposite (or another) direction. Support for the claim that the source is loca tedApril 2004 Page 10-21 at the origin, and dominates exposures in the area around the monitor, would then be strongest if the ambient concentration increa ses significantly when the wind blows towards the east and drop s signif

icantly when it blows in other

directions. I f the dat

a did not in

dicate this effect, then the source is not at the center, or the re is an additional, and perhaps more significant, source in the area.

• Take into account shielding and concentrating effects. Buildings, hills, and trees can have shielding and conc entratin

g effe cts. Thes e effe cts may cause a ssessors

to undere stimate exposure i f either

measure ment sites

are shie lded from normal a ir flow or if these s ame structures produce high concentra tions downwind due to lee effects. Unless there is a pattern of movement of people that make sites near buildings and other struc tures of particular interest, assessors should perform measurements away from the influence of these structures. It is p articula

rly important

to locate monitors a

way from such structu

res if the goal is t o locate s ources, as the flow patter ns for ai r are hi ghly

complex ne ar these

structur es, gre atly complicating the ability to identify the source location from monitoring data.

• Be aware that sources of air toxics from mobile sources (car s, trucks, etc.) can complicate measurem ents of ambient air concentrations produced by stationary sources . For the

estimate s of exposur

es from st ationar y so urces, i t may be pref erable to make measurements at locations away from roads. Monitoring should occur at distances ranging from 3 to 61 meters from a ma jor traffic artery

(see Exhibit 10-5). These roads provide, in a sense , a “bac kgroun d” level , or noise , above w hich the s ource mu st rise to create a discernible signal. Of course, if total ambient exposure from all sources is to be estimate d, and the

exposed po

pulation s pends a si gnific ant fra

ction of ti me near roads, th is factor may be captu red by selectin g a samp le of site s near those road s.

• Make sure

that the heights of m onitoring a nd sampling devices a re consis tent with t he breathing zones of people when public exposures are being evaluated . This is g eneral ly between 1 and 2 meters (the lower end be ing for children and the upper end for adults). While less important for highly disperse d gases (i.e., gases with hig h diffusion coefficients), this consideration can be important for heavy gases and particulates, which produce significant vertical gradients of c oncentration.

• Keep in m

ind that bac kground conc entrati ons can be diffic ult to det ermine . Alt hough background concentrations can be difficult to determine, it is important to estimate this factor as accurately

as possible at the location of measurement (see below for a discussion of backg round concentra

tions). Unfortuna

tely , even ba

ckgro und level s can var

y dr amatica lly over time and over a geog raphic a rea, an d so asses sors shoul d exercis e caution in using past studies and studies from other geographic areas in establishing background f or a measurement location. Meteorological and pollutant source information must also be carefully considered in selecting an appropriate background monitoring location. Thelocation must not be near major sources of the contaminant, or in the predominant downwind direction of those sources. The number of backg round samples should be determined during p lanning

/scoping /problem f

ormulation stage , and be b ased on st atistica l testing criteri a specifie d in the DQ Os.April 2004 Page 10-22 The choice of monitoring or sampling locations depe nds on the spatial scale of the assessment being supported by

the measurement program (i.e., micro, middle, ne ighborhood, urban, regional, or national). Note that samples collected ( generally ) at the micro-scale, middle-scale, or neighborhood-scale for the spec ific purpose of determining the impact of a sourc e or co-located groups of sources on a specific population ar e called source-oriented monitoring samples

. In each ca se, selection of sites for the monitoring progra m should consider whether:

• A mean value is needed for a region (in which c ase, the sampling must be sufficient to allow interpolation of a surface concentra tion across that region, from which a mean may be estimated, or a mobile monitor/sampler must be used while moving throughout the reg ion).

• A mean value is neede d for an a rea. I n this cas e, the mon

itor would

be place d so as to capture the average

of all the sources in the area (i.e., it is usually not oriented towards one source).

• A maxim um value

is needed (for example, for a screening assessment or an estimate of the maximum exposu

re to an i ndividual from a pa

rticular source or co-loc ated gr

oups of sou

rces; in this case, the task is to identify a loca tion as close as possible to this point of maximal exposure).

• A distribution of exposures across t he popula tion in the r

egion is needed , in which

case sampling might be performed across a region. Infor

mation on the number of monitoring stations needed to perform this analy sis with an acceptable level of accura cy/prec ision was recently e valuated and discussed by the Lake Michiga

n Air Directors Consortium ( http://www .ladco.org/toxics .html ).

• A test of a model

is being conducted (in which case the loca tion is selected to provide the most meaningful and unambiguous test of the model predictions under e stablished source term and meteorological conditions).
In all five cases a

bove, it is important to determine compounds that might interfere with the measurement of target compounds and, to the extent fea sible, locate sampling devices in areas where such interference is small (without compromising the need to cover a geog

raphic region). It also is important to establish one or more “backg round” and/or “control” locations so the elevation of concentrations or exposures at sampling locations due to source s not located in the assessment area can be determined.
In each ca se, site selection can improve through use of re lease data (source terms) and dispersion models. An accurate estimate both of average exposures and distributions of exposure (i.e., concen tration me asured a

cross dif ferent monitors)gener

ally will requ ire ade quate sa

mpling in geog raphic r egions c haract erized by the highest conc entrations in addit ion to samp ling in le ss impacted areas. Since suc

h regions may

repres ent a sma

ll fract ion of the area in the over all study region, it may be nece ssary to “over -sample” in the highest exposed areas to ensure the points of maximal exposure are not missed. This process might be accomplished, for example, by sampling on a g

rid, with the grid density hig her in the area surrounding the suspected point of maximal exposure; this will be particularly important if initial monitoring/sampling indicates high spatial variability in the a rea around the point of maximal exposure. For example, regionsApril 2004 Page 10-23 Background and C ontrol Samples Background monitors are m onitorsthat are

place i n the pr edom inant up wind di rection (relativ

e to sources ) in the

assessm ent area to me asure the conce

ntration s of the

COPC in air t hat is m oving into the asse ssmen

t area. The resu

lts of su ch monitoring

is help ful in u

ndersta nding the m onitorin g resu lts obtained in the assessm

ent area; however, background monitoring results should not be subtracted from assessm

ent area monitoring

results because of the un certainties in the backg

round monitor as

a truly representative measure of long term am bient backgrou

nd concentrations. I

nstead, EPA recommen

ds bar charts that com

pare contemporane ous concentrations of a c hemical in a back

ground monitor to the sam

e chemical at assessment area m onitors; these charts prov ide a sense of the potential influence of back ground concentrations

on the assessment area. Unlike a back

ground m onitor, which is located upwind of the assessm ent area, a control monitor is locat ed with in the a ssess ment are a and is sited in such a way as to d etermi ne the a verage concent ration o f all po llutant sources , once mixin g has occurre d (inclu

ding c hemic als blow

ing in to the assessment area from outside source s, mobile source emissions, and

stationary source em

issions within the assessment area). Control m onitors should be located away from direct influence of any one or group o f sources in the assessm ent area. Similar to backgroun d monitoring

results, control monitor results shou

ld not be subtracted from other assessment area monitoring results (or modeling results). Instead , a simple bar chart comparison is usually adequate to compare the g eneral “urban soup” to more focused monitors. near known, larg e emission s source s, and dow nwind of the predom

inant wind

direction, should probably re ceive increased attention in sampling if a distribution of conce ntration is being developed across a large

r assessment area. I f samples were taken onlyin relatively nonimpacted areas, the resulting distribution might not reflect the actual exposure of many area residents. (Ultimately, this is one of the prime r easons for using modeling to evaluate exposure; namely, that models can e stimate exposure concentration at as many geographic points in a assessment area as the analy st wishes and for which sufficient emissions inventory data and computing power are available. Thus, modeling obviates these monitoring concerns.)

For the case of model testing, random sampling is not required or even desired. I nstead, sampling is performed specifically in one or more locations where the conditions of emissions and dispersion are well established, and where ther e are no interfering sourc es or compounds. An ideal situation is a single, known source and a stable wind pattern dur ing the period of sampling. Even in such cases, however, it will be nece ssary to provide a sampling grid covering the plume dimensions, since small errors in assigning wind direction can r esult in significant differe nces bet ween mod

el result s and mea suremen ts. By sampling at a var iety of locat ions in the plume, it is possible to adjust the model to determine whether a better fit might be obtained by m ore acc urate in formation on the wi nd field, effective stack heigh t, and othe

r parameters. As part of the national-scale assessment component of the 1996 Na tional-Scale Air Toxics Assessment (NATA) activities, EPA compared monitoring to modeling r esults by using selec

ted locations and compounds (seven HAPs) throughout the U.S. (see www.epa .gov/ttn

/atw/nat a/mtom_pr

e.html ). The comparison goal was to assess the closene ss of modeling and monitoring results, which would expose the overall uncertainty in estimating exposures. They found, for example, that modeled results generally underestimated results at monitors when the modeling was performed to predict air concentrations at the precise locationApril 2004 Page 10-24 (Equation 10- 1) of the monitor; however, results were more compara ble when the maximum concentration that the model predicted was compared ag ainst the maximum monitor concentration, without the requirement that modeling and monitoring be at the same location. These results indicate that uncertainties in the modeling produced errors that shifted the location of the point of maximal exposure, but not necessarily the ma gnitude of maximal exposure. A significantly more detailed uncerta inty analy sis curre ntly is underway , with res

ults expec ted in 2004

(these w ill be ava ilable at the NATA website).
10.5.1.2 Selecting Sample Size With respect to determining the quality of any estimates of

mean concentration or exposure at a location, the coefficient of variation (CV) should be ca lculated to determine the number of samples needed to meet DQOs established by the decision problem. If F is the standard deviation of a set of N measurements performed r andomly throughout a

geographic reg

ion and randomly in time, and : is the mea n for tha t sample se

t, the val ue of CV is :

The target value of CV depends on the de cision criteria establishing the needed acc uracy of an estimate of conce ntration or exposure , but a g eneral targe t of less than 0.5 (5 0 perce nt) is suggested and a value of 0.2 or less should be possible. (This discussion assumes that the samples are representative of the g eographic area a nd time period for which the average is being calculated.) The above calculation of CV requires knowledg e of F and : , which can only be obtained after the sampling program has been unde rway. I t is possible, however, to estimate F from an initial guess of the mean concentration or e xposure, : , throug

h regr ession fu nctions su

ch as thos e established by Bor

tnick and Stetzer. (5) An example of such a regression is shown below based on a scatt er plo t of da ta fro m benz ene mo nito ring.

Note that F increa ses as : increases. The authors use a lognor mal relationship between F and : :April 2004 Page 10-25 (Equation 10- 2)

(Equation 10- 3) They per form a weighted least-squares r egression (solid line in the figure above) and obtain for the case of benzene:

The approximate size of N needed to produce the desired value of CV may then be estimated from the above equation if an estimate of : is available from either past monitoring data, similar geog raphic r egions, or models

. 10.5.1.3 Setting Up a Monitoring/Sampling Program While the design of a monitoring program will depend in many ways on the kind of monitoring to be cond

ucted, there ar

e some g eneral

aspects of all moni

toring progra

ms that ass essors should consider . EPA gui dance d escribe s many

of these issues in d

etail. (7) The general aspects re lated to designing a monitoring prog ram that supports risk assessment are de ve lo pe d a nd wr it te n d ow n i n t he pl an ni ng , s co pi ng , p ro bl em fo rm ul at io n p ha se (p ar ti cu la rl y, much of the following information is included in the study-specific conceptual model and the analysis plan and QA

PP for monitoring activities). This activity involves three steps: (1) identify the source s, including the contaminants, the concentrations, the timing and locations of releases, as well as the hy potheses you want to test (e .g., whether a source exists, its relative contribu tion to ove

rall exposu

res, etc .); (2) d

etermine the exposur

e pathwa ys ( which in the case of air monitoring is inhalation and perhaps dermal absorption throug h immersion in air); and (3) determine the receptors of interest, including a ny sensitive subpopulations, their locations, how they are exposed, and relevant health benchmarks (e.g ., IURs or RfCs). The conceptua l model can be u sed to ide

ntify where s ignific ant exposur es are l ikely

to occur to rece ptors of in

terest, which in turn helps to guide the selection of monitoring sites. The following ste ps are then often used t o deve lop, condu ct, an d eval uate t he resu lts o f moni torin g: 1. Collect a

nd revie w existing

air monitoring inf

ormation f

or the site . This information should include d ata on concentra

tions, sour

ces, loc ations of

recept ors, and other env

ironmenta

l data (e.g., meteorological data ) needed to guide decisions. The source s of these data will depend on the location of the site, but a good start is to consider results from some of the national monitoring

network s. 2. Determi

ne the le vel of sophisticati on neede d by the monitor

ing prog ram. This

level is established in the QAPP and the DQOs. The

sophistica

tion migh t range from simpl e screening procedures ( e.g., to determine whether there a re any e xposures of concern) to more sophisticated methods intended to develop accurate maps of exposure ac ross the region.
3. Develop

a clear air monitoring pla

n, includi ng dete rmining the follow

ing: ty pes of ai r monitors (these depend on the compounds identified as being of intere st); the number andApril 2004 Page 10-26 A typical m

onitoring station, located at a site with easy access, power, and protection for the equipment location of monitors; the frequency and duration of monitoring, sampling and analy sis of samples; a

nd any QA/QC pro cedure s that must b e in plac e to meet D QOs.
4. Develop a detailed, written plan for day -to-day a ctivities related to how equipment will be maintaine d and ca

librated , and how t

o documen

t results a nd QA/QC pr

ocedure s. The da

ta maintenance plan should include development of a sy stem of logbooks for entering data, along with procedures to ensure the data are entered correctly and the logbooks a re archived. There should be a clear procedur e for maintaining chain-of-custody for both the samples and the logged resul

ts.
5. Evaluate the air monitoring results for their validity and reliability, including summar y indicators of data quality ( e.g., the data qualifiers discussed elsewhe re in this chapter), and summarize

these re sults so de cision-ma kers ca n underst and this qu

ality and ensu re the qu ality meets decision needs. This evaluation should include a summary of the statistical procedures used and the air c oncentr

ation re sults, and

an estima

te of unc ertainty

in results deemed u sable by the analy

st (including uncertainty

due to monitoring equipment, handling of samples, and sample analysis). There are a number of spec ific issues that arise in Step 3 above that relate to the development of the monitoring program. These issues are summarized here in roughly the order in which they would be a

pproached in deve loping a real pr ogram:

• Establishing s ampling lo cations . Sampling may be purposive, random, or syste matic. Purposive sampling refers to locating the monitor at a particular location because that location is of special interest. While such sampling can be useful to address specialized questions (such as the impacts of a spe cific sou rce, or the relia bility of model r

esults), t hey gener ally are les s useful f

or risk assessment purposes, and care should be taken when averaging the re sults along with results from the other forms of sampling. Random sampling involves selecting monitoring locations in a random and unbiased manner, with no correlation between locations (other than, perhaps, the fact that they are all in a defined region). Assessors could establish locations by cr eating a grid, and then ran domly selectin g the tw o coordinates (x and y) in that gr id. Random

sampling has the a dvantag e of well established and relatively easy to apply

statistical methods for eva luating results, b ut runs the

risk of mis sing some “hot spots” of exposure. Systematic sampling involves establishing a grid and placing monitors sy stematically on the grid nodes. This ensures that sampling is uniform across a n area, although statistical analysis is more complex because the samples are not truly random. Exhibit 10-6 illustrates common ty

pes of sa mpling pr ograms
. There also are practical conside rations in selecting locations, regardle ss of which of the three procedures above is used. Monitors and samplers will require a ccess to land, both in terms ofApril 2004 Page 10-27 permission to locat

e the equ ipment and the abil ity t

o reach the site. I

t must also b e possible

to provide electrical power, and some protec tion of the equipment against theft, vandalism, and other disturbance; therefore, a fe nce may be

needed.

Exhibit 10-6. Common Types of Sampling

Purposive sam

pling focuses the sa

mpling effort in specific locations (in this ex ample, the area estimated to hav

e the highest conce

ntration). Most air quality sampling is purposeful (i.e., monit oring stations

are loc ated in areas w

here m onitorin g is fe asible ( e.g., locations that are accessible), areas of direct (e. g., ma ximum) im

pact, or where concern s about potential exposures have be en raised). Grid sam pling consists of reg

ularly-spaced

samples in a predetermined g

rid. Random

sampling consists of samples in locations selected by chance.

• Determining the types of equipment and samples . The samp ling/mon itoring method wil l depend on the comp ound bein g sample d, as wel l as the ne ed for g rab samp les or composite (continuous) monitoring. See Section 10.6.1 for more detail on this issue.

• Conducting fi eld scre ening . Before establishing the monitoring site, it is useful to conduct some limited screening of the region using relatively simply

methods. This will help identify locations likely to be of interest (e .g., likely loca tions of maximal exposure). If this isn’t possible, modeling results might be used. Guidance on this issue can be found in EPA’s Field Screening Methods Catalog

. (8) These re sults ge nerally should not, however, be used inApril 2004 Page 10-28 Field Blanks A field blank is a

clean sample, carried to the sampling site, exposed to sam pling conditions, returned to the laboratory , and treated as an enviro nmen tal sam ple. F ield bla

nks ar e used t o demonstrate that:

• Equipment cleanin

g has adequately

removed contamination introdu

ced by sam

pling at previous sites;

• Sampling and sample proc essing have not resulted in contamin

ation; and

• Samp le hand

ling a nd trans port, la b transp ort, and lab measurem

ent have not introd

uced contamination. the risk assessment of chronic exposures because a small number of sa mples taken over a short period of time will not provide an accurate estimate of long term exposure.

• Accounting for temporal and meteorological factors . Sampling must account for the fact that concentrations will fluctuate in time, in part because of meteorolog y (e.g ., the wind blows in different directions during the day , carry ing the contaminant to different locations). Where variability is high, a larger number of samples will be neede d to achieve a desired level of accuracy . The sampling program should include a full annua l cycle c overing the seasons for a chronic exposure assessment. Where this is not possible due to limits on resources, the sampling should at least include two temporal extremes (e .g., under windy condition s blowing

from major source s to the mon itor, and under ca lm conditions). I

t is essential to include the variability of the samples in any estimates of a ccuracy for the monitoring location.

• Implementing QA/QC measures . It is essential that well-established, clear and documented methods for assuring the quality and reliability

of data be developed. Many

of these issues are describ ed in the t

ext box on the QAPP discussed in Section 10.3. A sampling protocolmust be developed detailing (1) conditions under which samples are collecte d; (2) how

training of individ

uals will be conducted; (3) how the precision and accuracy

will be ensured so results are obtained reproducibly ; and (4) the analy tical str ategie s that will b e used to ensure quantitation limits are met. Measures are also put into place to ensure that samples are handled appropriately from collection through analysis (e.g., chain-ofcustody require

ments, allowable sample holding times). Sampling devices used to collect, store, preserve, a nd transport samples must not alter the sample in any way

that complicates analy sis. Samples should be stored in a way that ke eps the concentration as close as possible to that in the field. QC samples must be collected, stored, transported, and analy zed in a way that is identical to the tre atment of site samples. For example, both field and trip blanks, which are sampling device s that have not been used for samp ling in th e field b ut otherw ise are brough t throug h all of the

other pr ocedure s to which field samples will be subjected, must be treated identically to the actual field samples. These fi eld and tr

ip blanks provide information on the exte nt to which

samples mi ght bec ome contaminated by non-site-related materials during handling in the fie ld (field blanks) and subsequent transport back to the lab for analy sis (trip blanks).
10.5.2 Data Analy sis and Repor ting As Section 10.4.1 mentions, adequate data analysis, recording, and archiving is esse ntial to the design and conduct of a monitoring prog ram. It is important that assessors enter ea ch data pointApril 2004 Page 10-29 into a file with relevant qualifiers, including location of sample; date a nd time of sample; method of sampling and relevant operating characteristics (e.g., flow r ate); transfer process; storag e time; analy sis method; and ident

ity o f people

perfor ming all stages o f the mea suremen t. The inte grity of this da tabase should then

be asses sed peri odically

by c omparing

a random sample of

file information against hard copies (e.g ., laboratory books) to ensure reliability of tra nscription. For results using a common methodology , there should be a record of severa l key aspects of the me th od th at as su re re li ab il it y:

• A description of the calibration process, including ce rtification of any sta

ndards used in that calibration.

• Results of any inter-labor atory compar

ison of uses of the method, and certification that the labor atory perf

ormin g the an alysis fo r the s ampli ng progr am fal ls wi thin a reaso nable range of these inter-la borator y r esults.

• A recor

d of back ground levels a nd levels in blanks, allowing

a compar ison of the se aga

inst sample results.

• A summary of the f

requency of “detects,” or fraction of samples with values a bove the MDL or SQL (see Exhib it 10-7). If th

is fract ion is small

and the c hemical i s though

t to be pre sent, it may indicate that improvements in

the metho d are ne eded. Of course, if the SQL s are we ll below any he alth benchmark, a small fraction of detects or quantifia ble results need not trigg er a ca

ll for impr

ovements

.

• A policy

on signif icant digits and how

these ar e relate d to the ac curacy

of the method. All res ul ts sho ul d be rep ort ed o nl y wit h a n um ber of d igi ts con si st ent wit h t hi s ac cur acy. In addition, rounding rules should also be established and followed.

• A description of how summary quantities such as means are calculated. This descr iption includes such factors as how outliers are identified and de alt with, the possible influence of this process on sample mean and variance, and how r esults below the SQL are handled. F or example, some laboratories will report a chemical that they detect below the SQL as “not detected” simply be cause it is below the SQL and they cannot accurately

quantify it. Other labs will report such a chemical as detected, but with an estimated c oncentration and qualify the value as “J.” In ge

neral, labs should report detected chemicals, re gardless of whether they can accurately

quantify their conc entration. The use of J-qualified data for risk assessment purposes is described below.

• A detailed description of the QA/QC flags that are used by the lab to re

port data and a clear description of how the lab deals with samples that are associated with blanks that a re contaminated.
10.5.3 The Use of M onitoring D ata to Calculate Ex posure Con centrat

ions As the ab ove noted , monitorin g data c an, unde

r limited c ircumsta

nces, be used to es timate exposure c oncentr

ations in the vicinity

of the mon

itor. Some

gener al rules t hat apply to this activity are as f ollows:April 2004 Page 10-30 • Data from different monitors should not be combined to estimate exposure concentra tions (with the exception of co-located duplicate monitors – see below).

Exhibit 10-7. Illustr ation of SQL and MDL

People of

ten ref er t o the SQL as the “de tection l imi t. ” When a l ab reports a res ult as “ undet ecte d” (U) or “not detecte d” (ND), thi s is the l evel t o which t hey are usual ly r eferr ing. The SQL is really a li mit of quanti tati onthat can va ry betw een sa mple s (the M DL , on the o ther ha nd, is a tru e “de tection limit”). Fo r exam ple, a re sult of “5 U : g/m3” usua ll y mean s “n ot det ecte d at asampl e-s pecif ic qu anti tat ion l imi t of

5 : g/m3 .” W hen a c hem ical is detecte d, but bel ow the SQL (i. e., a “J” val ue), r isk as sessor s oft en use t he J valu e as is (i. e., t

he J valu e is u sed wit h no modi fi cati on). When a chemi cal is not de tect ed in a sampl e, bu t t here is reas on to be li eve i t may be present, even at very

small amounts (e.g. , the chemical i s found in some samples, but not i n others), ri sk assessors oft en us e 1⁄2 the SQL as a s urrog ate concen tra ti on for ris k ass essme nt pu rposes (in the exampl e above, hal f of “5U” is 5/2 = 2.5). It is usually not appropriate to use 1⁄2 the MD L as a surrogate for concentrat ion for exposure assess ment purposes . The process of ass essi ng and combini ng monit oring dat a for expos ure ass essment purposes is discuss ed in more detail in Appendices H and I.

• Monitoring data at a location are not ge nerally used to de scribe variation of exposure concentrations experienced by individuals in a population of people, although temporal differences for the population as a whole ( e.g., exposure to the population during the winter versus exposure to the population during the spring) may be appropriate. Variation in exposure concentration within a population is preferably described by looking at exposure concentrations across a set of monitors in the assessment ar ea.

• The representativeness of the exposure conce ntrations, as represented by any one monitor’s data, depends on the amount and quality of the data collected, and the individua l chemica ls involve d. For example, some pollutants

may be “reg ional” in natur e, mean ing th at the ir con centra tion tends to be r elati vely homo geneou s over a large area. In that c ase, a given monitor may be broadly r

epresentative of ambient concentrations throughout the region. Some compounds, on the other hand, show sharpApril 2004 Page 10-31 concentration gradients over spac e and the monitor may only be reflective of exposure concentrations for people living very near to the monitoring station.

• To assess acute exposures with monitoring samples, the results from the individual samples (not their average) should be c ompared to acute health benchmarks, and the sampling time should match the averaging time of the acute health benchmark (see Chapter 13).

• For chronic exposure assessment, all the valid samples collected and a nalyzed for a monitor (taken routinely throug hout the course of at least one y ear) are averaged (see below) to provide an estimate of the long term exposure conce ntration. Appendix I provides a gene ral overview of how monitoring data should be evalua ted, processed, and displayed to deve lop estimates of exposure concentration.
10.6 Monitoring Methods, Technologies, and Costs EPA has developed a number of methods to measure the conc entration of air toxics in ambient air. T he maj ority of t his i nform ation is fou nd on E PA’s A mbien t Mon itori ng Techn ology Information Center (AMTI C) website (Exhibit 10-8), and assessors involved in monitoring should bec ome famil iar with th is website and its contents. Gi

ven the br

eadth an d scope o f this website’s contents, it is not possible here to fully re view all of the information here. This section only provides an introduction to the methods. Appendix E summarizes relevant informa tion from two k

ey EPA compe

ndia of me

thods, pri marily for ambie

nt air moni

toring. In a

ddition, th

is chapter does not examine indoor air measurements, as EPA has provided monitoring recommendations only for radon. EPA has developed a Compendium of Metho ds for the D etermina tion of Toxi c Organic Compounds

in Ambien t Air

to assist federal, state, and local regulator y personnel in deve loping and maintaining necessary expertise and up-to-date monitoring technology for characterizing organic pollutants in the ambient air (Exhibit 10-9). (9) The Compendium contains a set of 17 peer-reviewed, standardized methods for the de termination of volatile, semi-volatile, and selected toxic organic pollutants in the air. The Compendium, along with updates and a ddenda, is availa ble at EPA ’s AMTI C Website a t http://www .epa.g ov/ttn/amt ic/airto x.html

.
Exhibit 10-8. EPA’s Ambi ent Monitoring Technology Infor mation Center (AM TIC) Infor mati on on ambie nt co ncen tra tio ns fo r a wi de var iet y of co mpoun ds ca n be f ound thro ugh AM TIC ( htt p:/ /www .epa .gov/ ttn /amt ic/ welc ome.h tml ). This Center facilitates the exc hange of am

bient monit oring- related inform

ation co llected throug

hout th e U.S. , and c an prov ide va luable i nsigh ts into the selection of mo

nitoring metho ds. Established in 19 91 as an electronic bulletin board system (BBS), AMTIC

has ev olved with changin

g tech nolog y into a pag e on the

World Wi de Web. It i s operated by EPA ’s OAQPS throug h the Monitoring and

Quality Assuranc e Group (MQAG). The database contains inform ation on all the Reference and Equivalent Metho ds for the criteria pollutants, the toxic organic (TO) Meth ods for air toxics and other noncriteria pollutant m ethodologies, Fede

ral Regulations pertaining

to ambient m

onitoring, am

bient monitoring

QA/QC related inform

ation, information on am

bient monitoring

related publications, am

bient monitoring

news, field and laboratory studies of in

terest, and updates on any new or de veloping EPA Ambient A ir standards.April 2004 Page 10-32

Exhibit 10-9. EPA’s Toxic Organ ic (TO) Monitoring Methods
M ethod Descri ption TO -1 Method for the Determinati on of Volatile Organic Compo unds (VO Cs) in Ambient Air using

Tenax ® Adsorpt ion a nd Gas Chr omatogr aphy/ Mass Spect

romet ry ( GC/MS)
TO -2 Method for the Determination of

VOCs in Ambi ent Air by Carbon Molec ular

Siev e Adsorpt ion and Gas Chr omatogr aphy/ Mass Spect

romet ry ( GC/MS)
TO -3 Method for the Determination of

VOCs in Ambi ent Air u sin g Cry ogeni c Precon cent rat ion Techni ques a nd Gas Chr omatogr aphy w ith Fla me Ioni zati on and El ectr on Capture Det ecti on TO-4A Dete rmi nat ion of Pe st ici des and Pol ych lor in ate d Biph eny ls in Ambie nt Air Usi ng Hi gh Vol ume Polyu rethane Foa m (PUF) Sampl ing Foll owed by Gas Chromat ograph ic/Multi -Det ector Det ec ti on (GC/

MD)
TO -5 De termin ation o f Alde hyde s and Ke tones in Am bient A ir Usin g Hig h Pe rform ance Liquid

Chrom ato graphy (HPLC)
TO -6 Determination of Phosgene in Ambient Air Using High Performance Liquid Chrom atography

(HPLC)
TO -7 Method for the Determinat ion of n itr osodimethy lamine (NDM A) in Ambient Air Using Gas Chromatography
TO -8 Method for the Determinati on of Phenol and M ethylphenols (Cresols ) in Ambient Air Using

High Performance Liquid Chro matography
TO-9A Determi nati on of Pol ychl orin ated, Polybr ominat ed, an d Bromina ted/ Chlor inat ed Dibenz o-pDio xins and Dib enzo furans in A mbie nt Air
TO-10A Dete rmi nat ion of Pe st ici des and Pol ych lor in ate d Biph eny ls in Ambie nt Air Usi ng Lo w Volume Polyu rethane Foa m (PUF) Sampl ing Foll owed by Gas Chromat ograph ic/Multi -Det ector Det ec ti on (GC/

MD)
TO-11A Determination of Formaldehyde in Ambient Air using Adsorbant Cartridge Followed by High

Perf orman ce Li qui d Chr omat ogra phy (HPLC)
TO-12 M ethod for the D eterm ination o f No n-meth ane O rganic Co mpo unds (NM OC ) in Am bient A ir

Usi ng Cr yo ge ni c Pr ec onc en tr at io n a nd Dir ec t F la me I oni za ti on Det ec ti on (PDF ID)
TO-13A Determinat ion of Poly cycli c Aromatic Hydr ocarbons (PAHs) in Ambien t Air Using Gas

Chromat ograph y/ Mass Spect romet ry ( GC/MS)
TO-14A Determi nati on of VOCs i n Air Using Speci all y Prepa red Cani ste rs wi th Su bseque nt An aly sis by Gas Chrom atography
TO-15 Determinat ion of VOCs in Ai r Collect ed in Speci ally -Prepared Cani ster s and Analy zed by Gas

Chromat ograph y/ Mass Spect romet ry ( GC/MS)
TO-16 Long-Path Open- Path Fourie r Transfor m Infrared Monitorin g of Atmospher ic Gases
TO-17 Determinat ion of VOCs in Ai r Using Act ive Sampli ng Onto Sorbent TubesApril 2004 Page 10-33 10.6.1 Ambient

Air Monit oring Methods and Technologies The term “monitori

ng metho d” is a comprehen sive term that inclu

des ever yth ing from the sampl e collection devices to analy tical laboratory

methods. These methods fall into three broad categories related to the time scale over which concentration will be averaged :

• Grab samples provide a quasi-instantaneous measurement of a conc entration. They generally are obtained in the field usually over a period of 24 hours or less and then returne d to the laboratory f

or analysis. The sa

mpling may be a

utomated, allowing a time-series of samples to be drawn, but all samples still are generally returned to the laboratory for analy sis. In rare instance s, a mobile laboratory ma y be co-loc ated with the sampling location, in which more “real-time” data is possible.

• Continuous monitors provide a time series of measurements in the field, with a stream of data at selected intervals (e.g., onc e each 24 hours). These monitors may be fully automated versions of grab sampling, taking sa mples at a set interval but then analy zing the samples interna lly r ather th an retur

ning to the lab. An

alterna tive is a c ontinuous f

low monitors, which draw ambient air through a cha mber and analy

zes it in real time (e.g., the semi-continuous formaldehyde monitor developed by the EPA, whic h runs through one complete cyc le of sampling and analysis in 10 minutes).

• Time-integrated samplesare collected over an extended period of time. Only the total pollutant collected is measured, and so only the average conce

ntration during the sampling period can be determined. As with gra b samples, these measurements genera lly are obtained in the fie ld and re

turned to a labora tory for ana lys is. Monitoring methods/systems can a lso be divided into a different set of categorie s based on the method of collection :

• Integrated air sampling devices use a pump to draw a ir continu ously into the sa

mple chamber, over a reactive medium, or throug h a filter during a prescribed pe riod of time; the sample is r

eturned to the lab oratory

for ana lys is.

• Direct-read monitorsdraw air through a measureme nt system and provide a dire ct reading of the concentration without returning samples to the lab.

• Automa ted mon itori ng sy stems collect samples, perform the analy sis, and report results at regular intervals in the field.

• Air deposition monitors rely on deposition proper ties of compounds (e.g., particulates), and may consist of

active a nd/or pas sive, wet and/or d ry sampling methods.

• Passive monitors allow the compound

to diffuse

into conta

ct with an active ma

terial; these ge ne ra ll y ar e a na lyz ed in th e l ab , a lt ho ug h s om e i nd ic at e t he pr es en ce of a c om po un d b y a color change.April 2004 Page 10-34 Exhibit 10-10. 33 Urban HAPs (Na tionwide Basis) acetaldehyde acrolein acrylonitrile arsenic and compoun ds benzene beryllium and compou nds 1,3-butadiene cadmium and com

pounds carbon tetrachloride chloroform chromium and com

pounds coke over e

missions 1,2-dichloropropane dioxin ethylene dibrom

ide ethylene dichloride ethylene oxide f ormaldehyde hexachlorbenzene hydrazine lead and compound

s manganese and com

pounds mercury and

compounds methylene chloride nickel and compound

s polychlorina

ted biphenyls polycylic org anic matter propylene dichloride quinolene 1,1,2,2-tetra

chloroethane tetrachloroethylene trichloroethylene vinyl chloride

Compounds moni tore d in t he NATA Nationa l Sca le As sess ment pil ot sites are in dicate d by ital ics
. • Grab sampling devices use an essentially instantane ous sampling method, such as an evacua ted cham ber into w

hich ambi ent air is

allowed t o enter a t a fixed ra

te; the sa mple collecte d is retur

ned to the laborat ory for ana lys is. In some circumstances, g rab samples may be

collected by voluntee

rs (for example, when residents near an industrial complex organize to capture samples whe n a strong odor is present). This process is commonly ref erred to as a “bucket briga de.” Bucket brigades may provide useful informat ion that a problem ma

y exist that war

rants mor e in-depth evalua

tion. They

are als o helpful, in some cir

cumstanc es, to hel

p the aff ected c ommunity

become m

ore invol ved in the air toxics evaluation process. Nevertheless, care should be taken to e nsure that all of the necessary sampling and analy sis protocols and QA/QC are established, understood, and followed by the bucket brigade team members to ensure that the grab samples are of suffic ient quality to be used for decision making purpose at hand. Mobile air monitoring platforms are sometimes used to evaluate air qua lity parameters. A “mobile platform” can be any thing from a VOC sampling apparatus on a movable trailer to a sophistica ted multi-pollutant s ampling and ana lyti cal mobile

trailer . The utili

ty o f mobile platform s is that the

y c an be move d from pla

ce to pla ce rela tively easily (e.g. , for hots

pots analysis) and may

only require a

place to park the platform and an electric al hookup (as opposed to the more

difficul t proces s of esta

blishing fixed monitoring loc ations, wh ich requ ires ac cess to land, often by esta

blishing a leasing agree

ment, and permanent security measures, such as fencing). Most existing air toxics monitoring programs have focused on the 188 HAPs, and especia lly on the 33 ur

ban HAPs identified by OAQPS on a nationwide basis (Exhibit 10-10) as generally

presenting the great est contr ibution to r

isk to public he alth from

air toxics i

n urban areas. Note that the highest-risk HA

Ps in a spec ific region or community

may differ from this list. A significant database exists on national exposures to these c ompounds, especially those monitored by the National-Scale Air Toxics Assessment (see Chapter 2 and the website at www.epa .gov/ttn

/atw/nat a ). A gener al starti ng point f or most monitoring efforts should be an initial screening analysis to identify the COPCs. A description of the ge neral process for screening analy

ses of this type is provided in Chapter 1.April 2004 Page 10-35 EPA has no t develop ed method s for man y c ompounds, including

some of th e 33 urba

n HAPs. Potential deficiencies in particular monitoring methods include:

• Quantita

tion limits a

re not low

enough relative to environmental le

vels and/ or healt h benchma rks;

• Lack of available

standards for monitoring protocols (e.g., standa rds developed by

the National Institute of Science and Te chnology );

• Methods a

re not pr actica l or easy to impleme

nt;

• Compound st

ability is so poor t

hat the c ompound de grade s signif icantly betwee n the time i

t is collected and the time it is analyzed, re sulting in poor to no recovery at the time of analy sis;

• Recover efficiencies are too low, resulting in poor precision and/or quantitation limits that are not l ow enoug

h for use relative to health benchma rks;

• Methods have not been sufficiently tested in the laboratory

and field;

• Methods are not producing results that are compa rable to established methods; and

• Po or re li ab il it y. The deficiencies noted in Exhibit 10-11 are particularly important and have been identified by EPA as nee ding met

hodolog y de velopmen t. (10) Becau se they

present a similar challen ge, EPA has tar get ed s eve ral VOC s fo r pr ogr ams to im pro ve m oni to rin g ca pab il it ies (Ex hi bi t 1 0-1 2). In addition, both diesel exhaust (a complex mixture), acrolein, and arsenic require additional method de

velopmen t to y ield acc urate, r eliable, and fiel d-tested

monitoring

methods.
10.6.2 Sampling Costs There is no general g

uideline for the costs associated with monitoring progra ms, as they depend on quite an array

of factors. Several of the more critica l include:

• Whether s amples a

re anal yze d “in house ” or cont

racted out.

• Whether monitoring equipment is available or must be purchased or lea sed.

• The number of monitoring results or samples required (ther e is some economy of sc ale, but increased numbers of results also increases c ost).

• Whether personnel must be hired and/or trained.

• The pote

ntial cost of lease s and insur

ance for monitori

ng sites

.
• Laborato ry analytical costs for sp ecial analytes . For ex ample , dio xin sampl es can r un as h igh as $1,000 per sample, making an extensive dioxin sampling scheme gene rally out of rea

ch for most studie s. 10.7 Archiving Air Toxics Monitoring Data When appropriate, results of a monitoring progra m should be submitted to the relevant air toxics database, such as EPA’s Air Quality System (AQS). (11) The AQS w ebsite ( www.epa .gov/ttn

/airs/ai rsaqs/sy sovervie w.htm ) provides detailed information on submitting and retrieving such data, including instructions on the file for mat for the data. Archived data may be accessed at the AQS site. (12)April 2004 Page 10-36

Exhibit 10-11. Identified Deficiencies in Available Monitoring Methods Compound Candidate Method Deficiency 1,3-butadiene 1,2-dibrom

oethane 1,2-dichloroetha

ne TO14A/15 sensitivity issue; false highs
acrylonitrile TO14A/15 NIST standard n eeded; rec over y prob lems ethylene oxide None/NIO SH 1614 poor sto rage stability 1,1,2,2-tetra

chloroethane TO-15 NIST standard n eeded arsenic and comp ounds IO-3 sensitivity issues; filter contamination; resou rce in tensi ve beryllium and

compounds None resource intensive; XRF sensitivity issu

e mercury and

compounds IO-5 requires special equipm ent acrolein None TO-11A re sults in unst able de rivati ve poor re cover

y 2,3,7, 8-tetrachlor

odibenz o-p- dioxin TO-9A resou rce in tensi ve

Exhibit 10-12. VOC Compounds Needing Improved

Monitoring Methods vinyl chloride 1,2-dichloroethe

ne dichloromethane chlorof orm 1,2-dichloroetha

ne benzene carbon tetrachloride 1,2-dichloropro

pane trichloroethene cis- and trans- 1,3-dichloropro pene 1,1,2-dicho

roethane 1,2-dibrom

oethane tetrachloroethylene 1,1,2,2-tetra

chloroethane hexachlorobutadiene acrylonitrile 1,3-butadiene ethylene oxideApril 2004 Page 10-37 Use of His torical Monitoring Data Historical monitoring

data for an assessm

ent area may be of use in develop ing the analy sis plan. They ca n help wi th a ran ge of use s, incl uding:

• Identifying

the types of che

micals that m

ay be present in the a ir;

• Selecting locations for monitors;

• Performing p

reliminary screen

ing level risk estimates; and

• Establishing acceptable monitoring p rotocols. The utility of historical data w ill, of course, be based on an assessm

ent of the quality o f the data. For exa mple, data th at were

not col lected w

ith sufficient QA/QC, m

ay not be useful for any of the above purpose s. 10.8 Using Air Monitoring Data to Evaluate Source Contribution Caution should be used in interpreting the results of a measur ement as being u niquely associa ted with a given so urce. Mo st measurements from monitoring data are, depending on the chemical, a combination of backg round concentra

tions and t

he same chemica l releas ed from p ossibly

multiple sources . Benzene, for example, is pr esent in backg round air, is relea sed from mobile sources, and is used and released from multiple ty

pes of sta tionary sources . This is not to say that monitoring data c annot be used to identify relea ses from a source. Under certain circumst ances, a naly sis of multip le measurements at different locations may indicate a spatial pattern consistent with the known air dispersion pattern accompany ing that source (and inconsistent with the patterns from other sources).
EPA also ha s develop ed “rec eptor mod els” whic h make use

of monitor

ing data , together with emissions inventories, to perform source apportionment analy ses, which provide a quantitative estimate of what percent of each pollutant comes f rom each identified source. EPA’s Chemical Mass Balance Model is one such example (available on EPA’s SCRAM website at http://www .epa.g ov/scra

m001/tt23. htm ). This mod el uses chemical c oncentr ations mea sured in samples from sources (emissions) and receptor loca tions to estimate the contributions of source types to ambient air pollutant conce ntrations. The model is used primarily in the deve lopment of State I mplementa

tion Plans f

or PM 10 . The model allows the user to select samples, chemical species , and sour

ce ty pes for modeling , calcul ate sour ce contr ibutions a nd their s tandard

errors , evaluate goodness-of-fit and validate the model results, prepare output documentation, and gra ph results.April 2004 Page 10-38 1. U.S. Environmental Protection Agency . 2004. National Air Toxics Monitoring Strategy, Draft . Office of Air Quality Planning and Standards, Research Triangle Park, NC, January 2004. Ava

ilable at : http://www.epa.gov/ttn/amtic/files/ambient/airtox/atstrat104.pdf

. 2. U.S. Environmental Protection Agency . 2003. Latest Findings on National Air Quality; 2002 Status and Trends. Office of Air Quality Planning and Standards, Research Triangle Park, North Car

olina. EPA

454/ K-03 -001. Ava

ilable at : http://www.epa.gov/air/airtrends /2002_airtrends_final.pdf 3. U.S. Environmental Protection Agency . 2004. Technology Transfer Network Ambient Monitoring

Technol ogy

Information Ce

nter. Up dated Ma

rch 4, 20 04. Avail able at: http://www .epa.g ov/ttn/amt ic/airtxfi l.html . (Last accesse d March 2004.) 4. U.S. Environmental Protection Agency . 2003. Laboratory Operations and Quality Assurance Manual. Science and Ecosy stem Support Division, Analytical Support Br anch, Region 4, Athens, G

A. Januar y 20 03. Avail able at: http://www.epa.gov/region4/sesd/asbsop /asbsop.h tml U.S. Environmental Protection Agency . 2003. Quality Assurance Management Plan for Region 4. U.S. Environmental Protection Agency , Region 4, Atlanta, GA. May 2003. Availabl e at: www.epa .gov/re gion4/se sd/oqa/r 4qmp598.h

tml

. 5. S. Bortnick and S. Stetzer. 2002. Monitoring Methods and Network Design . Air Toxics Exposure Assessment Workshop. June 25-27, 2002. San Francisco, California. 6. U.S. Environmental Protection Agency . 1998. Quality Assurance Handbook for Air Pollution Measure ment Sys tems, Vol

ume II, Part 1: Am bient Air Q uality Mon itoring Pro

gram Quali ty System Development . Office of Air Quality Planning and Standards, Research Triangle Park, NC, Augu st 1998. EPA -454/R-98 -004. Ava ilable at : http://www .epa.g ov/ttn/amt ic/files / ambient/qaqc/redbook.pdf 7. U.S. Environmental Protection Agency . 1989. Risk Assessment Guidance for Superf

und, Volum e I, Huma n Health E valuationManual (

Part A) , Interim Final . Office of Emergency and Remedial Response, Washington, D.C., December 1989. EPA/540/1- 89/002. Av ailable a t: http://www .epa.g ov/oerr

page/s uperfun d/progr ams/risk/ ragsa /index.htm

. U.S. Environmental Protection Agency . 1989. Procedu res for Dis persion M

odeling an d Air Monitoring

for Superf

und Pathway

Analysis (Volume IV of Procedu res for Cond ucting Air Pathway An alyses fo r Superfun d Applicat ions) . Office of Air Quality Planning and Standards, Research Triangle Park, N C. EPA/450/1-89/004. 8. U.S. Environmental Protection Agency . 1988. Field Screening Methods Catalog: User’s Guide . Office of Emergency and Remedial Response, Washington, D.C., September 1988. EPA 540-2-88-005 (NTIS / PB89-134159) . ReferencesApril 2004 Page 10-39 9. U.S. Environmental Protection Agency

. 1999 . The EPA Compendium of Methods for the Determi nation of Toxic Organic

Compounds

in Ambien t Air. Center for Environmental Researc h Inf ormation, Washingt

on, D.C., Ja

nuary 1999. EPA/625/R-96/1 0b. Avail able at: http://www .epa.g ov/ttn/amt ic/airto x.html

. U.S. Environmental Protection Agency

. 1999 . The EPA Compendium of Methods for the Determi nation of I

norganic T

oxics Com

pounds in A mbient Air . Office of Research and Development, Washington, D.C., June 1999. EPA/625/R-96/01a. A vailable at: http://www .epa.g ov/ttn/amt ic/inorg .html

. 10. Whitaker, D. 2002. New Trends in Monitoring Methods. Air Toxics Exposure Assessment Workshop. June 25-27, 2002. San Francisco, California. 11. U.S. Environmental Protection Agency . AirData. About the AQS Database. Updated October 3, 2003. A

vailable at: http://www .epa.g ov/air/da ta/aqsdb .html (Last accesse d March, 2004.) 12. U.S. Envir

onmental Protection Agenc

y. 2 004. Technology

Transfe r Networ k Air Qua

lity System. Download Detailed AQS Data. Updated March 11 , 2004. Av ailable a t: http://www .epa.g ov/ttn/airs/airsaq s/archived%20da ta/downlo adaqsda

ta.htm . (Last accesse d March 2004.) U.S. Environmental Protection Agency . 1999. 1997 Urban Air Toxics Monitoring Program (UATMP). Office

of Air Qu

ality Planning and Stand

ards, Re search

Triang le Park, N C, January 1999. EPA- 454/R-99- 036. Avai lable at: www.epa.gov/ttnamti1/files/ambient/airtox/r99-036a.pdf

.

# Chapter 1 1 Estimating Inhalation Exposur e

Table of Contents

11.1 Introduction

............ ............ ............ ............ ............ ...... 1
11.2 Estimating I

nhalation Exposure Concentrations

............ ............ ............ . . 1 11.2.1 General Approaches for Deriving Ex posure Concentrations

............ ......... 2 11.2.2 Commo

n Ways to Estim

ate Exposure Concentrations
............ ............ . . 3
11.3 Exposure Modeling

............ ............ ............ ............ ............ 6 11.3.1 Inhalation Exp osure Modeling

............ ............ ............ ......... 9 11.3.2 Microenvironmen t Concentration: How

is it Developed?

............ .......... 12 11.3.3 Sources

of Data for Hu man Activity

for I nhalatio n (and other) Ex

posure Assessm

ents

............ ............ ............ ............ ............ ........ 13 11.3.4 Exam

ples of Inha

lation E xposure Models

............ ............ ........... 16 11.3.5 Exposure Modeling Examples

............ ............ ............ ........ 19
11.4 Personal Monitoring

............ ............ ............ ............ .......... 20
11.5 Exposu

re to a P opulati on: Co mmon Desc riptors

............ ............ ............ 22
11.6 Evalu

ating Uncerta

inty

............ ............ ............ ............ ........ 22
11.7 Presenting the Resu lts of an Exposure A ssessment

............ ............ .......... 23 References

............ ............ ............ ............ ............ ............ 23April 2004 Page 1 1-1 11.1 Introduction The pre vious thre e chapte

rs discus sed how to

quantify exposure a

nd relea se rate s and esti

mate chemica l fate an d transport. This chapter d iscusses the fina l step of e stimating

exposure. This chapter will discu ss inhala

tion exposur

e only . Unless p ersisten

t bioacc umulative hazardou s air pollutants (PB-HA Ps) are present in

source e missions, mo st air toxic s risk asse ssments wi ll only estimate inhalation exposure concent rations . Limit

ing the e xposure as sessment t

his way is possible because the dose-response values that cha racterize inhalation risk (e.g., refe rence concentrations, inhalation cancer unit risk estimates – see Chapter 12) take into consideration the complex physical and pharmac okinetic processes that influence how the chemical r eaches the target organ, which may

be a region of the respiratory tract or a remote site (see Chapter 12 for a more detailed discussion). Specifically , other than exposure modeling to account for things like time in different microenvironments and microenvironment concentra tions, no adjustment for other exposure parameters (e.g., body weight and inhalation rate) are wa rranted. For multipathway risk assessments, howeve r, where ingestion intake rate is the exposure par ameter, it will be necessary

to consider parameters such as body weight and contact rate (e.g ., amount of soil ingested, fish eaten) for the indirect exposure pathwa y metrics of exposure (se e Chapter 19). Assessors determine human exposure to an environmental pollutant via inhalation by estimating the conc entration of that p ollutant in the ambie

nt air and the cont

act of a n individua

l with that a

ir (along with the characteristics of the c ontact). Because concentr ations in the air vary ove

r space and time, it is important to know where and how long people spend their time in relation to the contaminated air under study . Through air quality modeling and monitoring, the ambient concentrations of pollutants in air can be estimated ge ographically and temporally. Throug

h the use of exposure modeling, estimates of exposure via the inhalation route can be a djusted from modeling data to take into account the demogra phics of people in the study ar ea and the time they may spend in va

rious micr

oenviron ments. The remainder of this chapter discusses how to estimate inhalation exposure conc entrations for the risk assessment (Section 11.2); exposure modeling (Section 11.3); personal monitoring (Section 11.4); common descriptors (Section 11.5); evaluating uncer tainty (Section 11.6); and presenting the results of an exposure assessment (Section 11.7).
11.2 Estimat

ing Inhalatio n Exposure C oncentr ations The ambient air exposure concentrations (ECs) can be e stimated using either (or both of) two gener al method s: air qua

lity modeling

and air q uality monitoring . As discu ssed in Cha pter 9, a ir quality modeling involves def ining the pollutant sources and release char acteristics and modeling pollutant fate and transport (how the air toxic is transported, dispersed, and transfor med over the area of interest). As Chapter 10 discussed, monitoring involves mea suring ambient concen trations o f chemic als. Be cause of the time/e

xpense and

other limi

tations as sociate d with monitoring (most notably, questions about repr esentativeness), modeling is the most common approach for estimating ambient air conc entrations to be used in the air toxics risk assessment. Monitoring is often used, instead, as a secondary tool to provide input data to the models and validate the model results and to look for important gaps in the emissions inventory used to run the model

.April 2004 Page 1 1-2 11.2.1 Genera

l Approache s for Deri

ving Exposure

Concentr ations There a re two g eneral

way s to deriv e the EC f

or a giv en risk a ssessmen t (see Exhi

bit 11-1) . Both may incorpor ate the r esults of a

ir quality modeling

and/or monitoring

efforts

.
Exhibit 11-1. Two General Ways to E stimate Inhalation Ex posure Concentration

The left- hand si de illus trates the use of ambi ent air c oncentr

ations a s a surr

ogate for the EC. I n this example, the an

alysis assume s that individuals spen d 100 percent of their tim e at a given

location, so the estimate of am

bient concentration thus repres ents the EC. The rig ht-hand side illustrates the use of exposure modeling . In this example, the an alysis assume s that an individual spends 50 percent of his/her time at hom

e; 15 percent at a schoo l; and 35 percent at an office. The EC is the weighted sum of the product o f the am

bient concentra

tions at each lo cation a

nd the a moun t of tim e spent there. Both indoor and outdoor concentrations usually are considered at each location.

• Ambient Air Concentrations as a Surrogate . For screening-level e valuations, assessors use the c oncentr

ations of air toxics g enerat

ed at ea ch modeli ng node (or inter polated n odes) or the concentrations determined by a monitor (if modeling is not performed) as surrog ates of the inhal ation expos

ure conc entrations for the populations in the stu

dy l ocations . The def

ault assumption in such a screening assessment is that the population of interest is breathing outdoor air continuously at the modeled or monitor location. This is believed to be a conservative assumption since indoor air concentrations of air toxics are e xpected to be the same or lower than the outdoor concentrations (when the indoor concentrations are produced solely by inflow from outside air).

• Exposure m odeling . More comprehensive inhalation exposure assessments combine estimates of ambient pollutant concentrations (e.g., from air qua lity models) with information about the population of interest, including the ty pes of people present (e.g., ethnicity , age, sex), time spent in different microenvironments, and microenvironment concentra tions. The assessme nt object ive is to ide

ntify a repre sentative

estimate of the pol lutant concentra tion in the inhal ed air in

each mic roenvir onment an

d combine

it with an e stimate of

the time sp ent in different microenvironments (and the activities within these microenvironments) thr oughout the daily routine of diff

erent groups of people with similar attributes (called cohorts ).April 2004 Page 1 1-3 11.2.2 Comm on Ways to Es timat e Exposure Concentr ations Risk asses sors commonly use seve ral diff erent wa ys t o estimate

exposure c oncentr

ations. Some ways ar e used primarily f

or screening-level (Tier 1) assessments; others are used primarily for more refined assessments. Exhibit 11-2 illustrates several different way s to estimate exposure concen trations w hen ambie

nt air concentra

tions are used as su

rroga tes.

• Monitori ng locatio ns . Sites where air monitors are located provide a dire ct measure of ambient air concentrations at those locations. However, these locations may or may

not be representative of ambient air concentr ations in other parts of the study a rea. If monitors are not locat ed wher e people live, the monitoring

results ma

y not be of muc

h value f or the risk assessment other than to check the accura cy of modeling . Monitoring results may be used as inpu ts to expo sure m odeli ng.

• Point of maximum m odeled concentration . This is the modeling node where the maximum modeled ambient air concentration occurs, regardless of whether there is a person ther e or not. This generally

provides a conservative estimate of exposure and could be used a s the EC in a screening-level evaluation (f or example, using the SCREEN3 model). This point can be used to provide an estimate of “high-end” e xposure to the risk manager because, althoug h no one may actually be living there a t the pre sent, some one migh t move the ir in the f

uture. Th is point may be ref

erred to as the point of the “maximum exposed individual (MEI).”

• Point of maximum m odeled concentration at an actual receptor location . This is the modeling node whe

re the ma ximum ambien

t air concentra

tion occu rs to an a ctual pe rson in the area of impact, usually at an actual residence (or, if the r esidence falls between modeling nodes, an interpolated value). To identify this point precisely, it is necessary to know detailed information about the location of actual people in the study area. As with the point of maximum modeled concentration above, this point can be used to provide an estimate of “high-end” exposure to the risk manage r (in this case, based on current actual exposures). This point may be refe

rred to as the point of the “maximum individual risk (MIR).”

• Census tr act/bloc k internal po int . The U.S. Census Burea u provide s informa

tion about

populations in geographic units called ce nsus tracts, which are subdivided in to bl ock gro ups /en um era ti on d is tri cts and bl ock s. In cases where there is only

limited information about the census tract (e.g., nothing is known other tha n the number of people living within the tract), the Census Bureau’ s “internal point” (sometimes referred to as a c entroid) for the tract typica

lly is used as the point of exposure for all the population in the tract. The internal point is a set of geographic coordina

tes that generally

represents the approximate geographic cente r of a geographic

subdivison (see box on next page). The Census Bureau provides a n internal point for each of its geogr aphic subdivisions (i.e., tracts, blocks, and block groups). Note that the inter nal point is not population weighted (i.e., it is not located “in the direction of where the people are”)

.April 2004 Page 1 1-4 Exhib it 11-2.Illustr ation o f Common W ays to E stimate E xposu re Usin g Ambie nt Air

Concentrations as S urrogates for Exposu re Concentration 

### A

100 me ter mod eling g rid In this hyp othetic al exam ple, the risk asses sors ha ve mo deled a relea se of a v olatile o rganic HA P fro m a fac ility using a computerized air quali ty model, and the ambient air concentration is used as a surrogate for t he exposure concentration (EC). The area of impact surrounds the faci lity and is generally

greater in t

he direction of t

he primary wind fl ow (and decreases in concentration wit h distance fr om the source). The mod el was set t o make estima tes of an nual co ncen tration a t 100 -meter distanc es from the sou rce in a rectan gular gr id pa ttern. T he po ints where the model makes estimates are cal led “modeling nodes” or “receptors.” Note, however, that modeling receptors do not neces sari ly coi ncide wi th act ual people ( who are al so someti mes ref erred to a s recept ors) – t hat is, there m ay or m ay not b e a pe rson a t any give n mo deling nod e. Th ere also is one m onito ring site. Knowing only the informati on displayed in the f irst ver sion of the map (A), it is dif ficult to say much about exposu re s ince we do not know wh ere t

he peopl e are in r elation t o the fac il ity or t he ar ea of

impact . To reme dy this, our next step is to obtain demographic data (usually f rom the Census Bureau) and overlay it on the above map . W e ma y also ha ve first-han d kno wled ge of e xactly wh ere p eop le live in the v icinity of the o f the facility which we can also incl ude on the map. Performing this analy sis and redrawing the map gives picture B (next page).
In the second version of the ma p (B ), we have include d the census tract bo undaries (d otted lines) and we also know from stu dy are a reco nnaissa nce tha t there is an uninha bited nation al fores t to the we st of the fac ility, a farmer ( Mr. MacD onald) di rectl y to t he north , and a s mall t own in t he north east. (Note tha t th e town, Smallvi lle, actually can be further subdivi ded into smaller census blocks; however, they are not s hown here to keep the pict ure s impl e.) Now tha t we have abett er i dea of wher e peopl e are in r elation t o the fac il ity (a nd th e area of impa ct caus ed b y the V OC release ), we are in a be tter po sition to sta rt mak ing som e statem ents ab out ho w pe ople are exposed. Some of the more common ways t o charact erize t he exposures that

may be occurr ing i nclude:
1. M onitoring Site . The mon itoring site is lo cated in one of the hig her p arts of the area o f impa ct, but it is sout hwest of t he f acil ity and far

fr om most of t

he ar ea’s popula ti ons. This mon it orin g si te wou ld not be appropriate for describing exposure for the people of Smallvi lle, but it could be used for people in the immediate vicini ty of t he facili ty and to check the accuracy of the modeling.April 2004 Page 1 1-5 Exhibit 11-2 (co ntinued) 

### B

2. Point of

M aximum Modeled Concen trati on . In this exa mple , this poin t is located on the facility bou nda ry, where no one cur

rentl y li ves. This point is cal led th e Maximum Exposed Individua l or MEI which i s defi ned as the highes t est imated r isk t o a hypothetical exposed indi vidual , regar dless of whet her people ar e expected to occupy t

hat ar ea.
3. Po int of M axim um C onc entr atio n at a Lo catio n O ccu pied by P eop le . In t his ex ample, t

his poi nt occur s at Mr. M acDonald’s farm. This point is cal led the Maximum Individual Risk, or MIR, which is defined as t he highest estimated risk t o an exposed individual in areas that people are believed to occupy. Actually, the con centra tion use d to re prese nt M r. M acD ona ld co uld b e des cribe d using either an estimate of exp osure at a point (e. g., his house ) or s ome oth er es ti mate of exposure f

or th e la rger

far m if the re wer e a good j ust if ication for doin g so (e. g. , a n av era ge of al l t he f arm’ s mode ledpoin ts , s in ce Mr. MacDonal d spe nds much of h is ti me working around the farm).
4. Census Trac t Int ernal Point . In this exa mple , we co uld sim ply use th e cen sus tract inte rnal po int to repre sent

exposure f

or al l peopl e li vin g inthe cens us t ract . Thi s i s some ti mes us ed, es pecia ll y whe n youdo not have any fi rst-hand knowledge of the ar ea (i.e., you only have general demographic data from the Census Bureau) . Howeve r, i n th is exampl e th e cens us t rack int ernal

point woul d not be a ver

y good e sti mate of exposu re con cent

rat ion be caus e it

is hig her i n conce ntr ati on th an t hat experi enced by most of t

he popul ati on (i.e., the pe ople of Sm allville) and it is lower in c onc entratio n than tha t of the high est exp osed perso n (i.e., Mr. M acDonald).
5. Census B lock Internal Points . So far, th is exam ple ha s focuse d on chara cterizing an ind ividua l perso n’s exposure living at

defined points wit

hin the st

udy area (either a r eal person like Mr. MacD onald, or a hypo thetical p erson like the M IR). W hat if we w anted to kno w som ething m ore a bou t how many peo ple in the st udy area a re li ving at

diff erent level s of exposu

re? One way t o do this is t o develop a fr equency diagram that display s the exposure concentration at each of the census block int ernal points and identi fies t he numbe r of peop le living in that block (see below ). This kind of rep resentation is very helpful to the risk managers becau se it

gives them a s ense of the ra nge of expos ures and t he numbers of

people li ving at differen t levels o f expo sure. (In add ition, the a ssesso r may a lso cho ose to repre sent the e xpo sure with isople ths of

ris k (as i n the above g raphic) and by li sti ng the a pproximate number people li ving wi thi n each isople th.)April 2004 Page 1 1-6 The internal point with the highest impact in the study area may also be referred to as the point of maximum concentration at a receptor location, although it may not be as precise as the example

above where mor

e local k nowledg e is applie d to locat e this poin

t.

• Population-based approaches . Exposures may be evalua ted by tracking

individual members of a population and their inhalation through time and space . Such analyses may incorporate a user-specified number of simulat ed individuals or popula

tion gro ups ( cohorts ) to repre sent the p opulation in the stud

y a rea. A c ohort is de fined he re as a g roup of pe ople within a population with the same demographic variables who ar e assumed to have similar exposures. In this approach, the exposure ana lysis process consists of rela ting chemical concentrations in air (outdoor and/or indoor) and tracking the movement of a population cohort th rough lo cations w

here chemical exposure ca n occur a ccordin g to a spe cific ac

tivity pattern. Population-based analy sis is generally accomplished using exposure models (as described in Section 11.3 below).

• Perso nal monit oring . Exposures may be estimated direc tly by placing monitors on individuals, which allows collection of more detailed information specific to the exposure pattern for that i ndividual . Such monit

ors are refer red to as personal monitors because they provide information on exposure to that individual, rather than to the general a rea in which an individual might be moving. Personal monitoring is discussed in Section 11.4 below. Note that the units for the EC estimates are ty pically expressed in terms of microg rams (or milligrams) of pollutant per cubic meter of air. For pollutants adsorbed to particles, inhalation exposure e stimates should be provided a s the conc entration of these pollutants on the particles , not the concentra

tion of the

particle s themselv es. 11.3 Exposure M odeling This section discusses exposure modeling, which uses the ambient air concentra tion estimates along with information about the population of interest and information on how the pollutant concentration can vary

in different microenvironments to derive estimates of exposure concen tration over the pe

riod of exposure. I

nformati on on huma

n exposure

modeling

for air toxics can be found on EPA’s Fate, Exposure, and Risk Assessment (FERA) website at http://www.epa.gov/ttn/fera/

. For example, suppose an analy st uses the air quality model, I SCLT3, to estimate the annual average concentr

ation of benzene from a petroleum refinery at each census tract internal point for eve ry cen sus tra ct w it hi n 50 km of t he s our ce ( for il lu st rat io n, a ss um e th is is 25 c ens us t rac ts ). In a screening level analy

sis, the analyst may

simply use the predicted a mbient air concentration as a surrogate for the population chronic e xposure concentration of benzene at each of the 25 interna l points.April 2004 Page 1 1-7

Interna l Point o r Centr oid: Wh ich is C orrect? When evaluating exposure to people in a given place, the modeled a ir quality at the “interna

l point” of a geog raphic e ntity (such a s a cens

us tract or cens us bloc k) is o ften use d as a s tarting point t o represent exposure for the p eople in that geog raphic entity. Ac cording to the U .S. Census Burea u: An inte rnal po int is a set of g

eograp hic coo rdinate

s (lati tude an d longi tude) that is located within a specified geographic entity. A single point is identified for e ach entity; for many entities, this point represents the app roximate geographic cen ter of that entity. If the shape of the entity causes this po int to be located outside the boundary of the entity o r in a water body, it is relocated to land a rea within the entity. In computer-read able products, internal p oints are shown to six decimal places; the dec imal po int is im plied. The fir

st chara cter of the lati tude or longitu de is a plus (+) o r a minus (–) sign. A plus sign in the latitude identifies the point as being in the Northern Hem isphere, while a minu s sign identifies a location in the Southern Hemisphere. For longitude, a plus sign identifies the point as being in the Eastern Hemisphere, while a

minus sign identifies a location in the W estern Hemisphere. To illustrate how internal points a re established, consider the following two ex amples. I n census tract A, the internal poi nt (q) is simply the geographic cen ter of the square . In census tract B, a river flows along the we stern ed

ge of the trac t and m

akes a sharp b end towards the

tract’s eastern edge. In this case, the “geog

raphic center” of census tract B is actually outside the tract itself. Since the Census Bureau requires that the in ternal point be within the physical boundaries of the geogra phic entity, the Bureau phys ically

moves the p oint int

o the tr act, as shown (to a po int that is no lo nger t he geo graph

ic center).

Note that the internal point is generally set to reflect the geog raphic center of the entity in question, regardless of where people actually live in that entity. In other wo rds, the point is not “p opulation weigh ted” (th e Censu s Burea

u does not pro vide p opulati on weig hted int ernal po ints for

census tracts or block gro

ups). Without population weighting, an exposure concentration estimated at the intern al point m ight n ot be re present ative of the c oncentr

ations t o which person s livin

g in the censu s entity might be exposed. Analy sts routinely m odify the Census Bureau internal points for census tracts and census block

group s (using

census block data) to locate them to a spo t mor e repres entative of wh ere people are actu

ally lo cated w

ithin th e geog

raphic e ntity (e.g. , a “po

pulatio n weig

hted” in ternal p oint).
Source : U.S. Departm

ent of Comm

erce, U.S. Censu s Bureau. 2000. Geographic Glossary (Census 2000) . Av ailable at: http://www.census.gov/geo/www /tiger/glossry2 .pdf

.a Th ese term s are intro duc ed an d de fined in P art V I of this R eferen ce Lib rary.

April 2004 Page 1 1-8 However, a limitation of this is that each person in a census tract is not brea thing air at the ambient c oncentr

ation con tinuously . There

are a va riety of reas ons why

this is so. F or example :

C People come and go from the census trac t for work, play, or

travel. They

may g o to another census tract in the vicinity with either a higher or lower concentr ation of benzene.

C People do not spend all their time outdoors (which is what our analyst has presumed in our hypothetical example). I

n fact, most people spend most of their time (with some estimates of about 90 percent) indoors. The chemical conce ntration of benzene may

be higher or lower indoors than outdoors.

C The benzene concentration throughout the ce nsus tract, in our example, is probably not always the sa

me as that at the internal point we selected (we have just assumed it was for computational ease). Exposure mo deling was devel oped to tr y a nd help mov e an ana

lys is into con sidering

these details. Thus, air quality modeling estimates how contaminated the air is in the different location s within a s tudy

area. E xposure mod

eling si mulates how differ

ent ty pes of pe ople interact differently

with that contaminated air to derive integrated (e .g., time weighted) estimates of their e xposure for the dur

ation of in

terest. This section focuses on exposure models to evaluate inhalation exposures. Exposure models are also available for other routes of exposure as well (e.g ., a model may be e mployed to track patterns of food and drinking water consumption acr oss a population). These indirect pathway exposure models are discussed in Chapter 18. The estimation of population exposure is a very dif ficult task because it requires information on the activity patterns of the population as well as information on the air toxics concentrations (indoor and outdoor) to which that population is exposed. Although several databases ha ve been develope d to char

acterize activity pattern s (see Se ction 11.3 .3), var ious sourc es of var

iability (e.g., among individuals and ge ographical regions) introduce uncertainty. Thr

ee main factors affect the ov erall accurac y of expo sure m odeli ng:

• Uncertainties associated with indoor air toxics concentrations (note that most people spend the majority of their time indoors);

• How well the subgroups (or cohorts) selecte d for analy sis provide a realistic description of the population composition in a given area; and

• Uncerta

inty and vari ability

associa ted with the inputs a

nd parameters of

exposure models. Exposure models can be formulated in a determ inistic framework, where the value for each input and o

utput var iable is c haract erized by a point es timate (i. e., a sing le value assumed t

o apply uniformly

). Alternatively, the

framework may

be stochast

ic or probabilist

ic , with one or more input variables characterized by a frequency

or probability distribution (a) (see Exhibit 11-3).April 2004 Page 1 1-9 If the input dis tributions

repres ent vari ability (a) across the population, the resulting output distribution correspondingly r epresents the variability

of exposures across the population. On the other ha nd, if the input distr

ibutions re

present uncerta inty (a) about input parameters, the output distributions will represent uncertainty a bout exposure levels. Some of the newer exposure models address both variability and unc ertainty sepa rately (se e Section 11.3.4).

Exhibit 11-3. Deterministic versus Stoch astic/Probabilistic Approaches to Exposure Mo deling

In the Determ

inistic Approach, the assessment assum

es that each input to the model is a specific number (and the answer is a nu mber)

.

In the Stochastic/Pr

obabili stic Approach, the assessm

ent assumes that the inputs to the m

odel may be specified as a distribution (and the answer is a dis tribution).

11.3.1 Inhalation Ex

posure Mo deling Inhalation exposure is characte rized by the pollutant concentra tion in the air (i.e., the exposure concen tration)reachi

ng an individual’ s nostrils a nd/or mout

h (in units o f μg/m 3 ). Estimates of air con centra tion s from model ing or m onit oring c an be u sed in inhal ation expo sure m odeli ng. When derived from monitoring measurements, exposure concentra tions are an aggregate of the contributions from all emissions sources impacting the monitor. When derived from modeling studies, the estimated exposure concentrations reflect only the sources that were included in the modeling exercise. Models have an added bene fit of allowing the analy st to determine the contribution of a source to the estimated exposure concentration for a ny of the exposed population groups. (Try

ing to determine “what source” contr ibuted “how much” to a monitoring result can be a challenging and perhaps impossible task, depending on the chemical and numbe r of sources in the study a

rea).April 2004 Page 11-10 Lead Exposu re Modeling Lead (Pb) poisoning

presents potentially sig

nificant risks to the he alth and welfare of children all over the wor ld today . The I

ntegra ted Exp osure U

ptake Biok inetic Mo del for Lead in

Childr en (IEUBK) attem pts to predict b lood- lead co ncentra tions (P bBs) for childr

en expo sed to l ead in t

heir environment. The model allow s the user to input relev ant absorption param

eters (e.g., the fra

ction of lead absorbed from

water) as well as intak

e and exposure rates. Using these inp uts, the IEU

BK model rapidly calculates and recalculates a complex set of equations to estim ate the potential concentration of lead in the b lood fo r a hy potheti cal chil d or po

pulatio n of chi ldren (6

mont hs to 7 years of age ). Measured or estimated blood-lead con centration is not only

an indication of exposu

re, but also a widely-u sed index for discerning future health problem s. For additional inform ation see http://w ww.ep a.gov

/superfu nd/prog rams/ lead/ieu bk.ht m .

(Equation 11- 1) Becau se air po llutant concentra tions var y ov er time a

nd space , inhalat

ion exposur

e models combine i nformati on on huma

n activity pattern s and micr

oenviron mental concentra

tions to estimate exposure concentrations. Activit y patter ns are defined by an individual’s or cohort’s allocation of time spent in different activities in various microenvironments and various geographic locations. A micro environm ent is a defined space

that can be trea ted as a well-characterized, relatively homogeneous location with respect to pollutant concentration for a specifie d time per

iod (e.g ., rooms in

homes, re stauran

ts, school s, offic es; inside vehicle s; outdoors). A common exposure model for inhalation that combines information on microenvironment concentrations and activity patterns calculates a time-weighted average of all exposures from the diffe rent micr oenviron ments in wh

ich a person spend s time duri ng the p eriod of

interest :

where:
EC A = the adjus ted aver age inhalation exposure concentra tion (μg/ m 3 ), T = total averaging time (T = 3 t j ; years), C j = the aver

age c oncentr

ation for microen vironmen t j(μg/m3 ), and t j = time spent

in the micr

oenviron ment j(year

s). Note that the two critical parameters that need to be e valuated in this equation are the concentration of a chemical in a microenvironme nt and the amount of time spent in that microenvironment. Exhibit 11-4 presents a simple example. General information on how assessors go about obtaining such data is provided below. A s a practical matter, most air toxics risk assessments will not actually g ather such activity pattern data for study-specific exposure assessme nts. Rathe

r, avail able expos

ure mode ls have a

lready incorpor ated much of this informat ion for us e by

the general risk assessment commu nity . Howeve

r, ever y mo del is differe nt and the data inpu t require ments var y f rom model t

o model. U sually , assessors care fully review each model’s documentation before deciding to use it to determine if it will answer theApril 2004 Page 11-11 question that needs to be answered and what resourc es would be needed to develop the required inputs.

Exhib it 11-4.Simple Examp le of How to E stimate E xposu re Con centrat ion (EC ) for Exposure Modeling EC . The following ex

posure profile has been d eveloped for one year (which represen ts, for example, the 30 y ears of “work

”) for a represen

tative indiv idual within th e popul ation of interes t:
Duration Spent in Each Microenvironment (% yea r) Averag e Conc entrati on of Pollu tant A in Each Microen vironme nt (μg/m 3 ) 10 = outside 50 = at work 40 = inside house 80 20 10

The EC for that indiv

idual is calculated as: EC = (0 .1 × 80 ) + (0.5 × 20) + (0.4 × 10) = 2 2 μg/m 3
Lifetim e EC . To der

ive a lifetim

e expos ure concentrati on for t

hat indi vidua l, annu al estim ates are combined as follows:

Duration Exp osed to Each An nual Concentration (no. years) Annual Average Concentration of Pollutan t A (μg

/m 3 ) 1 = ne wborn 4 = pre- school 12 = school 4 = colle ge 30 = work 19 = retirement 10 40 30 30 22 40

The Lifetime EC is calculated as: Lifetime EC = (1 × 10) + (4 × 40) + (12 × 30) + (4 × 30) +(30 × 22) + (19 × 40) = 30 μg /m 3 70
Screen ing ex

posure estimate . One way t

o perfo rm a s creenin

g level asses sment

using these d ata is to set the EC equ

al to th e highest air c oncentr ation m odeled (e.g. , 80 μg

/m 3 for annual adjusted or 40 μg/m 3 for li fetime adjus ted – se e example s above) for all microen vironmen ts. If the hazard a nd risk, respectively, prove to be below acceptable risk v

alues, the risk m

anager may

conclude that no further evalua tion i s neces sary.April 2004 Page 11-12 (Eq uation 11-2)

(Equation 11- 3) 11.3.2 Microe nvironm ent Conce ntration: How is it Dev eloped? Microenvironments can be indoors (e.g., school, off ice, car, bus) or outdoors (e.g., f illing station, roadwa y) . Ind oor micro environment conc entrations are c omprised of contr ibutions fr

om a chemical in outdoor air penetrating the indoor environme nt and from indoor emission sources of that same chemical (if indoor sources are w ithin the scope of the analy sis). They may be derived from d irect measu rement s or es tima ted fr om mo delin g. There a re two common appr

oaches t o modeling

indoor mic

roenvir onment concentra

tions. One

is the microenvironment factors m ethod , where the outdoor contribution is estimated from the outdoor concentration and a microenvironment factor that represents the ratio of the microenvironment concentration to the outdoor concentration. Microe nvironment factors are typically derived from concurrent measureme nts of concentrations in the microenvironment (contai ning no in

door emis sion sourc es) and o utdoors. T

he indoor contribu tion is then

added to estimate the overall microenvironment concentration (whe n indoor sources are included in the scope of the asse ssment). A

gener al equat ion for th e microe

nvironme nt factors method

is:

where:
C j = concen

tration in microen vironmen t j M j = microen

vironmen t factor for micr oenviron ment j C o = concurrent outdoor concentration C s = concen

tration c ontribution to the mic roenvir onment jconcentration from an indoor emission source The second approach is the mass-balance method . The mass balance method ty pically assumes that an enclosed microenvironment is a single well-mixed “box,” although multi-chamber configurations are possible. The time-vary ing concentration of an air pollutant in such a microenvironment is estimated from several variables (see Exhibit 11-5). A general formulation for the c hange in conce ntration i n an enc losed micr

oenviron ment over

time is:

where:
V = volume of microenvironment enclosure C j = concen

tration in microen vironmen t j p = penetration factor (only applies to incoming air) Q = air flow

rate k = po ll ut an t r em ov al ra te (i nc lu de s a ll typ es of re mo va l, in cl ud in g a tm os ph er ic de ca y, surface reactivity , surface adsorption, wall deposition, etc.) C o = concurrent outdoor concentration S = indoor sou

rce emis sion rateApril 2004 Page 11-13 The solution to this differential equation can be used to predict a time sequence of microen vironmen t jconcen trations.

Exhib it 11-5.Illustr ation o f Mass Bala nce Meth od for Mo deling Indoor Microen vironme nts

11.3.3 Sources of Data for Human Activity for Inhalation (and other) Exposure Assessments Numerous EPA and related databases provide informa tion useful for conducting exposure assessments, including information on activity pattern and demographic information useful for inhalation exposure

modeling . Ty

pes of inf ormation i

ncluded a re human activity survey s, standard values for phy siological processes and consumption of food and water, measured exposure data, health status survey s and measurements, nutrition survey s, and data on the spatial distribution of populations. This section provides several of the more notable information sources, some of which are important for inhalation exposure modeling, a nd some of which are important for modeling exposures through pathway s other than inhalation (e.g., ingestion of contaminated fish, soil, and groundwater). Be cause they are so important for an understanding of exposure, we in

trodu ce th em he

re (e ven t houg h the focu s of th

is Cha pter is on i nhal ation ). We will revisit many of these sourc es in Part III (Multipathway Exposure Assessment).April 2004 Page 11-14 Indoor vs. Outdoor Concentrations Indoor air con

centrations may

be an importan

t consideration in an air tox ics risk assessm

ent. Depend ing on the pol lutant a

nd the s ources being

assesse d, conc entration leve ls ma y be s ubstant ially highe r outdo ors, in

one or more indoor micro enviro nmen ts, or i nside v ehicles. In gener

al, pol lutants that have im

portant indoor em

ission sources will have higher concentration s indoors than outdoors. Importa nt indoor emission

sources include com

bustion sources, building materials, consum

er product s, and occupan

t activ ities lik e ciga rette sm oking . Sim

ilarly, polluta

nts that are prim arily emitted by motor vehicle s would be expected to have higher in-veh icle concentrations than at ou tdoor locations distant from roadways.
Informa tion that may

be useful to the v

arious methods u sed to estimate m icroenvironmen

t concent rations is ava

ilable fr om st udies in volv ing m easurem ents of indoor and per sonal ex posure concentrations. These include the following EPA studies:

C The Building Assessm ent, Survey

and Evaluation (BASE) study, which was a cross- sectional study of 100buildin

gs. I nformation re lating

to BAS E is cur

rently being update d to inc lude ba sic summ ary re sults from the

100 bu ilding s studie d. The r

aw data collect ed for t he 100 buildin

gs is scheduled for release soon. (1)

C The Longitudinal Tem poral Indoor Monitoring and Ev aluation (TIME) Study

in federal buildings. (1)

C The Lo s Angele s Tota l Expos ure Ass essment Metho dology (T EAM) st udy, (2) which collected concurrent indoor and ou tdoor samples of 18 VOCs for two con secutive 12-hour periods in 1987, around 45 homes in Februa ry and 40 h omes in J uly.

• EPA Consolidated Human Activity Database (CHAD) . CHAD contains data obtained from huma

n activity studies tha

t were pe rformed at city, state, a

nd nationa

l levels. C HAD is intended to provide

input data

for exposu

re/intak e dose mod

eling a nd/or sta tistical a naly sis. (3) CHAD i s a mas ter dat abase p rovid ing acce ss to other human activ ity datab ases u sing a consistent format. This facilitates access and retrieva l of activity and questionnaire informat ion from those data

bases. The studies contained in CHAD cover a rang e of geographic a reas. In addition to the National Human Activity Pattern Study (NHAPS) with information about residents from 48 states, there ar

e studies targe ting re sidents of

Baltimor e, Cincinn

ati, Denv er, L os Ange les, Valdez, Washington DC, and the states of California and Michigan. B ecause the individual studies differed based on what information was collected, not a ll fields in the CHAD databas e are po

pulated f or all the record s. Each CHAD diary

record consists of a 24-hour sequence of activities. Specified for each activity is a start time, end time, dura

tion, one o f 113 loca

tion code s, and one

of 145 activity codes. Each diary

record is tagged with a CHAD ID, which relates it to a re cord in the demogr aphic da tabase i dentify

ing information a

bout the su

bject of the diar y. D emogra

phic fields include personal characteristics (age, gender, ethnicity , weight), social character istics (education, occupation, income), residential location (state, c ounty, zipcode) and housing charac teristic s (heatin

g fuel, cooking

fuel). I n addition , CHAD ha s the cap ability to estimat

eApril 2004 Page 11-15 the relative metabolic rate for eac h activity in a rec ord using random sampling from distributi ons deriv ed from c linical st udies.

• EPA Exposure Factors Handbook . The Exposure Factors Handbook provides a statistical summary of the ava

ilable data on various parameters and varia bles used in assessing human exposure. This Handbook is used by risk assessors w ho need to obtain data on standard factor s to calc

ulate human exposur

e to toxic chemicals. These fa ctors inc lude huma

n activity factors and residential characte ristics. Recommended values are for the g eneral population and also for various segments of the population who may have characteristics differe nt from the general pop

ulation. I ncluded a re full di scussions

of the issu

es that a ssessors

may want to consider in deciding how to use these data and e xposure parameter recommendations. (The Exposure F actors H andbook i s in final f orm, but a s new dat

a become availab le update s will be posted). (4)

• EPA Hum an Exposure Database

System (HEDS) . HEDS is a web-enabled data repository for huma n exposure

studies. (5) Its mis sion is to pr ovide dat a sets, do cuments, and meta data for human exposure studies that can be easily accessed and understood by a diverse set of users. HEDS provid

es only data and accompa nyi ng docu mentation from res earch s tudies; it does not pr

ovide inte rpreta tions. I t allows a user to download do cuments f or revie w or data sets for analy sis on their own computer system. Curre ntly contained in HEDS are various components of the National Human Exposure Assessment Survey (NHEXAS).

• National Hum an Exposure Assessm ent Surve

y (NHEXAS) . The National Human Exposure Assessment Survey was deve loped by US EPA’s Office of Research and Development (ORD) in the 1990’s to provide information about multimedia and multipathway population exposure to chemicals of va rious types. Phase I

consists of demonstration/scoping studies using probability-based sampling designs. Volunteer participants were randomly selected from several area s of the U.S. These studies included personal exposure, residential concentrations, and biomarker me asurements. The Arizona study measure d metals, p esticide s, and VO Cs. The Ma ry land study

measure d metals, pesticide s, and poly

cyclic aromatic hy

drocar bons (PAH s). The Re gion 5 stu dy, conducte d in Ohio, Michigan, Illinois, I ndiana, Wisconsin, and Minnesota, measured metals and VOCs. Researchers worked with the participants to measur e the level of chemicals in the air they breathe d, in the f

oods and b everages they

consumed

(includin g drinki ng wate r), in the soil and dust around their homes, and in their blood and urine. Participants completed questionnaires to help identify possible sourc es of chemical exposure. Sample collection occurred between 1995 and 1997. The c onfidentiality of participants is strictly protected. Information about the studies can be f ound in the related study e ntries in EIMS and in the Journal of Exposure Analysis and Environmental Epidemiology

. (6)

• CDC Nati onal Healt h and Nutrit ion Exami nation Surve

y (NHANES) . NHANES i s a survey conducte d by

the National Cente

r for He alth Statis tics (NCH S), Cente rs for Di sease Control and Prevention. (7) This survey has bee n designed to collect information about the health a nd diet of people in the Unite d States. N

HANES is un

ique in tha

t it combine s a home interview with health tests that are done in a Mobile Examination Center. The current NHANES is eighth in a series of national examination studies conducted since 1960. The results of these survey s are compiled in databases and summarized in a variety of tables and reports. Data from direct examination, testing, and measure ment of national samples of theApril 2004 Page 11-16 ci vi li an no ni ns ti tu ti on al iz ed po pu la ti on pr ov id e t he ba si s f or (1 ) e st im at es of

me di ca ll ydefined prevale nce in the US and the

distributi on of the p opulation with resp ect to phy

sical, physiological, a

nd psychologic al characteristics, and (2) analy sis of relationships among various measure

ments without refe

rence t o an explic it finite u

niverse of persons. Repor

ts also present information about dietary patterns in various segments of the US population.

• U.S. Census Data . The U.S. Census provides data on the spatial distribution of population and population subgroups at several geog raphic levels: national, state, county , tract, block group and block. (For detailed ana lysis, Summary

File 3 is most useful.) Examples of useful spatially-resolved da

ta for exposure assessment include: population by age, gender, a

nd ethnic group; house heating fuel use; e stimated travel time to work by var ious modes of transpor tation; an d levels o f employ

ment in va

rious indu stries. A ssociate d geog raphic d ata specify ing boun daries o f the var

ious geo graph

ic entitie s for map

ping ar e also av ailable i

n Topolog ically

Inte grate d Geogr

aphic Encoding

and Refe rencing (TI GER) file s. (8)

• LandScan USA . LandScan is a high re solution population distribution database for the continental U.S. currently under development, following the methodology used to create a similar global database called L andScan1998 (updated in 2000). (9) Land Scan use s satellite imager y in population distribut

ion modelin

g to prod uce popu lation dist ribution d ata at a much fine

r resolu tion than previousl

y a vailable . Lan

dScan 199 8 and 2000

have a g rid cell size of 30 seconds (<1 kilometer) and use census data in combination with many other geospa tial data , such as l and use/c over, top ography, slope, ro ads, and nighttime

lights, in order to improve the estimation and prediction of the spatial distribution of residential populations. Future LandScan update s will use a much smaller grid cell size of 3 seconds (<100 meters). Currently , a pilot study in a 29 county area in southeast Texas (around Houston and Port Neches) is being conducted. LandScan will be very useful for exposure modeling , environ

mental jus tice studi es, and o ther ty pes of ri sk assess ments.
11.3.4 Example s of Inhalatio n Exposure M odels Several exposure models have been or are be ing developed by EPA and others for a variety of purposes. Some of the important characteristics that vary among the models include:

• Ambient concentrations

– Modeling or monitoring estimates

– Time scales (e.g., averag ing time)

• Exposure c oncentr

ation time scale

– Ti me in cr em en t f

or ca lc ul at io ns (e .g ., by m in ut e, ho ur ly, se as on al ly, an nu al ly)

– Av er ag in g t im e f

or re po rt in g ( e. g. , h ou rl y, a nn ua ll y)

• Spatial sc ale

– Geogr

aphic re solution of

predict ions (e.g ., Census t

racts, Ce nsus blocks, grids )

– Potential size of modeling domain (e.g., neighborhood, county , nation)

• Population

activity data

– Type (e .g., time in microenvironments, commuting locations, food and water ing estion rates)April 2004 Page 11-17 – Te mp or al re so lu ti on (e .g ., by m in ut e, ho ur ly, se as on al ly, an nu al ly)

– Area sp ecific r

esolution (e.g. , nationa

l or regional)

– Demographic resolution (e.g., by age, gender, or

ethnic group)

• Framework

– Deterministic: inputs and outputs are characterized as point estimates

– Stochastic or probabilistic: inputs and outputs are characterized as distributions repres enting v ariabili

ty a nd/or unc ertainty ; Monte Car lo techni ques are

used to ra

ndomly select input values from the distributions for repeated simulations The remainder of this section provides brief descr iptions of some of the most recently de veloped inhalation exposure

models. The featu

res of e ach mode l describ ed are s ummarized

in Exhibit 11-6.

Exhibit 11-6. Comparison of Inhalation Exposure Model Features Model Population

Activit y Data Source of Ambient

Concentrations Spatial Resolution Framework
HEM-3 none

(screeni ng m odel) IS CST3 census blocks (additional poin

ts can be specified) determ inistic

HAPEM micro-en vironment time/sequence, commuting external model or

monit oring data census tract stochas tic

TRIM.Expo (a.k. a. APE

X) micro-en

vironment time/sequence, commuting external model or

monit oring data depends on

resoluti on of ai

r quality and demo graph

ic input s stochas tic

CPIEM micro-en vironment time/sequence, commuting external model or

monit oring data user-specified for

the selection of activity patterns (e.g., state, reg

ion) stochas tic

Human Exposure Model (HEM) The Huma

n Exposure

Model ( http://www .epa.g ov/ttn/fe ra/huma n_hem.htm

l ) was de signed t

o screen major stationary

sources of air pollutant emissions efficiently , ranking the sources accord ing to the potentia l cance r risks an d noncancer haza

rd assoc iated wit

h long-t erm (an nual) average exposure

concen trations. (10) The current version, Version 3 (HEM-3), is implemented on a Windows p latform f

or ease of use. HEM-3 con

tains a ve rsion of the Gaussia

n atmosphe

ric dispersion model ISCLT2 ( with included meteorological data), and U.S. Census Bure au population data (2000) at the Census block level. A limited amount of source data are required as model inputs (e.g., pollutant emission rates, facility location, height of the emission release, stack gas exit velocity, stac k diameter, temperature of the off-gases, pollutant properties, and source location). HEM-3 estimates the magnitude and distribution of ambient air concentrations of pollutant in the vicinity of each sour ce. The model usually

estimates these concentrationsb EP A ha s dev elop ed the To tal Risk In tegrate d M ethod olog y (TR IM ) for use in the asse ssmen t of air

pollu tants (bo th haza rdo us and criteria). A PE X c omp rises the inh alation expo sure co mpo nent o f TR IM

. April 2004 Page 11-18 within a radial distance of 50 kilometers (30.8 miles) from the source. Exposure conc entrations for the residents of each Census block are a ssumed to be the outdoor concentration at the Census block “internal point.” This actually represents a surrogate f

or exposure, as important exposure variables (e.g., indoor-outdoor conce ntration differences, human mobility patterns, residential occupancy

period, breathing rates) are not explicitly addressed. Multiple facilities (including clusters of facilities, each having multiple emission points) can be addr essed by HEM-3. Variability and unce

rtainty in input data and par ameters are not considered. The Hazardous Air Pollutant Exposure Model (HAPEM5) The late st version

of EPA’s H azardous

Air Polluta

nt Exposure

Model (HA

PEM5) is a s tochastic screen ing-le vel inhal ation expos

ure mode l appropr

iate for assessin g aver age lon g-ter m (annua l) exposures of the general population, or a specific sub-population, over spatial scales ranging from urb an to nati onal (http://www .epa.g ov/ttn/fe ra/huma n_hapem. html ). This application require s a moder

ate leve l of compu

ter mode ling skill s. HAPEM5 uses the general approac h of tracking representatives of spe cified demographic groups as they move among

37 indoor, in-vehicle, and outdoor microenvironments and among geographic locations. The e stimated pollutant concentrations in each microenvironment visited are combined into a time-weighted aver age concentration, which is assig ned to members of the demographic group (the c ohorts). Microenvironment concentrations are estimated fr om outdoor concentrations with the factors method. HAPEM5 uses five primary sources of information: population data fr

om the U.S. Census; po

pulation a

ctivity data fr om CHAD commuting d

ata develope d by

the Bur eau of the Census; u ser suppl ied air q uality data either from measure ments or an air dispersion model; and microenvironmental factors data

. The pre vious version of HA

PEM5, name ly H

APEM4, wa s used in th e NATA na tional sca le assessme nt of the 1 996 NEI

to develo p estimate s of risk, by c ensus tra ct, for e ach of the 33 HAPs ( http://www .epa.g ov/ttn/amt ic/netamap.html ). Specifically, H APEM4 was used to predict population exposure

for eac h of 10 de

mographic gro

ups in eac h tract.
Total Risk I ntegrat ed Metho dology Exposu re Event Model (TR

IM.Expo Inhala tion ), also known as Air Pollutants Exposure Model (APEX) The Air Po ll ut ant s Ex pos ure Mod el ( AP EX) com pri ses th e in hal ati on p ort io n of th e TR IM exposure module, TRIM.Expo ( http://www .epa.g ov/ttn/fe ra/huma n_apex.htm

l ). (b) TRIM.Expo (a.k.a. APEX) uses a persona l profile approach rather than a cohor t simulation approach. That is, individuals are selected for simulation by selecting combinations of demogr aphic characte

ristics a nd finding

an activ ity p attern to

match it, r

ather th an directly selecting an activity

pattern. If the selec

tion probabilities for the demographic charac teristics are the same as within the population to be simulated, this approach will provide a representative sample of that population’s activity patter ns without the need for post-simulation weighting of results.April 2004 Page 11-19 The current version (APEX3, available on the we b) includes a number of useful features including automatic site selection from large ( e.g., national) databases, a serie s of new output tables providing summary statistics, and a thoroughly reorganized method of describing microen vironmen ts and the ir para

meters. T

he model has the ca

pability to estimat

e microenvironment concentration from the mass-balance method, but also provides the option of using the factors method. Most of the spatial and temporal constraints wer e removed or relaxed in APEX3. T

he model’ s spatial r

esolution is flexible

enough to allow f

or the use of finely resolved modeled air quality values, as well as sparser measured value s. Averaging times for exposure c oncentr

ations ar e equall y f lexible. L

ike HAPEM 5, the use

r must supp ly the air qua

lity data (f rom modeli

ng or mon

itoring) to the mod

el. California Population Indoor Exposure Model (CPIEM) The CPIEM (11) is a stochastic inha lation expo sure mode l develop ed for the Califor nia Air Resources Board’s (ARB’s) I ndoor Program to evaluate indoor exposures for the g eneral Californ ia popula tion as we ll as cer

tain sub-populations. CPI EM combine s indoor a ir concentration distributions with Californians’ location and activity information to produce exposure a

nd dose distributions

for diff erent ty pes of ind oor envir onments. The temporal resolution and averaging time are user-selected from the options of 1-hour, 8-hour, 12-hour, and 24-hour. The spatial resolution and modeling domain similarly are specified by

the user according to county, state region, or the entire state. Although outdoor concentrations may be included in the application, the focus is on indoor exposures and indoor emission sources. The model is implemented on a Windows-based platform for ease of use. The model uses location/activity prof iles that were collected in ARB studies. Microenvironment concen trations a re deri ved from measure ment studie s for up to

nine micr

oenviron ments. Concentration distributions from measurement studies for many pollutants and microenvironments are included in the CPIEM da tabase. However, for pollutants and microen vironmen ts not inclu

ded in the databas e, the CPI

EM prese nts two alt

ernativ es. The f

irst is to estima

te indoor air conc entration distribut ions base d on distrib

utional inf

ormation f

or mass balance parameters with a mass-bala nce module. The second is for the user to directly specify concen tration di stribution s. 11.3.5 Exposure Modeling Examples The following applications of air quality modeling and exposure modeling at real-world sites provide useful insights into air toxics modeling. The TRIM.Expo (a.k.a. APEX) inhalation exposure model has also been used with the ISCST3 air quality model to predict human inhalation exposure s. A report docume nting thi s aspect

of the ca se study

will be av ailable a

t: http://www .ep a .gov/ttn/fera/human_apex.html. National-scale Air Toxics Assessment (NATA) . EPA’s NATAis designed to provide a comprehensive evaluation of air toxics exposure and risk across the U.S. Activities include expansion o f air toxic s monitorin g, improving and periodi cally updating emission in

ventorie s, improving national- and local-scale modeling, c ontinued research on health effec ts and exposures to both ambient and indoor air, and improvement of assessment tools. As noted previously , one component of NATA is a National Scale Assessment conducted with the ASPEN and theApril 2004 Page 11-20 HAPEM4 to e stimate a

nnual ave rage e xposure concentra

tions of the

33 urban air toxic po llutants in every US Census trac

t. Specific examples of the results of the National Scale Assessment and additional information on NATA activities can be found on-line. (12)
Houston Cas e Study . This stud y was carri ed out by EPA’s O ffice o f Air Q ualit y Planni ng & Standards (OAQPS) and the Office of Transporta tion and Air Quality (OTAQ ) as a component of th e In te gr at ed Ur ba n S tr at eg y. (13) For the Houston metropolitan area, I SCST3 modeling was applied, using emissions data for point, non-point, and mobile sources from EPA’s 1996 National Toxics Inventory . Ambient air concentrations for numerous air toxics were predic ted at the census tract level with ISCST3 and HAPEM, which we re then employe d to obtain estimates of population exposures. Modeling results were compared to the re sults obtained through studies of this area carried out as part of the N ATA National Scale Assessment. The study demonstrated that mode ling usin

g I SCST3 and a

n improve d emissions

inventor y pr ovides mor

e realistic patterns and better agree

ment with monitoring data. In addition, elevate d concentrations (hot spots) we re found that wer e not dete cted in th e nationa l scale a naly sis.
11.4 Perso nal Monitor ing Thus far, we have focused on monitoring de vices that generally

are located in a secure compound (and some

times on ro of tops) that measu re air q uality that is re present ative of some spec ific geog raphic s cale. A

n alternative to su

ch an ap proach i s to place

monitors di

rectly on individu

als, which allows collection of more detailed information specific to the exposure patter n for that individua l. Such moni

tors are refer red to as personal monitors because they provide information on exposure to that individual, rather than to the general are a in which an individual might be moving. An advantage is that per sonal monitors reflect the time-vary ing concentrations (unle ss th ey are inte gratin g moni tors) an ind ivid ual ex perien ces as h e or sh e move s abou t thro ugh various a ctivities . Persona

l monitors have see

n increa sing use

in rece nt years due

to two fac

tors: they are more readily

available, reliable, and cheaper than in the past, and there is growing evidence that personal exposures may at times be correlated poorly with average values derived for larger ge ographic areas (see Exhibit 11-7). Two modes of personal monitoring have been deve loped. One relies on direct measurements of air concentration for toxics in the breathing zone or otherwise on/nea r the body of a

n individual (these are called direct measur ement methods). The other relies on change s in biological properties such as blood level of an air toxic (or metabolite). The latter is not consider ed here because it does not strictly me asure ambient air concentrations or estimate exposure. Personal monitors, as with area or fixed monitors described previously in this chapter, are available in two typ es:

• Active monitors use a small air pump to draw air through a filter, pa cked tube, or similar device. They

can be b oth contin

uous and in

tegra ted. Such a personal exposur

e monitor i s availab le to meas

ure PM 10 and PM 2.5 in air using a 37 mm Teflon filter and a 4 L /min flow rate. The pump and

battery pack ar e worn in a bag, while the

filter c an be loc ated ess entially anywher e on the body. I

n addition, cyclone pe

rsonal samplers are available for mea suring particulates in air (the term “cy clone” refers to the fact that the sampler measures the particulates by “ spinning” the particles in an air stream, which then c ollect on the sides of the device for collection and analy sis). Combinations of impactor and denuder filter packs areApril 2004 Page 11-21 availab le to sampl e both ae rosols an d gase s such as SO 2 , NH 3, and HNO 3 . Different coating materia ls on the di ffuser

tube can be used to

collect d iffere nt gase s.

Exhibit 11-7. Examples of the Use of Personal Monitoring

• Relationship of Indoor, Outdoor and Personal A ir (RIOPA) study

. (14) Indoor a nd outdoor concentrations of 30 poly cyclic aromatic hydrocarbons (PA Hs) were meas

ured in 55 hom

es in Los Ange les, CA , Hous ton, TX, and Eli zabeth , NJ. Th e study focuse d on are as in ea

ch city characterized by worst-case con ditions in the outdoor air, generally locate d close to major source s. Integrating

MSP samplers, polyurethane foam cartridges, and qu

artz fiber filters were used for the field sampling, and the samp les were analyz ed subsequently in the lab. Am ong man y results, the study showed that indoor air

was dominat

ed by outdoor source s for th ese com pounds , with reasonably strong

correlations between the in door and outdoor air co

ncentrations.

• National Human Ex posure Assessment Sur

vey (NHEXAS)

. (15) The NHEXAS prog ram was design ed to “d escribe the dist

ribution of hum an expo sure to multi

ple chemical s from

mult iple routes on a com

munity and

regional scale, and

its association with env

ironmental concentration s and personal activities.”

It is being

conducted in three stag es: (1) design, field evaluation and demonstration projects; (2) ex posure field studies; and (3) special studies to exam ine issues such as hig hly ex posed p opulati ons and

long- term exposure s. Ext

ensive statisti cal anal

yses o f the da

ta have been perform

ed, including cha

racterizations of back

ground levels of exposure to selected chemicals, as well as correlations amo ng environm

ental concentrations, indiv idual exposures, biomarkers, and survey data on personal activ ities.

• EPA’s T otal Ex

posure Assess ment Meth odolog y (TEA M) studies (16) estimated exposu res of about 800 persons to 25 VOCs; about 300 persons to 32 pesticides; an d 1,200 persons to carbon mono xide. The gen

eral approach in

all fou r of the main TEAM stu

dies wa s the sa

me: a probability-b ased selection of responden ts, so that they w

ould represent a m

uch larger population (e.g., the 800

persons in the TEAM VOC studies actually represen ted about 800,000 p ersons in 8 cities); the use of personal m onitors as well as outdoo r monitors to estim

ate actual personal exposu re; and the use of an O ffice of Manag emen

t and B udget

(OMB)- approved ques tionnai re and activity dia

ry to try to pinpoint local sources. In two of the TEAM Studies for VOCs and carbon monoxid

e, an effort was m

ade to measure body burden, by collecting a breath sample from each of the 2, 000 per sons in

volv ed. This

was im portant in iden tifyin g acti ve sm oking as the m ain source of exposure to b enzene and sty rene, for examp le. Also, the breath measureme nts identified a “dirty dozen

” pollutants that were prev alent in almost ev

ery person. The C enters for Disease Cont rol late r col lec ted bloo d samp les from 8 00 di ffe rent pers ons a nd fo und e sse nti all y the s ame dozen pollutants prev

alent in blood.

• Passive monitors rely on sorption, entra pment, etc., driven largely by diffusion. They

are primarily integ

rated sampling devices, giving a estimate of average exposure over the sampling period. Examples include diffusion tubes, badges, and detec tor tubes. Diffusion badges currently

are available for measurement of NO 2 , O 3 , SO 2 , CO and formaldehy de. Organi c vapo rs can b e measu red in passi ve dev ices u sing ac tivat ed char coal b adges, altho ugh the rang e of comp ounds, as ide from o rganic s, that ca

n be sampl ed in this w ay

is small. Reviews of such methods of personal sampling can be f ound in Bower et al. (1997). (17) However, many of the same

limitations as ambient methods exist, and in some cases additional quantitation limit and pr

ecision problems a

re pres ent.April 2004 Page 11-22 In g eneral , air toxics

risk asse ssments tha

t rely on monitor

ing to characte

rize exposu

re will generally not rely on persona

l monitoring because of the highly complex and resource intensive nature of this technique, and because persona l monitoring and its findings are currently more geared toward basic re search.
11.5 Exposure to a Population: Common Descriptors There are a wide variety

of ways to desc

ribe exposure to a population, some of which may be legally required, others which may be chosen based on the requirements of the risk mana ger. No matter w hat spec ific mea sure is chosen, the risk asse ssment ne eds a cle ar and sc ientific ally supportable rationale for the approach take n; risk assessors generally describe that approach clearly a nd thoroughly in the e

xposure assessment portion of the risk assessment documentation. Risk asses sors aim f

or there to be no am

biguity about wha

t was done

in the expos

ure asse ssment. EPA policy and g

uidance recommend that exposure to a population be described using several different way s to give the risk manager a sense of the range and mag

nitude of the exposures. For example, a “high end” exposure estimate might describe the exposure experienced by a ctual pe op le in th e m os t h ig hl y co nc en tr at ed pa rt of th e a re a o f i mp ac t, wh il e a “c en tr al te nd en cy” exposure estimate might describe the exposure experienced by people in the study are a who experien ce more modest concentra

tions. A variety of

statistical values are used to describe high-e nd and central tendency

exposures, including 95 th percentile exposures (for high-end) a nd 50 th percentile values for central tendenc y. Risk asses sors will w ant to obta in and bec ome famil iar with EPA’s Risk Characterization Handbook to better

understa nd variou s ways exposure and risk c an be ad

equatel y characterized. (18) EPA’s Guidelines for Exposure Assessment (19) is also invaluable in this regard. Some of the

alterna tive appr oaches f or char acterizi ng air t oxics exposur

es are i llustrate d in Exhibit 11-2 above.
11.6 Evaluating Uncertainty
Uncerta inty includes the assum

ptions and

unknown f

actors in herent i n the exposu

re asse ssment. Discussing uncertainty

places the risk estimates in proper perspective. Specific uncertainties associa ted with the chemic al monitor ing data , fate a

nd transp ort models , and the i nput data (especially

emissions inventory data) that asse ssors use to estimate exposure concentrations usually accoun t for the b ulk of unc ertainty

within the

assessme nt. Exposure

models als o contribute to the overall uncertainty in exposure assessment. The assessor needs to understand the extent to which variability and uncer tainty are c onsidered in all the fate and transport and exposure models that are used. HAPEM and other exposure models can acce pt input data on the distributions of time spent in different micro-environments and produce time-avera ge exposure estimates for defined populations. The assessor should be familiar with the extent to which the various components of the exposure assessme nt can an d do acc ommodate

uncerta inty and vari ability

analy ses. I n addition , it is important to consider the compatibility of models in the various steps in the exposure asse ssment (emissions, transport, etc.) with regard to addre ssing important sources of uncertainty . Once the capabilities and data requirements of the various models are known, the assessor should considerApril 2004 Page 11-23 1. U.S. Environmental Protection Agency . 2003. Building Assessment, Survey and Evaluation Study (BASE) Study

, updated June 17, 2003, and Longitudinal Temporal I ndoor Monitoring and Eval uation (T IME)study

in feder al building s. Availa

ble at: http://www .epa.g ov/iaq/la rgebld gs/base _page .htm . (Last accesse d March 2004). 2. Wallace L., W. Nelson, R. Zieng enfus, E. Pellizzari, L. Michael, R. Whitmore, H. Zelon, T. Hartwell, R. Perritt and D. Westerdahl. 1991. The L os Angeles TEAM Study : Personal Exposures, Indo or-Outdo or Air Concentra tions, and Breath Concentr ations of 25 Volatil e Organic Compounds. J. Expos. Anal. Environ. Epidem. , 1(2):157-192. 3. U.S. Envir

onmental Protectio n Agenc y. 2 003. Consol idated Hu man Activ

ity D atabase (CHAD). Updated

Novembe

r 20, 2003 . Availab le at: http://www.epa.gov/chadnet1/ . (La st accessed March 2004). 4. U.S. Environmental Protection Agency . 2002. The Exposure Factors Handbook. Updated Decemb er 30, 20 02. Avail able at: http://cf pub.epa .gov/nc ea/cfm/

exposfac .cfm?ActTy

pe=def ault . (Last accesse d March 2004). 5. U.S. Environmental Protection Agency . 2003. Human Exposure Database Sy stem (HEDS). Updated Februa ry 11, 2003. A

vailable at: http://www .epa.g ov/heds/ . (Last accesse d March 2004). 6. Journal of Exposure Analysis and Environmental Epidemiology , Vol. 5, No. 3, 1995. 7. Ce nt er s f

or Di se as e C on tr ol . 2 00 3. Na ti on al He al th an d N ut ri ti on Ex am in at io n S ur ve y. Updated October 30, 2003. A

vailable at http://www .cdc.g ov/nchs/n hanes.h tm . (Last accesse d March 2004). 8. U.S. Census Bureau. 2004. Index of /census_2000/datasets/Summary_File_3. Availabl e at: http://www2.census.gov/census_2000/datasets/Summary _File_3/ (Last accesse d March 2004). the appropriate level of detail for addre ssing uncertainty in specific variables, and approaches f or integr ating un certain ty a naly ses acr

oss the mod

els.
11.7 Prese nting the R esults of an Ex

posure Ass essme nt The summary of e

xposure assessment for air toxics consists of presenting the ECs for each chemical of potential concern (COPC) with the duration of exposure for the popula tions of interest , as well a s chara

cterizing salient f eature s of the stu

dy p opulation (s), par

ticularl y those that may be influenc ing their exposure and resultant risk (e.g., size and proximity to sources and /or loca tions of hig

hest ambie

nt conce ntrations ). The a ssumptions u sed to dev elop thes e estimates should also be presented and discussed. I n addition to the summary tables, it is useful to sho w sa mp le c alc ul ati ons for eac h pa th way t o ai d i n t he r evi ew o f th e ca lcu lat io ns. (If exposure modeling

is used, a thoroug h discussi on with sam

ple calc ulations is

usually also provided.)
ReferencesApril 2004 Page 11-24 U.S. Census Bureau. Topologically Integra ted Geographic Encoding a nd Referencing sy

stem (TI GER). Upd

ated Mar ch 5, 2004 . Availab le at: http://www .census. gov/g eo/www/t iger/in dex.html . (Last accesse d March 2004). 9. U.S. Environmental Protection Agency . 2003. Port Neches Environmental Justice Study and Demonstration Project for the Development of L andscan USA. Updated December 30, 2003. Availabl e at: http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=22406 . (Last accesse d March 2004). 10. U.S. Environmental Protection Agency . 2003. Human Exposure Modeling - Human Exposure Mo del (HEM ). Updat ed April 2 3, 2003. A

vailable at: http://www .epa.g ov/ttn/fe ra/huma n_hem.htm

l . (Last accesse d March 2004). 11. ICF Consulting. 2002. California Population Indoor Exposure Model, Version 2: User’s Guide . Prepar

ed for the Califor

nia Envir onmental Protection Agenc

y, C aliforni a Air Resources Board. 12. U.S. Envir

onmental Protectio n Agenc y. 2 002. The N ational- Scale Air Toxics Asse ssment. Updated Septembe

r 18, 2002 . Availab le at: http://www.epa.gov/ttn/atw/nata/ . (Last accesse d March 2004). 13. U.S. Environmental Protection Agency . 2004. Technology Transfer Network. Air Toxics Strateg y: O verview . Update d Febru ary 10, 2004. A

vailable at: http://www .epa.g ov/ttn/atw /urban/u rbanpg .html . (Last accesse d March 2004). 14. Naumova, Y., Eisenreich, S., Turpin, B., Weisel, C., Morandi, M., Colome, S., Totten, L., Stock, T., Winer, A., Alimokhta ri, S., Kwon, J., Shende ll, D., Jones , J., Maber ti, S. and Wa

ll, S. 2002. Poly

cyclic Aromatic Hy

drocar bons in the

Indo or and Ou

tdoor Air of Three Cities in the U.S. Environmental Science and Technology , 36: 2552-2559. 15. U.S. Environmental Protection Agency . 2003. Human Exposure Measurements: National Human Expos

ure Asse ssment Sur

vey (NHEXA S). Updat

ed July 21, 2003. A

vailable at: http://www .epa.g ov/heasd /edrb/nhexas.htm . (Last accesse d March 2004). 16. Ozkaynak H., J. Xue, R. Weker, D. B utler and J. Spengler. 1995. The Particle TEAM (PTEAM ) Study : Analysi

s of the Dat

a, Volume

III, Dra ft Final Re

port . U.S. Environmental Protection Agenc

y, R esearc h Triangle Park, N C. Ozkay nak H., J. X

ue, J. Spengler, L . Wallace , E. Pellizza ri, and P. Je

nkins. 199 6. Personal exposure t

o airbor ne parti cles and

metals: r

esults fr om the Par

ticle TEA M Study in Riverside, California. Journal of Exposure Analysis and Environmental Epidemiology , 6(1):57-78. Pellizzari, E. D., K. W. Thomas, C. A. Clayton, et al. 1992. Particle Total Expo

sure Assessment Methodology (PTEAM): Riverside, CA Pilot Study Vol. I . U.S. Environmental Protection Agency , Research Triangle Park, NC. (EPA 600/R-93-050).April 2004 Page 11-25 Sheldon, L ., A. Clay

ton, J. Kee

ver, et a l. 1992. PTE

AM: Monitoring of Phthalates and PAHs in In door and Ou tdoor Air Sa mples in R iverside , California

. Final Rep ort . Califor nia Air Resources Board. Sacramento, California

. Wallace, L. 1987. The Total Ex posure As

sessmen t Methodo logy (TE AM) Stu dy: Summ ary and Analysis: Volume 1 . U.S. Environmental Protection Agency , Office of Research and Development, Washington, D.C. EPA/600/6-87/002a. Wallace, L. 1989. Major sources of benzene exposure. Environ. Health. Perspect ., 82:165-169. Wallace L., W. Nelson, R. Zieng enfus, E. Pellizzari, L. Michael, R.Whitmore, H. Zelon, T. Hartwell, R. Perritt and D. Westerdahl. 1991. The L os Angeles TEAM Study : Personal Exposures, Indo or-Outdo or Air Concentra tions, and Breath Concentr ations of 25 Volatil e Organic Compounds. J. Expos. Anal. Environ. Epidem ., 1(2):157-19. 17. Bower et al. 1997. A Practical Guide to Air Quality Monitoring . National Environmental Technology

Centre, England. 18. U.S. Environmental Protection Agency . 1992. Guidance on Risk Cha racteriza tion for Risk Managers

and Risk A ssessors . Risk Assessment Council, Washington, D.C., February 26, 1992. 19. U.S. Environmental Protection Agency . 1992. Guidelines for Exposure Assessment

. Federal Register 57:22888- 22938, Ma y 29 , 1992. Av ailable a

t http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=15263

# Chapter 1 2 Inhalation Toxicity Asses sment

Table of Contents

12.1 Introduction

............ ............ ............ ............ ............ ...... 1 12.1.1 Hazard I dentification and Dose-Response Information

............ ............ . . 1 12.1.2 Dose-Respo

nse Assessment Methods

............ ............ ............ ... 5
12.2 Hazard I dentification

............ ............ ............ ............ .......... 8 12.2.1 Weight

of Ev idence – Hum

an Carc inoge

nicity

............ ............ ........ 9 12.2.2 Iden

tification of Cr

itical E ffect(s) – Non- Cancer Endpoi nts

............ ......... 11
12.3 Dose- Respon se Asse ssmen t for Ca ncer Ef fects

............ ............ ............ . 12 12.3.1 Determination of th e Point of Departur e (POD)

............ ............ ...... 13 12.3.2 Derivation of the H uman Equiv

alent Concentration

............ ............ . . 14 12.3.3 Extrapolation from POD to Derive Carcinogenic Potency Estima tes

............ . . 17
12.4 Dose-Respo

nse Assessment for Derivation of a Reference Concentration

............ .... 19 12.4.1 Determination of the Point of Departure an d Human E quivalent Concentration

..... 20 12.4.2 Applica

tion of Uncerta

inty F actors

............ ............ ............ .... 22
12.5 Sources of Chronic D ose-Response Values

............ ............ ............ .... 24
12.6 Acute Exposure Refere nce Values

............ ............ ............ ........... 26
12.7 Evaluating C

hemicals Lack

ing Health Referen

ce Values

............ ............ ..... 31 12.7.1 Use of Available Data Sources

............ ............ ............ ....... 31 12.7.2 Route-to- Route Extrapolation

............ ............ ............ ........ 31
12.8 Dose-Respo

nse Assessment for Mixtures

............ ............ ............ ...... 32 References

............ ............ ............ ............ ............ ............ 35April 2004 Page 1 2-1

Risk = ƒ (metric of ex

posure, metric of toxicity) Toxicity Assessment is a 2-Step Process : 1. Hazard Identification– What types of effects does the chem ical cause? Under w

hat circumstances? 2. Dose-response Assessment – How potent is the chemical as a carcinogen and/or for noncancer effects? 12.1 Introduction The purpose of the toxicity assessment is to weig h available evidence rega rding the potential for toxicity in exposed i

ndividual s ( hazard identification ) and to quantify the toxicity by deriving an appropriate dose-response value ( dose-re sponse asse ssment ). Toxicity assessment is the second part of the genera

l risk equation. Although the toxicity assessment is an integ ral and important part of the overa

ll air toxics

risk asse ssment, it i s usually accompl ished pri or to the r isk assessme nt. EPA has

complete d this toxicity

assessme nt for man

y H APs and ha s made av ailable the resulting toxicity information and dose-r esponse values, which have undergone extensive peer review (see Appendix C). 1

In most air toxics risk assessments, little new toxicological evaluation of primary data will be require d. Howev er, it is imp ortant to u ndersta nd how the

availab le data w ere ana

lyze d to produce the dose-responses values used in a risk asse ssment. In the risk characterization step, the risk assessor will need to describe the nature of the ava ilable toxicological evidence and the uncerta inties inhe

rent in the

development of the

dose-re sponse va

lues used in the inha

lation risk assessment (see Chapter 13). Additionally, in the event that there a re significant data analy

sis and interpretation issues, or if a dose-re sponse va

lue does n

ot exist and n

eeds to be develope d for a pa

rticular air toxic of

interest , this chapter provides information about how to locate toxicity a ssessments, accompanying doseresponse values, and relevant guidanc e documents. However, development and interpre tation of toxicity information and dose-response value s requires toxicological expertise and should not be undertaken by

those without appropriate training and experience.
12.1.1 Hazard Identification and Dose-Response Information As part of the hazard identification step, evidence is ga thered from a variety

of sources regarding the potential for an air toxic to cause adverse health eff ects in humans. These sources may include h uman data , experime

ntal anima

l studies, a

nd suppor ting inf ormation s

uch as in vitro laboratory te sts. The source of data affects the overall uncertainties in the resulting human doseresponse values, as discussed below.a A PBPK model e sti mates the dose t o a ta rget ti ssu e or or gan by

tak ing int o accoun t t

he ra te of

absor pti on into the body, dist

ribution among target organs and t issues, metabolism, and excretion. April 2004 Page 1 2-2 Epidemiology

is the study of the distribution and determinants of disea se or health status in a p opulation. • Human data . Human to xicity data ass ociated

with exposur

es to air t oxics may

be locat ed in epidemiological studies, controlled exposure studies, or studies of accidental exposures. Well-cond

ucted ep idemiological studi es that show a positi ve assoc iation between exposure to a chemic al and ad

verse he alth eff

ects ofte n provide evidenc e about hu

man heal th effe cts associated with chronic exposures. Such data , howeve

r, are a vailable only for a limi ted numbe

r of air toxics. Epidemiological data also are very diffic

ult to interpret, because the number of exposed individuals may be small, the incidenc e of effects may be low, doses are usually not well-char

acterized, and there may

be complicating factors such as simultaneous exposure to multiple chemicals and heterogeneity among the exposed group in terms of age , sex, diet, and other factors. Controlled exposure studies provide stronger evidence , since both the exposure duration and exposure concentrations are more ac curately know n. However, such studies with humans are gene rally limited to acute exposure dura tions. Studies re porting health e ffects a ssociate d with acc idental exposures ma y be helpful, although exposure concentrations to air toxics may be high, and effects may

be acute rather than chronic. Also note that small sample size is often a significant limitation to interpreting controlle d and ac cidenta

l exposure

studies.

• Animal data . The toxicity database for most air toxics is drawn from e xperiments conducted on non-human mammals such as rats, mice, rabbits, guinea pig s, hamsters, dogs, or monkey s. The underlying

assumption is that the susceptibility of humans and these animals to the effects of the chemicals is broadly similar because we share many common biological attributes (e.g., similar organs, similar and, in some ca ses, identical metabolic processes). Howeve r, some ob servations in anima ls may be of unc ertain r

elevanc e to human s (e.g. , if tumors are observed in an animal experiment, but the organ in which the tumor is formed does not exist in humans ). Also, it is neces sary

to adjust the results

from animal studies

to humans due

to differ ences in body mass, ana

tomy , metabol ic rate, and other species -specif

ic factors (see, for example, Section 12.3.3). This is why derivation of dose-response values from animal studies requires considerable expertise.

• Supporting data . Metabolic, pharmacokinetic, and genotoxicity studies are sometimes used to infer the likelihood of adverse effects in humans. Metabolic studies on a bsorption, distribution, metabolism, and elimination can provide information about the mechanisms of toxicity associated with a particular c hemical in humans. In phy siologically base d pharmac okinetic

(PBPK) mo dels, (a) the body is subdivided into a series of a natomical or physiological “ compartments” that represent specific org ans or lumped tissue and organ groups, and the behavior of the chemica l is modeled in each compartment. Data on a chemical’s pharmacokinetics, genotoxicity, and possible mode of action can be used to refine a toxicity assessment. I

n some cases, computer models using structure-ac tivity relationships (i.e., predictions of toxicological activity based on analy sis of chemical structure) also may be used as supporting evidence. EPA considers these ty pes of data to be supportive, not de fi ni ti ve , e vi de nc e o f a ch em ic al ’s to xi ci ty.b W hile the majority of RfC s are derived for effects other than cance r, RfCs may b e derived for all effects,

includ ing can cer, wh en a no n-linear m ode of actio n has b een d emo nstrated for car cinog enicity. April 2004 Page 1 2-3 Information f

rom these sources is conside

red in the hazard a nd dose-r esponse a ssessmen

t steps in characterizing a chemical with re gard to the ty pe(s) of effect a chemica

l produces (the hazard) and the circumstances in which this occurs, as well as the leve l of exposure required to produce that effect. The output of the dose-response asse ssment is the relationship between dose (the level of ex posur e) and t he resu ltin g response (the increased incidenc e and/or

severity of adver se effects). A dose -respon se asses sment is the process of quanti tatively evaluat ing toxicit

y information, characterizing the relationship betwee n the dose of the contaminant received (or the inhalation exposure

concen tration, f or inhala tion asse ssments) a

nd the inc idence o f adverse health effects in the exposed subjects (which may be animal or human) and then, as appropriate, extrapolating these results to human populations. Depending on the ty pe of effect and the chemica l, there are two t ype s of dose-response

values that tradit

ionally may be deriv ed: predictive cancer risk estimates, such as the inhalation unit risk estimate (IUR) , and predictive non-cancer estimates, such as the reference concentration (RfC)

. (b) Both types of dose

response values may

be developed for the same chemical, as a ppropriate.

The relationship of dose to response can be illustrated as a g raph called a dose-response curve

. There are two gener

al types of re sponse data that may be considered and graphed. One is termed “continuous” and refers to responses such as the se verity in chang es to a physiologic al parameter in a given individual as dose increases (see E xhibit 12-1, A) . The second describes the incidence of a particular response in a population (see Exhibit 12-1, B). By convention, dose or exposure is represented on the x-axis; response on the y- axis (Exhibit 12-1). Inhalation Dose-Resp onse Values (a)
Inhalation Unit Ris k (IUR) : The upper-bou nd excess lifetime c ancer risk estim ated to result from continu ous exp osure to

an ag ent vi a inhala

tion per μg/m

3 over a lifetim

e. The interpretation of the IUR wo uld be as follows: if I

UR = 2 × 10 -6 μg/m 3 , not m ore than 2 exces s tumors are expecte d to devel op per 1 ,000,0 00 peop le if exp osed continuou sly for a lifet ime to 1 μg of the chemic al per c ubic meter of inhaled a

ir. The number o f expected tumors is likely to be less; it may even be none.
Reference Conce ntration (Rf C) : An estimate (with uncertainty span ning perhaps an order of magnitude) o f a continuous inhalation exposure to the hum an population (including

sensitive subpopulations) that is likely to be without an ap

preciable risk of deleteriou s effects during a lifetim e. Generally used in EPA's no ncancer health assessm

ents.

(a) The phras e “dose-r

esponse” i s used ge

nerally here and e lsewhe

re in the doc

ument. EPA ’s value s for inha lation, howe

ver, are derived for exposure concentration, although with consideration of dose. Consideration o f the relationship between exposure c oncentration,dose, and dosi metry (how the body

handles a chemic al once it is inhaled) is inherent in the deriv ation of these exposure concentration-response values.April 2004 Page 1 2-4

Exhibit 12-1. Examples of Dose-R esponse Curves

A. Co ntinuo us Resp onse D ata Simple exam

ple of a dose-response curve for graded responses o f a specific physiolog ical parameter to increasing

exposure. B. Different Responses in a Popu lation Simple exam

ple of the incidence of th ree differen t effects in an exposed

popula tion in response to different exposu re concentrations (over the sam

e durati on).
While the pr

imary focus of this chap ter is on d

escripti on of dose -respon se value s releva nt to chronic (long-term) exposures, the information reviewed for developing those values may include e ffects a ssociate d with acu te (shor t-term)exposures . Additiona lly, informat

ion on acu

te exposures is essential to the development of acute exposure referenc e values (see Section 12.6).

• Acute exposures are usually r elatively short in duration, but rela tively high in conc entration an d m ay r es ul t i n i mm ed ia te re sp ir at or y an d s en so ry i rr it at io n, ch em ic al bu rn s, na rc os is , e ye damage , and var

ious other effects. Acute

exposures

also may result in l onger

-term he alth effects.

• Chronic exposures are usually r elatively long

in duration, but relatively low in conc entration and may re sult in health effects that do not show up immediately a nd that persist over the long term, such as cardiovascular disea se, respiratory disease, liver and kidney disease, reproductive effects, neurological damage, and cance r. Generally , chronic reference va lues are derived for exposure periods betwee n seven yea

rs and a lifetime. Acute reference va lues (see section 12.6) are ge nerally deve loped for very short exposures (e.g., hours to days; Exhibit 12-2). For intermediate exposures, subchronic reference values a re avai lable fr

om some sou

rces (e .g., AT SDR). Most a ir toxics risk assessments will focus on c

hronic a nd acute evaluat ions; howe

ver, und er more limited cir

cumstanc es, subch

ronic evaluations may be pe

rformed.April 2004 Page 1 2-5

Exhibit 12-2. Reference Values of Different Durations In the Agency’s Review of the Reference Dose and Reference

Concentration Processe

s , 2 it was recom mend ed that in addit

ion to t he tradi tional chronic r

eferenc e valu

e (i.e. , RfC o

r RfD) include d in the IRI S database, value s of several shorter d urations also be developed, where possible. As a first step in this direction, the Review proposed the following

definitions. EPA c

urrently is considering these and other recom

mendations m

ade in the Review . These definiti ons are based o n expos ure durations for hum

ans, and were not intended to be rigid specifications, but sim ply general d

escriptions of the relevant expo sure time period.

• Acute : Exposure by the oral, dermal, or inhalation route for 24 hours or less.

• Short-term : Repe ated ex posure (a) by the oral, d

ermal, or inhalation

route for more than 24 hours, up to 30 days.

• Longer-term : Repeated exposure by the oral, derm al, or inhalation route for more than 30 days, up to approxima

tely 10 percent of the life span in hum ans (b) (mor

e than 3 0 day s up to 90 day s in typically used laboratory anim al species (c) ).

• Chron ic : Repe ated ex posure by the

oral, d ermal , or inhalation route for mor e than a pproxim ately 10 perc ent of t

he life s pan in h uman s (more than approxi matel y 90 d ays to 2 yea rs in ty pically used lab oratory

animal speci es).
(a) A rep eated expo sure m ay be e ither co ntinuo us, per iodic , or interm ittent. A co ntinuo us exp osure is a daily exp osure for the to tal dura tion of inte rest. A p eriod ic exp osure is one o ccurr ing at reg ular interv als (e.g., inhalatio n exp osure 6 ho urs/da y, 5 da ys/week ; or or al exp osure 5 da ys/week ). An inte rmittent e xpo sure is o ne in which there is no effect of one exposure on the effect of t he next; thi s definition implies suf ficient time for t

he chemical and its metaboli tes to clear t he biological sys tem before the subsequent (i. e., noncumulative pha rmac okine tics). A p eriod ic exp osure may o r may n ot be intermitten t.
(b) An avera ge of 70 years is typical default used for chro nic expo sures.
(c) Exam ples of typically used labo ratory species includ e rats, mice, and rab bits.

12.1.2 Dose-Re sponse Asse ssment Methods Depending on whether a substance c auses cancer and whether its dose-r esponse curve is thought to have a threshold, EPA may use either of two approaches in a dose-r esponse assessment. One approach produces a predictive e stimate (e.g., inhalation cancer risk estimate), a nd the other produces a reference

value (e.g., RfC). Historically, the use of a predictive estimate has been limited to cancer assessment. That is, dose-response assessments for c ancer have been expressed as predictive cancer risk estimates based on a n assumption that any amount of exposure poses some risk. Assessments of effects other than cance r usually have be en expressed as reference values a t or below

which no harm is expe cted. Ma ny s ubstance s have be en asses sed both w ay s: the first for cancer and the second f or adverse effects other than c ancer. While this use of predictive estimates for cancer and r eference values for other

effects is still the practice for the vast major

ity o f chemic als, EPA now recog nizes that t

here ar e chemic als for w

hich the d ata support an alternate approach.April 2004 Page 1 2-6 All substances are poison s: there is none which is not a poison . The right dose differentiates a poison and a remedy. – Paracelsus

Different Responses Exhib it Different Dose-Response Cur ves

Line A – A sharp

increase in response with increasing dose Line B – A m

ore gradual increase in

response with increasing dose An importa

nt aspect of dose-response relation ships is whe

ther the availab le evide

nce sug gests the existence of a threshold. For many types of toxic responses, there is a threshold d ose or dose rate below which there are thoug ht to be no adverse effects from exposure to the chemica l. The huma n body has defe nses ag ainst many

toxic age nts. Cells in

human organs, espe cially in the liver and kidneys, br

eak down many

chemicals into less toxic substances that can be eliminate d from the

body in urine a

nd fece s. In

this way , the huma

n body can withstand some chemical exposure (at doses below the threshold) and still remain healthy . For example, many air toxics are naturally oc curring substances to which people routinely receive trace exposures at non-toxic le

vels.
Identification of a threshold dose de pends on the typ e of res

ponse and the way

in which the toxic chemical produces it. EPA has developed guideli nes 3 for assessing the dose-response for various types of a

dverse effects, which provide more information a

bout eval uating e vidence to determine if a thr

eshold exis ts. Both the point at which the dose-response curve begins to ascend (its threshold, which may be zero)

and the slope of the cu rve (its steepness) provide information about the toxicity of a chemical (Exhibit 12-3). The potency of a che mical is a measure of its stre ngth as a toxicant compared w

ith other chemicals. Therefore, the lower the threshold dose, the more potent (or toxic) the chemical. The slope of the curve is a measure of the range of doses from the thre shold dose (at which the adverse effect is first measur ed) to the dose at which the effect is complete (i.e ., higher doses pro duce no a

dditional i

ncidenc e of that

effect, althoug

h other a dverse e ffects may

begin to appear). The steeper the dose-response curve, the smaller the rang e between the first appearanc e of an effect and a substantial response.April 2004 Page 1 2-7 Exhibit 12-3. Dose-Response R elationships for Carcinogen s and Noncarcino gens A. Example Linear Carcinogen

In t he abs ence of

cle ar ev idenc e to t

he cont rary , EPA ass umes a s a mat ter of s cien ce pol icy

tha t ev en a ve ry l ow exp osure to a ca ncer-c ausing polluta nt can in creas e the risk o f cance r (albe it a small am ount) . Exp erime ntal da ta are u sed t o const

ruct a dose- respon se r elations hip a nd ide nti fy the point of depa rture – t

he dose tha t ca n be co ns i de re d t o b e n ea r o r i n t he ra ng e o f

ob se rv ed re sp on se s and , t hu s, no si gn i f i ca nt ex t rapol at i on i s n ee de d. To estima te the d ose-re spo nse rela tionship at do ses be low the poin t of dep arture, th e do se-resp onse relation ship betw een the poin t of dep arture a nd ze ro is assu med to be linear. T hus, at d oses b elow the po int of de partu re, with each unit of i ncrease in exposure (dose), there i s an increase i n cancer response. Where evidence supp orts the ac ce pt an ce of

a n on- li ne armod e of

ac ti on, a r ef

er en ce con ce ntra ti on app ro ac h may b e e mpl oy ed, as sh ownin “B” bel ow. LEC 50 = l et hal

ef fe ct iv e co nce nt ra ti on f

or 5 0 per cen t o f t

he p opul at io n; EC 10 = effec tiv e concentr ation that causes an observable adverse effect i n 10 percent of the population. B. Example Non -linear Approach

A do se ma y exist be low the minim um he alth effect lev el for wh ich no adve rse effec ts occu r. EP A typic ally assumes that at

low doses the body's nat ural protective mechanis ms prevent or repair any damage caused by the poll utan t, so there is no il l ef fect at l ow doses . Even

long -t erm (c hroni c) ex posure s bel ow th e th resh old ar e not expected t o have advers e eff ects. The dose-res ponse rel ations hip (t he respons e occurri ng wit h incr easing dose) varies w ith pollu tant, indiv idual se nsitivity, and type o f health effe ct. NOEL = n o- obs er ved -e ff ect -l eve l; NOAEL = no -obse rved -adve rse-effec t-level; LO AE L = lo west-ob serve d-ad verse -effect-leve l.April 2004 Page 1 2-8 Items to Include in the Hazard Identification of an Air Toxics R

isk Assessment

• List of chemicals d

etected

• Summaries

of toxic effects and qua

lity of the toxicological evid

ence

• Discussion that focuses the risk assessm

ent on chem icals m ost lik ely to cause a dvers e effect s Epidemiologic and toxicologic data on air toxics typic ally result from exposure leve ls that are high relative to environmental levels. Therefor e, low-dose extrapolation (prediction) is necessa ry to derive an appro priate d ose-re sponse va

lue. For a few a ir toxics (e .g., the

criteri a air pollutants ozone and

carbon m

onoxide), d

ata are sufficie nt to char acterize dose-re sponse relation ships at e

nvironme ntal leve ls. In

such cas es, ther

e is no nee d for extra

polation o f toxicity data to lower doses. Such is not the case for most air toxics. Low- dose extrapolation requires either information or assum ptions abo

ut the ty pe of dose -respon se curve likely under low

dose situations . EPA risk a ssessmen t guidel ines prov

ide more detailed informat ion on how E

PA performs low-dose extrapolation for chemicals with various toxic effects, such a s developmental effects or neur

otoxic eff ects. (3)
12.2 Hazard Identification The haza rd identif ication, which is us

ually part of an existing dose-response assessment for each chemical, provides a summary of the availab le toxicity

informat ion for th e air toxics being studied, and includes the weight of evidence determination and identification of critical effects. This step should answer the follow

ing que stions:

• Can exposure to a chemical be linked causall y to particu lar hea lth effe cts?

• Could these

effects occur

at envir onmental ly r

elevant concen trations?

• What is the nature and strength of the evidence of causation? By def inition, all HAPs and many other air toxics have the potential to cause adverse effects in the e xposed population. Exhibit 12-4 provides examples of cancer and non-cancer effects, and Appendix C identifies which HAPs have been associated with carcinogenic (c ancer) effects or non-c ancer effects, along

with the st

rength and rati ngs of the toxicity evidence that has been evaluated by EPA or other international environmental ag encies. Exhibit 12-4. Examples of Advers e Health

Effects

• Birth d

efects

• Tremor s

• Infe rtility

• Skin rash

• Mel anoma

An air toxic s risk asse ssment sho uld includ

e in its haza

rd identif ication a summary of the qua

lity of the toxicological evidence (i.e., the nature and stre ngth of the evidence of causation) f or the chemicals of concern. Study factors such as the route of exposure used, the ty pe and quality of health e ffects, the biolog

ical plau sibility

of findin gs, and the consi stency

of findin gs acr oss studies a ll contrib ute to the streng th of the hazard ide

ntificat ion statem

ent.April 2004 Page 1 2-9 12.2.1 Weight of Evidence – Human Carcinogenicity A major determination made during the hazard identification step c oncerns the potential of a chemical to cause cancer in humans. This deter mination, which involves considering (or weighi ng) all the avai lable evidence, is called the weig ht of evid ence de terminat ion. This determination is complicated by possible inade quacies of the published studies, as well as differe nces in bo dy processe s betwee n people a

nd labora tory animals. E

PA’s Guidelines for Carcinogen Risk Assessment guide scientists in interpreting available studies to assess the potential human carcinogenicity of environmental pollutants. (EPA’s carcinogen risk asse ssment guideli nes wer e first pu blished in 1986. Revi sions wer

e propose d in 1996 a

nd 2001 an

d the July 1999 draft of the revisions was adopted as interim guidanc e. A subsequent 2003 draft of the Guidelines has been released for public and sc ientific review prior to adoption as final. The guidelines are available on the web.) 4 When compared with EPA’s original 1986 guidelines, the 1999 inter im Guidelines recommend a more compr ehensive evaluat ion of the evidenc e with reg ard t o a chemic al’spotential

mode of action, and a

more comp lete descripti on of

the c onte xt of a che mical’s c arcinog enic pote ntial (e. g., “lik

ely carcinogenic

by i nhalation and not li

kely carcinogenic by

oral exposure”). The weight of evidenc e determination now includes one of five descriptors, and is accompanied by additional text that more completely summarizes EPA’s interpretation of the evidence. The narr ative statements consider the quality and adequacy

of data and the consistency

of responses induced by

the agent in question (see Exhibit 12-5).

Exhibit 12-5. Information Regularly Includ ed in a Narrative S tatement Describing the Characterization o f Weight of Evidence for Carcinogen icity (1999 Interim Guidelin es)

• Name of ag ent and

Chem ical Ab stracts S ervice s num ber, if availa

ble

• Conclu sions (by rout e of exp osure) a

bout hu man c arcinog enicity , using one of five s tandard descr iptor s: “Car cinogen ic to Hu mans” “Li kely to be C arcin ogenic t o Humans” “Suggest ive Evide nce of C arcinog enicity , but N ot Suffi cient to Assess Human Carc inoge

nic Potential” “Data are Inadequate for An Assessm ent of Human Carcinogenic Poten tial” “Non Likely

to be Carcinogenic to H

umans”.

• Summary

of human an

d animal tum

or data on the ag ent or its structural analog ues, their relevance, and bio logica

l plausi bility

• Other key data (e.g., structu re-activity

data, toxicokinetics and metabolism , short-term studies, other relevant toxicity

or clinical data)

• Discussion of possible m ode(s) of action and app

ropriate dose-respo

nse approach(es)

• Conditi ons of expressi on of ca rcinog enicity , inclu

ding r oute, d uration , and m

agnitu de of ex posure Source : EPA ( 1999) Guideli nes for Carcin ogen Ri

sk Asse ssment. Review

Draft

(4) Many existing carc inogen assessments were developed pursua nt to EPA’s 1986 Guidelines for Carcinogen Risk Assessment , which used a simpler but less informative weight of evidence system (see Exhibit 12-6). Information bearing

on the qualitative assessment of carcinogenic pote ntial may be g ained from human epi demiological data , animal st udies, comparativ e pharma cokineti c and met

abolism studies, genetic toxicity studies, structure- activity relationship (SAR) analy sis, and other studies of an agent’s properties. I nformation from these studies helps to elucidate potential modes of action and biological fate and disposition.April 2004 Page 12-10 Exhibit 12-6. EPA’s Weight of Evidence C lassification for Carcinogens (1986 Guidelines) Group A: Human Carcin ogen (sufficient ev

idence of carcinogen

icity in hum

ans) Group B: Probable Huma n Carcinogen (B 1 - lim

ited evidence of carcin

ogenicity in h

umans; B2 - sufficie nt evi dence o f carcinogeni city in animals with

inadeq uate or lack o f evid

ence in humans) Group C: Possibl e Hum an Carc inoge

n (lim ited ev idence of carci

nogen icity i n anim als with inadequate or lack o f human data) Group D: Not Classifiable as to H uman Carcinogenicity (inadequate or no evidence) Group E: Evide

nce of Noncarci

nogen icity f or Hum ans (no evide nce of c arcinog enicity

in adeq uate studies)
Source : EPA (1986). Guidelines for Carcinog en Risk Assessment (4) Upon such consideration, both EPA systems assig n a consensus interpretation to the weight of evidence, evaluating the likelihood that the ag ent is a human carcinogen. Toxicological evide nce is characterized separately for human studies and animal studies as: sufficient, limited, inadequate, no data, or evidence of no e ffect. The characterizations of these two ty pes of data are combined , and bas ed on the e xtent to whic

h the ag ent has be en shown t o be a ca rcinog en in experimental animals or humans, or both, the chemical is given a weig ht of evidence classification. Generally , no single factor is determinative. For example, streng th of association is one of the criteri a for ca

usality . A strong

associa tion betwe en exposur

e and ca ncer in a nimals is more likely to indicate causality than a weak association. However, finding of a large cancer incide

nce in a single study must be balanc ed against the lack of consistency as reflected by

null results from other equally we ll-designed and well-conducted studies. I n this situation, the positive association of a single study

may either suggest the presence of cha nce, bias, confounding factors, or different exposure conditions. On the other hand, evidence of weak but consistent associations across s everal

studies su

gges ts either

causali ty or that the

same confounder

may be opera ting in all of the se studie s. If information is availa

ble to con sider the

mode of a

ction for carcinogenic ity, the car

cinoge nicity assessme nt will eva

luate tha t informa tion and dr

aw conc lusions tha

t influenc e the dose -respon se method for the substance. Ifthe evidence is sufficient to support a conclusion of nonlinear doseresponse, then the information on carcinoge nicity may

be considered in combination with the information on other effects in deriving a re ference value such as an RfC (see section 12.4). Otherwi se, a line ar dose-response approac h leading to a pred ictive ri sk estimat

e, such a s an I

UR, will usually be pursued. If the information supports it, the guidelines also accommodate the development of a non-linear predictive risk estimate.c A similar, more re cent term, “key event,” is defined as “an emp irically observed p recursor” to an adverse

eff ect (e. g., li ver c ancer

or ot her l iver

toxi cit y) c onsi ste nt w ith a parti cular mode of act ion. The phras e “mode of action” refers to the wa y a given chem ical may act in the bod y to initiate one or more adverse effects. April 2004 Page 12-11 Biological Effects of Carcinogens Carcinogens are chemicals that in duce cancers. Exam

ples include:

• 4-Aminobyphenol , which targets the b ladder;

• Benzene , which targets the tissue that mak e white blood cells;

• Asbestos , which targets the lung’s tissue;

• Benzidene , which

targets the b ladder;

• Beryllium , which targets the lungs;

• Chromium , which

targets the re spirator

y trac t;

• Radionucleotides , which targets b one marrow an d the lungs; and

• Vinyl chloride , which

targets the li

ver. Ther e are var ious typ es of ca rcino gens, in cludi ng:

• Primary Carcinogens : A primary carcinogen is a su

bstance that is carcinog enic as it occurs in the enviro nmen t.

• Procarcinogen : A procarcinoge n is a substance that bec omes carcinog enic only after conv ersion from som e benign form . Most environm ental carcinogens are of this type.

• Cocarcinogen : A cocarcinogen is a substance that is n

ot carcinogenic by

itself, but potentiates the carcinogenic effect of o

ther chemicals.
Chemicals also ca

n serve as mutagens , causi ng ch anges

in ge netic m aterial t hat can disrupt cell function and lead to can

cer or other health problem s.
12.2.2 Identification of Critical Effect(s) – Non-Cancer Endpoints As part of the characterization of the available information on non-cancer health effe cts (or including cancer, if a threshold mode of a ction has been established), the targets of c hemical toxicity within the

body are ide ntified, a long with what hav e been te

rmed “critical e ffects” associated with the toxicity. A critical effectis descr ibed as “ either the adverse effe ct that fi rst appears in the dose scale as dose is increa sed, or as a known precursor to the first adver se effect.” Underly ing this d esigna tion is the a ssumption that if the c ritical e ffects a

re prev ented, then all other adverse effects observe d at higher exposure concentrations or doses are also prevented. (c) Note that not all observed effects in toxicity studies a re considered adverse eff ects. The identific ation of the critic al effe ct(s) de pends on a

comprehensive r

eview of the avai lable da

ta with car eful con siderati on of the e xposure conditions a ssociate d with eac h observe d effec

t, so that comparisons of effect levels or potential refe rence values are made on a c ommon basis (see Section 12.4). A more comprehensive discussion of hazard identification and the e valuation of the under lyin g datab ase for

non-can cer ef fects is i ncluded i n the EPA do cuments Methods for Derivati on of Inhal

ation Refe

rence Co ncentrat

ions and Ap

plication o

f Inhalatio n Dosimet

ry (1994) and A Revie

w of the Re

ference Dose and R eferenc e Concen tration Pro cess(2002). 5April 2004 Page 12-12 12.3 Dose-Response Assessment for Cancer Effects The process for deriving a quantitative dose -response estimate for cancer ( e.g., a cancer slope factor ) involve s the following thr ee steps : 1. Determination of the concentration associated with the point of depa rture or POD (Section 12.3.1); 2. Derivation of the human equivalent concentration corr esponding to the POD (Section 12.3.2); and 3. Extrapolation from the POD (expressed as human equivalent concentration) to derive carcinogenic potency

estimates (Section 12.3.3). The first two steps are also performed in the deriva tion of reference values such as the RfC (Exhibit 12-7); in that case, these steps are followed by the application of uncertainty factors (see Section 12.4).

Exhibit 12-7. Steps involved in deriving an RfC or IU R From an Animal Studyd Note that the c orresponding value for ing estion exposure s is the benchm ark do se (BMD ). This often is used as the g eneral term for the BMC/BMD process. April 2004 Page 12-13 Example POD for Benzene EPA’s characterization of the carcinogenic e ffects of benzene was updated in 1998. The IUR for benzene is based on epidemiolog ic studies showing clear evidence of a causal association between exposure to benzen

e and leukem

ia. The specific mec

hanisms by

which benzene and its metabolites lead to cancer rem

ain uncertain.
EPA selected the Rin sky et al. 1981 epidemiolog ic study of 1,16 5 Pliofilm rubber male work ers at three facilities in Ohio as the data set for the dose- response relationship for determ ining the I

UR. The worke rs had b een em

ploye d betwe en 1940

and 19 65 and were fo llowed throug h 1981. Rinsk

y et. al. expanded the study

to include additional wo rkers and published it in 1987. The Rinsk y data suffers - as ma ny ep idemi ologic

studies do - from uncerta inties a bout ex posure levels

in the e arly y

ears. There are no measurem

ents of benzene in

the facilities’ air prior to 1946 , so exposures for thes e years mu st be estimated. Using one set of expo sure est

imate s with t

he Rinsky e

t al. stu dy, EPA concluded that exp osure to benze ne incre ases the

risk o f leuk

emia at a lev

el of 40 ppm- years of occu

pationa l exposure (8 hours /day, 5 da ys/week, 50 weeks/ year). Below th is number , the sh ape of t he dose -respons e curve cannot be determin

ed. Converting

the occupational exposu re of 40 ppm-

years to an equiv

alent lifetime of env

ironmental exposure yields 120 ppb, as a POD, belo w which the shape of the doseresponse curve is u

ncertain.
EPA decided there is not sufficient evidence to demonstrate that the dose-respon se relationship below the POD is non- linear. As a science p olicy default, EPA assumed low- dose linearity for extrapo lation from the POD

to zero. Giv

en a range of p lausible exposure estim ates for the Rinsky

et al. study, the Agency det

ermined that the ben

zene i nhalatio n unit r isk at 1 μg/ m 3 ranges from

7.1 × 10 -3 to 2.5 × 10 -2 depending on the exposur e estimates and modeling approa ch used to derive the POD.
Source : U.S. EPA. 1998. Carcin ogenic Effects of Benz ene: AnUpdate . Office of Research a nd Development, National Center for Environmen tal Assessment, Washington, D.C . EPA/600/P97/001F.; Rinsk

y, R.A., Young, R .J., and Smith, A.B. 1981. L eukemia in benzene work ers. American Journal of Industrial Medicine . 2(3) 217:245. 12.3.1 Determination of the Point of Departure (P OD) Dose-response assessment for cance r and other effects begins with identifica tion of the point of departu re (an e xposure concentra

tion or inta

ke) from the exper

imental da

ta. This po int (in ter

ms of its human equivalent), while within the range of observa tion, is the point from which extrapolation begins, either for the purposes of deriving a cancer risk estimate (the I UR) or a RfC for non- cancer

health e ffects.

The POD may be the

traditional no observed adverse effec t level (NOAEL), lowest obser ved adverse effect level (L

OAEL), or benchmark concentration (BMC)

. (d) EPA has recommended the use of the BMC approach, where possible, be cause the traditional use of the L OAEL or NOAEL in determining the POD ha s long been recognized as having several limitations (andApril 2004 Page 12-14 generally is not used in dose-response for cancer ef fects. In particular, the LOAEL -NOAEL approach:

• Is limited to one of the doses in the study and thus is dependent on study desig n;

• Does not a

ccount f or varia bility and unce rtainty in the esti

mate of the dose-r

esponse relationship;

• Does not account for the slope of the dose-response curve; and

• Cannot be applied, where there is no NOAEL , except through the application of an uncertainty f

actor. If the dose-response

data are of high quality, a mathematical dose-response model may be fitted to the dat a to dete rmine a more prec ise POD tha n the NOA

EL or LOAEL. Whe

n a model i s used, the POD is cal culated

as the sta tistical lower confidence

limit of the

dose at w

hich ther e is a low t oxi c respo nse (u suall y 5 or 10 pe rcent i ncide nce in popul ation s wit h an eff ect or a change in a physiologica

l measurement indicating adversity ). (6) The se lecti on of t he resp onse p ercent age is intended to coincide with the sensitivity limit of the experimental design or pr ofessional judgmen t. This ca lculated POD is cal led the B MC. The BMC a

pproach is an alte rnate wa y of determin ing the p oint of de

parture for low- dose extrapolation. It can be used in canc er and noncancer risk assessment as the star ting point for linear low-dose e

xtrapolati on, calc

ulation of a marg in of exposu

re, or a pplication of unce

rtainty factors for calculating RfCs or other dose -response values. BMC methods involve fitting various mathemat ical mode ls for dos e-resp onse to re ported da ta and usi ng the d iffere nt results to selec t a BMC that is

associa ted with a predete rmined be

nchmark response , such as a

10 perce nt incre ase in the inc idence o f a part

icular le sion or a 1 0 perce nt decre ase in bod

y w eight g ain (Exhibi

t 12-8). EPA has developed the Benchmark D ose Software (BMDS) to facilitate these oper ations. BMDS currently

offers 16 different mathematical models that can be fit to the laboratory da

ta. EPA plans t

o continua

lly i mprove a nd expand the BMDS sy

stem. 6 It is likely that there will continue to be situations that are not amenable to BMC modeling and for which a NOAEL

or LOAEL

approach should be used. I n some cases, there may

be a combination of benchmark doses and NOAEL s to be considered in the assessment of a particular agent.
12.3.2 Derivat

ion of the Hum an Equivalent Concentr ation Because inhalation toxicity studies ty pically involve discontinuous exposures (e.g ., animal studies ro utinely involve in

halation exposures

of 6 hours

per day , 5 days per we ek), the

POD will usually need to be e

xtrapolated to a continuous exposure scenario (as appropriate for the RfC and IUR). This duration adjustment step is essential in interpreting inha lation studies, but is not routinely nece ssary for the interpretation of oral exposures. Operationally , this is accomplishede “C × t ” is a comp one nt o f Hab er’ s Law t hat refe rs t o th e def aul t ass umpt ion (in lie u of info rmat ion to t he co ntr ary) that effects observed are related to the cumulative exposure or “area under the curve” (quantified by concentration, C, multiplied by durati on, t). It is

noted that w

hen going f

rom a dis continuous inhala tion exposure

regim ent to a cont

inuous exposur

e, the result w ill alwa ys be a lower v alue for concentrat

ion, thus providi

ng an autom

atic m argin of protectiv eness f or chem icals f or which C alon e (vs. C × t) may be appro priate, whi le provid ing the ap propri ate conversi on for sub stances for whi ch cumulati ve exposure is the appropriate measure. (4) April 2004 Page 12-15 (Equation 12- 1) by apply ing a concentration-duration product, or C × t product (e) for both the number of hours ina daily exposure period and the number of day s per week that the exposures are performe d. For examp le, for a

POD of 100

mg/m 3 derived from an animal study in which animals are exposed by inhalation for 6 hours per day, 5 day

s per week, the adjustment to a continuous

exposure c oncentr

ation wou ld conside

r both hour

s per day and day s per we ek:
Thus, 18 mg

/m 3 is the POD c oncentr

ation adj usted for

continuou s exposure

versus 10 0 mg/m 3 unadjusted. This approach assumes there is no dose-rate e ffect (i.e., that the same total inhaled material produces the same effect regardless of the time over which this material wa s inhaled).

Exhibit 12-8. Example Deriva tion of Benchmark Dose Lev el

Illustration of the computation of a benchmark dose (BMD) and BMD L (a lower one- sided confidence limit on the BMD) for an e xtra r isk of 0.1 0 (as su ggested b y the BMDS guidanc e documen t), usi ng a one- sided 9 5 perce

nt confi dence i

nterva l. A n umbe r of m odels w ere fit t

o the da ta, and the log -log

istic model illustrated prov

ides the best fit to the exa mple data. The pred

icted curve com

es well within the confide nce lim its for e ach data point. Other data and mod els are i llustrated in ex ample s prov ided in the BMD S gui dance d

ocument. (6)April 2004 Page 12-16

Choice of a Defau lt DAF for

Extr apolati on from A nimal Da ta Depends on the Physical and Chemical Properties of the Pollutant
Gases

• Category 1 (effect in respiratory sy stem) – default DAF based on inhalation rate, and surface area of target portion of re spiratory tract

• Category 2 (some characteristics interm ediate or comm

on to category

1&3) – default DA

F is the more restrictive of t he defaults for category 1 & 3

• Categ ory 3 (syste

mic e ffect[s])– defau

lt DAF based o

n blood :air par

tition c oefficie

nt Particles

• Respiratory toxicant – default DAF based on fractional deposition, inh alation rate, and surface area of target portion of re spiratory tract

• Systemic tox

icant – default DAF based on inhalation rate, body weig ht, and fractional deposition
Source : U.S. EPA. 1994. Methods for Derivation of In halation Reference Conc entrations and Application of Inhalation Dosimetry

. (5) Exposures documented from human occupational epidemiological studies are most often reported as 8-hr time-weighted a verages (TWAs) and theref ore, also are discontinuous. Adjustment of these exposures is usually done as par t of the dosimetric adjustment to derive a human equ

ivalent c oncentr

ation (HE C), rathe

r than as a discre te step, a nd is explained below

in Section 12 .3.3. The

duration adjustmen

t step also is explicitly

incorpor ated into phy siologic ally based pharmacokinetic (PBPK) models used to extrapolate an animal or oc cupational study -derive d POD into a

n HEC. After duration adjustment, the POD is converted into a human equivalent concentration (HEC) from the experimental animal dose. This conversion may be done using default methods specific to the par

ticular c hemical c lass of c oncern or more re fined methods such as PBPK modeling. The Agen cy’s inha lati on dos imet ry method ology (5) provides a recommended hierarchy , as well as defaul t genera lize d proc edures for der ivin g dosimetric adjustment fac tors ( DAFs ) for this extrapolation. Application of DAFs to an animal exposure value y ields an estimate of the corres pondi ng conc entrat ion re levan t to h umans (i.e. , the H EC) giv en dif ferenc es in p hysiolo gy and in the form of the pollutant that influence how the chemical exerts its effec t. The DAF depends on the che

mical ca tegory (i.e., g as or par

ticle) a nd whethe

r the adv erse ef

fect oc curs in the resp iratory

tract or outside of

the resp iratory

tract. HECs are d

erived u sing DAF s for both RfC development (noncancer effec ts) and IUR development (ca ncer).

When data

are ade quate to s

upport it, the pref

erred EPA approa

ch for c alculati

ng a HE C is to use a chemical-specific PBPK model paramete rized for the animal species and regions (e .g., of the respiratory

tract) involved in the toxicity (Exhibit 12-9).April 2004 Page 12-17 In PBPK models, the body is subdivided into a series of anatomical or phy siological “compartments” that represent specific or gans or tissue and organ g roups. The transfer of chemicals between compartments is described by a set of differential equations. The parame ters of the model are of three ty pes: physiologica

l parameters (such as tissue perfusions or tissue volumes), physicochemica l parameters (such as partition coefficients that de scribe the degree of partitioning of a given chemical to a g iven tissue), and biochemical parameters descr ibing metabolic processes. The structure of a PBPK model is deter mined by the intended use of the model, the biochemical properties of the chemical studied, and the e ffect site of concern.

Exhibit 12-9. Extrapolatio n of Inhalatio n Exposure to Calculate the HEC

EPA employs a hierarchy of ap proaches for deriving

the human eq

uivalent concentration. Preference is given to the use of a phy siologically- based pharmacok

inetic model, followed by intrerm ediate, less detailed approaches, wh

ich are followed by

the default approach, w

hich utilizes a DA

F specific to the type o f chem

ical and how it exerts its effect.
With suffic ient data , a PBPK mo del is ca pable of calcula ting inte rnal dose s to a target orga n in an anima l from any

exposure s cenari o and then estimatin g what h uman exposu

re would result in this same internal dose (i.e., the HEC). A formal DAF is not calcula ted in this process; rather, the model itself se rves as a DAF in e stimating

HECs. How ever, c onstruct ing a PB PK model is an infor mation-in tensive process, requirin

g much c hemical- specific

data. Con sequentl

y, t hese models ar e usually availab le for on

ly a subset of chemica ls. For example, EPA ’s IR IS toxic ity assessme nt for vin yl c hloride r elies on a PBPK mode l.
12.3.3 Extrapolation from POD to Derive Carcinogenic P otency Estimates Observable cancer rate s in laboratory or huma n occupational epidemiologic studies tend to be several orders of magnitude hig her than cancer risk levels that society is willing to tolerate from involuntary chemica

l exposures. To obtain observable results, laboratory studies need to beApril 2004 Page 12-18 Risk = EC × IUR , where
EC = lifetime estimate of continuous inhalation

exposu re to an indiv idual ai r toxic IUR = the corresponding in halation unit risk estimate for t

hat air t oxic conducte d at exposur

es usuall y w ell above

environmentally

releva nt conce ntrations . Thus, extrapola tion from t

he POD-HEC to lower

doses is us

ually necessa ry . This extra

polation is performed consistent with the mode of action, if adequately supported. Where the mode of action su pports a b iologic ally-based model and t he data s et is not ri ch enoug h to suppor

t a biologically ba sed model, a non-linear referenc e concentration approach is employ ed (see Section 12.4.2). When the da

ta are in sufficie

nt to suppor

t a mode of

action de cision, or

where the data support a linear mode of action, a linear extrapolation is employ ed.
For line ar extrap olation, a straig ht line is dr

awn fro m the point o f depar ture expr essed as a human equ

ivalent d ose to the origin ( i.e., zer

o incremental dos e, zero incremen tal response) to give an incremental probability dose unit. That is, the slope of the line expresses extra risk per dose unit ( e.g., t

he I UR, expres sed as extr

a risk pe r μg/m3 of lifetime exposure). EPA’s 1999 proposed guidelines (4) for carcinogen risk assessment re commend the use of the lowest effective dose using a 10 percent response leve l (LED 10 ) (as estimated by the lower one-side d confidence limit on the benchma

rk conce ntration [or BMCL 10 ]) as the PO D for line ar extrap olation. T his approac h is to dra w a strai ght line

betwee n the estimated poin

t of depa rture, g eneral ly, as a default, the LED 10 . The LED 10 is the lower 95 percent limit on a dose that is estimated to cause a 10 perce nt respon se. The li

near extr apolation approa

ch to asse ssing ri sk is consi dered g eneral ly conservative of

public he alth, inc luding se nsitive sub

populations, in the a

bsence o f specif

ic informat ion about t

he extent o f human va

riability in sensitiv

ity t o effec ts. The inha lation ca ncer dos e-resp onse valu

e derive d by

linear extrapolati on is the I UR. I t is present ed as an u pper-bo und estima

te of the excess ca ncer ri sk resulti

ng from a lifetime (assume d 70-y

ear) of continuou s exposure

to an ag ent at a c oncentr

ation of 1 μg/m 3 in air. As illustrated previously in Exhibit 12-2(A), risk is the product of the slope and the estimated exposure. The IUR is a plausible upper-bound e stimate of the risk (i.e., the risk is not likely to be higher but may

be lower and may

be zero). When adequate human epidemiology data are available, maximum likelihood estimates may be used instead of uppe r bounds to generate the IUR. When only

animal data are available and linear e xtrapolation is used, the IUR is derived from the largest linear slope that is consistent with the data (within the upper 95 pe rcent confide nce limit) . In o ther wor

ds, the tr ue risk to humans, w

hile not ide

ntifiable , is not like ly to exceed the upper-bound estimate (the I UR), and is likely to be lower. This mea ns that any estimate of risk for air toxics using an IU R is likely to be protective of all potentially exposed populations. In addition, this means that air toxics risk estimates are likely to be conservative, that is, protective of public health.
The evidence for the carc inogenic mode of action may lea

d to a conclusion that the dose-response relationship is nonlinear, with resp onse fal ling much more qui ckly than line arly

with dose, or may

be most influenc ed by

individua

l differ ences in sensitivity. I

n some cases this may be due to th e mode of carc inogen ic act ion b eing a seconda ry effect of toxicity

or of an i nduced phys iologic al chang e that is it self a thr eshold phenomenon. EPA does not generally try to distinguish between mode s of action that might imply a “true threshol d” from th

ose with a

nonlinea r dose-r esponse r elationsh ip. Except i

n unusual cases where extensive information is available, it is not possible to distinguish betweenApril 2004 Page 12-19 these empirically. The

refore, as a matter of science policy, nonlinear proba

bility functions are only fitted to the response da ta to extrapolate quantitative low-dose risk estimates when the carcinogenic

mechani sm of the to xicant is ve ry well-und erstood. When the e

vidence indicate s a non-line ar dose r

esponse f unction c ontaining

a signif icant change in

slope, an d alternate nonlinear approach may

be considered. For example, when carc inogenesis can be shown to be a secondary effect of threshold toxicity, the EPA dra ft carcinogen guidelines r ecommend derivation of a reference c oncentration.
12.4 Dose-Response Assessment for Derivation of a Reference Conce ntration The reference conc entration is defined as an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive sub-p opul ation s) tha t is l ikely to be wit hout an app reciab le ris k of de leter ious effect s duri ng a lifetime. The RfC is e

xpressed a s a chron

ic exposur

e level to the chem ical in am bient air (in units of mil ligrams

of the sub stance p er cubic meter of air, or mg

/m 3 ). This va

lue is usua

lly derived for use with effects other than c ancer. But when a chemica l’s carcinogenicity

has been shown to be associated with a nonlinear mode of action (see A gency ’s Cancer Guidelines), (4) a reference concentr

ation may be der

ived for use with all effects, including canc er. Inherent in the derivation of a reference concentr

ation is the recognition of an exposure level likely to be witho

ut an appr

eciable risk of a dverse e ffects ( e.g., a

sub-thre shold leve l for adv erse effects). The objective of this ty pe of dose-response assessment, then, is to estimate that exposure l evel for

humans. T

he RfC is de

rived af ter a thorough r

eview of the heal th effe cts database for an individual chemical and identification of the most sensitive and r elevant endpoint (the “critical effect”) a

long with the principal study( ies) demonstrating that endpoint. In a ddition to an ana lys is of the st

udy data ava ilable for the che

mical, ri sk assess ors also u se uncer tainty factors to account for differenc es in sensitivity between humans and la boratory a

nimals, the possibility of heightened se nsitivity of some population groups (e.g ., people with respiratory disea se, ve ry young child ren, t he aged) , and a ny limit ation s of th e datab ase. T he met hodol ogy for der ivation of an inhala tion refe rence c oncentr

ation is de scribed

in detail i

n EPA’s Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetr y . (5) The first part of this type of assessment, which involves a careful qualitative and quantitative analy sis of the study

data, pa rallels t hat perf ormed for linear

cancer dose-re sponse as sessment (i.e., derivation of the point of departure in terms of a human e quivalent concentration [POD HEC ]). The qualitative analysis is descr ibed in Section 12.2.2, while the quantitative analysis is described in Sections 12.3.1 and 12.3.2. The latter part of this type of assessment involves the application of uncertainty factors to address limitations of the data used (e.g., the factors raised above).
In I RIS, EPA includes with each RfC a statement of hig h, medium, or low confidence based on the compl eteness

of the da tabase f or that su bstance . High c onfidenc e RfCs ar e conside red less likely to chang e substantially with the collection of additional informa tion, while low confidence RfCs may be espec ially vulnerable to cha nge. (5)April 2004 Page 12-20 12.4.1 Determination of the Point of Departure and Human Equivalent Concentration In earlier sec

tions (Section 12.2.2, 12.3.1 and 12.3.2) the analy sis of the database and identification of the critical effect, as well as the de rivation of the POD in terms of human equivalent concentrations are discussed. In developing a dose -response assessment, toxicologists evaluate the available data for a substanc e. Studies

of high q uality are sel ected, a

nd the ass essment is

focused on the most appropriate studies. As the RfC is a chronic value, prefe rence is given to long-term studies over short-term ones, to studies using animals that exhibit effects similar to those experienced by humans, to studies using an appropriate exposure route (e.g ., inhalation exposure for developing an RfC), a

nd to studie s showing

a clear pattern of incre asing f

requenc y or severity of response with increasing dose. Toxicologists use the information to identify the critical effect(i.e., the adverse effect that appea

rs at the lowest dose). Afterwards, appropria te human data are chosen as the ba sis for the

RfC or, if h

uman data

are not a dequate , data fr

om the most a

ppropria te species are ide ntified. I f this is not

known, the

data fr om the most s ensitive s

pecies i s usually chosen. This analysis is descr ibed in Section 12.2.2. The objective in identifying the critical effect or effects is to identify the effect(s) - among a

ll those associated with exposure to the chemica l of inter est - that occur a t the lowe st exposure

and would

lead to de

rivation o f the lowe st RfC (Exhibit 12-10).

Exhibit 12-10. Overview to Develop a Reference C oncentration

The LOAEL (HEC ) and NOAEL (HEC) are illustrated with low-dose ex trapolation with the application of uncertainty or modify

ing factors to deriv

e the human health-protective RfC. Note that this figure represents data from appropriate animal species.
Using the dose-response relationship for the cr itical effect, toxicologists identify the POD f rom the experimental data. This exposure concentration (in terms of its human equivalent) which marks the boundary

between the range of obser vation and that of extrapolation, is the point fromApril 2004 Page 12-21

Derivation of RfC Using BMC Meth odology – 1,3-dich loropropene A review of the

available anim

al studies indicated chang es to the surface cells of the nasal portion of the respiratory tract as the critical effect for 1,3- dichloropropene. Ben chmark m

odeling was perform ed on the data d

emon strating

this eff ect. The seven statisti cal models for dichot omou s data from the Ag ency’s benchm ark dose m

odeling software (B MDS Version.1b) were app lied to the incidence data for the adjusted ad ministered doses. The best model fit w as determined by

eliminating all mo dels tha

t did no t have

a statis tically

signi ficant goodnes s-of

-fit ( p <0.05) . The re

maini ng m odels were then ranked by best visu al fit of the data, especially for the lower doses, as observed in the graphical output of the Benchmark Dose Software. The model with statistically significant goo dnessof-fit and best visual and statistical fit was used to estimate the BMC at 10 perce

nt risk and the 95 percent lower c onfiden

ce lim it of the BMC (the BMCL ). The gamm a, log istic, m

ultistag e, Weibu

ll, and qua ntal- quadrat

ic mo dels provided

statisti cally signif

icant fit s. The gamm

a mod el was t he best fit overa ll becau se it provided

the bes t visual fit. This model y ielded a BMC 10 of 5.9 mg

/m 3 and a BMCL 10 of 3.7 mg

/m 3

. The BMCL 10 was identified as the P OD and was adjusted from experim ental conditions to a continuous inha lation exposur e value (POD adj ). Because the critical target was the nasal m ucosa, algorithms for extrathoracic effects for Categ ory 1 gases were used to adjust continuou s animal exposure concen tration to HEC. The POD HEC for a Category 1 gas was d

erived by m

ultiplying the animal BMCL 10 by an interspecies dosimetric adjustmen t for gas:respiratory effects in the extratho racic ar ea of th e respir

atory tract. Using

default value s, the a

djustme nt factor was eq

ual to 0.2. For exam

ple, for 1,3- dichloropropene:
POD HEC = BMCL 10 (HEC) = BMCL 10 (adj) × 0 .2 = 3. 7 × 0.2= 0.7 m g/m 3
The POD HEC was divided by uncertainty factors for interspecies extrapolation (UF of 3) and intraspecies variation (UF of 10) and rounded to one significant fig ure to yield the R

fC for 1,3- dichloropropene:
RfC = POD HEC / 30 = 0 .02 m g/m 3 which extrapolation begins for derivation of a RfC. The POD may be derived from benchmark modeling (see Section 12.3.1 regar ding the derivation of a BMCL ). If the data do not meet require ments for benchma rk modelin

g, the PO D is deriv ed by

the use of

a statistic al analy

sis to identify the no-observed-adverse-effect-level , or NOAEL , defined as the highest dose level administered to laboratory animals that did not cause statistically or biologically signific ant obs erv abl e ad ver se e ffe cts aft er c hro ni c (u sua ll y lif eti me) ex pos ure in th e st udi ed p opu lat io n. In some cas es, a L OAEL

is used in the absenc e of a NO AEL. In e ither ca se, the PO D is transformed into a continuous inhalation exposure (e.g., from an intermittent animal exposure, 6 hours/day, 5 day

s/week) and then into a human equivalent concentration (a s described in Section 12.3.2). In or

der for the appr opriate critica l effect to be ide

ntified, a compari son of POD s across differe nt endpoin

ts is done in

terms of h

uman equi

valent c oncentr

ations (or potentia

l RfC value s, which incorporate the application of UFs, nee d to be compared). (5)April 2004 Page 12-22 (Equation 12- 2) 12.4.2 Application of Uncertainty Factors The RfC is an estimate derived from the POD HEC for the critical effect (based on e ither a BMCL

HEC , NOAEL HEC or LOAEL HEC ) by consiste nt applic ation of U

Fs. The U

Fs are a pplied to account for recognized uncer tainties in the use of the available data to estimate an exposure concentration appropriate to the assumed human scena rio. The general formula for de riving an RfC from a POD HEC is:

A UF of 10, 3, or 1 is applied for each of the f ollowing extrapolations used to derive the RfC (see Exhibit 12-11):

• Animal to human;

• Human to se

nsitive hu man popula

tions;

• Subchronic to chronic;

• LOAEL

to NOAEL; and

• Incomplete to complete databa se. The UFs are gener

ally an order

of magnitude (10), although incorpora tion of dosimetry adjustments or other information may r esult in the use of reduced UFs for RfCs (3 or 1). The composite UF applied to an RfC will vary in magnitude depending on the number of uncertainties involved; however, an RfC will not be derived when use of the data involves more than four areas o f extrapo lation. The composi te UF wh en four f

actors a re used general ly is reduced from 10,000 to 3,000 in recognition of the lack of independence and the conservatism of these fa ctors. The 2002 A

gency review o f the re ferenc e dose (R fD)/re ferenc e conce

ntration p rocess (2) encouraged the development of g uidance in the area of chemical-spe cific adjustment factors (CSAFs). These factors utilize specific data to replac e the default UFs for interspecies or inter

individua l variati on. The r

eview pa nel noted , howeve

r, that the CSAF appr

oach for any single substance is determined principally by the availability

of relevant data. For many substances there are relatively

few data available to serve as an a dequate basis to replace defaults for interspe cies diff

erence s and huma

n variab ility

with more

informat ive CSAFs

. Because of this procedure to addr ess the lack of information on the translation from experimental data to a human scenario, the resulting RfC for many HAPs is on the order of 100 to 300 times lower tha n the NOA

EL ac tually observe d in the an

imal testin

g (see Exhibit 12-1 2). This r

eflects the lowering of the RfC to address the uncertainties in the e xtrapolations mentioned above. For those HAPs that have had their effects well documented in human studies, the RfC may be much closer to the highest concentration at which an a dverse effect was not observed ( e.g., within a factor of 3 to 10).April 2004 Page 12-23 Ex hib it 1 2-1 1. Unc ert ain ty F act orsUse d in the Der ivatio n of

an Inh alatio n R

fC Standard Uncertain ty Factors Processes Considered in UF Purview A = Animal to hu man Extrapolation from valid results of long -term stu dies on laboratory anim

als when results of studies of human exposure

are not available or are inadequate. Inte nded to accoun t for the uncerta inty i n extrapolating laboratory

animal data to the case of average healthy

humans. • Pharmacokin etics/Pharmacody

namics

• Relevance of labora

tory animal model

• Species

sensiti vity

H = Human to sensitive hu man Extrapolation of valid experimental results for studies using prolonged exposure to average healthy huma ns. I ntended to acco unt for the va

riation in sensitivity am

ong the m

embers of the hu

man population. • Pharmacokin etics/Pharmacody

namics

• Sensitiv

ity

• Differences in mass

(children, obese)

• Concomitant exp osures

• Activity Pa

ttern

• Does not account for idiosyncrasies S = Su bchron ic to ch ronic Extrapo lation f

rom l ess than

chronic exposu re resul ts on laboratory anim

als or humans w

hen there are no useful long-term hum

an data. I ntended to account for the uncertainty in extrapolating from less than chronic NOAELs to chronic NOAEL s. • Accumula tion/ Cumulat ive damage

• Pharmacokin

etics/Pharmacody

namics

• Severity of effect

• Recovery

• Duration of study

• Consistency of effect with duration L = LOAEL to NOAEL Derivation from

a LOAEL instead

of a NOAEL. Inte nded to accoun t for the uncerta inty i n extrapolating from

LOAELs to N OAELs. • Severi ty

• Pharmacokin

etics/Pharmacody

namics

• Slope o f dose-re sponse curve

• Trend, consistency of e ffect

• Relationship o f endpo ints

• Functional vs histop athological evide nce

• Exposure uncertainties D = In complete

to comp lete dat a Extrapo lation f

rom v alid res ults in l aboratory ani

mals when the data are “incomplete”. I ntended to account for the inability of any

single laboratory

animal study to adequately address

all possible adverse outcomes in humans. • Quality of critical study

• Data gaps

• Power of critical study /supporting studies

• Exposure uncertainties

Source : U.S. EPA. 1994. Methods for Derivation of In halation Reference Conc entrations and Application of Inhalation Dosimetry

. (5)f As noted earl ier, see ht tp: // www. epa. gov/

tt n/ atw /t oxs ourc e/s ummar y. ht ml for a cu rrent listing o f this information.

April 2004 Page 12-24 Exhibit 12-12. Examples of the Use of Uncertai nty Factors in Der iving RfCs RfC from NO AEL Example: Diesel E ngine Emissions RfC from LO AEL Example: Toluene Toxicity data: 144 μg chem ical/m

3 air (NOAELHEC from

chronic rodent study) Uncertainty factors : 3 x 10 = 30 3 = anim

al-to- human extrapolation 10 = human

to sensitive hum

an subpopulations

RfC = 144/30= 4.8 μ g/m 3 = 0.00 5 mg

/m 3 Toxicity data:

119 m g chem ical/m

3 air (LOAEL HEC from

chronic occupa tiona l study) Uncertainty factors : 10 x 10 x 3 = 300 10 = hum

an to sensitive hum

an subpopulations 10 = LOAEL -to-NOAEL extrapolation 3 = database deficiencies
RfC = 1 19/300mg

/m 3 = 0.4 m

g/m 3 NOAEL HEC = No-O

bserved-A

dverse-E ffect Level (Hum an Equivalent Co

ncentration) LOAEL HEC = Lowest- Observed-Adverse- Effect Level (Hum an Equivalent Co

ncentration)
Source : EPA’s I

RIS database http://www.epa.g ov/IRI S/

. In so me of the

older I RIS as sessments

a “modif yin g fact or” may have be en applie d in addition to the traditional uncertainty factors. It had bee

n used with professional judgement when it was determined that another uncertainty factor was needed; its magnitude depe nded upon the professional assessment of scientific uncertainties of the study and database not explicitly trea ted via the ot her unce rtainty factor s. (5) The 2002 Agency review of the RfD/RfC process, however, recommended against continued use of the modify ing factor. I

t was felt that the traditional factor s could ac count for

any remainin g unce rtaintie s. (2)
12.5 Sources of Chronic Dose-Response Values Appendix C provides a current listing of appropriate chronic dose-response values (i.e., RfCs or compara ble value s and I

URs) for HAPs. (f) References for acute e xposure levels are provided below in Exhibit 12-13. Hazard identification and dose-response assessment information for chronic exposure, presented in Appendix C, was obtained from various sources and pr ioritized according to (1) conceptual c onsistency with EPA risk assessment g uidelines, and (2) level of review received. The prioritization process was a imed at incorporating into our assessments the best available science with respect to dose-re sponse information. The sources listed below were used, and provide

this infor mation for

chemica ls bey ond the 18 8 Clean Ai

r Act haza

rdous air pollutants listed in A

ppendix C.

• U.S. Environmental Protection Agency (EP A) . EPA has d

evelope d dose-r

esponse assessments for chronic exposure to many pollutants. These assessments typically specify a n RfC (to protect against effects other than c ancer) and/or I UR (to estimate the probability ofApril 2004 Page 12-25 contracting ca

ncer). Backg

round doc

uments, pa

rticular ly f or the mor

e rece nt files, a lso contain information on physical a nd chemical properties, toxicokinetics, and hazard charac terization. EPA disse

minates do se-res

ponse ass essment information in sever al forms, based on the level of review. Dose-re sponse assessments that have achieved full intra-ag ency consensus are incorporated in the Integrated Risk Infor mation Sy stem ( IRIS) , which is regularly updated and available on-line (www.epa.gov/iris). All IRIS asse ssments since 1996 also have underg one indep endent external pe er revi ew. I n the past , dose-r

esponse assessments for some substances were prepar ed by the EPA Off

ice of Research and Development, but w ere nev er submitt ed for EP A consen sus. EPA ha s assemble d the res ults of many such a ssessments in the Health Effects Assess ment Sum mary Ta bles (HEAST)

. Although the values in HEAST have undergone some review and have the concurre nce of individual Agency

program offices, they

have not had enough review to be re cognized as Agency -wide consensus information. In addition, since H EAST has not been updated since 1997, other sources described here ar e, for many

chemicals, more reliable.

• Agency for Toxic Substances and Disease Registry (ATSDR) . ATSDR, which is part of the US Dep artment o f Health and Huma

n Service s, develo

ps and pub lishes Mini

mum Risk Leve ls (MRL s) for ma

ny t oxic substa

nces. The MRL i s define d as an estimate of daily human expos

ure to a s ubstance that is like ly to

be withou

t an appr eciable risk of a dverse effects (other

than can cer) ov er a spe cified d

uration o f exposure . MRLs a re deri ved for a cute (1-14 days) , intermediate (>14-364 days), and chronic (365 days and longer) exposures by inhalation and oral routes. ATSDR describes MRLs a s substance-specific estimates to be use d by h eal th ass ess ors to sel ect env iro nm ent al c ont ami nan ts for fur th er e val uat io n. M RLs are presented with only

one significant figure and a re considered to be levels below which contaminants are unlikely to pose a health threat. Exposures above an MRL do not necessarily

represent a threat, and MRL s are therefore not intended for use a s predictors of adverse health effects or for setting cleanup levels. The MRL data undergo a rigorous r eview process, including internal ATSDR review, peer reviews, and public comment periods. Appendix C shows the ATSDR chronic MRL where no I RIS value is available, because the MRL's concept, def

inition, and derivation are philosophically c onsistent (though not identical) with EPA's guidelines for assessing noncancer effects. ATSDR publishes MRL s as part of pollutant-specific toxicological profile documents, and also in reg ularly-update d on-line ta bles. 7

• California Environmental Protection Agency (CalEPA) . The CalEPA Office of Environme

ntal Hea lth Hazar d Assessme

nt (OEHH

A) has de veloped d ose-re sponse assessments for many substanc es, based both on carcinogenicity and health effects other than cancer . The pro cess for

developi ng these assessme nts is simila

r to that us ed by

EPA to develop IRI S values and includes significant external scientific peer re view. The non-cancer information includes inhalation health risk guidance values expressed as c hronic inhalation reference exposure levels (REL s). CalEPA defines the REL as a conc entration level at (or below) w hich no he alth eff

ects ar e anticip ated, a c oncept that is substa ntially similar to EPA’s approach to

non-can cer dose -respon se asses sment. App endix C shows the chronic REL ( including

both fina l and prop osed valu

es) whe re no I RIS RfC /RfD or AT SDR MRL exists. CalEPA’s quantitative dose-response information on carcinogenicity by inhalation exposure i s expr

esse d in te

rms of the I UR, de fine d simi larl

y t o EPA’ s I UR. Ap pend ix C shows spe cific Ca

lEPA UREs w

here no I RIS va lues exist. C alEPA’s d

ose resp onse assessments for carcinogens and nonc arcinogens are available on-line. 8April 2004 Page 12-26 • International Agency for Research on Cancer ( IARC ). The I ARC, a bra

nch of the World Health Organization, coordinates and conducts re search on the causes of human canc er and develops scientific strategies for ca ncer control. The I

ARC sponsors both epidemiological and labor atory resear ch, and d isseminat es scien tific inf ormation t

hrough meeting s, publications, courses and fellowships. As part of its mission, the IARC assembles evidenc e that substances cause cancer in humans and issues judg ments on the strength of evidence. IARC’s categor

ies are Group 1 (carcinog enic in humans), Group 2A (probably carcinogenic), Group 2B (possibly c arcinogenic), Group 3 (not classifiable ), and Group 4 (probably not carcinogenic). The ca

tegorization scheme may

be applied to either single chemicals or mixtures; however, IARC does not develop quantitative dose-re sponse metrics such as UREs,. IARC’s categor

ies for substances are included in Appendix C to support or augment EPA’s we ight-of

evidenc e (WOE) d

etermina tions, whic

h do not co ver all su

bstance s and in some cases may

be out-of-date. The list of IA RC evaluations to date is available on-line ( http://193. 51.164.11 /monoeva l/grlist .html ). Additionally, the EPA has compiled fac t sheets for the 188 CAA hazardous air pollutants and makes them available on the Air Toxics website ( http://www .epa.g ov/ttn/atw /hapinde x.html ). This collection is called the Health Effects Notebook for Hazardous Air Pollutants , and provides for each HAP a summary of available information in the following categ ories: hazard summary, phy

sical properties, uses, sources and potential exposure, and health hazard informat ion. Thes e fact sheets ar

e useful for desc ribing hazards a ssociate d with the 1 88 HAPs.
12.6 Acute Exposure Reference Values Many air pollutants can c ause adverse health effec ts after acute or short-term exposures lasting from a few minutes to several day s. For some pollutants, acute exposures may be of greater concern than chronic exposures. The severity of effects from acute exposures may vary widely

. Agenc y- wide gu

idance on how to as sess toxic e ffects f rom short- term exposu

res is cu rrently being d evelope d. This g

uidance for Acu te Refer ence Expo sure (AR

E) level s is intende d to assist acute r isk asses sment ac tivities. A variety of other short-te rm, acut e exposure

limits are also described in Exhibit 12-12. 9 Appendix C provides a current listing of acute dose-response va lues for HAPs. Methods for dose-response assessment of acute e xposures are usually similar to the approa ch for chronic exposure, with their derivation involving the identification of a “cr itical effect,” determination of a

NOAEL or compa rable va lue for that effe ct, and a

pplication of unce

rtainty factors (e.g., animal to human population). Howeve r, the process by

which most acute inhalation dose-response assessment values are der ived differs from the chronic RfC methodology in two important ways. First, “ acute” may connote exposure times varying from a few minutes to two weeks. The time frame for the value is cr itical, because the safe dose (or the dose tha t produces some def ined eff ect) may vary substantia

lly w ith the len

gth of exposure. Se cond, some acute dose-response assessments include more than one level of se verity. A ty

pical assessment may have values for level 1 (at which only mild, transient effects may occur), level 2 (above which irreve rsible or other se rious ef fects ma y oc cur), a

nd level 3 (above w

hich life -threa

tening e ffects may occur) . There fore, ma

ny a cute ass essments p

resent d ose-re sponse as sessment values as a ma tr ix , w it h o ne di me ns io n b ei ng le ng th of ex po su re an d t he ot he r a se ve ri ty- of -e ff ec t c at eg or y.April 2004 Page 12-27 Exhib it 12-13 . Ex amples o f Availab le Shor t-Term, Acute Expo sure Le

vels Acronym Full

Name Group or

Agency Purpose/ Defini tion Source/W ebsite
AEGL Acu te

Exposure Guideline Level National Research

Counc il (NRC) National Advisory Committee (NAC) The AEG Ls repres ent sh ort- term t hreshol d or ceil ing exposu re val ues int ended f

or th e prot ecti on of t

he gen eral publi c, i nclu ding sus cepti ble or sensitive individuals, but not hypersu sceptible or hyp ersensitive individuals. The AEG Ls repres ent bi ological refer ence val ues f

or thi s defi ned human pop ulation a nd co nsist of thre e bio logica l endp oints for four d ifferent single emerge ncy ( acci denta l) e xposur e peri ods (30 mi nut es, l hour, 4 hours , an d 8 hour s). In som e instanc es, AE GL s also a re de velop ed fo r 5 or 10 m inutes. The biological end points are de fined as follows:

• AEG L-1 is the ai rborne concentration (expressed as parts per mil lions [ppm] or mi ll ig rams [mg] /me ter s [ m] 3 ) of a sub stance at or ab ove which it is predicted that

the general population, including “s usceptible” but exclud ing “hyp ersusc eptib le” ind ividua ls, could expe rience notab le discomfort. Airborne concentrati ons below AEG L-1 represent exposure levels that could pro duce m ild odor, taste, or o ther sensory irritations.

• AE GL -2 is the airb orne conc entratio n (exp ressed as pp m or mg/m 3 ) of a substance at or above which it is predicted that the general population, including “susceptible” but excluding “hyp ersusceptible” ind ividuals, could experi ence i rrev ersi ble or othe r se riou s, long -l ast ing eff ects or impa ired a bility to esc ape . Airbo rne co ncen trations b elow the AE GL -2 but at o r abo ve A EG L-1 re prese nt exp osure levels tha t may ca use no table disco mfor t.

• AE GL -3 is the airb orne conc entratio n (exp ressed as pp m or mg/m 3 ) of a substance at or above which it is predicted that the general population, including “susceptible” but excluding “hyp ersusceptible” ind ividuals, could experience life- threatening effects or death. Airborne concentrations below AEGL -3 but at or above AEG L-2 represent exposure levels that

may cause irreversible or other serious, long-lasting eff ects or impaire d abili ty t o escape. http://search.nap.edu/b ooks/ 030 907 294 8/htm l/

ARE Acu te

Reference Exposure U.S.

Environment al Protec ti on Agency The AR E is an informed esti mate of the highest inhalati on exposure (concentration and duration) t hat is not likely

to cause adverse effect s in a human popu lat ion, inc ludi ng se nsi ti ve su bgroups , ex posed t o tha t s cenar io, even on a n intermittent basis. 10 For these purposes, acute exposures are singl e conti nuous exposur es las tin g 24 hours or l ess; AREs may be derived for any durati on of in teres t wit hin t hat peri od. “Int ermittent ” impli es suffi cient ti me between exposures such that one exposure has no effect on t he heal th ou tcome pr oduced bythe next exposu re. EPA is i n th e proces s of finalizing the methodo logy for develo pmen t of ARE s.Exhib it 12-13 . Ex amples o f Availab le Shor t-Term, Acute Expo sure Le

vels Acronym Full

Name Group or

Agency Purpose/ Defini tion Source/W ebsite

April 2004 Page 12-28 BEI Biological

Exposure Indices American

Confer ence of Governmental Indust rial Hyg enists BEIs ® are health- based values for use by indust rial hy gienist s in making decisi ons regar ding saf e level s of exposu

re to var ious che mical a nd physi cal agents found i n the wor kplace. http :/ /w ww .a cg ih .o rg /TL V/

CEEL Co mm unity

Emergency Exposure Level National Research

Counc il (NRC) National Advisory Committee (NAC) CEELs are ceiling exposure values for t he publi c applicabl e to emergen cy exposures of foreseeable magnitude and duration, us ually not exceeding 1 hour. Three CEELs w ere es tabl ish ed:

• CEE L-1: Concentration above which discomfort, f or example eye and nose irrit ation or headaches, becomes increasingly commo n;

• CE EL -2: Co ncen tration a bov e which disab ility, for exam ple, sev ere eye or respiratory i rritat ion, becomes increasingly commo n;

• CEEL-3: Conc entr ati on above w hich deat h or l if

e-t hreat enin g ef

fect s, for exam ple , pu lmon ary

edema , c ardi ac f

ail ure , or can cer , be come increasingl y common. Guidelines for Developing Community Emergen cy Exposur e Level s f

or Hazardous Substan ces (NRC , 1993)

EEGL Emergency

Exposure Guidance Level NAS Co mmitt ee

on To xicology Exposure levels judged to be acceptable for militar y personnel performing tas ks dur ing emerg encysi tuations . Not c onsi dered s afe exposu re l evel for routine or no rmal op erations.
ERPG Emergency

Respo nse Planning Guideline American

Indust rial Hygiene Asso ciation ’s (AIHA) Emergency Respo nse Planning Committee Th ese gu ideline s are inte nde d for a pplic ation b y perso ns traine d in

emergency response planning. ER G-1 : The maxim um co ncen tration in a ir belo w whic h it is believ ed ne arly all indi viduals could be exposed for up to one hour without experiencing other t han mil d trans ient

adverse he alth

eff ects or perceivi ng a cle arly

defin ed objectionable odor. ER G-2 : The maxim um co ncen tration in a ir belo w whic h it is believ ed ne arly all indi vidu als coul d be expos ed for up to on e hour

wit houtexper ienc ing or dev elop ing irrev ersible or oth er serio us hea lth effects o r symp toms th at cou ld impair their abilit ies to take protect ive action. ER G-3 : The maxim um co ncen tration in a ir belo w whic h it is believ ed ne arly all indi vidu als coul d be expos ed for up to on e hour

wit houtexper ienc ing or dev elop ing life-threa tening h ealth effec ts. htt p:// www.bnl. gov/scapa/ er pgp ref.htm http://www.bnl.gov/scap a/sc apa wl.htm

IDLH Imm ediate ly

Da ngero us to Life or He alth Concent rat ion Na tional In stitute for Occupati onal Safety a nd H ealth (NI OSH) An imm ediately dange rous to life or health con dition is one “that pose s a

threat o f expo sure to airbo rne co ntamin ants wh en that e xpo sure is likely to caus e deat

h or i mmediat e or del ayed pe rmanen t adv erse heal th e ff ects or prevent escape fr om such an env ironment . The purpose of establ ishi ng an IDLH is to ens ure th at the worker can escape f rom a givencontamin ated enviro nme nt in the ev ent of failur e of the r espira tory pr otectio n equ ipme nt. NIOSH Respira tor Dec isi on Log ic [N IOS H 1 987 ], http://www .cdc.g ov/nio sh/id lh /i nt ri dl4. ht mlExhib it 12-13 . Ex amples o f Availab le Shor t-Term, Acute Expo sure Le

vels Acronym Full

Name Group or

Agency Purpose/ Defini tion Source/W ebsite

April 2004 Page 12-29 LOC Level of

Concern U.S.

Environment al Protec ti on Agency, Federal Emergency Mana gement Agency , U.S. Departme nt of Transportat ion Defined by t he Technical Guidance for Hazards Analy sis (a gui de developed to as sis t i n pla nni ng f

or acci denta l ch emica l re leas es). As t he conc entr ati on of anext remel y haz ardous subs tanc es i n ai r above whi ch t here may be seri ous i rrev ersi

ble h ealt h ef fect s or de ath as a r esul t of a si ngl e expos ure f

or a rel ati vely

shor t per iod of

ti me. I n th e 1987 Techni cal Guidanc e for Hazards Analy sis document , an LOC was es ti mated by usi ng one- tent

h of t he IDLH level p ublishe d by the Na tional In stitute for O ccup ationa l Safety an d H ealth. For t he pur

poses of of

fsi te c onsequ ence a naly sis perf ormed as part

of accid ental re lease re quire men ts unde r Sec tion 11 2(r) o f the CA A, this va lue is supe rced ed b y ER PG -2 value s as ava ilable, an d the A genc y intend s to supercede those values with AEGL-2 values as they are developed and adopted. Techni cal Guidanc e for Ha zard s Ana lysis. Emergen cy Pl anni ng f

or Extremely Hazardous Subs tan ces . ( USEPA, FE M A, U SD OT , 198 7). 61 FR 31672; Ju ne 20, 1996

MRL Acu te

Minimum Risk L evels U.S. Ag encyfor

Toxic Substance s and D isease Registry (ATSDR) The MRL is an e sti mate of human e xposur e to asubs tanc e th at i s l ikely t o be without an appreciable ri sk of adverse eff ects (other t

han cancer) over a specified durati on of exposure, and can be derived for acute exposures by the inhal ation and oral r outes. Unlike t he one-hour

focus of most of t

he other values listed he re, acu te M RLs are d erived for exp osure s of 1 to 14 d ays duration. ht tp: // www. ats dr. cdc. gov/

mr ls .h tml

REL Reference

Exposure Level Cal if

orn ia EPA Offi ce of Environment al Health Hazard Assessment (OEHHA) The acute RE L is an exposure that i s not li kely to cause adverse ef fects i n a human populat ion, i ncludi ng sens iti ve sub- populati ons, expose d to that concentration for one hour on an inter mittent basis. RELs are based on the most sensiti ve, relevant, adverse health eff ect reported in the medical and toxicological l iteratur e. RELs are designed to protect the most sensi tive indiv iduals in t

he populati on by the incl usion of margins of saf

ety. Since margins of safety are incorp orated to a ddress d ata gaps and uncertainties, exceeding the REL does not automati call y indi cate an a dverse heal th i mpact OEHH A has de fi ned t he l owest avai labl e acut e sev eri ty lev el a s t he REL. http:/ /www.oehha.ca.gov/air / pdf/ acut erel .pdf

SPEGL Short-term

Pub lic Emergency Exposure Guidance Level National

Academy of Scien ces (NAS) Committ ee on Toxicology Th e N AS d evelo ps sho rt-term p ublic e merg ency e xpo sure gu idanc e levels

(SP EG Ls) to a pply to the exp osure s of the ge neral p ublic to conta minan ts durin g airbo rne ch emic al relea ses; SP EG Ls are gener ally set at a lev el of 0.1 to 0.5 times the EEGL and are me asured as 60 minute or 8 hour exposure time frames. Crit eri a and Methods f

or Preparing Emergency Exposure Guidance Level (EE GL ), Sho rt-Term Pu blic Emergency Guidance Level (SPEGL), and Conti nuous Exposure Guidance Level (CEG L) Do cum ents. 1986. Nationa l Acade my Press, Nation al Aca demy of Sci ence s , Washi ngt on, D.C.Exhib it 12-13 . Ex amples o f Availab le Shor t-Term, Acute Expo sure Le

vels Acronym Full

Name Group or

Agency Purpose/ Defini tion Source/W ebsite

April 2004 Page 12-30 STEL Short-Term

Exposure Limit American

Confer ence of Governmental Indust rial Hyg enists (AC GIH) STE Ls are time weight ed average (TW A) guidelines f or the control of short term exposure in the workplace. These are important supplements t o the eight -hour TWA exposure standar ds whichare more concer ned wit h the t otal intake o ver lon g per iods o f time. G enera lly, ST EL s are es tablishe d to minim ize the risk of the o ccurr ence in nearly a ll worke rs of: intole rable irri tati on; chroni c or irr eversi ble ti ssue ch ange; an d narcosi s to an e xtent that could precipitate indust rial accidents , provided the eight hour TWA exposure standar ds are not

exceeded. STELs are re commended for thos e subst ances only when there is evide nce either from hu man or a nimal studies that adverse health eff

ects can be caused by high short t erm exposure. STE Ls are expres sed as air

borne c oncent

rat ions of s ubst ances , av erage d over aperi od of 15 m inutes.April 2004 Page 12-31 Route t o route

extra polatio ns shou ld only be done by qua

lified toxicologists. 12.7 Evaluating Chemicals Lacking Health Reference Values
12.7.1 Use of Available Data Sources If EPA-derived I

RIS assessments are available f or the chemicals being examined, these values should generally

be used in the risk assessment. Use of IRIS or other EPA-derived doseresponse values pr events du plication of effort in toxicity

assessme nt and ens

ures con sistency

in the dose-response values among risk assessments. If EPA-derived dose-response values are not availab le, the ot

her sour ces desc ribed in Se ction 12.6

should be g

iven next pr

iority . Use of these sources in a hierarchical manner ha s been implemented in tables developed for the 188 hazardous air pollutants (see Appendix C and http://www.epa.gov/ttn/atw/toxsource/table1.pdf ). The Toxicology Excellence forRisk Assessment (TERA) maintains a database of international doseresponse values (see www.TERA.org/iter ).
If those sources also lac

k inhalation dose-response values, then route-to-route extrapolation (discussed below) may

be considered. This approach, however , may be quite deta iled, and requires assistance from a professional toxicologist. I f all sources and approaches have been researched, and no dose-re sponse value is available, the assessor should describe the ef fects of the chemical qualitatively and discuss the implications of the absence of the chemical from the risk estimate in the

uncerta inty section o f the risk

assessme nt.
12.7.2 Route-to-Route Extrapolation For cases in which appropriate dose-r esponse values are not available for the route of exposure being c onsidere d, but are availab le for a

nother r oute, it ma

y be possible to

use route -to-rou

te extrapolation. Route-to-route extrapolation is recommended only from oral to inhaled exposure and only for car cinoge ns. The a bility to perform quantit

ative ro ute-to-r oute extra polation is critically depe

ndent on the amount and type of data available. Regardless of the toxic endpoint being considered, a minimum of information is required to construct plausible dosimetry for the routes of interest. This information includes both the nature of the toxic effect and a description of the re lationship

betwee n exposure

and the to xic effec

t. Data from other routes of exposure may be useful to derive an RfC (for carcinoge ns only; discussed below) only when r

espiratory tra

ct effects and/or “first pa ss” eff

ects ca n be rule d out. First pass effects are c ases whe

re metab olism take s place i n the port al-of-e ntry tissues, prior to en

try into the systemic circula

tion. The respiratory

tract can exhibit a first-pass effect after inha lation. Unless the first-pass effect and dosimetry are adequately

understood, there can be substantial error in troduce d in route -to-rou

te extrapo lation tha

t does not a

ccount f or these consider ations. Oral toxicity data should not be used for route-to-route extrapolation in the following case s (unless these effects can be ac counted for in a PBPK model):

• When gro

ups of che

micals ha ve diffe rent toxici

ty by the two diff

erent r outes (e .g., metals, irritants, and sensitizers);

• When a first-pass effect by the respiratory

tract is expected;g CalEPA has developed IURs based on RPFs for severaladditi onal PAHs that have been cl assi fied a s probably or possibly human carcinogens (e.g., IARC). April 2004 Page 12-32 • When a first-pass effect by the liver is expected;

• When a re spirator

y tr act eff ect is est ablished , but dosime

try compari son cannot be cle arly established betwe en the two routes;

• When the respiratory

tract is not adequately

studied in the oral studies; and

• When short-term inhalation studies, dermal irritation, in vitro studies, or characteristics of the chemical indicate potential for portal-of-entry effects at the respiratory

tract, but studies themselve s are not

adequa te for a n RfC deve lopment. The actual impact of exposure by different routes can only be estimated by taking

account of factors that influence absorption at the portal of entry , such as (1) phy sicochemical characteristics of the chemical; (2) exposure factors; and (3) phy siologic parameters. The prefe rred method for performing route-to-route extrapolation involves the development of a PBPK model that describes the disposition of the chemical for the routes of interest. As pre viously discussed, PBPK models account for fundamental physiologic and biochemical parameters and pr ocesses such as blood flow, ventilatory pa rameters, metabolic capacities, and rena l clearance, tailored by the phy sicoche mical and biochemi cal prop erties. If appropriate toxicity

information is not available, a qualitative rather than quantitative evaluation of the chemical is recommended. The implications of the a bsence of the chemical from the risk estimate should be discussed in the uncertainty section.
12.8 Dose-Response Assessment for Mixtures The recommended approach for a ssessing risks from exposure to a mixture of pollutants (e.g., coke oven emissions, diesel exhaust, etc.) is to utilize a dose-response assessment developed for that mixture or a mixture judged similar. 11 12 Where such an assessment is not available, a component-by-c omponent approach may be employed. The

re are several commonly

used approaches. Selection among the approa ches involves consideration of the similarity of the mixture components with regard to their toxicological activity . There are a few g roups of toxicologically similar chemicals for which the Agency

recommends the use of relative potency factors (RPFs) or toxicity equivale nce factors (TEFs). These f actors have been developed by EPA and other organizations for two classes of compounds: PAHs and dioxins/furans. The World Health Organization (WHO) has developed TEFs for polychlorinated bipheny

ls (PCBs) as an extension of the factors for dioxins/furans (see Exhibit 12-14).

• Polycy clic Aro matic

Hydrocar

bons (PA Hs) . EPA has not developed IURs or CSFs for carcinogenic PAHs other than benzo(a) pyrene. E

PA recommends use of a RPF based on the potency of e ach compound relative to that of benzo(a)py rene. 13 Although several referenc es may be found in the litera ture with proposed RPFs for PAHs, EPA recommends the following RPF values for seven PAHs, which are c lassified as B2, probable human carcinogens: (g)April 2004 Page 12-33 PAH RPF Benzo(a)py

rene Benzo(a)anthracene Benzo(b)fluoranthene Benzo(k)fluoranthene Chrysene Dibenz(a,h)anthracene Indeno(1,2,3-c,d)py

rene 1.0 0.1 0.1 0.01 0.001 1.0 0.1 Thus, for these se ven PAHs, the I

UR for be nzo(a)py rene is mu

ltiplied by

the appli cable RPF to derive the I UR.

• Dioxins, F urans, and P CBs . For carcinogenic dioxins and furans, the TEF approach has an underlying

assumption of additivity across mixture components. EPA currently recommends TEFs for specific congene rs, rather than isomeric groups (see Exhibit 12-13). TEFs were determined by inspec

tion of the available congener-spe cific data and an assignment of an “order of magnitude” estimate of r elative toxicity when compared to 2,3,7,8- TCDD. The cancer potency

of certain dioxin and furan congeners is estimated re lative to 2,3,7,8-TCDD based on other toxicity information that is available for the congeners. Scientific judgment and exper t opinion f

ormed the basis for these TEF value s. Externa l review of the toxic ity and pharmacokinetic data utilized in setting these TEF values supporte d the basic approach as a “reasonable estimate” of the r elative toxicity of poly

chlorinated dibenzo-dioxins (PCDDs) and polychlorinate d dibenzo-furans (PCDFs). 14 TEF valu es devel oped by

scientif ic groups over the past 15 y ears are provided in Exhibit 12-13. The most recent consensus of the scientific community (including representation by

EPA scientists) is represented by the WHO 1997 va

lues.
TEFs based on the relative cancer potencies are used to adjust the exposure concentrations of mixture components, which are subsequently summed into a single exposure concentration for the mixture. That exposure concentration based on TEFs is then used, along with the 2,3,7,8- TCDD I

UR or nonc ancer r

efere nce valu e, to estimate can

cer risks or other

health hazards for the mixture.April 2004 Page 12-34 Exhib it 12-14 . Tox

icity Eq uivalen ce Factor s for Dio xins, Furans and PCBs Congener EPA (1987) 15 NATO

(1989) 16 WHO

(1994) 17 WHO

(1997) 18 TCDDs 2,3,7,8-TCDD 1,2,3,7,8-PeCDD 1,2,3,4,5,8-HxCDD 1,2,3,7,8,9-HxCDD 1,2,3,6,7,8-HxCDD 1,2,3,4,6,7,8-HpCDD 1,2,3,4,6,7,8,9-OCDD 1 0.5 0.04 0.04 0.04 0.001 0 1

0.5 0.1 0.1 0.1 0.1 0.001 1 1 0.1 0.1 0.1 0.01 0.0001 TCD Fs 2,3,7,8-TCDF 1,2,3,7,8-PeCDF 2,3,4,7,8-HxCDF 1,2,3,4,7,8-HxCDF 1,2,3,7,8,9-HxCDF 1,2,3,6,7,8-HxCDF 2,3,4,6,7,8-HxCDF 1,2,3,4,6,7,8-HpCDF 1,2,3,4,7,8,9-HpCDF 1,2,3,4,6,7,8,9-OCDF 0.1 0.1 0.1 0.01 0.01 0.01 0.01 0.001 0.001 0 0.1

0.05 0.5 0.1 0.1 0.1 0.1 0.01 0.01 0.001 0.1 0.05 0.5 0.1 0.1 0.1 0.1 0.01 0.01 0.0001 PCBs IUPAC # Structure 77 3,3',4,4'-TCB 81 3,4,4',5-TCB 105 2,3,3',4,4'-PeCB 114 2,3,4,4',5-PeCB 118 2,3',4,4',5-PeCB 123 2',3,4,4',5-PeCB 126 3,3',4,4',5-PeCB 156 2,3,3'4,4',5-HxCB 157 2,3,3',4,4',5'-HxCB 167 2,3',4,4',5,5'-HxCB 169 3,3'4,4'5,5'-HxCB 170 2,2',3,3',4,4',5-HpCB 180 2,2',3,4,4',5,5'-HpCB 189 2,3,3',4,4',5,5'-HpCB 0.0005

– 0.0001 0.0005 0.0001 0.0001 0.1 0.0005 0.0005 0.00001 0.01 0.0001 0.00001 0.0001 0.0001

0.0001 0.0001 0.0005 0.0001 0.0001 0.1 0.0005 0.0005 0.00001 0.01

–

– 0.0001 Source: EPA’s dioxin reassessment activ ities 19April 2004 Page 12-35 1. U.S. Environmental Protection Agency . Air Toxics Website: Summary. Updated November 28, 2003. A

vailable at: http://www .epa.g ov/ttn/atw /toxsource

/summary .html . (Last accesse d March 2004). 2. U.S. Environmental Protection Agency . 2002. A Review of the Reference Dose and Referen ce Conce

ntration Pr

ocess . Risk Assessment Forum, Washington, D.C., 2002. EPA/630/P- 02/002F. Availabl e at: http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=55365 3. U.S. Envir

onmental Protection Agenc

y. 2 002. Off

ice of Re search

and Deve lopment, National Center for Environmental Assessment. Risk Assessment Guidelines . Updated June 30, 2002. A

vailable at: http://cf

pub.epa .gov/nc ea/raf

/rafg uid.htm . (Last accesse d March 2004). 4. U.S. Environmental Protection Agency . 1999. Guidelines for Carcinogen Risk Assessment. Review D

raft . Risk Assessment Forum, Washington, D.C., July 1999. NCEA-F-0644. Availabl e at: http://cf pub.epa .gov/nc ea/raf

/rafg uid.htm U.S. Environmental Protection Agency . 1986. Guidelines for Carcinogen Risk Assessment

. Federal R egister 51(185): 33992-43 003, Septe mber 24, 1 986. Avai lable at: http://cf pub.epa .gov/nc ea/raf

/rafg uid.htm U.S. Environmental Protection Agency . 2003. Draft Final Guideline

s for Carcin ogen Risk Assessme nt (Ext ernal Review Draft) , Risk Asse ssment Forum, Washin gton, D. C. NCEA-F- 0644A. Av ailable a t http://cf pub.epa .gov/nc ea/raf

/rafg uid.htm 5. U.S. Environmental Protection Agency . 1994. Methods for Derivation of Inhalation Referen ce Conce

ntrations a nd Applica tion of Inh alation Dos imetry . Washing ton, D.C. U.S. Environmental Protection Agency . 2002. A Review of the Reference Dose and Reference Concentration Process. Risk Assessment Forum, Washington, D.C. December 2002. EPA/630/P-02/0 02F. Ava ilable at : http://www.epa.gov/iris/RFD_FI NAL%5B1%5D.pdf

(Last accesse d April 2004). 6. U.S. Environmental Protection Agency . 2002. Benchmark Dose Software (B MDS). Office of Research and Development. Updated June 30, 2002. Available a t http://cfpub.epa.gov/ncea/cfm/bmds.cfm . (Last accesse d March 2004). U.S. Environmental Protection Agency . 1996. Benchmark Dose Technical Guidance Document . Risk Assessment Forum, Washington, D.C., August 1996. EPA/600/P-96/002A. Availabl e at: http://www .epa.g ov/cgi-

bin/clar itgw?op-D isplay

&docume nt=clse rv:ORD:06 03;&ra

nk=4&te mplate=epa

. 7. Agenc

y f or Toxic Sub stances

and Dise ase Registry

(ATSDR). 2004. Toxic ologica l Profile Information Sh eet. Upd

ated Mar ch 19, 200 4. Availa

ble at: http://www .atsdr.c dc.gov /toxpro2.ht

ml . (Last accesse d March 2004). ReferencesApril 2004 Page 12-36 Agenc y f or Toxic Sub stances

and Dise ase Registry

(ATSDR). 2004. Minimal Risk L evels (MRLs ) for Ha

zardous Sub stances . Update d March 2 2, 2004. A vailable at: http://www .atsdr.c dc.gov /mrls.html . (Last accesse d March 2004). 8. State of California Office of Environmental Health Hazar d Assessment. 2003. Air - Hot Spots Guide lines. Ava

ilable at : http://www .oehha. ca.gov/air/hot _spots/ind

ex.html . (La st accessed March 2004). 9. Office of Response and Restoration, National Ocea n Service, National Oceanic and Atmospheric Administration. 2002. Occupational Exposure Limits. Updated March 26, 2002. Ava

ilable at : http://re sponse.r estorati on.noaa .gov/c ameo/loc s/worklims .html . (La st accessed March 2004). 10. The ARE is described in http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=60983

. U.S. Environmental Protection Agency . 1998. An SAB Re port: Dev elopmen t of the Ac ute Referen ce Expo

sure . Science Advisory

Board, Washington, D.C., EPA-SAB-EHC-99-005. Novembe r 1998. Av ailable a

t: http://www.epa.gov/sab/pdf/ehc9905.pdf

. 11. U.S. Environmental Protection Agency . 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures . Risk Assessment Forum, Washington, D.C. EPA/630/R-98/002. Federal Register 51(185):34014-34025, Septembe r 24, 1986. 12. U.S. Environmental Protection Agency . 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures . Risk Asse ssment Forum, Washin gton, DC , EPA/630/R- 00/002. Available a t: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf

. 13. U.S. Environmental Protection Agency . 1993. Provision al Guidanc e for Quan titative R isk Assessment of Polycyclic Aromatic Hydrocarbons . EPA/600/R-93/089. 14. Olson, J.R., Bellin, J.S., Barnes, D.G., et al. 1989. Reexamination of data used for establishing toxicity equivalency factors (TEFs) for chlorinated dibenzo-p-dioxins and dibenzofurans (CDDs 45 and CDFs). Chemosph ere 18(1-6):371-381. 15. U.S. Environmental Protection Agency . 1987. Interim Procedures for Estimating Risks Associated with Exposures to Mixtures of Chlorinated dibenzo-p-dioxins and -dibenzofurans (CDDs a nd CDFs) . EPA/625/3-87/012. 16. NATO/CCMS. 1989. Scientifi c Basis for

the Dev elopmen t of Intern ational Tox icity Equivalent Factor (I-TEF) Me thod of Risk Assessment for Complex Mixtures of Dioxins and Related Compounds . Report No. 178, December, 1998. 17. Ahlborg, U., Becking, G.C., B irnbaum, L.S., et al. 1994. Toxicity equivalence factors for dioxin-like PCBs: Report on a WHO-ECEH and IPCS consultation, December 1993. Chemosph ere

28(6):1049-1067. 18. van Leeuwen, F .X.R. 1997. Derivation of toxic equivalency f actors (TEFs) for dioxin-like compounds in humans and wildlife. Organohalogen Compounds 34:237.April 2004 Page 12-37 19. For more

informat ion on EPA’ s dioxin rea ssessmen t activitie s, see: http://cfpub.epa.gov/ ncea/cfm/recordisplay .cfm?deid=55264&CFID=12120688& CFTOKEN=95507561,

# Chapter 1 3 Inhalati on Risk Ch aracter izati on

Table of Contents

13.1 Introduction

............ ............ ............ ............ ............ ...... 1
13.2 Quantif

ication of Canc er Risk

and Noncancer

Haza rd

............ ............ ......... 4 13.2.1 Cancer Risk Estimates

............ ............ ............ ............ ... 5 13.2.1.1 Characterization of I ndividual Pollutant R

isk

............ ........ 5 13.2.1.2 Charact

erizat ion of C ancer R

isk from Ex

posure to Multip le Pollu

tants

............ ............ ............ ............ ........ 6 13.2.2 Noncancer Hazard Estimates

............ ............ ............ .......... 8 13.2.2.1 Characterizing I ndividual Pollutant H azard for Chronic Exposures

... 8 13.2.2.2 Characterizing Multip le Pollutant Hazard for Chronic Exposures

.... 9 13.2.2.3 Characterizing H azard for Acute E xposures
............ ........ 11 13.2.3 Quantifying R

isk From Background

Sources

............ ............ ........ 12
13.3 Interpretation and Presentation of I nhalation Cancer Risk

s and Noncancer H azards

........ 13 13.3.1 Presenting Risk

and Hazard E stimates

............ ............ ............ . 17 13.3.2 Exposure Estimate s and Assumptions

............ ............ ............ . . 17 13.3.3 Toxicity Estimates and Assumption s
............ ............ ............ ... 18 13.3.4 Assessment and Presentation of Uncertainty in Risk Characterization

............ . 19 13.3.4.1 Practical Approaches to U ncertainty Assessm

ent

............ .... 20 13.3.4.2 Presentation of Uncertainty Assessment

............ ........... 23 13.3.5 Additional Information

............ ............ ............ ............ . 23 References

............ ............ ............ ............ ............ ............ 25April 2004 Page 1 3-1 Ris k = ƒ (me tri c of exp osu re, met ric of t oxi cit y)
Risk characterizatio

n combines the information from the exposu re asses sment

and the toxicit y asse ssmen

t to provide a quantitativ

e estimate of poten

tial cancer risk and/or hazard for other adverse effects, a long with a statement of confiden

ce about the data and methods used

Informa tion should be presented on the range of exposures derived from exposure scen arios and on the use of m

ultiple risk descriptors (e. g., centr al ten dency, hi gh end of i ndividu al ris k, populat ion risk , imp ortant s

ub-p opulati ons, if known) consistent with terminolog y in the Guidance on Risk C

haracterization , Agency

risk assessm ent gu idelines , and program

-spe cific guidance

. EPA Policy for Risk Charac terization (2) 13.1 Introduction The last component of risk assessment, risk characterization, integr ates the information from the exposure assessment (Chapter 11) and toxicity asse ssment (Chapter 12), using a combination of qualitative and quantitative information and including a discussion of uncertainty and va ri ab il it y. (1) The risk c

haract erization and its components should be presente d so that th e details of the analy sis are tr

anspare nt, clea r, consis tent with EPA guida nce and policy , and will general ly support the conclusion that the analy sis is reasonable for its intended purpose. Risk assessors aim for the risk summary a nd risk conclusions to be complete, informative, and useful for decision-makers. One way of accomplishing this is to make sure that major uncertainties associated with determining the nature and extent of the risk ar e identified and discussed. EPA has developed several key polic ies a bout how to c

hara cter ize and present risk assessment informat ion. EPA’s Policy fo r Risk Characterization (2) specifie s that a risk characterization “be prepare d in a manner t hat is cle ar, tra

nsparen t, reasonable, and consistent with other risk characterizations of similar scope prepared across progr

ams in the Agency .” The purpose of the memorandum was to ensure that risk management decisions are well-supporte d and well-understood, both inside the EPA and outside the Agency . The confidence in the data, science policy judgments, and the uncertainties in the database should be clearly communicated. The 1995 Guidance for Risk Characterization has been updated by

the Handbook for Risk Characterization , which provides more background and appr oaches t o presen ting the risk cha racter ization re sults. (3) Risk asses sors may want to become familiar with the information provided in both the policy and handbook before beginning a risk as sessment. A 1992 memorandum from EPA’s Office of the Administrator provides g uidance on describing risk asse ssment re sults. (4) This memorandum focuses on communicating the full rang e of informat ion used in

developi ng the a ssessmen

t, rathe r than pr oviding only

point estimates of r

isk to the public. The risk characterization guidance a nd handbook (3) recomme

nds prese nting a f ull and complete picture of risk that includes: a statement of confidenc e about data and methods used to de

velop the assessme nt; gre ater consistency a

nd compar

ability in risk ass essment a

cross EPA programs; and statement of the level of scientif ic judgment inher

ent in risk manage ment dec isions. I

nformati on should be presented on the range of exposures derived from exposure scenarios using multiple r isk descr iptors (e .g., ce

ntraltendency, hig

h-end of individual risk, population risk, important sub -populat ions, if known). For risk management dec isions, the risk estimates ar

e compar ed to leg ally mandate d or other

risk obje ctives (s ee Part V of this Reference Manual).April 2004 Page 1 3-2 Incidence is defin

ed by the Na tional C ancer I

nstitute as “The number of new cases of a disease diagnosed each year.” F or exam

ple, a S tate’s c ancer re gistry might rep ort th at the state wide 5-year average incidence of lung c ancer (i.e., the average numb er of actual people that were d iagnosed by a doctor over the 5 year period) is 700 new cases per 100,000 people (5-y

ear averages are

often used to prov

ide an est imat e tha t is mo re st abl e over time) . In comp arison, air tox ics risk assessm ents provide only a theoretical estimate o f the likelihood that a n individual in the exp osed population will con tract canc er as a res ult of ex posu re ove r a pe rio d of t ime (e.g. , 50 or

100 years of a facilit

y lifet ime).

Steps in an Inhala

tion Risk Characteriz ation
1. Organize ou

tputs of inhalation exposure and toxicity assessm ents. 2. Derive inhalation can cer risk estimate s and noncancer hazard quotients for each po llutant in each pathway for each type of receptor bein g studied. 3. Deriv e cum

ulativ e inhala

tion can cer risk estimates and

noncan cer haz ards for

each re ceptor f

or all chemicals in a pa

thway and then across pathways. 4. Identify k

ey features and assu

mptions of expo sure and toxicity asse ssments. 5. Assess and cha

racteriz e key

uncerta inties a nd va riability associa ted with the ass essme

nt. 6. Consid

er addit ional re levan

t inform ation (e .g., related

studies ).
Risk characterizatio

n should include a risk

summa ry and risk conclusions that are com plete, informative, and useful for decision- makers, an

d which clearly ide

ntify and discuss the

major uncertainties associated with d etermining the nature and extent of risk . See references 2 an d 3 at the end of this chapter for m

ore information. Risks are often evaluated initially for individuals within the

potentia lly exposed population. Populati on risks for the exposed population may also be estimate d, which may

be usefu l in estimating potential economic costs and benefits from risk reduction. Sensitive subpopulations should also be considered, when possible. Estimates of incidence also are possible (see Exhibit 13-1). The pote ntial risks calcul ated for

specific inhalation exposure s are ty pically increm ental ris ks ; that is, they are potential risks that are in additionto those risks already

faced by the population under study for r easons other than exposure to air toxics (e.g., hereditary

, lifestyle risks such as smoking ). The risk estimates are used to answer questions concerning the gene

ral risks posed to the exposed population, the risk levels of various groups within the

population, and the

potentia l range of risks a cross the population (e.g ., centr

altendency ( e.g., average ) or high-end (e.g., maximum) risk for individuals within the populations of interest).

Estimated cancer risks and noncanc er hazar

ds are g eneral ly d

evelope d for ea

ch chemi cal to which people are exposed in the study area and each exposure pathway through which exposure can occur. The results are then summed in a spe cific way to provide total estimates of risk and hazard. The general steps involved in risk char acterization are:

• Quantify risks and ha zards for each chemical through e ach pathway for each receptor;

• Review exposure es timates a nd assumpt

ions;

• Review to xicity

estimate s and assu

mptions;

• Assess uncertainties and variability ; and

• Consider additional relevant information (e.g., relate d studies).April 2004 Page 1 3-3 Exhibit 13-1. Estimates of Risk Individual risk . Estimates of cancer risk are usually expressed as a statistical prob ability represented in scientific notation as a negative exponent of 1 0. For exam ple, an additional risk

of contracting cancer of one chance in

10,000 (or one ad

ditional person in 10,00 0) is written as 1×10 -4 (or 1E-04). This means that for ev

ery 10,000 peo

ple that are exposed, in the way that w e have presumed

, one of those p eople m

ay dev elop ca ncer ov er their lifetim e. Lik ewise, a risk

of one person in one m

illion is written 1×10 -6 (or 1E-06) and a risk or one in one hundred tho usand is written 1×10 -5 (or 1E- 05). Population Risk . Estimates of cancer risk can be expressed as the num ber of people in the po

pulation who may have the sam

e risk level (e. g., 1,000,0 00 people in the expo sed population under stud y may have a risk of 1×10 -6 , 2,495 m ay have a risk of 1×10 -5 , and 300 m ay have a risk of 1×10 -4 ). Incidence . Estimates of c ancer risk can be expressed as the incidence of cancer cases in a po pulation. For example, the estimated incidenc e of cancer in a popu lation of 500,000 indiv

iduals where the individual risk is 1×10 -5 (based on a 100 yea r expos ure sce nario ) is si mply:

Note th at sin ce the i ndividu al canc er ris k value is a lifet ime value , it is d ivided by 70 years (average lifetime length) p

rior to multiply

ing by the exposure period duration (100 years). I t is also important to note the assum

ptions in this exam

ple calculation (e.g., average po pulation size of 500 ,000 individuals and indiv

idual lifetime risk value of 1×10 -5 for the 100 yea r period). Given

these assum ptions, these p ossible

seven new c ases are

the exp ected n

umbe r of cas es ove

r the to tal expo sure duration of 100 years. If on e wanted to estim

ate the number o f new cases per y ear, simply use an exposure duration of one year. I n our example,

This points out two problem s with using risk

estimates to deriv

e incidence estimates. First, a fraction of a can cer cas e (whic h often resul ts fro m this exe rcise ) is not a very help ful st atist ic when assess ing a potential air toxics problem . Second, people living in differe

nt areas with the sam

e individual risk

s, but with very

different exposed population sizes can end up with very different incidence rates. F or example, if our population above only had 10,0 00 people, the incidenc e rate would have been predicted to be no m

ore than 0.1 (ve

rsus seven). While the first situation indicates a hig her potential population impact, the second situation nev ertheless indicates identical indiv idual risk predictions for members of the population. B oth metrics are inform ative to the risk

manager, and reflect different considerations which m

ay have differe

nt weights in differen

t decisions. Other ways of describing risk to an exposed population are also possible.April 2004 Page 1 3-4 Risk versus Hazar d...What’s the Difference? Risk asse ssor s pur pose ful ly use the t erm risk to mean the stat istical p robabil ity of

devel oping

cancer over a lifetime (even if exposure only occ urs over a portion of that lifetime). Noncancer “risks,” on the other hand, are not expressed

as a statistical probability of developing a disease. Rather they are expressed as a simple com

parison of the exposure concentration to a reference concentration associa ted with the observable ad

verse health e ffects. To help mak e this distinct ion, th e poten

tial harm from exposu re to carcino gens i s called

“risk ” and the poten

tial harm from noncarcinog ens is called “hazard. ” Risk estimates in screening-level (Tier 1) ana lyses ty pically are

deterministic estimates based on point estimates of exposure and toxicity. Deterministic estimates are usef ul screening tools in a tiered analy sis, but need to be qualified by tra nsparent discussions of the nature and extent of uncerta inties in the input va

riables a nd the subs equent li

kely impact on

the ultima

te risk characterization. Deterministic analy ses with appropriate uncertainty characterization can be used to ide

ntify situations

of low inc

rementa l risk and to focus on areas

where a dditional a

naly sis might impr

ove the ba sis for se lection o f a risk ma nageme nt action . At higher tiers

of analy ses, risk asse ssors com

monly describ e exposure

(and les s freque

ntly , toxicity

) by probabil ity distributions rather than by point values and pr opagate these distributions through the exposure assessment and risk characterization process. This ty pe of probabilistic analy sis, which may address uncerta inty and vari ability

as distinct issues, w

ill result i n an estima

te of risk that is a probability distribution rather than a point value. A more detailed discussion of the assessment and presentation of uncertainty in the risk characterization process is provided in Section 13.3.4. Probabilistic uncertainty ana lysis is discussed in Chapter 31.
13.2 Quantification of Cancer Risk and Noncancer Hazard Quantification of risk and hazard is the step whe re exposur

e conce ntrations in air are combined with applicable inhalation dose-response values. Predictive cancer risk estimates ar

e prese nted sepa rately from noncancer hazard quotients. Risks are qua ntified f or the pa thways, rece

ptors, and exposure scenarios outlined in the concep tual site model.
Information about the distribution of exposure and risk for the population is an important componen

t of risk characterization. Distributions are often more useful than point estimates. However, since developing fully distributional estimates of risk is usually out of the scope of most risk a ssessmen

ts, asses sors can

provide a sense of the rang e of risk s by

developi ng both centra l tendenc y a nd high- end estima

tes. (5)

• Central tendency

estimates are intended to give a char acterization of risk for the ty pical individua l in the pop

ulation. T

his is usua

lly e ither ba sed on the

arithmet ic mean r isk (average estimate) or the

median risk (median estimate).

• High-end

estimates are intended to estimate the risk that is expected to occur in the upper range of the distribution (e.g., risk above about the 90 th percentile of the population distribution). For example, the maximum exposed individual (MEI) risk or maximum individua l risk (MI R) might b e used to e stimate hi gh-end risks. An evaluation of the uncertainty in the risk descriptors is an important component of the uncertainty discussion in the asse ssment. Both quantitative and qualitative evaluations of uncertainty c an be useful to users of the assessment (see Section 13.3.4 and Chapter 31).a EP A is cur rently rev iewing m ethod s for asse ssing ca ncer r isk for less th an lifetime expo sures o ccurr ing in

childh ood . EP A’s D raft Do cum ent Sup plem enta l Gu idan ce for A ssessing Ca nce r Sus cep tibility from Ea rly-Life Exposure to Carc inogens ( ht tp: // www. epa. gov/ sab/

pane ls /s gacs rp. ht ml ) rec ommends a chang e to t he cur rent meth od for st rong mutag ens. This document i s undergoi ng publi c and Science AdvisoryBoard review and wi ll be c ompleted somet ime i n th e fu tur e wit h cons ider ati on of t

hat revi ew. EPA’s met hods f or ai r tox ics ass essme nts wil l be con sistent with the final do cum ent. April 2004 Page 1 3-5 13.2.1 Cancer Risk Estimates Estimated individual cancer risk is expressed as the upper bound probability that a person may develop c ancer over the c ourse of their lif etime as a result of the exposur

es under study . This predicted risk is the increm ental ris k of cancer from the exposure being a nalyzed that is above the risk that the individuals in the population have already (i.e., due to non-air toxics related issues). Due to the nature of the assumptions in their derivation, inhalation unit risks (I URs) are generally considered to be “plausible upper-bound” estimates of potenc y. As such, the ca

lculated risks are usually a

conservative estimate (i.e., the true risk may be lower).
As descr ibed above, risks ma

y be estimate d for both

the cent ral tend ency (avera ge expos ure) ca se and for the high-end (exposure that is expected to occur in the uppe r range of the distribution) case. However, for both ty pes of estimates, the same estimate of toxicity (i.e ., an IUR or reference concentr

ation [RfC]) is generally used to ca lculate the risk. In other words, w hile the estimate of exposure may be allowed to vary to de

rive a sense of the range of exposures in a population, the same

estimate of toxicit yis used to calculate risk for both average and high-end risks. With few exceptions, toxicity values are not currently presented as a range. Cancer risk characterization ty pically is perfor

med first for individual air toxics, then is summed over all of the air toxics to which a person may be exposed at the same time. These steps are described in separate subsections below.
13.2.1.1 Charact erizat ion of Individual P ollutant Ri sk For inhalation exposures, chronic cancer risks for individual air toxics are typically

estimated by multiplying the estimate of long -term exposure concentration (EC) by the corresponding I

UR for each po llutant to e stimate th e potenti al incre mental ca ncer ri sk for an

individua

l: Ris k = EC L × I UR (Equation 13- 1) where:
Risk = Cancer risk to an individual (expressed as an upper-bound risk of contra cting cancer over a lifetime); EC L = Estimate of long-term inhalation exposure concentration for a spe cific air toxic;

and IUR = the corresponding inhalation unit risk estimate for that air toxic. Performing the estimate in this way provides an estimate of the probability of developing cancer over a lifetime due to the exposure in question. Because of the way this equation is written, the underlying

presumption is that a person is exposed continuously to the EC L for their full lifetime (usually assumed to be 70 y ears). (a) The EC L

is an estimate of this long-term exposure evenApril 2004 Page 1 3-6 though it is probably base d on only one y ear’s worth of monitoring data or a modeling run that covers only one

year’ s worth of time. (As noted in Chapter 11, exposure modeling can be used, in some ca ses, to de

rive a be tter esti mate of the amount o f time peo ple inter act with contaminated air. Nevertheless, the probability of developing cancer is still averag ed out over the full lifetime of the individual.)
Estimates of canc er risk a

re usual ly e

xpressed a s a statist

ical pro bability repres ented in s cientifi c notation as a negative exponent of 10. For example, an additional risk of contra cting cancer of one chance in 10,000 (or one additional person in 10,000) is written as 1× 10 -4 (or 1E-0 4). This means that for every

10,000 people that are exposed, in the way that we have presumed , one of those people may de

velop cancer over their lifetime. L ikewise, a risk of one person in one million is written 1×10 -6 (or 1E-06) and a risk or one in one hundred thousand is written 1× 10 -5 (or 1E-05).
Because I URs are typica

lly upper-bound estimates, a ctual risks may be lowe

r than predicted (see Chapter 12), and the true value of the risk is unknown and may be as low as zero. (5) These statistical projections of hypothetical r isk are intended as screening tools for risk mana gers and cannot ma

ke real istic pre dictions o f biolog ical eff ects. Suc h risk esti mates als o cannot b e used to determine whether someone who already has cancer is ill because of a past exposure. Part VI of this volume

provides an overv iew of the Public He alth Asse ssment pro cess use d to evalu ate whether past exposu

res res ulted in c urrent il lness. Risks for cancer are ge

nerally expressed a s individual risks (i.e., the risk borne by a n individual in a larg er expose d populati on). The number of

people in the popula

tion who ha

ve the sa me risk level may also be pr

ovided (e.g., 1,000,000 people in the exposed population under study have a risk of 1×10 -6 , 2,495 have a risk of 1×10 -5 , and 300 have a risk of 1×10 -4 ). It is also pos sible to calculate the number of expected cases of c ancer expected over a 70-y ear period by

multiplying the cancer risk to an individual by the number of individuals; however, even though the calculation might y

ield an estimate of incidence, low predicted canc er incidence rates (even vanishing ly s mall) do not mean tha t individua

ls within the

populatio

n will not g et canc er beca use of air toxic s exposure s. 13.2.1.2 Characterization of Cancer Risk from Exposure to Multiple Pollutants People ma

y r eceive exposure t

o multiple c

hemicals , rather

than a sin

gle che mical, at the same time. The concurrent exposure to multiple carcinogens may occur through the same pathway or across several pathway

s. With a few exceptions (e.g., coke oven emissions), cancer doseresponse values (e.g., I

URs) are usually a

vailable only for individual compounds within a mixture.
The following eq

uation es timates th e predicted cumul ative inc rementa l individua

l cance r risk from multiple substances, and assumes an additive effect from simultaneous exposures to sever al carcinogens: Risk T = Risk 1 + Risk 2 + ... . + Risk i (Equation 13- 2) where:April 2004 Page 1 3-7

Examp le Calc ulation

to Esti mate Ca ncer R

isk (Hyp othetic al) A Tier 1 mode ling a

nalysi s was p erform

ed to es timate

risk t o the m aximu m exp osed individu

al, assumed to re side at the poin t of maximu

m con centrati on for A

BC Factory. Four H

APs we re potentially of concern: benzene, dichloro ethyl ether, form aldehyde, and c admium compounds. Cancer risk estim

ates were obtained for eac h HAP by multiplying

the estimated annual average EC by the IUR fo

r each HAP. The re sulting upper boun d cancer risk estim

ates ranged from

2×10 -6 (benzene, formaldehyde ) to 8×10 -4 (dichloroethyl ether). The cancer risk estim ates for each HAP were summ ed to obtain an estim

ate of total inhalation cancer risk (9×10 -4 ). Note that 97 pe rcent of the estimated total risk res ults f

rom dichl oroet hyl ethe r, and t hat more than 99 percen t resu lts fr om dichl oroet hyl ether and cadmium

compounds. In this hy

pothetical example, the risk assessor wo uld need to decide which HAPs to ca

rry to higher tiers by weig hing the sm

all proportion of risk posed by benzene and formaldehyde

against the fact that these risks nevertheless exceeded one in one million.

HAP EC

μg/m 3 IUR

1/(μg/m 3 ) Cancer Risk

Estimate (a) Percent of

Total Risk Benzene 0.3 7.8 × 10 -6 2 × 10 -6 < 1% Dichloroethyl ether 2.5 3.3 × 10 -4 8 × 10 -4 97 % Formaldehy

de 0.2 1.3 × 10 -4 2 × 10 -6 < 1 % Cadmium compounds 0.01 1.8 × 10 -3 1 × 10 -5 2 % Total 9 × 10 -4

(a) Standa rd rules for rou nding apply which will com

monly lead to an answer of one sign

ificant f igure in both risk and hazard estimate s. For presentation pu rposes, hazard qu

otients (and hazard in

dices) and cancer risk estim

ates are usually reported

as one significant fig

ure. RiskT = total cumulative individual pathway-spe cific cancer risk (expressed as an upperbound risk of contracting cancer ove r a lifetime); and Riski = individual risk estimate for the i th su bs ta nc e i n t he in ha la ti on pa th wa y. In screening -level assessments of carcinogens for which there is an assumption of a linear doseresponse , the can

cer risks predicted for in

dividual c hemicals may be adde d to estima

te cumulative cancer risk. This approach assumes that the r isks associated with individual chemicals in the mixture are additive. In more r efined assessments, the chemicals under assessment may be e

valuated to determine whether effec ts from multiple chemicals are synergistic (greater than additive) or anta gonistic (less than additive), although sufficient data f or this evaluation are usually la cking. In those ca ses where IURs ar e available for a chemical mixture of c oncern, risk cha racter ization ca n be cond ucted on t he mixture u sing the

same procedures used for a single c ompound. When more than one pathway is involved, the pathway specific risks are gener ally summed first, and the

n summed a

cross pa thways. This pro cess is described in Part II

I of this refere

nce manual. Note that for carcinog ens being assessed based on the assumption of nonlinear dose-response, for which an RfC consider ing cancer as well as other effects has been derived, the hazar d quotient approach will be appropriate (see Section 13.2.2).April 2004 Page 1 3-8 13.2.2 Noncancer Hazard Estimates For noncancer effe cts (as well as carcinogens be ing assessed based on the assumption of nonlinear dose-response), exposure concentra tions are compared to RfCs, which are estimates (with unc ertainty

spanning perhaps an order of magn itude) of a continu ous inhala

tion exposur

e to the human population (including sensitive sub-populations) that is likely to be without an appreciable risk of deleterious noncanc er effects during a lifetime (se e Chapter 12).
13.2.2.1 Characterizing Individual Pollutant Hazard for Chronic Exposures For inha lation expo sures, noncance r hazard s are es timated by dividing the estima

te of the chronic inhalation EC by

the RfC to y

ield a ha zard quoti ent (HQ)

for individual che

micals: HQ = EC C ÷ R fC (Equation 13- 3) where: HQ = the hazard quotient for an individual air toxic; EC C = estimate of chronic inhalation exposure to that air toxic; and RfC = the corresponding refere nce concentration for that air toxic. In screening

inhalation risk assessments, which are routinely built around a particular y ear’s estimate of emissions, the exposure estimate is usually based on a n assumption of continuous long-term exposure using an annual aver age as the estimate of exposure concentr ation. A more refined assessment (e.g., by use of an exposure model) may generate an estimate of a mor e realistic exposure (e.g., by the application of an exposure model or refined emissions estimates over the longer time period).
Based on the def

inition of t

he RfC, an

HQ less th an or equ

al to one i ndicate s that adv erse noncancer effects are not likely to occur , and thus can be considered to have negligible hazard. Unlike cancer risks, however, HQs g reater than one are not statistical probabilities of harm occurring. I

nstead, they ar

e a simple statement of whether (and by how much) an exposure concen tration exceeds the

RfC. More over, the

level of concer n does not i

ncreas e linear

ly or to the same extent as H Qs incre ase abov e one for differe nt chemic als beca

use RfCs do

not generally have equal accuracy

or precision and are gene rally not based on the sa

me severity of e ffect. Thus, we can only

say that with exposures increa singly g reater than the RfC, (i.e., HQs increasingly

greater than 1), the potential for adverse effects increases , but we do not know by how much. An HQ of 100 does not mean that the hazard is 10 times grea ter than an HQ of 10. Also an HQ of 10 for one substance may not have the same meaning (in terms of hazard) a s another substance resulting in the same HQ.April 2004 Page 1 3-9 Examp le Calc ulation

to Esti mate Ch ronic N oncanc er Haz ard (Hyp othetic al) A Tier 1 modeling

analysis was perfo

rmed to estim

ate chronic noncancer hazard to the maxim um exposed individual, assumed to resid e at the point of m

aximum concentration for ABC Factory. Four HAPs were potentially

of concern: benz ene, dichloroethyl e ther, formaldehy

de, and cadm

ium compounds. Noncancer hazard estimates were obtain ed for each HAP by dividing the estimated Exposu re Conc entration (EC) by the Inh alation Referen

ce Conc entration (RfC)for each HAP (note that the EC is expresse d in un

its of m g/m 3 for this analysis) . The resultin g Hazard quotient (HQ) estimates ranged

from 1×10 -3 (formaldehyd e) to 1 (cadmium

compounds). Note that no RfC was available for dichloroethy l ether. The HQs for e ach HAP were su mmed to obtain an estimate

of the Hazard I ndex (HI) o f 1. Note that cadm ium com pounds account for 95 percent of the HI , suggesting that the other HAPs may not need further consideration (altho ugh this determ

ination should be m

ade in cons ideration of all

relev ant informatio

n, incl uding uncerta inties such as c onfiden

ce in th e expos

ure concentration and uncertainty factors used to derive each RfC).

HAP EC

mg/m 3 RfC

(mg/m 3 ) HQ (b) Percent of HI Benzene 6 × 10 -4 6 × 10 -2 1 × 10 -2 1 % Dichloroethyl ether (a) 5 × 10 -3

--- --- --- Formaldehy

de 4 × 10 -4 1 × 10 -2 1 × 10 -3 4 % Cadmium compounds 2 × 10 -5 2 × 10 -5 1 95 % Hazar d Inde x (HI) 1

(a) note that the absence o f an RfC value means that w e cannot quantitatively assess a HAP. (b) Standa rd rules for rou nding apply which will com

monly lead to an answer of one sign

ificant f igure in both risk and hazard estimate s. For presentation pu rposes, hazard qu

otients (and hazard in

dices) and cancer risk estim

ates are usually reported

as one significant fig

ure.

13.2.2.2 Characterizing Multiple Pollutant Hazard for Chronic Exposures Noncancer health effec

ts data are usually

available only for

individual compounds within a mixture. In these cases, the individual HQs can besummed together to calculate a multiplepollutant hazard index (HI): HI = HQ 1 + HQ 2 + ...+ HQ i (Equation 13- 4) where
HI = hazard index; and HQ = hazard quotient for the i th air toxic. For screening-level a ssessments, a simple HI may first be calculated for all chemicals of conc ern within the inhalation pathway (a dding hazards across pathway s is discussed in Part III). I f the HI is less than your decision cr iterion, a more refined analy sis is usually not performed. Adding HQs in this fashion is based on the assumption that even when individual pollutant levels areApril 2004 Page 13-10 Procedure for Segregation of HIs by Effect Segregation o f HIs requires identification of the major effects of each chem ical, including those seen at higher doses than

the critical effect (e.g. , the chemical m

ay cause liver damage at an EC of 20 μg/m 3 and ne urotoxi city a

t an EC of 50 μ g/m

3 ). Majo r effe ct cat egorie s incl ude neu rotox icity, develop mental t oxici ty, repr oduct ive toxi city, immunoto xicity, and adv erse effects by targ et organ (i.e., hepatic , renal, respira tory, cardiov

ascular, gastrointestinal, hem

atological, m

usculoskeletal, and dermal/ocular effects). lower tha n the cor respondi ng ref erence levels, s ome polluta

nts may work tog ether su

ch that th eir potential for harm is additive and the combined exposure to the group of che micals poses greater likelihood of harm. Some groups of chemicals can also beha ve antagonistically , such that combined exposure poses less likelihood of harm, or sy nergistically , such that combined exposure poses harm in greater than additive manner . Where this type of HI exceeds the criterion of interest, a more refined ana lysis is warranted. Although the HI appr

oach encompassing all chemicals in a mixture is commonly used for a screening-level study, it is important to note that application of the HI equation to compounds that may produce differe nt effe cts, or th at act by

differe nt toxicological mec

hanisms, c ould overest imate the potentia l for eff ects. Con sequentl

y, i t is more a

ppropria te to calc ulate a s eparat

e HI for eac h endpoint of concern for which mechanisms of action are known to be similar. Because the assumption of dose additivity is most appropriate for compounds that induce the same ef fect by similar mod

es of action, EPA’ s Guidance for Conduc ting Healt

h Risk Assessment of Chemical Mixtures and Supplementary Guidance (6) sugge st subgr ouping p ollutantspecific HQs by toxicological simila

rity of the pol lutants for subsequ ent calc ulations; t hat is, to calculate a target

-organ-s pecific-ha

zard index

(TOSHI) for each subgrouping of pollutants. This calculation allows for a more appropriate estimate of overall hazard. Segregation of hazard indices by effect and mechanism of action can be complex and time- consuming

because it is necessa ry to identif y a ll the major effe cts and target organism for e ach chemical and then to cl assify

the chem icals ac cording

to target organ(s) or

mechanism of action. This analysis is not simple and a toxicologist with familiarity in developing TOSHI s is best sui

ted to per form this function. If the seg

regation is not carefully done, an underestimate of true hazard c ould resu

lt. Acute HQs are developed in the same manne r as chronic HQs, with the caveat that the exposure duration associated with the exposure concentration should match the exposure duration embodied in the ac ute toxicity

value. Whe

reas sum ming chr onic HQs t

o a total hazard ind

ex is a relatively straig

htforward exercise, the issues related to developing acute HI ar

e more subtle and complex. A t

oxicologi st familia

r with ac ute exposur

e and risk analy

sis should b e consult

ed to perfor m this proc ess.April 2004 Page 13-11 13.2.2.3 Characterizing Hazard for Acute Exposures Risk assessors can derive estimates of acute noncanc er hazard for each HAP by combining the applicable short-term exposure concentration (EC) and ac ute dose-response value (AV) for the HAP to obtain the acute Hazard Quotient (HQ) for the HAP using the following equation: HQ A = EC ST ÷ AV where: HQ A = the acut

e hazard quotient f

or an indi vidual HA

P; EC ST = estimate of short-term inhalation exposure to that HAP; and AV = the corr

esponding

acute do se-res

ponse va lue for that HAP. Note that ambient air concentrations are ca lculated for an exposure duration compatible with the acute dose-response value used. Available acute dose-response values a re more diverse than chronic values, bec ause they we re develope d for dif

ferent purposes and cons idering

differe nt exposure

duration s. The most effec tive cha

racter ization of a

cute risk often is to

compare the maximum e stimated hourly concentrations with a range of a cute dose-response values from sources de scribed in Chapter 12. If the ambien

t concen tration is lower tha n all the a cute ben chmarks , it is ge

nerally reasona ble to conclude that the potential for significant acute ha zard is negligible. If

the concentration exceeds some benchmarks but not others, the assessment should include a discussion of the implications for the chemical of interest, with attention to the details of both the exposure scenario a nd the benchma rks inclu ded in the analy sis.
Acute noncancer health effe cts data are usually available only for

individual HAPs within a mixture. In these cases, it may be possible to combine the individual acute HQs to calculate a multi-pollutant acute hazard index (HI) using the following formula: HI A = HQ A1 + HQ A2 + ...+ HQ Ai where
HI A = acute hazard index; and HQ Ai = acute hazard quotient for the i th HAP. Althoug h this appe ars simila

r to the pr ocess for combinin

g chronic HQs, t

he summing

of acute HQs is complicated by several issues that do not pertain to chronic HQs. First, acute doseresponse values have been developed f or purposes that vary

more widely than c

hronic values. Some sourc es of ac

ute value s define

exposures

at which a

dverse e ffects a

ctually occur, w hile other sources develop only no-effect acute values. Second, some a cute values are expressed as concentration-time matrices, while others are e xpressed as single concentrations for a set exposure d

uration. Third, some acute values ma

y sp ecifica

lly c onsider m

ultiple expo sures, whereas others consider exposure as a one- time event. Fourth, some sources of acute values a re intended to regulate workplace e xposures, assuming a population of healthy workers (i.e., without ch

ildren, s eniors, or other s ensitive i ndividual s). Such o ccupati onal valu es may also consider cost and feasibility , factors that EPA considers the province of the risk manag er rather than the risk assessor.April 2004 Page 13-12 Given these differences among acute values with regard to their purpose s, and the different ty pes of acute exposure c

haract erization that may be perf ormed, the acute

HI a naly sis is most informative when limited to acute values from the same source, the same level of effects, and the same duration. Analy ses that mix sources, effects levels, and durations are likely to be misl eadin g. Risk asses sors commonly evaluat e acute noncanc er hazar

d using a variety of diffe rent ac ute values f rom diffe rent sour ces, and discuss th e resulti

ng haza rd estima tes consi dering

the purpo se for which each of value was deve loped. This kind of evaluation should only be done by an experienced toxicologist. The significa nce of thes e HQs and HIs woul d need to be considered in the context of the purpose of the risk assessment and the characteristics of the dose-response values, such as their purpose, averaging time, and health endpoints . EPA is working to provide more comprehensive g uidance on what benchmarks to rely upon and plans to de ve lo p a re le va nt ac ut e b en ch ma rk me th od ol og y. 13.2.3 Quantifying Risk From Background Sources In some cases, it may

be appropriate to quantify background concentrations of the a ir toxics of concern. For example, backgr ound concentrations may

be a critical element in determining the need for further reductions of emissions from a par ticular source. Backgr

ound concentrations are the levels of contaminants that would be present in the absence of c ontaminant releases from the source(s) under evaluation. Bac kground concentrations may occur naturally

in the environment or originate from other human sources (e .g., an industrial area upwind from the source s of concern). The ge neral a pproach in risk ass essments a

nd risk ma

nageme nt decisions has oft

en been to assess the incremental risk posed by emissions from a particular source or group of sources. Various EPA programs, however, have taken specific approaches to considering background risks, some

of which are summa

rized in EPA ’s Residual R isk Repor t to Congres

s . (7)
A detaile d analy

sis of bac kgroun d conce ntrations typ ically would re quire exte

nsive dat a gathering and modeling be yond that require d for the incremental risk analysis. For example, numerous nearby

(and possibly distant) air toxics sources of var ying ty pes would need to be characterized in sufficient detail to support release a nd exposure modeling. The data needs for assessment of background concentra tions may differ de

pending on what will be done with the data. F or example , if the question is si mply “what is t

he risk to the popula

tion in a sp ecific place,” then an assessment of backg round may be unne cessary (monitoring data in the study a rea may be all that is require d). On the other hand, if the question is “what is the risk and what can we do about it,” then a knowledge of how much r isk is contributed from both local and backg round sou rces ma y be necessa ry . If t he risk is u

nacceptably

high, bu t most of the

risk is backg round in na

ture, the re may be no appropriate

risk red uction str ategy (espec ially

in rega rd to local sources). Interpreting ba

ckground concentrations may be difficult for anthropogenic chemica ls and for chemicals formed through chemical re actions. For example, when try ing to estimate background formalde hyd e conce ntrations , it is diff

icult to sc reen out the rea ctive pr ecursors which change

in the study area

from those that change before e ntering the study

area. Also, if a source of nitroge n oxides (NO x ) is not present, secondary formation of formaldehy de may be slowe d.April 2004 Page 13-13 The pre sence of

high ba ckgro und conc entrations of anth ropoge nic chem icals co uld incre ase public concerns i

n some situa

tions (see Part V of t

his refe rence manual for discuss ion of risk communic ation). On the othe r hand, k nowledg e of backgroun

d risks co uld help pl ace the

air risks from a particular source or source area in better perspective. In g eneral , the most a

ppropria te way to evalua te the contribution

of backg round concentra

tions to the ri sk est imat e is to simp ly compar e the ri sk att ribut able t o know n or es tima ted (e. g., th rough monitoring

) backg round concentra

tions in a b ar char

t again st the risk

attributa ble to the source( s) being evaluated (see Exhibit 13-3). Note that the study -specific risk estimate will be based on a metric of total exposure (when monitoring data are ava ilable) or incremental exposure (when modeling data are available. I t generally

is not appropriate to subtract background conc entrations from monit

ored val ues.

Exhibit 13-3. Example Compar ison of Risk Estimates from Study-sp ecific and Background So urces

In this exam

ple, the estimated risk from the specific sources being

evaluated (2.8×10 -5 ) and the estimated risk from backg round sources (1.0×10 -5 ) are compared s ide-by- side. This places the risk estimates fro m the

source s of con

cern in an appr

opriate region

al context.
13.3 Interpre

tation and P resenta

tion of Inhalat

ion Cancer

Risks and Nonc ancer Haz ards In the final pa rt of the r isk char acteriza tion, risk a ssessors

commonly

present estimate s of heal

th risk in the context of uncertainties and limitations in the data and methodology . Exposure estimate s and assu

mptions, toxic ity e stimates a nd assumpt

ions, and t

he asses sment of u

ncerta inty are usually discusse d. Additionally, information relevant to the public he alth context of the estimated risks is presented.
EPA’s Policy for Risk Characterization (2) de sc ri be s a ph il os op hy o f t ra ns pa re nc y, c la ri ty, consistency, and r

easonableness (TCCR), and provides detailed approac hes to achieving TCCR. Exhibit 13-4

provides an overv iew of EPA ’s TCCR prin ciples.April 2004 Page 13-14 Exhibit 13-4. Transparency, C larity, Consistency, a nd Reasonableness Principles Principl e Definition Criteria for a Good R isk Characterization Transparency Explicitness in the risk

assessment process • Describe assessmen t approach, assum

ptions, extrapo lations, and us e of m odels

• Describe plausible alternative assumptions

• Identify d

ata gaps

• Distinguish science from policy

• Describ e uncer

tainty

• Describe relative streng th of assessment Clarity The assessment itself is free

from obscure

langu age an d is easy to understand • Empl oy bre vity

• Use plain English

• Avoid tech nic al te rms

• Use simple tables, graphics, and e quations Consistency The conclusions of the risk

assessment are chara

cterized in harmony

with EPA actions • Follow statutes

• Follow Agency

guidance

• Use A genc y inf

ormat ion s yste ms

• Place assess ment in co ntext with si milar ri sks

• Define level of effor

t

• Use rev

iew by peers Reasonableness The risk assessme nt is based on sound judgm

ent • Use rev iew by peers

• Use best available s cientific information

• Use good judg

ment

• Use plausible alternatives
Sour ce: EPA Risk Character izat ion Guidance (3)
The risk characterization document should allow the risk manag er, and the public, to know why risk was a ssessed t

he way it was, by

clearly summarizin

g the available d

ata and i ts analy sis, uncerta inties, al ternativ e analy

ses, and t he choic es made. A good r isk char acteriza tion will sta

te the scope of the assessment, express results clearly, articulate major assumptions and uncertainties, identify r

easonable alternative interpretations, and sepa rate scientific conclusions from science policy

judgments. The Policy for Risk Characterization calls for the explanation of the choices made to be highly visible. The goal of risk character ization is to clearly communicate the ke y findings a

nd their strengths and limitations so that decision-makers can put the risk results into context with other informat ion critic al to eva

luating risk mana

gement options (e .g., ec onomics, s ocial va lues, publ ic perception, policies). The risk character ization will provide a means of placing the numerical estimates of risk and hazard in the context of what is known and what is not about the potential exposures and should include the elements listed in Exhibit 13-5. Exhibit 13-6 provides examples o f graphical pr

esentat ions of risk estimate s.April 2004 Page 13-15 Exhibit 13-5. Elements Commonly Includ ed in the Risk C haracterization Disc ussion

• Agreement that the key contaminants were identified

• A discussion of m

odeled or measu

red air concentrations relative to backgro und

• The magnitude of the estimated can cer risks and nonc ancer hazard indices, and a description of the types of health risk

s potentially present, distinguishing b etween known effects in humans and those found o nly in experim ental an imals

• The le vel of co nfide nce in t he toxi city dat a used t o esti mate ris ks

• A presentation of qualitativ e information abou

t the toxicity of sub stances not included in the quantitative risk a ssessment

• Level of conf idence in the exposure

estimates for

key exposu re pathways an

d relate d expos

ure parameter assum

ptions

• The major factors driving the risks (e.g ., substances, pathways)

• The major factors reducing the certainty in the results and the significan ce of these uncertainties (e.g. , a cha

nge in the ass umpti on for a

certain param eter cou ld incre ase/decrease the risk estimate).

• The exposed population cha racteristics

• A com parison with lo cation- specific

health studies , if availableApril 2004 Page 13-16 Exhibit 13-6. Example Compar ison of Risk Results for a Hypo thetical Risk Assessment

The risk of develo

ping cancer is plotted

as shown. A risk of 1×10 -4 (1 E-04) indicates a probability o f one chance or less in 1 0,000 of an indiv idual developing

cancer. Risks of 1×10 -5 (1 E-05) and 1×10 -6 (1 E-06) correspond to probabilities of o ne chance or less in 10 0,000 and one m illion, respectively. Values in parentheses represent E PA’s Weight- of-Eviden

ce classification of the ag ent as a potential human carcinog en: A = hum an carcinogen; B2

= probable hum

an carcinogen (with

sufficient evidence in animals and in

adequate or no ev

idence in human s).

The hazard index is eq

ual to the sum of the hazard quotients (i.e., exposure co ncentration/RfC) for each chemical. It is not a p robability. A ha

zard index < 1 indicates that it is unlik ely for even sensitive populations to

experience adverse health effects. Thus, haz ard is negligible.April 2004 Page 13-17 13.3.1 Presenting Risk and Hazard Estimates Risk and hazard estimates will usually be prese nted both to risk managers and to the public. Dependi ng on the audienc e, risk charact

erization s can pre sent information w ith diffe rent amo unts of technical detail as required, although a voiding the use of technical terms gene rally improves clarity. Presenta

tions may include the assumptions the risk assessment used, as well as the distributi on of risks estimate d for the assessme nt. Multiple

point estimates and

risk ran ges co uld be discus sed in both

narrat ive and ta bular forms. The d

iscussion of resul ts may include i tems such as:

• The range of risks estimated within specified distance s from the source(s) of concern;

• An estimate of population size associated with different risk levels; and/or

• A compar

ison of the

magnitu de of the risk estimate to ba

ckgro und risks. Key issues and c onclusions should be clearly hig hlighted in any summar

y. Exhibit 13-7 identifies several summary products that can facilitate risk communication. (See also Part V of this Reference Manual for a descr iption of various techniques for communicating risk.)

Exhibit 13-7. Summary Products to Facilitate Risk Communication

• Executive summary – a summ

ary with som

e technical detail, for audien ces with some technical knowled ge (e.g., f

irst l ine manage rs). T his exe cutive summary may someti mes be the execut ive summary of the technical risk characterization itself depend ing on the audien

ce.

• Bulleted list – a list highlig hting the key

issues and conclusions culled from the tech nical risk characterization with little or no technical detail; for aud iences with little or no tech nical knowledge

(e.g. , hig her- level mana

gers, decision mak

ers).

• Briefing packages – written products that describe key issues and conclusions for managers, decision mak

ers, and other public o fficials.

• Fact sheets, press releases, and public relations notices – written products that describe key issues a nd conc lusions

for non-tech

nical au diences (e.g. , affect

ed or in terested public)

.

• Slide shows, speeches, and talks – visual pres entation s (perha ps acco mpan

ied by audio present ations) and tran scripts of oral present ations o f key

issues and the ir context; for m ostly non-technical au

diences.

13.3.2 Exposure Es timat es and Assum ptions
For ea ch exposur

e pathwa y e valuate d in the risk assessment, che ck that a ll informa tion need ed to characterize exposure is available. For ea ch exposure pathway

evaluated, exposure estimates and assumptions should be reviewed to assure the consistency and validity of key

assumptions. These assumptions may include, f or example, the period of exposure and the modeling assumptions.April 2004 Page 13-18 The risk characterization section on exposure may summarize the following exposure information:

• Estimated exposures (chronic, subchronic, and shorter-term, as a ppropriate); and

• Importa nt ex posur e mode ling as sump tion s, in cludi ng:

– Chemical concentration at the exposure points; and

– Frequency

and duration of exposure. Other items that could be addressed in the risk characterization summary of the exposure assessment include:

• The most significant sources of environmental exposure:

– Data on sources of exposure from different media ( when multimedia analyses a re performed);

– Estimates of the relative contribution of different sources of e xposure; and

– Identification of the most significa nt environmental pathway s for exposure (when multimedia analyses are

performed);

• Descriptions of the

populations that we

re asse ssed, inc luding th

e gene ral popul ation, hig

hly exposed g roups, an d highly suscepti ble gro ups;

• Description of the basis for the exposure assessment, including any monitoring, modeling, or other analy ses of expo sure distr ibutions (e .g., pr

obabilisti c techni ques – see Part VI I of t his Reference Manual); and

• Key desc

riptors of exposure:

– Description and illustration of the (range of) exposures to: “average” individuals, “highend” individuals, the general population, and special subpopulations such as c hildren and th e e ld er ly;

– Description of how the central tendency estimate was developed, including the factors and/or methods used in developing this estimate;

– Description of how the high-end estimate was deve loped;

– Description of how population estimates of risk were developed; and

– Description of how any incidence calculations were perf ormed.
13.3.3 Toxicity E stimat es and Assum ptions During the risk c haract erization step, the risk asse ssor usua

lly r eviews w hether a ll toxicity informat ion neede d to char

acterize risk is ava

ilable. T he risk charact

erization section on toxicity often summarizes the following information:

• IURs f

or all ca rcinog enic che

micals;

• Discussion of weig

ht of evid ence an d classif ications f or all ca rcinog enic che

micals;

• Typ e of huma

n cance r for Cla ss A carc inogen s;

• Chronic and subchronic dose-response values and shorte r-term (acute) dose-response va lues (if appropriate) for all chemicals (including carcinogens and developmental toxicants);

• Critical effect associated with each dose-response value;April 2004 Page 13-19 • Discussion of uncertainties, uncertainty factors, and modify

ing factors used in deriving eac h dose-re sponse va

lue and de gree o f confid

ence in d ose-re sponse va

lues;

• Whether the dose-response values are expressed a s absorbed or administered doses (applies primarily to inge stion exposures - See Chapter 22);

• Pharmacokinetic data that mayaffect the extrapolation from animals to humans for doseresponse values; and

• Uncertainties in any

route-to-route extrapolation.
13.3.4 Assessment and Presentation of Uncertainty in Risk Characterizat ion The risk estimates used in air toxics risk assessments usually are not fully probabilistic estimates of risk but conditional estimates given a considerable number of a ssumptions about exposure and toxicity. Air toxics risk assessments make use of many different kinds of scientific concepts and data (e.g., exposure, toxicity, e pidemiology), a

ll of which are used to characterize the expected risk in a particular environmental context. Infor med use of reliable scientific information from many differ

ent sources is a central feature of the risk assessment process. Reliable information may or may not be ava

ilable for many

aspects of a risk assessme nt. Scient

ific unc ertainty

is inherent in the risk assessment process, and risk manage rs almost always must make dec isions using as sessments

that are not as def

initive in a

ll importa nt area s as would

be desir able. Risk assessments also incorporate a variety of professional and science policy judgements (e.g., which models to use, where to locate monitors, which toxicity studies to use as the ba sis of developing dose-response values). Risk managers ther efore need to understand the streng ths and the limitations of each assessment, and to communicate this information to all participants and the public. (2) A critical part of the risk character ization process, therefore, is an evaluation of the assumptions and uncertainties inherent in the risk assessment in order to place the risk e stimates in proper perspective. One of the key

purposes of uncertainty

analysis is to provide an understanding of where the estimate of exposur

e, dose, or risk is l ikely

to fall wit

hin the ra nge of possible v alues. O ften this is expressed as a subjective confidence interval within which there is a high probability that the estimate will fall. A relate d analy sis, terme d “sensit

ivity analy sis” or “ analy

sis of unc ertainty importance,” is often performed to identify the relative contribution of the uncertainty in a given parameter value (e.g ., emission rate, in gestion rate) or model c omponent t o the tota l uncert ainty in the exposure or risk estimate. (8) Often this is used either to identify whic h parameter values should be varied to provide high-end vs. central-te ndency risk e stimates, or to identify parame ter values where additional data collection (or modeling effort) can increase the c onfidence in the resulting risk estimate. The Presidential/Congressional Commission on Risk Assessment and Risk Management (CRARM) recommends that risk assessors respect the objective scientific ba sis of risks and procedures for making infere nces in the absence of adequate da ta. (9) Risk asses sors shoul d provide risk managers and other stakeholde rs with plausible conclusions about risk that can be made on the basis of the available information, along with eva luations of the scientific weight of evidence supporting those conclusions and descriptions of major sourc es of uncertainty and alterna tive view s.April 2004 Page 13-20 Source s of Unce rtainty

• Scenar

io unce rtainty . Informa

tion to fully define exposure

or risk is mi ssing or incom

plete

• Model u ncertai nty . Algorithm s or assumptions used in models may not adequately represent reality

• Parameter

uncert ainty . Values for mod el parameters ca

nnot be estimated pre cisely

• Decisio n-rule uncerta inty . Policy and other choices made du

ring the risk a ssessment m ay influence risk estim

ates The ri sk cha racter izat ion t ypically sho uld ad dress the fo llow ing:

• Considering the hazard and the exposure, what is the nature and likelihood of the he alth risk?

• Which individuals or groups are at risk? Are some people more likely to be at risk than other s?

• Ho w se

ve reare t hean tic ipa te d a dve rs e i mpa ct s or ef fe ct s?

• Ar e t heef fe ct s r ev er sib le ?

• Wha t sc ie nti fic

ev ide nc e s upp or

ts t heco nc lus ion s a

bou t ri sk? Ho w st

ro ng is t heev ide nc e?

• Wha t isunc er ta in a bou t th e n at ur e o r ma gnitu deof the risk?

• Wha t isthe ra ng e o f info rme d vi ew s a

bou t th e n at ur e a nd pro ba bil ity of the risk?

• Ho w c onf

ide nt a rethe risk a na ly sts ab out the ir p re dictions o f r

isk?

• Wha t ot he r s our ce s c au se the

sa me ty peof ef fe ct s or risks?

• What contr

ibution doe s the par

ticular s ource ma

ke to the overall r

isk of this k

ind of ef fect in the af fe ct ed co mmu nit y ? To the

ove rall h ealth

of the co mmu nit y ?

• Ho w is

the risk di str ibu te d in re la tion to other risks t o the c omm

uni ty?

• Does the risk have impacts besides those on health or the environment, such a s social or cu ltu ral c ons eq

ue nc es ?

• The level of detail considered in a risk assessment and included in a risk characterization should be commensurate with the problem’s importance, expected health or e nvironmental impact, expected economic or social impact, urgenc y, and level of c ontroversy, as well as with the expected imp act and c ost of pro tective measure s. Risk characterizations should include sufficient information to enable:

• Risk managers to make a useful risk manag ement decision, and

• Stakeholders to understand the importance and context of that decision.
13.3.4.1 Pract

ical Appro aches to U ncertai nty Asses sment There are numerous sources of uncerta inties in a ir toxics risk assessme nts, and e ach mer

its consider ation. The

degre e to which

these sources of uncertainty

need to be quantifie d, and the

amount of

uncerta inty that is acceptable, varies considerably on a stud y-specifi c basi s. For a scree ninglevel (Tier 1) analysis, a high degree of uncertainty is often a

cceptable, provided that cons ervativ e assumpt ions are used to bias potential error toward protecting human health. The use of conservative assumption s is intende d to resul t in a situa

tion wher e the risk assessor is confid ent that the risk estimate is unlikely to be greater than the point estimate of risk. In other words, the point estimate of risk is e

xpected to

be at the high-e nd of the r

ange o f possible

values. T

he unce rtainty characterization for a Tier 1 analy sis commonly is limited to a qualitative discussion of the major sources of uncer tainty and their potentia l impact on

the risk e stimate. A

t higher tiers of a naly sis, sensitivity analy

sis to quantify the impact of vary ing input parameter values (or modelApril 2004 Page 13-21 Characterize Model Uncertainties

• List/summarize key m odel assumptions

• Indicate the potential impact of each assum ption o n the ex

posure and risk

estimate

– Direction

– Magnitude algori thms) on the

risk estimate, or more comp lete qua ntitative uncerta inty analy sis, common

ly are per formed to more full y de scribe the rang e of possi ble or pla usible va lues.
Practic al appro aches to

the asse ssment and presen tation of t he princ ipal sour

ces of un certain ty in risk assessments are summarized below. (10) Characterize Scenario Uncertainty . There are uncertainties associate d with the estimate of the magnitude and extent of chemical exposure or toxicity , the spatial and temporal aggre gation of chemical concentrations to calculate the exposure conc entration used in the risk characterization, the completeness of the analysis (e.g., important exposure pathways may not have bee n evaluated), and the manner in which the exposed population and/or exposure scena rio were specified for the analysis. Ideally , the key sce nario uncertainties have been discussed during planning , scoping , and pro blem form

ulation, a nd the ana

lys is plan ha s been de

veloped t o addres s these unc ertainti es. A limite d sensitivi ty a naly sis (e.g ., on key

assumption s associa

ted with exposure) may indicate the magnitude of uncertainty associated with specific aspects of the scenar io. At a min

imum, the a

naly sis of unc ertainty

should ide

ntify the key scenar io uncerta inties and indicate the poten tial impact of each on the dir ection a nd magn itude of the risk estimate. Characterize Model Uncertainty . There are uncertainties associate d with the selection of scientific models; these include dose-response models, models of environmental fa te and transport, and exposure models. There is always some doubt as to how well an exposure model or its mathematical expression approximates the true relation ships betw een site -specif ic environmental conditions. I deally one would like to use a fully validated mode l that accounts for all the known complexities in the parameter interrelationships for each assessme nt. Often , howeve

r, only partiall y validate d models a

re avai lable. As

a consequence, it is important to identify ke y mo de l a ss um pt io ns (e .g ., li ne ar it y, homoge neity , steady

-state c onditions, equilibri um) and their pote

ntial impa

ct on the r

isk estimate s. In

the abse nce of f ield data for mode l validati on, the ri sk assess or could p erform a limited sensitivity analysis (i.e., vary a ssumptions about functional relationships) to indicate the magnitu de of unc ertainty

that migh

t be assoc iated wit

h model form. At a min

imum, the a

naly sis of uncertainty

should list key model assumptions and indicate the potential impact of ea ch on the direction and magnitude of the risk estimate.
Characterize Param eter Uncertainty . During the course of a risk assessment, numerous parameter values are included in the c alculations of chemical fate and transport and human intake. Significant data gaps might have required that certain parameter va lues be assumed for the risk a ssessmen

t. For exa

mple, no information on the time spent outd

oors may be avail able for a specific population, and a national avera ge may be used instead. Even if data on the parameter of interest are ava ilable, they will be unce

rtain because the parameter e stimates are derived from a sample of the potentially exposed population. A first step in characterizing parameter value uncer

tainty is to identi fy

the key parameters infl

uencing the risk e stimate. T

his usually can be a

ccomplished by expert opinion or by an explicit sensitivity analysis. In aApril 2004 Page 13-22 sensitivity analy

sis, the values of parameters suspected of driving the risk estimates are varied, and the degree to which changes in the input variables result in changes in the risk estimates are summarize d and comp ared. I t may be possibl e to redu

ce para meter uncertain

ty in the most sensi tive p aramet ers by addi tion al, se lecti ve dat a gather ing. Characterize Decision-Rule Uncertainty . There are uncertainties associate d with policy and other choices made during the risk assessment. F or example, the exposure assessment might have evaluated an exposure duration (e.g ., a subchronic exposure) for which no appropriate dose

response value wa s availa

ble. Unc ertainty

would be a ssociate d with the c hoice of value to u se in the hazard characterization (e.g., a n acute versus chronic value). I n this situation, it might be possible to

assess ha zard twic e, once

with the a

cute val ue, and once with t

he chron ic value , to may indicate the ma

gnitude of uncertainty

associated with this decision. At a minimum, the analysis of unce

rtainty should identify

the key dec ision-rule uncertainties and indicate the potential impact of each on the direction and magnitude of the risk estimate. Tracking Uncertainty . Ide ally , one woul d like to qu antitativ ely

carry through the risk assessme nt the unc ertainty

associa ted with e ach par

ameter i n order t o chara cterize t he unce rtainty associated with the final risk estimates. However, this process ca n be highly c omplex and resourc e intensive and the more pra ctical a pproach for air t oxics risk a ssessmen

ts may be to describe qualitatively

how the uncertainties might be propaga ted through the risk analysis. Three differe nt approa ches to tr acking uncerta inty are des cribed b elow:

• Qualitative Approach . This approach involves developing a quantitative or qualitative description of the uncertainty for each parameter and indica ting the possible influence of these uncertainties on the final risk estimates given knowledg e of the models used.

• Semi-Quantitative Approach . This approach involves: (1) using available data to desc ribe the poten tial rang e of valu

es that the

parameters mig

ht assume ; (2) per

forming sensitivit y analysis to identify

the parameters with the most impact on the risk estimate; and (3) perfor ming sen sitivity

analy sis to compu

te the ra nge of exposure or risk est

imates tha

t result from c ombi natio ns of m inim um and maxi mum v alues for so me para meter s and m id-ra nge values f or others.

• Quantitative Approach . Probabil istic techniques su ch as Mont

e Carlo si mulation a

naly sis can explic itly

charac terize the extent of u

ncerta inty and vari ability

in risk ass essment, especia lly i n the exposu

re asse ssment ste

p. Using these te chniques , importan t variab les in the exposure assessment, as well as in the other parts of the risk assessment, are specified as distributi ons (rat

her than as singl e values ) accor

ding to w hat can b e express ed about t heir underly ing var iability and/or un

certain ty. Values a

re sample d repea

tedly from thes e distributi ons and c ombined in the anal ysi s to provid

e a range of possi

ble outcomes. While this technique can offer a useful summary of complex information, it must be noted that the analysis is only

as certain as the underly

ing data (and assumed forms of the distribution of data values in the population). It is important that the risk assessor c learly expresses individual modeled variables in a way that is consistent with the best information available. Highly qua

ntitative statistical uncertainty ana lysis is usually

not practical or necessary for most air toxics risk assessments. The general quantitative approach to propa gating or tracking uncertainty

through probabilistic modeling is described in Chapter 31.April 2004 Page 13-23 13.3.4.2 Prese ntation of Un certai nty Asses sment The fina l discussion of the r

isk char acteriza tion resu lts must pla

ce the nu merical estimate s of risk in the con text of the u

ncerta inties inhe

rent in the

analy sis. (2) The discussion should include:

• Leve l of conf

idence i n the quan

titative to xicity

informat ion used to

estimate risks;

• Presentation of qualitative information on the toxicity of substances not included in the quantita tive asse ssment;

• Level of confidenc e in the exposure estimates for key exposure pathways and r elated exposure p aramete

r assumpti ons;

• Major factors reducing ce rtainty in the results and the sig nificance of these uncertainties (e.g ., adding individual risk estimates for several substances or a cross multiple exposure pathways); a

nd

• Possible g

raphica l presen tation of k

ey parameter and r

isk uncer tainties.
13.3.5 Additional In format ion Other studies relevant to the risk assessment being perf ormed may be a

vailable, such as community

health stu

dies or pr evious ri sk assessments. For example, the Age ncy for Toxic Substance s and Dise ases Registry (ATSDR) may

conduct p

ublic hea lth assessments, he alth consultations, and other activities resulting in evaluations, assessments, and recommenda tions on specific public he alth issue s relate d to actua l or poten tial human

exposure t

o hazardo us materi als (see Chapter 30). ATSDR’s recommendations may include additional hazard characterization or risk reduction activities. In addition, these activities can initiate other activities within ATSDR such as exposure investigations, health studies, and health education.
If health or ex posur e stud ies ha ve been ident ified and ev aluat ed as ad equat e, the stud y findings may be incor porated into the ri sk chara

cterizat ion to stre ngthen the conc lusions of

the risk assessment. In gene

ral, a qualitative comparison of the results of available studies will usually be sufficie nt.April 2004 Page 13-24

Additio nal Refe rences Related to Unc ertaint

y Anal ysis Hattis. D.B. and

D.E. Burm

aster. 1994. Assessment of variability and uncertainty distributions for practical risk analy ses. Risk An alysis 14(5):713-73 0. U.S. Environ

mental Protection A

gency. 19 97. Guidin

g Princ iples fo r Monte C arlo An alysis . Risk Assessment Foru

m, Washing

ton, DC. March 19 97. EPA- 630/R-97- 001. U.S. Environ

mental Protection A

gency. 19 88. Nationa l Emiss ion Sta ndards for Ha zardou s Air Polluta nts

. Federal Register 53(145 ):28496 -280 56, Pro posed R ule and Notice of Pub lic Hea

ring. July 28, 1988. Finkel, A.M. 1 990. Confronting Uncertainty in Risk Management: A Guide for Decision-Makers

. Center for Risk Management, Res ources for the Fut ure. Washington, DC. U.S. Environ

mental Protection A

gency. 19 97. Policy for Use of Pro babilistic Analysis in Risk Assessment . Office of the Adm inistrator, Washington, D C. May 15, 1 997. National Council on R

adiation Protection and Measure ments. 1996. A Guid

e for U ncertai nty Ana lysis in Dose and Risk A ssessments Related to E nvironmental Contamin ation . NCRP Commentary No. 14; availa ble at htt p:/ /www .ncr p.co m/comm1 4.ht ml

.April 2004 Page 13-25 1. U.S. Environmental Protection Agency . 2003. Framework for Cumulative Risk Assessment (Exte rnal Revi ew Draft) . Office of Research and De velopment, National Center for Environmen tal Assess ment, Was hington, D. C., April 23, 2002. E PA/630/P -02/001F. Availabl e at: http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=54944

. U.S. Environmental Protection Agency . 1997. Guidance on Cumulati

ve Risk A ssessmen t, Part 1, Planning and Scoping . Science Policy Council, Wa shington , D.C. U.S. Environmental Protection Agency . 1984. Risk Asse ssment an d Manage

ment: Framework for Decision Making , Washington, D.C. EPA 600/9-85-002. Information Quality Guidelines The U.S. Office of Mana gement and

Budget (OMB) has directed all federal ag encies to develop inform ation qu ality g uidelin es for ri sk-related and oth er infor mation; EPA

has dev eloped draft guidelines pursuant to

the OMB directive. While these guideline s do not apply to S/L/T governm ents, they provide useful principles for developing and com municating the information dev eloped for the risk characterization. The OMB guidelines d

enote four substantive qualifiers for information disseminated by federal agencies. Quality

is defin ed as the encom

passing term, of whi ch utili

ty, o bjectivi ty, an d integ rity ar e the constituents. Utility

refers to the usefulness of the information to the intended users. Objectiv ity focuses on whether the disseminated inform ation is being presen

ted in an accurate, clear, complete, and unbiased m

anner, and as a m

atter of substance, is accura te, reliable, and unbiased

. Integri ty refers to security – the

protection of inform

ation from unauth

orized access or rev

ision, to ensure that the information is n

ot comprom

ised through corruptio

n or falsification. The guidelines provide some basic princi ples for agencies t o consider when develop ing their own guidel ines, includ ing:

• Guidelines should be flex ible enough to ad

dress all comm

unication media an

d variety of scope and importance of inform

ation products.

• Some agenc y informa tion may ne ed to meet higher o r more spe cific expect ation s for object ivity, utili ty, and in tegrit y.

• Ensuring and m

aximizing quality, objectivity , utility, and integ rity comes a

t a cost, so age

ncies should consider using

a cost-benefit approach.

• Agencies should adopt a comm on-sense appro ach that builds on existing processes and procedure s. It is im portant that agency

guidelines do not impose unneces sary administrativ e burdens.
EPA devel oped dr aft in formati on qual ity guide lines in resp onse to the OMB d irect ive ( www.epa.g ov/oei/qualityg uidelines ). EPA’s gu

idelines include two com ponents of particular relevance to air toxics risk manag ement: (1) g uidelines to ensure and m aximize the quality of “influen tial” inf ormat ion; and (2) g uidelin es to en sure and maximiz e the qu ality o f “influential” scientific risk assessm

ent information.
Source : Office of Managem ent and Budget. 2002. Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Informa tion Disseminated by F ederal Agencies . 67 Federal Register 36:8451. February 22, 2002 (www.w hit ehou se.go v/omb/ fedr eg/r epro duci ble .html ).

ReferencesApril 2004 Page 13-26 2. U.S. Environmental Protection Agency . 1995. Policy for Risk Characterization (“Browner Memoran dum”). Sc ience Pol icy

Council, Wa shington , D.C., Ma rch 1995 . Availab le at: http://64.2.134.196/committees/aqph/rcpolicy.pdf. 3. U.S. Environmental Protection Agency . 1995. Guidance for Risk Characterization . Science Policy Council, Wa shington , D.C., Fe bruary 1995. Ava

ilable at : http://www .epa.g ov/osa/sp c/htm/rc guide. htm. 4. U.S. Environmental Protection Agency . 1992. Guidance on Risk Cha racteriza tion for Risk Managers and Risk Assessment . Memorandum. Office of the Administrator, Washington, D.C. 5. U.S. Environmental Protection Agency . 1986. Guidance Mutagenicity Risk Assessment of Chemical Mixtures . Risk Assessment Forum, Washington, D.C., September 1986. EPA/630/R-98/003. (Published in the Federal R egister 51: 33997-8, Sept 24, 1986). Availabl e at: http://cfpub.epa.gov/ncea/raf/pdfs/mutage n2.pdf

. 6. U.S. Environmental Protection Agency . 1986. Guidance for Conduc ting Healt

h Risk Assessment of Chemical Mixtures . Risk Asse ssment Forum, Washin gton, D. C. EPA/630/R- 98/002. Available a t: http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=20533

. U.S. Environmental Protection Agency . 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures . Risk Asse ssment Forum, Washin gton, D. C. EPA/630/R- 00/002. Available a t: http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=20533

. 7. U.S. Environmental Protection Agency . 1999. Residual R isk Repor t to Congres

s . Office of Air Qual ity P lanning

and Stand

ards, Re search

Triang le Park, N C, March 1999. EPA- 45/R99/001. Available a

t: http://www.epa.gov/ttn/oarpg/t3/reports/risk_rep.pdf

. 8. Morgan, G. and Henrrion. M. 1990. Uncerta inty: A Gu ide to Dea ling with Uncertainty i n Quantitati

ve Risk a nd Policy

Analysis . Cambridge University Press, Cambridge, U.K. 9. Commission on Risk Assessment and Risk Management (CRARM). 1996. Risk Assessment and Risk Management in Regulatory Decision-Making (The “Whi

te Book” ). Draf

t Report, Washingt on, D.C.
CRARM. 1997. Framework for Environmental Health Risk Management

. Final Rep ort, Volume 1 . Washing

ton, D.C.
CRARM. 1997. Risk Assessment and Risk Management in Regulatory Decision-Making

. Final Report, Volume 2 . Washing ton, D.C. 10. U.S. Environmental Protection Agency . 1989. Risk Assessment Guidance for Superfund: Volume I. Human Health Evaluation Manual (Part A) . Office of Emergency and Remedial Response . Washing

ton, D.C., Decemb er 1989. EPA/541/1- 89/002. Available a t: http://www .epa.g ov/super fund/pro grams/

risk/rag sa/index.h

tm

.

# PART III

HUMAN HEAL TH RISK AS SESSMEN T: MULTIPA THWAY

# Chapter 1 4 Overview and Getting Started : Planning

and Scoping the Multipathway Risk Assessment

Table of Contents

14.1 Introduction

............ ............ ............ ............ ............ ...... 1
14.2 Overview o f Multipathway Air Toxics Risk Assessm ent

............ ............ ...... 3 14.2.1 Planning, Scopin g, and Problem

Formulation

............ ............ ......... 3 14.2.2 Analy sis

............ ............ ............ ............ ............ . . 4 14.2.3 Risk Characteriz ation

............ ............ ............ ............ .... 5
14.3 Overview o f Multipathway Expo sure Assessment

............ ............ ............ 5
14.4 Planning and Sc oping

............ ............ ............ ............ .......... 9 14.4.1 Iden

tifyin g the Concer

n
............ ............ ............ ............ . . 9 14.4.2 Iden

tifyin g The P articipa

nts

............ ............ ............ .......... 9 14.4.3 Determining the Scope of the Risk Assessment

............ ............ ....... 9 14.4.4 Describing the Prob lem

............ ............ ............ ............ . 10 14.4.5 Determ

ining How R

isk Man agers Will Evaluate t

he Concern
............ ....... 10
14.5 Tiered Mu

ltipathway Ri sk As sessments

............ ............ ............ ....... 10 References

............ ............ ............ ............ ............ ............ 11April 2004 Page 1 4-1 Bioconcentration is the net accum ulation of a su bstance by an organ ism a s a resu

lt of up take d

irectly from an en vironmen tal medi um (e.g., ne t accumul ation by an aqua tic or ganism as a resul t of upt ake directly from ambi ent wat

er, thro ugh g ill me mbran es or ot her exte rnal bod y surf aces).
Bioaccumulation is the net accum ulation (storage in tissue and/or organs) of a substance by an organism as a result of uptake from all environmental sources – the medium

in which they live, the water they drink, and the diet they consume – over a period of tim e. Biomagnification or Biological Magnification is the process whereby certain substances, such as pesticides or heavy

metals, transfer up

the food chain and increase in concentration. A biomag nifying chemical deposited in rivers or lak es absorbs to algae, which are ingeste d by aquatic organisms, such as small fish, w

hich are in turn eaten by

larger fish, fish- eating birds, terrestrial w ildlife, or humans. The chem ical tends to ac cumu

lates to highe r conce ntration level s with e ach suc cessiv e food c hain level. Biom

agnification is illustrated in Chapter 23. 14.1 Introduction
Part II of t

his Refer ence Ma nual disc ussed how

to plan for and con

duct a human healt

h risk assessment via the direct inhalation pathway . Part III provides the same g eneral discussion of the various aspects of the risk assessment process; however, the discussion is focused specifically on multipathway human health risk asse ssment. As noted earlier, all air toxics ri sk assessmen ts evaluate the direct inhalation pathway. In addition, m ultipathway risk assessment m ay be appropriate generally when air toxics that persist and which also m ay bioaccumulate and/or biomagnify are prese

nt in releases. These

generally

will focus on the persis

tent bioaccumulat

ive hazard

ous air pollut

ant (PB-HAP

) compounds (

Exhibit 14-1

), but spec

ific risk assessments may need to consider additional chem icals that persist and which also may bioaccumulate and/or biomagnify . For the se compou nds, the r isk asses sment ge

nerally will need to consider exposure pathways other than inhalation – in particular, pathways that involve deposition of air toxics onto soil and plants and into water, subsequent uptake by biota, a nd potential human exposures via consumption of contaminated soils, surface waters, and foods. Substances that persist and bioaccumulate readily transfer between the air, water, and land. Some may

travel g reat dist ances, a nd linge r for lon g perio ds of time i

n the envi ronment. The discussion of multipathway risk assessment f ollows the same general framewor k presented in Part II . This chapter presents an overview of multipathway risk assessment and discusses the initial planning, scoping and problem formulation activities. The remaining chapters of this Part focus on E

xposure As sessment ( Chapters 14 to 20), T oxicity

Assessme

nt (Chapte

r 21), an d Risk Characterization (Chapter 22). The discussions presented here supplement the information pro vi ded ear li er – rea der s ar e en cou rag ed t o re fer bac k t o t he c orr esp ond in g Ch apt ers in Par t II for addi tional ba ckgro und mater

ials.April 2004 Page 1 4-2 Exhibit 14-1. PB-HAP Compounds
PB-HAP Compound Pollution Prevention Priority P BTs Great Waters

Pollutants of Concern TRI PBT

Chemica ls
Cadmium compounds X Chlordane X X X Chlorinated dibenzod

ioxins and furans X (a) X X (b) DDE X X Heptachlor X Hexachlorobenzene X X X Hexachlorocyclohexa

ne (all isomers) X Lead compoun

ds X ©) X X Mercury comp ounds X X X Methoxychlor X Polychlorina

ted biphenyls X X X Polycyclic organic matter X (d) X X (e) Toxaphene X X X Triflurali n X
(a) “Dio xins and furans” (“” de notes the ph raseo logy o f the sou rce list) (b) “Dioxin and dioxin-like com pound s” (c) Alkyl lead (d) Benzo[a]pyrene (e) “Polycy clic aromati c compounds” and benzo[g, h,i] peryle ne See A ppen dix D for a discussion of the de rivation of this list of PB-HA Ps.April 2004 Page 1 4-3 For purposes of this Reference Manual, w e discuss planning, scoping, and proble m formulation for multipathway human health risk assessment separately from the correspondin g phase for inhalation risk assessment. In reality, the planning, scoping, and pro blem formulation phase for the multipathway assessment would be integrated with the inh alation analysis as early as feasible. 14.2 Overvie

w of Multipathway

Air Toxic s Risk Asse ssment The multip athway

risk asse ssment is or

ganized in the same

way as the dir ect inha lation ris k assessme nt into thre e gene

ral phas es: 1. Planning, scoping, and problem formulation; 2. Analysis, consisting of e xposure assessment and toxicity assessment; and 3. Risk characterization.
14.2.1 Planning, Scoping, and Problem Formulation The planning, scoping, and problem formulation phase of multimedia risk assessment focuses on developing a common understanding of wha t needs to be added to the risk assessment (bey ond the direct inhalation assessment) to assess risks associated with pathway s involving deposition (i.e., transfer of the compounds to soil, water, sediment, and biota) and subse quent ingestion exposure. The scope of the multimedia risk assessment genera lly is more extensive than that for in ha la ti on as se ss me nt , a nd th er ef or e s ig ni fi ca nt ad di ti on al ef fo rt is li ke ly.

It may be necessary

to include on the planning and scoping team experts in m ultimedia modeling, bioaccumulation, human exposure factors, and inge stion toxicology. The focus on additional exposure pathways may influence many

aspects of the risk assessment, including the size of the study area ; emission sources to be considered; the temporal and spatial resolution require d; the appropriate

level of detail an d documen

tation; tr ade-of fs betwe en depth a

nd bread th in the analysis; QA/QC require ments; analytical appr

oaches to be used; and the staff and monetary re sources to commit. The study-specific conceptual modelwould also reflect the specific concerns of air toxics that persist and which also may bioaccumulate. As with the inhalation risk assessment, the planning, scoping, and problem formulation proc ess is an iterative process that reflects changing information and concerns as the multimedia risk assessment unfolds.
The reader should become familiar with Part I I of this manual before r

eading this Part, since Part III focuses primarily

on those aspects of the risk assessment that are unique to multipathway analyses , incl udin g:

• How the study are a is defined;

• Potentiall

y exposed popu

lations;

• Exposure p athway

s and exposu

re route s;

• How exposure is assessed;

• Dose-response values for non-inhalation pathway s; and

• How risks are characterized.April 2004 Page 1 4-4 14.2.2 Analysis The analysis phase

of the multipathway assessment is divided into two components: exposure assessment and toxicity assessment. Exposure as sessme nt is likely to be considerably more complica ted than t he corr esponding

inhalation exposure

assessme nt for se veral r easons:

• People can be exposed to air toxics in many more wa ys, including in the food the y eat, the mi lk th ey d ri nk , a nd th e s oi ls on wh ic h t he y pl ay.

• Time is a critical variable. Air toxics that persist and which also may bioaccumulate can slowly build up in s oils, sediments, and biota ove r time. With s

ufficie nt time, ev en rela

tively small rel eases ha

ve the pot ential to r esult in hig

h exposure s.

• The spatial distribution of the air toxics can be complex. Chemicals can move away from deposition points due

to runoff , erosion , and the m

ovement o f contam

inated a nimals. Chemicals deposited over a wide area (e .g., a watershed) can c oncentrate in smaller areas (e.g., a pond).

• Multimedia

models of

ten use more extens ive input v ariable s.

• Sampling and analy sis may involve a wider r ange of media (e.g ., soil, sediment) and different types of biota (e.g ., fish, shellfish, plants). Each type of sampling and analysis has its own methods, protocols , and QA/Q C proced ures.

• Whereas the exposure concentration in air is the quantitative metric of exposure for inhalation, intake is the quantitative metric of ingestion exposure in multipathway analyses. To quantify intake, it is nece ssary to (1) e stimate the concentrations of chemicals of potential concern (COPC) in water, soil, sediment, and/or food items; (2) determine how much water, soil, sediment, and food are ingested; (3) determine the duration and temporal patterns over which ingestion occurs; and (4) adjust for body weight, to account for the differe nt types of people in the population who interact with the contaminated media. Multimedia exposure assessme nt uses a n

umber of differe nt exposure factors that provide quantitative estimates of the phy sical and behavioral attr

ibutes of potentia lly e xposed popu

lations (e .g., how

much fish a person eats per day

). Exposure factors can be treated as either constants or variables in the exposure assessment, depending on whether a deter ministic or probabilistic analysis is being performed.
Th e m ul ti pa th wa y toxicity assessm ent

is si mi lar to th e to xi cit y ass ess men t fo r in hal ati on. It consider s the same

gener al information: (1)

the ty pes of pot ential ad verse he alth eff

ects associated with chemical exposures; (2) dose-response re lationships; and (3) related uncertainties such as the weig

ht of evid ence for carc inogen

ic effe cts. Ther

e are two primary

differe nces:

• A chemical’s toxicity is influenced by the route of exposure. That is, the same chemical can result in different toxic effects (and have differ ent dose-response values) depending on whether the chemical is inhaled or ingested. The re are a number of reasons why this may occur. F or example , when a c hemical i s inhaled

into the re spirator

y tr act, the primary toxic effect may

occur in the respiratory

tract as a result of the inhaled chemical (a portal of entry effect). When swallowed, on the other hand, many chemicals are absorbed into theApril 2004 Page 1 4-5 bloodstream through the gastrointestinal tract wher e they ar e carried directly

to the liver. Chemical s in the live

r are of ten metab olized exten sively (either to more or

less toxic substances) before being transpor ted by the bloodstream to other pa rts of the body.

• The spec ific dose -respon se value s used for the inge stion path

way – refer ence do ses (RfD s) for non-cancer effe cts and oral cancer slope factors (CSFs) – differ in form and derivation from those

used for inhalation assessments. Spec ifically , RfDs and

CSFs are develope d to match the metric of exposure for ingestion and are expressed (usually) in terms of a mount of chemical ingested per unit of body weight per day

(i.e., mg/kg-d for RfDs) and risk pe r amount of chemical ingested per unit body weight per day

(i.e., (mg/kg-d) -1 for CSFs).
14.2.3 Risk Characterization The risk characterization for multipathway assessments also may be more complicated than that for the in halation risk asse ssment.

• Ing estion risk estimate s are fir st added a cross al l ingest

ion pathw ay s and then

added to inhalation risk estimates to calculate total (i.e., cumulative) risk. Although the summation process is relatively

simple for screening-level analy ses, it can become complex for more advanc ed tiers o f risk ass essment.

• The uncertainty

analysis for multipathway risk assessments may be c onsiderably more complex if multiple pathways are important bec ause many more exposure factors and variabl es will be

involved i

n the quan

tification of risk. As noted e arlier, many more spe cific exposure f

actors c an be tre ated as v ariable s for pro babilistic multipathway

risk asse ssments.

• The uncertainty

analysis for multipathway analysis is also much more complex due to the larger number of pathway s assessed and the larger number of measurement and modeling inputs that are needed.
14.3 Overvie

w of Multipathway

Exposure A ssessm ent As with inhalation r

isk asses sments, the

exposure a ssessmen

t for multip athway

risk asse ssments includes identify ing sour ces, cha racter izing re leases to

the air, estimatin

g conc entrations of air toxics in the environment, characterizing potentially exposed populations, and developing metrics of exposure. This section provides an overview of exposure assessment for multipathway

risk asse ssments. F amiliarit y with EPA’s Guidelines for Exposure Assessment (1) prior to b eginnin

g the mul tipathwa y exposure as sessment would be he lpful. The multipathway exposure assessment c overs a broader scope and may be more complex than direct in halation exposure assessmen

t.

• Exposure pathways to be eva luated include multiple media (soil, water, sediment, biota) and exposure routes in addition to inhalation (e.g., ingestion). Therefor e, the exposure setting may need a

dditional characterization (e.g., the location and natur e of water bodies and/or agricultural crops).April 2004 Page 1 4-6

Overview of Multipathway Exposure Pathways/Routes

• The evaluation of chemical fate and tra nsport accounts for the transfer of contaminants fr om air to soil and wate r and subs equent tr

ansport a nd transf er to othe r media. For examp le, air toxics that persist and which also may bioaccumulate are deposited onto soils and can enter surfac e waters via runo ff; some o f the com pounds tha

t deposit in

to water predominantly partition into sediments. Bioaccumulation – a concentration of contaminants in biological tissues – and subsequent transfer to humans via ingestion often play a major role in the exposure assessment. Multimedia models can be used to describe contaminant fate a nd transport through the use of partition coefficients a nd mass-balance techniques (see Chapter 6). Different monitoring methods (e.g., se diment or fish tissue sampling and analy sis) may be includ ed to aug ment or a ssist in the

evaluat ion of mode ling outp

uts.

• In c ontrast to

the dire ct inhala

tion asse ssment, in w

hich the q uantitati ve metric of exposur

e is the ambient air concentration at the exposure point, ingestion exposures are qua ntified using the chemical intake rate – the amou

nt of che mical ingested pe

r unit time

– gene rally expressed in units of milligrams of chemical per kilogram of body weight per day

. Calculat ion of che mical inta ke rate require s informa

tion on COPC c oncentr

ations in it

ems in ge st ed as we ll as in fo rm at io n a bo ut th e t ype an d a mo un t o f d if fe re nt it em s e at en ea ch da y, body weig

ht, and exposure durations for the sub-populations of interest. Intake rate is simply the amount of food (or other media), containing the contaminant of interest, that an individual ingests during some specific time period (units of mass/time). I ntake rate can be expresse d as a tota

l amount (e .g., mg

); as a do se rate

(e.g. , mg/day

); or as a rate nor malized to body mass (e.g ., mg/kg-day ). For most chemicals, the dose-response value ( e.g., reference dose, or RfD) is based on the potential dose (i.e., the amount of chemica l taken in), with noApril 2004 Page 1 4-7 explicit correction for the fraction absorbe d. For so me chemic als, it may be nece ssary

to adjust for such dif

ferenc es using

phy siologic ally

based ph armacokinetic ( PB-PK) mo dels, mathematical dosimetry models, and/or adjustment factors (see Chapter 8). Because exposure is quantified using chemica l intake rate, different ty

pes of people within a population (e.g ., childho od exposure s) may need to be

consider ed explicit

ly. Consumption

rates, dietary prefer ences, a nd body weight vary with ag e and wou

ld be acc ounted for in the ri sk assessme nt. (Note that not on

ly a ge, but sex, ethnic ity, cultura

l and rel igious pr

actice s may

also strongly influenc e the exposure patterns of people within a potentially exposed population.) Although it is possible to evaluate acute exposures for the ingestion pathway , EPA does not generally perform acute exposure assessments, because it is unlikely that PB-HAP compounds would concentrate to acutely toxic levels under any ty pical release scenario that did not pose a much more

substantia

l chronic risk. How ever, e ach asse ssment wou ld conside

r the ava ilable evidence in making this judgement. At a minimum, the risk charac terization would state the reasons why a

n analysis of ac

ute health effects for non-inhalation pathway s was not performed. The multip athway

exposure assessmen

t focuse s on two g eneral

categ ories of

ingesti on pathwa

ys: incidental ingestion and food chain (Exhibit 14-2). Inc idental ingestion pathway s consider exposures that may occur from ingestion of soils or surface water while a n individual is engaged in other activities (e.g., ingestion of soil while ga rdening or play

ing outside; ingestion of surface water while swimming). Food chain pathway s consider exposures that may oc cur if PB-HAP compounds accumulate in the food and water people c onsume.

Exhibit 14-2. Human Exposure Pathways Considered for Multipathway Air To xics A ssessmen tsApril 2004 Page 1 4-8 Analysis of Groundwater Pathways EPA’s Office of Solid

Waste has considerable exp erience in modeling and monitoring

the mov ement of conta minan ts in g roundw ater. Mu

ch of that expe

rience i s based

on exp osure a ssessm

ents associated with land-

based disposal units (i.e., where the source of contamination is in the subsurface). For exam

ple, EPA’s Center fo

r Exposure Assessm

ent Modeling (CEA

M) distributes multimedia models desig ned to quantify the

movem ent and concentration of contaminants (from land-based release s at hazardous waste sites) traveling through grou ndwater, surface water, and food chain m edia (av ailable at http://w

ww.ep a.gov

/ceam publ/ ). I n these mode ls, rele ases to the atm osphere from the sub surface may

be con sidered , but tr

ansfer f rom t he air th rough the sub surface are not. EPA do es not have su

fficent experie

nce wit h air to xics m

ultipath way a nalysi s to ide

ntify situations in which the ground

water may be contaminated. EPA’s Methodology for Assessing H ealth Risks Associated with Multiple Path ways of Exposure (3) identifies three site-s pecific conditions that m ight lead to greater g

roundwater impacts:

• Deposition rates that are sev eral times greate

r than the averag e;

• The existence of more soluble HAPs in em issions; and

• Higher recharg e rates such as would occur in areas with v ery permeab le soil and bedrock near the surface. As Exhibit 14-2 suggests, the focus of the multipathway assessment is on ingestion pathway s. Other exposure pathway s may be important for pa rticular risk assessments, including dermal exposures (i.e., di rect contact wit

h contami nated soi ls, surfa

ce wate rs, or sur face w ater se diments during outside activities such as gardening or swimming); resuspension of dust (e.g., from wind blowing across contaminated soils, or agricultura l activities such as tilling) and subsequent inhalation of the dust particles; and ingestion of contaminated gr oundwater. However, EPA does not have sufficient experience with multipathway air toxics risk assessments to identify the circumstances for which exposures via these additional pathway s may repre sent a potential concern.

• If si te-spec ific cir

cumstanc es sugg est that de rmal path way s may be of con cern, EPA’s Risk Assessment Guidance for Superfund (RAGS), P art D, Standardized Planning, Reporting and Review o f Superfun d Risk Asse

ssments , (2) inclu des a re lati vely stra ightfo rward m ethod ology for dermal exposure and risk assessment, starting with soil concentrations. The Planning Tables in the document are

simple to us e and inc orporat e into the multipathway

analy sis.

• Relative to the direct inhalation pathway , inhalation of soil resulting from dust resuspension by wind erosion g enerally is not thoug ht to be a significant pathway of concern for air toxics risk assessments. If site-specific c ircumstances suggest that resuspension of dust may repres ent a pote

ntial concern, EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combuster Emissions (MPE) (Ch apter 5 D ust Re su sp en si on ) d is cu ss es th e m et ho ds fo r e va lu at in g t hi s p at hw ay. (3)

• If site-specific c ircumstances suggest that gr oundwater may

represent a potential concern (e.g. , the pre sence of

extremely

shallow a

quifers used for drinking water pu rposes or a karst environment in which the local surface water sig nificantly aff

ects the quality of g

round water used as a drinking water source) , Total Risk Integrated Me thodology - F ate, Transport, andApril 2004 Page 1 4-9 Ecolog ical Exposu

re Module

(TRIM.FaTE)

has the a bility to assess chemica ls moving into the gro undwate r pathwa y. E PA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (3) and EPA’s Draft Tech nical Bac kground D ocument

for Soil Screening Guidance di sc us se s m et ho ds fo r e va lu at in g t he gr ou nd wa te r p at hw ay. 14.4 Planning and

Scoping As with inhalation analyses, the ke y steps in the planning a

nd scoping process include (1) identifying the c oncern; (2) identify ing who will be involved; (3) determining the scope of the risk assessment; (4) describing why there may be

a problem; and (5) determining how the concern will be evaluated. The planning and scoping process for multipathway risk assessment focuses on develo

ping a c ommon unde

rstandin g of wha t needs to be evalu ated to a ssess risks via deposition and transfer of the air toxics to soil, water, and biota, and subsequent inge stion. More detailed discussions of the p lanning and scop ing proc ess can b e found in Part II of t

his Volume and in guidance documents developed by EPA. (4)
14.4.1 Identifying the Concern The driving concern for the multipathway risk assessment generally would be the same as that for the inhalation risk assessment (e.g., regula tory require

ment, community need, hea lth concern). However, a number of additional specific conc erns may a rise. For example, the potential for bioaccumulation in food and subsequent ingestion may raise specific concerns about are as where people farm, economic issues such as recre ational fishing, or additional exposure pathways of potential concern (e.g., infants ing esting mother’s milk).
14.4.2 Identifying t he Participants The participants for the multipathway risk assessment generally would be the same as those for the inhalation risk assessment. However,

• A broader range of r

isk managers would be involved. For example, if there is a pote ntial for a fishery or

farm crops to become contaminated with air toxics, different persons or groups may have the

authority to make the risk mana gement decisions – the state, local, or tribal (S/L/T) fish and game de

partment or the agriculture depar tment may become involved.

• The risk assessment technical team would include additional experts (e.g ., in the areas of multimedia modeling, bioaccumulation, soil chemistry)

.

• The specific set of interested or affe cted parties may change or expand (e.g., f armers and fishermen may be

more concerned/involved).
14.4.3 Deter mining the

Scope of the

Risk Asses sment At a minimum , the scop e of the r isk asses sment will i

nclude a dditional e

xposure pa

thways, exposure routes, and potentially exposed populations or sub-populations. The deta ils of scope are developed during the problem for mulation step (see Chapter 15).April 2004 Page 14-10 Example Multipathway Problem Statemen t Air toxics emission s may be causing increased lo

ng-term health risk to peo

ple who eat fish in P

uffer Pond that may

be contaminated with mercury compound rele ases from the B ig Air Manufacturing Company . A multipa

thway risk a ssessment will be performed to ev aluate potential long-

term hum an health impacts assoc iated with consum ption of contamina

ted fish. In

gestion risks w

ill be assessed for recreational fishers who eat fish caught in Puffer Pond. I n addition, a m

odeling risk as sessment using air dispersion mode ling will be condu cted to estimate inha

lation risks for populatio

ns within 50 km

of the Acme prop erty boundary using residential exp osure conditions. 14.4.4 Describing the Problem As with inhalation, participants would develop a problem statem ent

that clearly a

rticulates the perceived problem to be evaluated. The pr oblem statement may also provide sta tements of what is and is no t include d in the mult ipathway risk asse ssment an

d why . (Note that, in ge

neral, only one problem statement is necessary to describe all exposure pathway s, including inhalation. A separate problem statement for each e xposure pathway is not usually necessary .)
14.4.5 Determining How Risk Managers Will Evaluate the Concern As with inhalation, the multipa thway risk asse ssment wou

ld be desi gned to

provide i nput to risk manage rs to help inform the

decision s they

must make. Part of th e plannin g and sc oping pr

ocess is developing an understanding of the ty pes of information needed by the risk managers and the level of uncertainty

in that information that can be tolerated.

14.5 Tiered Multipathway Risk Assessments EPA guid ance g eneral ly r

ecommen ds that a ti ered ap

proach t o risk ass essments b e taken t o identify the key

chemicals, sources, and pathway s that contribute most to the risk being evaluated. (5) A tiered

approac h can be particu larly valuable for multip athway

risk asse ssments becaus e of th e pote ntial compl exit y commonl y associa ted wi th su ch anal yses. Oft en, sc reenin glevel analyse s assume relatively hig h exposure factors (e.g., all of the fish a per son eats comes from a po tentially

contaminated pond

) to dete rmine whe

ther risk associa ted with a specific pathway a

ppears to be significant enough to wa rrant more robust analy sis. Subsequent tiers of analysis, using more

realistic exposure factors and perhaps involving more c omplex modeling and perhaps sampling and analy sis, are generally

undertaken only if

lower-tier analy ses continue to indicate the potential for risk. As with inhalation risk assessments, an iterative process of evaluation, deliberation, data collection, work planning a nd communication is used to decide:

• Whether or not the r

isk asses sment, in it s curre nt state, i s suffici ent to supp ort the ri sk management decision(s); and

• If the assessment is determined to be insuff icient, whether or not progression to a highe r tier of complexity (or refineme nt of the current tier) would provide a sufficient bene fit to warrant the addit ional ef fort.April 2004 Page 14-11 1. U.S. Environmental Protection Agency . 1992. Guidelines for Exposure Assessment

. Federal Register 57:22888- 22938, Ma y 29 , 1992. Av ailable a

t http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=15263 2. U.S. Environmental Protection Agency . 2001. Risk Assessment Guidance for Superfund (RAGS): Volume I -

Human Health Evaluation Manual (Part D, Standardized Planning, Reporting and Review of Superfund Risk Assessments), Final . Office of Emergency and Remedial Response, Washington, D.C., December 2001. EPA 9285.7-47. 3. U.S. Environmental Protection Agency . 1998. Human Health Risk Assessment Protocol for Hazardous

Waste CombustionFacilitie

s, Peer R eview Dr

aft . Office of Solid Waste and Emerge ncy Response , July

1998. EPA/ 530/D-98 /001 A, B , and C and subseque nt Errat a (EPA Memo , July

1999). Av ailable a

t: http://www .epa.g ov/eart

h1r6/6pd/ rcra_c /pd-o/co mb_risk.h

tm 4. U.S. Environmental Protection Agency . 1997. Guidance on Cumulati

ve Risk A ssessmen t. Part 1. Planning and Scoping. Science Policy Council, Washington, D.C., July 3, 1997. Availabl e at: http://www .epa.g ov/osp/spc /cumrisk2 .htm U.S. Environmental Protection Agency . 2003. Framework for Cumulative Risk Assessment

. Office of Resea rch and D evelopme

nt, National Center

for Envir onmental Assessme

nt, Washingt on, D.C., 2 003. EPA/6 00/P-02/00 1F. Ava ilable at : http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=54944 U.S. Environmental Protection Agency . 2001. Risk Assessment Guidance for Superfund (RAGS): Volume I -

Human Health Evaluation Manual (Part D, Standardized Planning, Reporting and Review of Superfund Risk Assessments) Final . Office of Emergency and Remedial Response , Washing

ton, D.C., Decemb er 2001. Publication 9285.7- 47. Avail able at: http://www .epa.g ov/super fund/pro grams/

risk/rag sd/index.ht

m 5. U.S. Environmental Protection Agency . 2001. Risk Assessment Guidance for Superfund (RAGS). Volume III

- Part A. Process for Conducting Probabilistic Risk Assessment . Office of Emergency

and Remedial Response, Washington, D.C., December 2001. EPA/540/R- 02/002. Available a t: http://www .epa.g ov/super fund/pro grams/

risk/rag s3a/index.htm References

# Chapter 1 5 Proble m Formul ation: Mu ltipat hway

Risk Assessment

Table of Contents

15.1 Introduction

............ ............ ............ ............ ............ ...... 1
15.2 Developing the Multipathway Conceptual Model

............ ............ ............ 1
15.3 Developing the Multipathway An alysis Plan

............ ............ ............ .... 2 15.3.1 Identification of the Sources

............ ............ ............ .......... 3 15.3.2 Iden

tification of the

Chem icals of Potent ial Concern
............ ............ ... 3 15.3.3 Identification of the Exposure Pathway s/Routes

............ ............ ....... 3 15.3.3.1 Characteristics of the Assessm ent Area

............ ............ . 4 15.3.3.2 Scale of the Assessm ent Area

............ ............ ........ 5 15.3.3.3 Use of Modeling v s. Monitoring

............ ............ ...... 5 15.3.3.4 Quantit

ation of Exposu re
............ ............ ............ 6 15.3.3.5 Evalu

ation of Uncert ainty

............ ............ ........... 7 15.3.3.6 Preparation of the Docum entation

............ ............ ..... 7 15.3.4 Identification of the Exposed Population

............ ............ ............ 7 15.3.5 Identification of E ndpoints and Metrics

............ ............ ............ . 7 15.4 Exposure A ssessment Approa ch

............ ............ ............ ............ .... 7 15.4.1 Scenario Approach

............ ............ ............ ............ ...... 8 15.4.2 Population-Ba sed Approach

............ ............ ............ ......... 11 References

............ ............ ............ ............ ............ ............ 12April 2004 Page 1 5-1 15.1 Introduction This chapter discusses the problem formulation step of the multipathway risk assessment, which takes the results of the planning and scoping proc ess and translates them into two critical products: the conceptual model, and the analy sis plan.
15.2 Developing the Multipathway Conceptual Model As with inhalation a

naly ses, the c onceptua

l model (E

xhibit 15-1)focuses

the multipa

thway risk assessment on several key elements, including sources, chemicals relea sed, fate and transport mechanisms, potentially exposed populations, potential exposure pathway s and routes of exposure, and potential adverse effects. Although disc ussed separately

here, as noted in Chapter 6, the elements

of the conc

eptual model t

hat are unique

to the multipathwa

y human health risk assessment should be integrated with th ose for the inhalation assessment as early as feasible

.
Exhibit 15-1. Generalized C onceptual Model for Multipathway An alyses

This figure highlig

hts the multipathway compo nents of the general air toxics risk assessm ent conceptual model introduced in Chapter 6. The conceptual m odel for a specific m

ultipathway risk assessment m

ay consider only

part of this general model, or m ay focus m

ore closely on spec ific subpopulations of concern.April 2004 Page 1 5-2 Elements of the conceptual model that may be unique to the multipathway asse ssment include:

• Sources . The spe cific sou rces inc luded in the analy sis may be focus ed on the s

ubset of a ll sources that release most or all of the identified air toxics that persist and which also may bioaccumulate.

• Chemicals of potential concern . The specific COPC will focus on those air toxics that persist a nd which a

lso may bioaccu mulate (i .e., persistent bio accumul ative ha zardous a ir pollutants (PB-HAPs) and other non-HAP chemicals that may be of concern for persistence and bioaccumulation).

• How the COP C move through the

environm ent . The con

ceptual model will need to consider the mechanisms by which PB-HAPs move through the environment, which include dispersion in the air; deposition (including vapor phase transfer) to soils, surface waters, and plant surfaces; erosion and other runoff phenome na; and uptake and bioconcentration by biota. The physical bounda ries of the study ar

ea may ne ed to include geographic a reas where COPC may be transported af ter deposition (e.g., PB-HAPs may have the potential to be deposited in a watershed and be carrie d out of the geographic ar ea defined for the inhalation pathway modeling

).

• The exposure pathways/media of concern . The potential exposure pathway s will include a number of

differe nt inges tion pathw ay s and, in some case s, derma l absorpt ion pathw ay s.

• The human populations potentially receiving exposure . The potentially exposed populations may

need to in

clude pe rsons who

do not live

within the

study area bu t consume food products that have the potential to become contaminated (e.g ., recreational fisher). Additiona

lly, differe

nt sensitiv e sub-pop

ulations ma

y be identifie d (e.g ., people

who consu

me large amounts of locally

-caught fish because of cultura l reasons).

• The potential adverse health effects (endpoints) that may result from exposure . The general ty pes of chronic health risks (cancer, non- cancer) may or may not change, depending on the specific COPC being evaluated. However, acute exposures generally are not a concern for multip athway

analy ses beca use it woul d be unlike ly f or air toxic s to accu

mulate in s oil, sediment, or food items to concentrations that would pose, in the absence of a c hronic hazard, an ac ut e h az ar d t hr ou gh th e i ng es ti on or de rm al pa th wa y.

• Metrics . The metrics used to characterize exposure and estimate risk may or may not be different from those used in the inhalation risk assessment. For example, the inhalation assessment may stop at a Tie r 1 analysis, while the multipathway assessment may g o all the way to a Tier 3 analy sis.
15.3 Developing the Multipathway Analysis Plan As noted in Chapter 6, the analy sis plan matches each element of the conceptua l model with the analytical a

pproach that the assessor will use to develop data about that element. This section describ es the el ements of the anal ysi s plan tha

t are uni que to the

multipathway

assessme nt, including (1) identification of sources; (2) identification of COPC; (3); identification of e xposureApril 2004 Page 1 5-3 pathway s/routes; (4) iden

tification of expose d populati ons; and ( 5) identif ication o f endpoin

ts and metr ics.
15.3.1 Identification of the Sources This part of the analy sis plan identifies the sources to be included in the risk assessment. As noted earlier, the focus of multipathway analysis is on sources of the air toxics that persist and which also may bioac cumulate. Within that subset, certain sources may be most important for a specific risk assessment. A tiered approach is re commended for focusing the risk assessment from the initial set of sources to the sources that will drive risk manage ment decisions. The initial tie r of ana lys is gene rally includes all sourc es of PB- HAPs. I n subsequent tiers

of analy sis, it may be possibl e to remo ve speci fic sour ces fro m the ana

lys is that con

tribute a very small fraction to the total risk estimate.
15.3.2 Identification of the Chemicals of Potential Concern This part of the analy sis plan ide

ntifies the chemic als that will be eva luated in the risk assessment. As noted earlier, the focus of multipathway analysis is on the subset of air toxics that persist and which may a lso bioaccumulate. Within that subset, certain chemicals may be most important for a specific risk assessment. A tiered approac h is recommended for focusing the risk a ssessmen

t from the initial list

of COPC to the

set of contaminan

ts that will

drive risk management decisions. The initial tier of analy sis generally

includes all of the air toxics released from the i dentifie d importan t source s. In

subseque nt tiers of analy sis, it may

be possibl e to remove specific chemicals from the ana lysis if they

contribute a very

small fraction to the total risk estimate.
15.3.3 Identification of the Exposure Pathways/Routes This part of the analy sis plan identifies the exposure pathways/route s to be evaluated. As noted in Chapter

6, an expos

ure path way consists o f four e lements:

• A source and mechanism of chemical rele ase;

• One or more environmental media (i.e., air, wate r, soil) in which the chemical is transported from the source;

• A point of potential human contact with the contaminated medium (referre d to as the exposure point); and

• An exposur

e route ( e.g., i

nhalation, inge stion) at t

he conta ct or expos

ure point . The rou

te may be actua l or poten tial, dep ending

on the pur

pose of the assessment. The exposure pathway

is complete if all four elements can be identified; otherwise the e xposure pathway is incomplete a

nd not considered further (see Exhibit 14-2, which presents the potential exposure pathways conside red for multipathway

assessments). The exposure points selected for the multipathway risk assessment also will depend on the choice of multipathway assessment appr oach and may

or may not be identica

l to those used in the inhalation risk assessment. In the example presented in Chapter 11, Mr. McDonald’s house was selected as the point of maximum inhalation concentration at a receptor loca tion. The multipathway assessment would likely also evaluate potential exposures via crops, meat, milk,a Maps, aer ial photos , an d tool s su ch as Geographi c Inf ormat ion Sy ste ms (GIS) can be v ery hel pfultool s f or characterizing t

he exposure setting ( see Part VI of this Reference Manual).

b Sou rces o f this inform ation m ay includ e any ex isting site de scriptio ns, pre liminary risk assessm ents, co unty soil surveys , wet lands maps, aeri al ph otogr aphs, U.S. Geol ogica l Sur vey t opograph ic maps , U.S. Depart ment of Agriculture So il Conservation S ervice repo rts, and information from state natural resources ag encies. April 2004 Page 1 5-4 and other foods. However, the focus would be on other exposure points or areas within the farm (e.g., the area wher

e forage fed to dairy

cows is grown or where veg etable crops are planted), not on the farmer’s house.
15.3.3.1 Characteristics of the Assessment Are a The physical setting

is important both in developing the study-spec ific conceptual model and selecting and providing input parameters for the appropriate multimedia models. As described earlier, the phy

sical set ting inc ludes inf

ormation s

uch as ur ban vs. ru ral setti ng, simpl e vs. complex terrain, climate and meteorology, and other important geographic f eatures (see Chapter 6). The most important additional information required for multipathway analyses is information on land use, soils, and surface water bodies within the assessment are a. (a) Many of the g eneral physical char

acteristics of the setting will influence the scope of multimedia modeling required. For example, if the sources being evaluate d are located in heavily industrialized area, there may be few, if any , agricultural areas or wa ter bodies close enough to receive sig nificant deposition. In t hi s e xa mp le , d ep os it io n t o s oi ls in ne ar by r es id en ti al ar ea s a nd su bs eq ue nt ex po su re pa th wa ys may be the most signific ant exposure pathways to examine.

• Land use . Information on land use is an important part of the physical char

acteristics of the assessme nt area discusse d in Chapte

r 6. For multipathway

analy ses, it is imp ortant to identify specific typ es of lan

d uses tha t may lead to exposures vi a ingestion pathway s, especially a

griculture, fishing, recre ation, and residential (indoor and outdoor, including gardening), as well as the location of particular areas where exposures via soil may be of concern (e.g., play

grounds, schools, day

care centers). Sources for land use data are discussed in Chapter 6.

• Soils . The type and c haracteristics of soils (e.g., sandy , organic, acidic, alkaline) in the assessme nt area affects phy sical phe

nomena su

ch as soil erosion r ates, the typ es and de

nsity of plants supported by the soils, and the phy sical and chemical characteristics that govern contaminant fate and transport. For example, the bioavailability of a compound may de

pend partially on soil pH. The spec ific information needed will depend in part on the input requirements of the multimedia fate and transport models selected f or the analysis (se e Chapter 19). (b)

• Water bo dies and the ir associ ated wate rsheds . Water bodies and their associated watersheds are important factors in evalua ting some of the major exposure pathways/routes considered in multipathway a nalyses. For

example, the identification of surface water bodies at locations in the assessment area receiving deposition from emission sources indicates the potential for exposures to contaminants from ingestion of fish, and possibly drinking water (drinking water is usually evaluated only if the

local population obtains drinking water fromc Use, a rea, and l ocati on of w ater bodies and their

ass ociat ed wat ersh eds ca n ty pical ly be ide nti fi ed by review ing the sa me lan d-use la nd cla ssification map s, topo grap hic ma ps, and aerial p hoto grap hs used in identification of land use discusse d in Part II of this Referen ce M anual. Add itional information on w ater bod y use can a lso be obta ined th roug h discu ssions w ith local a uthor ities (e.g., state e nviro nme ntal age ncies, fish a nd wild life agenc ies , or local

wate r cont

rol di str ict s) about v iabi li ty to s upport fi sh popul ati ons an d drinkin g wat er s ources , or current po stings of fish advisories. April 2004 Page 1 5-5 Multimedia Assessments: Modeling vs. Monitoring
Most m ultim edia air

toxics risk a ssessm

ents wi ll develop estim

ates of exposure concentration for noninha lat ion p ath ways p rima ril y thr ough mo del ing. In some instanc es, ana

lysts may use m onitorin g to e valua te the model. In m

ore rare instanc es, however, analy sts will use monitoring

to develop expo sure concentrations. surface water sources). (c) Information on fishing activity will also be useful in charac terizing the potentially exposed population.
Land use and human activities should be characterized in much the same way as Chapter 6 describ ed, except that a br oader r ange o f activit

ies/uses needs to be

consider ed. I t is importa

nt to identify all activ ities withi n the asse ssment ar

ea that c ould resu

lt in exposur

e to conta minants via non-inhalation pathways. The se would include hunting, fishing, growing crops (e.g., commercially, a s animal feed, or for private consumption), and incidental ing estion of soils. As noted earlier, the multipathway assessment may nee d to specifically a ddress special populations that are located in impacte d areas

because of unique

charac teristic s of the exposure se tting or to address particular community concerns. For example, a day care center or traditional Tribal fishing/hunting area may

be located in an area that is impacted by releases from a facility

or source area. Consequently, due to the site-specific exposure characteristics, exposure to children at the day c are center or tribal members may need to be addressed, because they may be especia lly s ensitive t

o the adve

rse eff ects and /or the exposure se

tting may be parti cularly conducive to exposur

e. EPA ha s develop ed a polic y f ocused on consiste

ntly and explic itly evaluat ing environmenta

l health r isks to infa

nts and ch

ildren in a

ll risk ass essments. (1)
15.3.3.2 Scale of the Assessment Area For inhalation assessments, the study a rea generally

is limited to a 50-km radius from the emissions sources (based on the dispersion models being used). The study area for the multipathway risk assessment g enerally w ill be limited similarly to the area in which deposition is modeled. However, certain potential exposure scenarios may require expansion of the study area bey ond the modeled deposition area. Examples include:

• The watershed for a lake or pond is within the modeled deposition are a, but the lake or pond (where contaminants may accumulate) is outside the deposition area.

• A commercial farm is within the deposition area, and a portion of the c rops are consumed by persons living outside the deposition area.

• A popular fishing area is located within the deposition area, and people from outside the deposition area come there to fish.
15.3.3.3 Use of Modeling vs. Moni toring As this doc

ument has

previous ly noted, ris k assessors can base estimates of current exposure concentrations on either actualApril 2004 Page 1 5-6 (Equation 15- 1) measurements (i.e., monitoring data) or modeling ( in this case, multimedia models). In many cases, m onitoring can be h elpful in r

educing uncerta inties in the exposure

assessme nt, beca use multimedia modeling is more complex and involves more uncertainties. Note, however, that the scope of potential monitoring for multipathway analysis is considerably

greater than that for inhalation analyses. A w ide range of ty

pes of sampling and analysis could be conducted, including sampling of soils, surface waters, se diments, and biota (human food items). Each ty pe of sample has its own methods, protocols, and QA/QC requirements (see Chapter 19)

. Multimedia sampling and analy sis may require a dditional expertise and effort. The analysis plan, in cl ud in g t he qu al it y as su ra nc e p ro te ct io n p la n ( QA PP ), wi ll ne ed to be mo di fi ed ac co rd in gl y. 15.3.3.4 Quantitation of Exposure In contrast to the inhalation assessment, in which the quantitative metr ic of exposure is the ambient air concentration at the exposure point, ingestion exposures are quantifie d using the chemical intake rate

– the amou

nt of che mical ing ested pe

r unit time

– gene rally expresse d in units of milligrams of chemical per kilogram of body weight per day . The fundamental equation for dieta ry intake a nd inge stion path

way s in gene

ral is g iven as:

where
I = Chemical

intake r ate, expr essed in u nits of mg

/kg-da y. F or evalu

ating exposure to non-carcinogens, the intake is ref erred to as average

daily dose (

ADD ); for evaluating expo sure t o carci nogeni c comp ounds , the i ntake is ref erred t o as li fetim e average daily dose (

LADD ). EC = Exposure concentration of the chemical in the medium of concern f or the time period being analy

zed, expressed in units of mg/kg for soil and food or mg/L for surface water or beverage s (including milk). CR = Consumption rate, the amount of contaminated medium consumed per unit of time,

event, or other measure. (e.g ., kg/day for soil and food; L/day

for water). EF = Exposure frequency (number of day s exposed per year ). ED = Exposure duration (number of ye ars exposed ). BW = Average body weight of the receptor over the exposure period (kg). AT = Averag

ing time, the peri od over w hich exposu

re is ave raged (days). For c arcinog ens, the averaging time is usually 25,550 days, based on a

n assumed lifetime exposure of 70 years; for non-ca

rcinog ens, ave

raging time equa

ls ED (year

s) multiplied by 365 days per y

ear. As noted above, modeling and/or monitoring (sampling a nd analysis) ca

n be used to determine the exposure concentration (EC) at specified exposure points. However, a variety of a

pproaches and assumptions can be used to determine the remaining va riables in the equation, as will be discussed in subsequent chapters. For example, calculation of the intake r ate requires assumptions about diet (i.e., how much the exposed individual eats and drinks each day ) and body weig

ht (how much the individual weighs). Dietary assumptions need to be specific to the type of food c onsumed (e.g., fish, milk, beef).April 2004 Page 1 5-7 As n ot ed i n S ect io n 6. 3.3 .4, th e ex pos ure dur ati on ( ED) use d t o ca lcu lat e ch emi cal in tak e ra te ( I) will h ave a

n impa ct on the choic e of t

oxicit y v alue s (e. g., a

cute vs. c hronic) u sed to

char acte rize risk and hazard. A s a general ru le, the ED values should match the exposu re assum ptions use d in developi ng the d ose-re sponse va

lues.
15.3.3.5 Evaluation of Uncertainty As with the inhalation assessment, the evaluation of uncertainty includes both a summary of the values used to estimate exposure, including their range, midpoint,and other values; and a qualitative or quantitative discussion that evaluates which variables or assumptions have the great est poten tial to af fect the overall uncerta inty in the expos

ure asse ssment.
15.3.3.6 Preparation of the Documentation The analysis plan ne eds to specify the a pproach used to document the multipathway exposure assessment, as discussed in Chapter 20.
15.3.4 Identification of the Exposed Population This part of the analy sis plan ide

ntifies the exposed p opulation that will be

evaluat ed in the r

isk assessme nt. The pr

ocedure for cha racter izing the potentia lly e xposed popu

lation g eneral ly w

ill be similar to that described for the inhalation pathway (Chapter 6). As noted pr ev io us ly, it may be nece ssary

to include

individua

ls who live

outside th e modele d depositi on area . The man ner in which potentially exposed populations are cha racterized depends on the gener al approach used for the multipathway a ssessment (see Section 15.4 below).
15.3.5 Identification of Endpoints an d Metrics This part of the analy sis plan identifies the specific human health endpoints that will be evaluated in the risk assessme nt and the metrics u sed to qua

ntify exposure a

nd risk. The multimed

ia assessment uses the same general endpoints (i.e., c ancer and non-cancer) and presents the central tendency a

nd high-end tendency

descriptors required as the rang e of risk estimates of the distribution. Risk characterization is discussed in more detail in Chapter 22. 15.4 Exposure Assessment Approach A variety of

approaches are available f or multipathway exposure assessments. This section describes two representative approac hes that range from a relatively simple approach based on scenar ios to a ve

ry complex an

d data-d riven ap proach b ased on ma ss-bala nce mode ls. This discussion is intended to illustrate some of the potential approaches available for multipathway exposure assessment. A given risk assessment might incorporate f eatures of either of the two approaches outlined below, or might feature a different approach. Regardless of the specific approa ch taken, EPA recommends a tiered approac h to multipathway exposure assessment, in which the exposure assessment moves from relatively simple to more complex as warranted by

the quality of available

information and its ability to be used to support the risk ma

nageme nt decision(s). Cha

pter 3 pr ovides an overvie w of tier ed appro aches to

risk assessme nt.April 2004 Page 1 5-8 Childhood exposures need to be considered explicitly in any non-inhalation scenario. This can be done w

ith a sepa rate sc enario ( e.g., a

“reside nt child” ) or by

incorpor ating change s in consumption rates, dietary preferences, and body

weight with age in the exposure factors incorporated into the scenario. EPA’s Risk Assessment Forum rece ntly published guidance

on selectin g appr opriate age g roups for assessin g childhood exposur

es. (2)
15.4.1 Scenario Approach Multipathway exposures may be evaluated by

developing a number of scenarios that desc ribe the po te nt ia l h um an ex po su re s t ha t m ig ht oc cu r v ia ea ch of th e p ot en ti al ex po su re pa th wa ys identified in the conceptual model. An exposure scenario is a combination of exposure pathway s by which a s ingle de fined human rece ptor mig ht be expose d to air toxic s that persist and which also may

bioaccu mulate. T

he speci fic exposu

re scenarios de fined for a give n risk assessment would be based on the characteristics of the exposure setting, potential exposure pathways, potential exposure points or ar eas, and predominant land uses and activities associated with the potentially exposed population. For example, if the study area included a small lake where fishing might occur, the a ssessment might include a “fisher” scenario that include d ingestion of fish caught in the lake.
The sce nario ap proach general ly in

volves re latively

simple mode ling and

fewer d ata require ments as mo deling i

nputs. This

can be p erforme d by

using “l inked mode ling sy stems” which ca n either be rela tively simple or i

ncorpor ate hig hly sophistica

ted sing le-mediu

m models into a single multimedia system. Howeve r, these types of

models do not assure conservation of mass and therefore may

under- or over-estimate exposure concentra tions for particular scenarios. The ge neral sc enario a

pproach involves:

• Identify

ing the potential exposure pathway s that may be important, including the areas where contaminants have the potential to accumulate in soils, surface wate rs, sediments, and biota; and specific activities that may result in ingestion of these contaminants (either via incidental ingesti on of soil w

hile in the contaminated ar

eas or by consuming

the conta minated p lants, animals, or surface water).

• Develop ing a se

t of scenarios tha

t describ e reasonable se

ts of pote ntial exposu

re pathway s, given the ty

pes of pe ople and a

ctivities that occ ur within t

he study area. T he scena rios would include specific exposure factors (e.g ., body weig ht, fish consumption) based on the particular activities identified above. The exposure scenar ios should consider children, either as a separate scenarios (e.g ., a “resident child”) or as part of an ove rall scenario (e.g., someone who is born in the exposure area and lives there for 30 y ears, and thus experiences exposure b oth during

childhood

and as an adult). T he exposur

e factors could b e set initi ally at conservative levels for screening -level assessments and then at more site-specific levels for hi gh er ti er s o f a na lys is . E ac h e xp os ur e s ce na ri o a ls o s ho ul d a pp ro pr ia te ly c on si de r s tu dyspecific sub-popu lations tha

t may experience diff

erent exposure conditions (e .g., be cause they eat diff

erent foods or parts of foods at different ra tes than the general population).

• Using r

elativel y si mple multime dia model ing techniques ( e.g., “ linked mod eling sy stems” described in Chapter 18) to estimate exposure concentrations in the media and biota of interest to each scenario. Monitoring (sampling or analysis) c ould be used to augment the modeling effort. For scree ning-level analyses, the scenarios can be based on the loca tionsApril 2004 Page 1 5-9 with the hi ghest mo deled concentra tions or de

position ra

tes. For example, a

scenar io involving consumption of fish could be based on the same location as a residential sc enario. In some cases, it may

be appropriate to evaluate an exposure scena rio assuming exposure through ingestion of fish from one water body and drinking water from a differ ent water body . Such ass

umptions ma

y ne ed to be r

efined i n subsequent model ing tiers.

• Quantifying

the dietary intake

for each scenario based on the modele d or estimated exposure concentrations and the specific exposure factors f or each scenario. Several exposure scenarios are commonly used in multipathway risk assessments (Exhibit 15-4). A recent description of several scena rios is provided in EPA’s risk assessment guidance for hazardou s waste in

cinera tors. (3) In a

ddition to the commonl

y a ssessed s cenari os provide d in Exhibit 15-4, other scenarios may be a ppropriate, depending on study -specific conditions. For example, if a contaminated surface water body is used for bathing and swimming, incidental ingestion, and dermal exposure from these activities may need to be considered. As another example, the resusp ension of contaminated soils

(i.e., windblown d

ust) may be importa

nt in some study area s. Note also that exposure of an infant to chlorinated dioxins/furans (and other lipophilic contaminants) via the ingestion of breast milk may be evaluated as an additional exposure pathway, sepa

rately fr om adult exposures, in each of the scenarios outlined below. Note also t

hat in each o

f these sc enarios, the

risk asses

sment needs to

look at both a

n adult and child (for example, the “farmer” includes both an adult farm er and a farmer child)

. Farmer . The farmer scenario is commonly evaluated to account for the combination of exposure pathway s to which a

person ma

y be exposed in a

farm or r anch expo sure set

ting. As indicate d in Exhibit 15-4, the farmer is commonly assumed to be exposed to air toxics through one or more of the follow

ing exposu

re pathway s:

C Direct i

nhalation of vapors and pa rticles;

C Inc idental in

gestion of soil;

C Ing estion of

drinking water f rom surfa ce wate r source s;

C Ingestion of homeg

rown produce;

C Ingestion of homeg

rown beef;

C Ingestion of dairy

products from homegrown livestock;

C Ingestion of homeg

rown chicken;

C Ing estion of

eggs from home grown c

hickens;

C Ingestion of homeg

rown pork; and

C Ingestion of bre ast milk (evaluated separately for an infant [for PCBs, dioxins, and furans]). In a Tier 1 assessment, the f armer commonly is assumed to c onsume a certain amount daily (e.g., grams/day

) of each food group (bee f, pork, poultry, eg gs, and milk) to make up a total consumpti on rate, and amoun ts consume d are as sumed to be homegr own. I f site-sp ecific information is available that demonstrates that a farmer does not ra ise beef, poultry, or

pork, and that rais ing any

of these livestock would not o ccur for a rea sonable p otential f uture fa rmer at a location, then elimination of one or more of these exposure pathway s could be justified. The farmer scenar io often d oes not inc lude the f

ish inge stion exposu

re pathway . Howeve

r, in some areas of the country , it is common for farms to also have stock ponds which are fished on a regula r basis for the fa

rmer’s c onsumption . Also, ing estion ra tes (e.g ., food, in

cidenta l soil ingesti on) ofte

n are age-depe

ndent.April 2004 Page 15-10 Exhib it 15-4.Common

Expo sure Sc enarios

Used in Multip athway Ex posure Assess ments

Exposure Pathways Scenarios aFarmResidentResidentResidentwithGardenLocalFishConsumers

Inhalation of Vapors and Particulates b • • • • Inci dental I

ngesti on of S oil • • • • Ingestion of Drinking Water from Surface Water Sources • • • • Ingestion of Homeg

rown Produce •

– • • Ingestion of Homeg

rown Beef •

– – – Ingestion of Milk from Homegrown Cows •

– – – Ingestion of Homeg

rown Chicken •

– – – Ingestion of Eggs from

Homeg rown Chickens • d d d Ing estion o f Hom

egrow n Pork •

– – – Ingestion of Fish d d d • Ing estion o f Breas t Milk c c

– c Notes:

• Pathway is include d in exposure scenario.

– Pathway is not included in exposure scen ario.

a Exposure scenarios are de fined as a comb ination of exposure pathways evaluated for a receptor at a specific exposure scenario location. Note that thes e scenarios are not exhau stive (i.e., addition al or other scenari os ma

y be r elevan t to a pa rticular exposu re asses sment

). Not e also t hat with in each scenario, the quan

titative exposure estim

ates will vary across age gro ups. b Note that inhal ation is include d in the

overa ll expos ure asse ssmen

t, but t he inha lation exposure assessment is perform

ed separately (as desc

ribed in Part I I of this Re ference Library).

c Infant exposure

to dioxins and/or furans via the ingestion of their mother’s breast milk is evaluated for in fants as an add ition al, sep arate exposu re pat hway. d Regional spec ific exp osure se tting characte

ristics ( e.g., presence of ponds on farms or with in semi-rural residential areas, presence of livestock within semi-rural residential areas) may warrant inclusion of this exposure pathway when evaluating a rec ommend ed exposure scenario.
Resident . The resident scenario is commonly evaluated to account for the combination of exposure pathways to which a person may be

exposed in an urban or rural (non-farm) setting. A s indi cated i n Exh ibit 15-4, the res ident is com monl y assumed to be e xpos ed to a ir tox ics th rough the follow

ing exposu

re pathway s:

C Direct i

nhalation of vapors and pa rticles;

C Inc idental in

gestion of soil;

C Ingestion of drinking

water from treated surface water sources; and

C Ingestion of bre ast milk (evaluated separately for an infant [for PCBs, dioxins, and furans]).April 2004 Page 15-11 The resi dent sce

nario of ten does n

ot include the fish in

gestion exposure p athway . Howeve r, in some areas of the country, ponds within semi-rural residential areas support fish for human consumption.
Resident with Garden . The resident with garden scenar io is commonly evaluated to acc ount for people w ho may be exposed

while g ardenin

g or thr ough the consumpti on of prod

uce gr own in their garden in an urban or rur al (non-farm) setting. As indicated in Exhibit 15-4, the resident with garden is commonly assumed to be exposed to air toxics through the same exposure pa th wa ys a s t he re si de nt sc en ar io , w it h o ne ad di ti on al ex po su re pa th wa y:

C Ingestion of homeg

rown produce.
Local Fish Consumer . The local fish consumer scenario is evaluated to ac count for the combination of exposure pathways to whic h a receptor may

be exposed in an urban or rural setting where fish is the main component of the person’s diet. As indicate d in Exhibit 15-4, the local fish consumer is commonly assumed to be exposed to air toxics through the following exposure p athway

s:

C Direct i

nhalation of vapors and pa rticles;

C Inc idental in

gestion of soil;

C Ing estion of

drinking water f rom surfa ce wate r source s;

C Ingestion of homeg

rown produce;

C Ingestion of fish; and

C Ingestion of bre ast milk (evaluated separately for an infant [for PCBs, dioxins, and furans]).
In ma ny c ases, loc al fish consumers a re assumed to gr

ow some of

their own

produce , but this may or may

not be relevant to a particular risk assessment. Also note that in some parts of the country, a pr

imary relianc e on fishing as a source or dietary protein is common - a circumstance known as “ subsistence fishin g.” Subs istence

hunting may

also be imp ortant for some g roups. 15.4.2 Population-Based Approach Multipathway exposures may be evaluated by

tracking individual members of a population and their inhalation and ingestion through time and space. Such a nalyses may

incorporate a userspecified number of simulat ed individualsor popula tion gro ups ( cohorts ) to represent the population in the study area. A cohort is defined here as a gr oup of people within a population with the sa

me demog

raphic v ariable s who are

assumed t

o have sim

ilar exposu

res. I n this approac h, the expos

ure ana lys is proce ss consists

of rela ting che mical con centra tions in environmental med

ia (e.g ., air, so il, water

) to chem ical concentra

tions in the

exposure media with which a person or popula tion has c ontact ( e.g., s oil, food, household dust). Expos

ure is estimated by trac

king the movement of a population cohort through locations wher e chemical exposure can occur according to a specific activity

pattern. (4) Models suc

h as the Sto chastic Human Exposure and Dose Simulation Model (SHEDS), Calendex, the Hazardous Air Pollution Exposure Model (HAPEM), and the Total Risk Integr ated Methodology , Exposure Event Model (TRIM.Expo) inc orporat

e this approach ( see Chap ter 19). The ge neral a pproach , which is analog ous to the i

nhalation exposure

modeling

techniqu es descr ibed in Cha

pter 9, in volves:April 2004 Page 15-12 1. U.S. Environmental Protection Agency . 1995. New Policy on Evalua ting Healt

h Risks to Children . Memora

ndum to Assi stant Admi

nistrators, Gener

al Counse l, Ins

pector G eneral , Associate Administrators, Regional Administrators. From Carol M. Browne r, Administrator, and Fred Hansen, Deputy Administrator. Washington, DC., October 20, 1995. • Defin ing exposure districts – geog raphic l ocations

within the

study area w here the re is potential contact between humans and a pollutant – and estimating che mical concentrations within each exposure district (through modeling and/or measure ment).

• Preparing inventories of chemical conc entrations in each microenvironment in each exposure district at selected time intervals (e.g., day s, hours). These inventories may be developed using mass-balance approache s that predict the partitioning of chemicals throughout the environment or thr

ough the use of mic

roenvir onment fa

ctors.

• Identify

ing the characteristics and activity patterns of eac h population cohort or set of representative individuals (e.g., the demog raphic characteristics of the individual; the locations where the individual lives, works, etc.; the amount of time spent in each location, and the individual’s activities within a location). These activity patterns should be representative of the exposed population of concern. F or representative individuals or cohorts, each sim

ulated pe rson/cohort is represente d by a “pers onal prof

ile” or “ activity pattern” developed by

selecting from a set of variables that include:

– Demographic variables (e.g . age, sex), which are ge nerated based on census data;

– Residential variables (e.g., where the person lives, works, etc., which are ge nerated based on sets of distribution data;

– Daily vary

ing variables (e.g., how long a person works in a garden), w hich are generated based on distribution data that change daily during the simulation period;

– Physiological var

iables (e.g., height, weig

ht), which are generate d based on age groupspecific distribution data; and

– Dietary va

riables (e.g., amount and ty pe of food consumed), which are g enerated based on sets of distribution data. Profiles or activity patterns ma y be deve loped using probability de nsity functions, allowing th e an alys is to in cor por ate pro bab il is ti c te chn iq ues suc h as Mon te C arl o an alys is (se e Pa rt V II of this reference manual).

• Summing the exposures for each exposure event over the assumed dura tion of exposure (e.g., lifetime or portion of a lifetime). The primary a

dvantage of the population-based approa ch is that it is a more realistic exposure assessment that simulates how people actually live and work within the study are a. It therefore can provide a more complete chara cterization of the spatial and temporal patterns of exposure. The primary disa

dvantage of the cohort approac h is the time and resources it requires, including signific ant input d

ata req uiremen ts. ReferencesApril 2004 Page 15-13 2. U.S. Environmental Protection Agency . 2003. Guidance on Selecting the Appropriate Age Groups for Assessing Childhood Exposures to Environmental Contaminants (External Review D raft) . Risk Assessment Forum, Washington, D.C., February 1, 2003. EPA/630/P- 03/003A. A

vailable at: http://cf

pub.epa .gov/nc ea/cfm/ recordisplay .cfm?deid=55887 3. U.S. Environmental Protection Agency . 1998. Human Health Risk Assessment Protocol for Hazardous

Waste CombustionFacilitie

s, Volume 1. Peer R eview Dr

aft . Office

of Solid Waste and Emergency

Response, Washington, D.C., July 1998. EPA/530/D-98/001A. Availabl e at: http://www .epa.g ov/combus tion/risk. htm

. 4. See the H

uman Exposu

re Model ing pag e of EPA’ s Fate, E xposure an

d Risk Anal

ysi s (FERA)website: http://www .epa.g ov/ttn/fe ra/huma n_gen. html

. See the T RIM.E

xpo web page at: http://www .epa.g ov/ttn/fer a /human_apex.html

. U.S. Environmental Protection Agency . 1999. Total Risk Integrated Methodology. TRIM.Ex po Techni

cal Suppo rt Docume

nt. Exter nal Revie w Draft . O ff

ic e o f A ir Qu al it y Planning and Standards, Research Triangle Park, NC, November 1999. EPA/453/D-99/001. Availabl e at http://www.epa.gov/ttn/fera/trim_fate.html#1999historical

.

# Chapter 1 6 Quantifi cation of Exposur e: Development of the Emissi ons Inventory for the Multipathway Risk Asses sment

Table of Contents

16.1 Introduction

............ ............ ............ ............ ............ ...... 1
16.2 Devel oping

the Em issions Inventory

............ ............ ............ .......... 1April 2004 Page 1 6-1 16.1 Introduction Chapter 7provides

an overv iew of the process used to de

velop an e

missions inv entory

for an a ir toxics risk assessment. As noted in that chapter:

• Emissions data are a source term for the risk assessment, primar ily as a key

input for computer models that estimate the transport of chemicals in the atmosphere; if and how they will be transformed by

chemical or phy sical processes; how and where they will be deposite d; and how

they will contin

ue to par tition and m

ove throu gh envi ronmenta l media following deposition.

• Developing the emissions inventory involves identifying the spe cific air toxics released from the sourc e and qua

ntify ing rel ease characte

ristics ( e.g., r

elease rates, te mperatu re, rel ease velocity).

• Local enhance

ments of existing air toxics emissions inventories may be advantag eous to a particular air toxics assessment effort as a very critical initial step, because air toxics inventories are not always at the quality that would provide the re sults desired in a modeling assessme nt.
16.2 Developing the Emissions Inventory The proc ess used t

o develop the emissi ons invent

ory for the mu

ltipathwa y r isk asses sment is similar to the process for inhalation analy ses (see Chapter 7 for a description of this process)

. However, there are a f

ew additional considerations that may apply to a given multipathway analysis (e.g ., information on particulate/particle-bound/vapor fractions if ISCST3 is used). See Chapter 1 8 for mor

e discuss ion on input s for mode ls used in mu ltimedia a ssessmen

t.

# Chapter 1 7 Quantifi cation of Exposur e: Chemi cal and Physical Properti es Affecting Multimedia Fate and Trans port

Table of Contents

17.1 Introduction

............ ............ ............ ............ ............ ...... 1 17.1.1 Measures of Persistence

............ ............ ............ ............ . . 1 17.1.2 Metrics of Bioaccumu lation

............ ............ ............ .......... 5
17.2 Chemical and P

hysical Properties that Affect Persistence and Bioaccu mulation

............ 8
17.3 Evalu

ating Persiste

nce and Bioacc umula tion in Exposu

re Asse ssmen

ts

............ ...... 10 References

............ ............ ............ ............ ............ ............ 11April 2004 Page 1 7-1 17.1 Introduction This chapter provides both an overview of the phy sical and chemical properties of chemicals that persist in the environment long enough to be of potential conce rn for multimedia exposure and informat ion on thei

r potentia l to bioac cumulate

in biolog ical tissues to leve ls that may result in significant exposures. This chapter also provides a brief discussion of va rious approaches for evaluat ing persistence

and bioac cumulation in an air toxics exposu

re asse ssment.

• The term persistence refers to air toxics that are slow to degrade in the a tmosphere or in the soils, wat er, and/ or sedime nts onto wh

ich they deposit a nd partiti on. Under

certain circumstances, persistent chemicals can incre ase in concentration over time. I n addition, some of these pollutants can bind tightly to soils and sediment and move from place to place by erosion. Examples of persistent che micals are metals (which never deg rade) and polychlorinated bipheny

ls (which degrade, but only over very

long periods of time). Measures of persistence are de scribed in Section 17.1.1.

• The term bioaccumulation refers to persistent chemicals that build up in the tissues of living organ isms to conc entrations that ar e highe r than in the surrou

nding e nvironme nt. These pollutants are often lipophilic in nature, which allows them to be taken up in and stored by fat tissues. A s an exampl e, the g roup of c hemicals known col lectivel

y a s dioxins (usu

ally some mixture of chlorinated dioxins and furans) persists for long periods of time in the environment and is

strongl y lip ophilic. Re

peated e xposure to d

ioxin in food

can lea d to increased body

burdens in fat tissue and human milk. Measures of bioaccumulation are discussed in 17.1.2.

• The term biomagnification refers to persistent chemicals that increase in concentration as they transfe r up the f ood chain so that the

y a ccumula te to hig her conc entration levels w ith each successive food chain level (se e Section 17.1.2). The air p ollutant’ s phy sical and chemic al prope rties and the char acteristics of the

environment to which the pollutant i s emitted a

ffect it s potentia

l to persis t and bioa

ccumula te. One o f the most important determinants in predicting persistence and ac cumulation in biota and other environmental med

ia is the pa

rtitioning behavior of the pol lutant. Pa

rtitioning refer s to where

in the envir onment a c

hemical w

ill tend to r

eside an d in what r

elative q uantities . When rel eased to the air, chemicals may

partition to air, water, soils, sediments, or biota, depending on a number of chemical and site-specific factors. Sec tion 17.2 highlights measures of partitioning and other chemical and phy sical properties. Section 17.3 discusses how measures of persistence , bioaccu mulation, a

nd partiti oning a

re used i n exposure

assessme nt.
17.1.1 Measures of Persistence Estimating the persistence of chemicals in the environment is a cha llenging exercise. Persistence depends on basic processes such as how the che micals are released (i.e., which envir onmental media the y a re rele ased to in

itially , in this ca se, air) ; how they move in the

environment (i.e ., to which en vironmen tal media they tend to pa

rtition); a nd their te

ndency to degr ade withi n those specific media (i.e., their persistence in air vs. in wate r). These basic processes, in turn, depend on a number of chemical-specific prope rties and site-specific conditions. However, despite theApril 2004 Page 1 7-2 Exhib it 17-1.Degra dation Processe s in Enviro nmental

Media Environmental Medium Applicable Degrada tion Process(es)

Air Photolysis

Photooxidation

Surface water Aerobic biodegradatio n Hydro lysis Photolysis Photooxidation

Soils Aerobic biodegradatio n Hydro lysis Photolysis (sur

face soi l) Oxidation - reduction reactions Sedim ents Anaerobic biodegrad ation complexity of the overall proce ss, some general screening methods are available to get a sense of persistence of chemicals in certain media. Within each environmental medium, several different deg radation processes can influence the persistence of chemicals. However, r egardless of the specific deg radation process, what gen eral ly occu rs is t hat o rga nic chemic als ( or or

gano-metalli c com

pound s) ar

e red uced in size and complexity, and, if complete deg radation occurs, converted to carbon dioxide, methane, ammonia, water, sulfates, phosphates, nitrates, and other end pr oducts. Explanations of the specific degradation proce sses and their applications within different environmental media are summarized below and in Exhibit 17-1:

• Aerobic biodegradation is the brea kdow n of chemic als by

micr oorg anism s that

utili ze oxygen;

• Hydrolysis

is the breakdown of chemicals by reaction with water;

• Photolysis or photodegradation is the process by whic h chemicals can be degra ded by the energ y in artific ial light

or sunlig ht;

• Oxidation or reduction (or redox) reactions involving the exchange of electrons between the pollutant and reactive compound s found in t

he envir onment;

• Photoox

idation is a process by w hich oxidation a

nd photoly

sis work jo intly

to break down chemicals and refer s to a reaction with oxyge

n in the presence of light (usually sunlight); and

• Anaerobic biodegradation is the breakdown of che

micals by microorganisms without the

use oxy gen (f or example , in sediments). Persistence is usually desc ribed using a term called half-l

ife , which is the time required for one-hal f of the origina

l mass of the chemic al to be degraded, transformed, or destroyed in a given medium. Half-life values may be measure d direct

ly or estimat

ed (e.g ., with computer models that predict half-life based on chemical structure). Alternatively, the literature may repor

t a degradation or transformation rate constant in units such as 1/day or day -1 . Assuming the reaction is first-order, the rate c onstant can be converted to the half-life and vic e versa using the equation t 1⁄2 = 0.693/k, where t 1⁄2 is the half-life (day s) and k is the first-order rate constant (day -1 )

. Exhibit 17-2provides a list of da

ta sources for deg radation half-li fe or ra

te consta nt values

.April 2004 Page 1 7-3

Exhibit 17-2. Data Sources for Half-Life and Ra te Constant Values Data Source Data E lements Comment s Howard et al. Handbook • High and low compartmen t half- life va

lues for soil, a ir, surface water, and sediment/groun

d water based on measu

rements and modeling e stimations; and

• High and low measured and estimated pro cess- specific half-life value s, including hydrolysis, red

uction, photolysis, photoo xidation (in water and air), and biodegradation. Howard et al. (1) compiled m

easured and estima

ted enviro nmen tal deg radation rates for orga

nic chem icals, i ncludin g poly cyclic aromatic hydrocarbons (PA

Hs), pesticides, and so lvents.

Mackay et al Handbo oks • Measured and estimated

half-life and rate c onstant data for air, water, soil, and sediment. Mackay et al. (2) compiled m

easured and estima

ted enviro nmen tal deg radation rates for orga

nic chem icals, i ncludin g monoarom

atic hydrocarbons, chlo

robenzenes, poly

chlorinated biphenyls, poly

nuclear aromatic hy

drocarbons, polychlorinated dioxins

and dibenzofurans , volatile organic c ompounds (VOC s), pesticides, and oxy gen, nitrog en, and

sulfur organ

ic compounds. Verschuren • Measured and estimated

half-life value s in surface water, groun

d water , sediment, soil, a

nd biota. Verschuren (3) compiled m easured and estima

ted degradation rates for o

rganic chem

icals.

Mercury Study Report to Congress • Measured demethy lation rate con stants c onver

ted to half- life data

for mercury (water, soil, s edime

nt). The Mercury Study Report to C ongress (4) was developed for a ru

lemaking and has underg one extensive Agency peer review.

HYDRO WIN • Estimated hy drolysis rate for water. HYDRO WIN, t he hyd rol ysis esti mati on pr ogra m, is part of EPA’s Estim

ation Program

Interface (EPI) Suite. (5) HYDR

OWIN uses a chem

ical’s structure to estimate the

acid- and b ase-catalyzed rate constants for certain chem ical classes (esters, carbam ates, ep oxides, halom

ethanes , and c ertain alkyl halides). Chemicals can be catalyzed (broken down) b

y acids (hyd

ronium) or bases (hydr oxide io ns). The rate co nstantsare use d to calculate hydroly sis half-lives at selected pHs.April 2004 Page 1 7-4 Half-life values for the same substance in diffe rent media, and for different substances in the same medium, can differ by several orders of magnitude. This is illustrated in Exhibit 17-3.

Exhib it 17-3.Exa mple Illu stratin g Rang es of Half-li fe Value s in Differ ent Environme ntal Media

HAP Measured or Estimated Half-life Value (Hours) (a) Air Water Soil Sediment acrolein 19 672 672 1,776 benzene 276 252 252 9,984 chlorobenzene 401 2,616 50 1,800 formaldehyde 4 192 96 384 methyl brom

ide 8,980 420 420 1,680 2,3,7,8-TCDD 200 768 12,096 14,400 Note: V alues are for I

llustrat ive Pur poses O nly (a) Values r epres ent the maximum measu red or est imat ed val ues f rom th e ref

erenc es ci ted i n Exhi bit 17-2. The fate and transport models used for char acterizing multipathway

exposure use metrics of persistence to account for loss of the chemical throug h degradation or transformation proce sses describ ed above . Ty

pically , for ea ch modele d media c ompartme nt, the fa te and tr ansport models require an overall metric of persistence for that media compartment (e.g., a ha lf-life value for air, a half- life valu

e for soi l, a half

-life va lue for s urface water) . The med

ia compa rtment ha lf-life is the half-life associated with the most important or fastest degr adation process or reaction. That is, the process-specific half-life of the fa stest degradation process is usually selected as the overall media compartment half-life.
Metal s may tran sform among d iffere nt com pound s or sp ecies ( e.g., d ivale nt mer cury can un dergo methy lation to y

ield methy

l mercur y, a nd it can b e reduc ed to for m element

al mercu ry ). Some metal species are more persistent and/or may be more toxic than others. Transformation half-life values in different media are more usef ul for evaluating the persistence of meta l pollutants of concer n. The spe ciation o f metals i s highly depende nt on ge ochemic al envir

onment (a nd in some cases, the presence of cer tain microbes). One species may dominate in water of one pH, Eh, DOC concentration, etc., and an entirely different species may

dominate in water of different geochemistry . [Also note that a further complicating factor is that analy tical laboratories often report the total amount of a metal present in a sample, rather than the amount of the various individua l metal spe cies pre sent (e. g., Cr 6+ versus Cr

3+ ), which usually have

different toxicities and different persistence values. An under standing of the concentrations of various metal spec ies is therefore desirable; however, it is often not analy tically achieva

ble.] (6) There are a number of concerns regarding

the assessment of risks posed by me tals in the environment. Information on this subjec

t can be f ound at the

following

website: http://cfpub.epa.gov/ncea/raf/rec ordisplay.cfm?deid=51736

.April 2004 Page 1 7-5 Note that BAFs an

d BCFs are not ca

lculated for huma ns; rath er, they

are som etime s used t

o est imat e air toxi cs co ncen tra tio ns in the f ood i tems eaten by people. What is a Persist ent Ch emical?
Becaus e envi

ronme ntal per sistence

is a com plicated phenom enon, n o sing le val

ue is un iversa lly accepted as indicating a “persistent” chemica l. EPA has used

a half-life v

alue of two m

onths (1,440 hours) in the Toxics Release Inventory (TRI) PBT final rule and the Preman ufacturing Notice evaluation process. (7) Other authors hav e suggested a half-life value of one mo nth (720 hours), using the following log

ic: Assuming

that a chemical will degrade in app roximately six half lives (when less than two percent of the mass rema ins), a chemical with a half-life o f one month w ould persist in the enviro nment for six month s. Thi s six mont h perio d would b e long eno ugh to enc ompass t he sens itive developmen

tal life stages of m

any organism

s. (8)
Note that the use of any single thr eshold value to define “p ersistent” chemicals may be misleading . An imp ortant consideration is data q uality (e.g., whether the half- life value is m easured or predicted and the ov

erall quality of the ex

perimental study

or computer algorithm used to d evelop the value). Ass essors must consid er other factors including the tendency of the chemical to p artition into various media. For example, vinyl chl oride has a half-life of six months in surface water and two years in sedim

ent – values that mig

ht sug gest t

his chem ical is “ persiste nt.” H owev er, its half- life in a ir is 53.4 hours a nd in so il is 10 days. Thus, d ependin g on i ts relati ve ten dency to part ition be tween a ir, soil, water, and sedim

ent, a significant am

ount of the mas s in air emissions might be degraded (in the air and/ or soil) prior to

the che mical reachin g surf ace wat er and s edime

nt. Ultimately, it is often difficult to select a single and reliable half-life because the half-life of a chemical depends not only on its physical properties (se e section 16.4), but also on the environmental conditions of the site to which the chemical is emitted. Temperature, sunlig ht intensity, the nature of the micr obial community, and conce ntrations of reactive species such as oxygen radic als can all affect the reactivity of a compound. (3)
17.1.2 Metrics of Bioaccumulation Several chemical-specific metrics ca n be used to evaluate the potential for a chemical to bioaccumulate in plants and animals and biomagn ify in food we

bs. (9) These values may be mea sured in laboratory tests or estimated with computer models based on chemical structure. These metrics include:

• Bioa ccumul ation Factor ( BAF) . The con centra tion of a s ubstance in tissue of

an org anism divided by

its conce ntration i n an envir

onmental medium in si

tuations w

here the organ ism and its food are exposed (i.e., accounting for f ood chain exposure as well as direct chemical uptak e). Su ch val ues are often used t o chara cteri ze th e trans fer of p ollu tants throu gh consumpti on of fish , beef, or dairy products

. (10)

• Bioco ncent ratio n Facto r (BCF) . The concentration of a substance in the tissue of an organism divided by

the concentration in an environmental medium (e.g., the c oncentration of a subst ance in a n aquatic organ ism [μg/kg

body weight ] divided by

the conc entration in the ambient water [μg/L water ], in situations where the organism is exposed through the water on ly) . The most c ommonly given v alue is a

n estimate

of the re lative concentra

tions in water and whole fish (or, in some cases, fish fillets, since most people in the U.S. do not ea tApril 2004 Page 1 7-6 whole fish), but BCFs for other orga nisms are also available. For plants, the Root Concentr ation Fa ctor (RCF ) indica

tes how re adily

pollutants

are tak en up from soil into underg round tiss ues.

• Octanol/wat er parti

tion coefficient . The ra

tio of a chemical’ s solubility in n-octa nol to its solubility in water at equilibrium (in th

is test, n- octanol i s used as a surrog ate for

lipophilic tiss ue). T his m etric , usua lly expr essed as a lo garith m (log K ow or log P) , is often used as a surrogate for (and is an impo rtant basis for estimat ing) BAF or BCF. As with pe

rsistenc e, bioac cumulation is a comp licated p henomenon, and no s ingle va lue is universally ac cepted as indicating that a chemical has a high tendency

to bioaccumulate, although EPA’s Off

ice of Pol lution Pre

vention a nd Toxics ha s used a th

reshold B CF value

of 1,000 in evaluat ing new and existing

chemica ls. (11) Exhibit 17-4 provides a list of sources for bioaccumulation data.
Importance of Trophic Lev els in Evaluating Bioaccumulation in Aquatic Food Chains An organism ’s trophic position in the a quatic food web can have an im portant effect on the m

agnitude of bioaccumulation of certain chemicals. Certain pollutants hav e the potential to biom

agnify, or increase in concentration at su ccessive trophic levels through a series of predator-prey associations. Chapter 5 (Bioaccum

ulation) of EPA’s Methodology for Deriving A mbient Water Q uality Criteria for the Protection of Hum an Health provid es gui dance a

bout de riving trophic level -spe cific bioaccumulation factors. (17)April 2004 Page 1 7-7

Exhibit 17-4. Sources of BCF and BAF Value s Data Source Data Element Comment s HWIR Technical Support Docum

ent Measured BAF Measured BCF Predicted BAF Predicted BCF The Hazardous Wast e Identification Rul e (HWIR)

Technical Support Docum ent (12) data were develo

ped for a rulemaking . Estimates are available for different classes of org anism s (fish a nd invertebrat

es). Mercury Study Report to Cong

ress Measured BAF for mercu ry The Mercury Study Report to C ongress (4) was developed for a rulemak

ing and has und

ergone extensive Agency peer review. Ambient Water Quality Criteri a docum ents Measured BCF Ambi ent Wate r Qualit y Crit eria doc umen ts (13) are developed for rulem

akings that esta

blish concentration limits for chem

icals in the surface waters o f the United States. They hav

e undergone exten sive Agency peer review. AQUIR E Database Measured BCF EPA’s Office o f Resea rch and Deve lopme

nt, Environmental Research Laboratory in Duluth, M N, maintains a database

of citations and aquatic b ioassay data including residue measures. (14) BCFW IN Predicted BCF BCFWIN, the Bioconcentration Fa ctor Estimation Program, is part of EPA’s EP I Suite. (5) This program estimat es BCFs b ased on log K ow data using the estimation m

ethodology p

resented in a 1997 stud

y prepare d for EP

A by Meylan

et al. (15) The metho dology was formulated u sing a training set of 694 com pounds with measured B CF values for fish . In ord er of prefe rence , the pr ogram uses (1) the log K ow entered by the use r, (2) t he expe rimenta l log K ow from the exper imental log K ow database for KOWWI N, and (3) the KO WWIN est imated l og K ow value.
BAF or BCF values are

used in some fate and transport models to calculate how much of the chemical will partition into organisms, such as fish or shellfish, that are consumed by humans or ecolog ical rec eptors of

concer n. The concentra

tions in the se biota c an then be used to ca

lculate the intake of the chemical by humans or ecological receptors of interest. It is recommended that BAFs be derived separa tely for spec ies of dif

ferent trophic le

vels to ac count for

differe nt levels of accu mulation f

or membe rs of diff erent tr ophic lev els. (16) EPA presents specific guidelines for deriving BAF values, including how to estimate BAFs using BCF values in its revised methodology for

developing Ambient Water Quality Criteria. (17) National-level BAFs developed by the Offic e of Water can be used in screening analyses. The se can be refined based on sitesp ec if ic ch ar ac te ri st ic s i n s ub se qu en t t ie rs of th e a ss es sm en t, if

ne ce ss ar y. As with ha

lf-life values, B CF value s vary

signific antly

among a ir toxics. Exhi

bit 17-5 pr

esents a few representative BCF va lues for several HAPs. Note that these represe nt the highest values id en ti fi ed in th e r ef

er en ce s c it ed in Ex hi bi t 1 7- 4 a nd ar e m ea nt fo r i ll us tr at iv e p ur po se s o nl y.April 2004 Page 1 7-8 Exhibit 17-5. Example BCF Values HAP BCF Value (a) acrolein carbon tetrachloride chlorof orm pentachlorophenol 2,3,7,8-TCDD 3 30 6 776 5,754 Note: v alues are for i

llustrat ive pur poses o nly (a) Values represen t th e maximum measur ed or esti mated val ues from t he re fer ences cit ed in Exhibi t 17- 4.
Note that the use of any single threshold value to define “bioaccumulative” chemicals may be misle ading . As with persistence, data quality is an important consideration (e.g., measured vs. predicted values; quality of the underlying

study/algorithm used to deve lop the value). Moreover, the specific orga nisms and tissues in which bioaccumulation is measured or predicted are important determinants of the relevance of a given BCF or BAF

value for the exposure assessme nt. Many substanc es that bio accumul ate tend to accum ulate in li pid tissues

(e.g. , fat, organs ), whi ch may not be the tiss ues th at mos t peop le eat (e.g., fish f ille t). Th erefor e, a hi gh BCF may not actua lly r esult in hig

h exposure s to humans

. 17.2 Chemical and Physical Properties that Affect P ersistence and Bioaccumulation Exhibit 17-6 identifies both direct metrics of persistence (i.e., half-life values), a s well as the physical and che

mical properties that are most important in determining persistence a nd bioaccumulation. In Exhibit 17-6, solubility and vapor pressure determine the pro pensity for pollu tants to dissolve in water and volatilize into the air, respectively . The Henry’s La w constant , which is simply the ratio of the chemica l’s water solubility to its vapor pressure , indicates whether a compound will partition into air or water at equilibrium. The speed with which the equilibrium occurs is affected by

the diffusion constants in air and water.
The coefficients that are abbre viated with a capital “K” (e.g., the oc tanol-water partition coefficient, K

ow ) in Exhibit 17 -6 provid e informa tion on how

strongl y or ganic a nd inorg anic compounds are likely to bind to soil or sediment particle s, or to partition into lipid versus aqueous phase liq uids. Stron

g binding indicate s a high p otential t o persist a

nd accu mulate in soils and s ediments; soil/sediment bindin g also te nds to be c orrela

ted with th e potenti al to bioaccumulate.
The 1998 Pe er Revie

w Draft of EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (HHRAP) p rovides ( Appendix A)

tables of recomme nded rat e constant s and che

mical/phy

sical pa rameter s for a la rge nu mber of a ir toxics. (18) Also, the user’s guide for EPA’s Risk Screening Environmental I ndicators (RSEI) tool includes phy sicochemical propert ies for TRI chemicals a

nd chemic al cate gories

. (19) Note, however, the values in these sources may ne ed to be updated.April 2004 Page 1 7-9

Exhibit 17-6. Chemical and Physical Prope rty Definitions Property Defini tion Significance/Comm ents Name Unique ident ifi er No t always reliable (many synonym s)
CAS No. Unique ident ifi er M uch m ore re liable tha n the ch emic al nam e; som e che mica ls have no CAS number; s ome CAS numbers refer to mixt ures MW Mo lecular weight In absence of data, can be used t o calcul ate D a , D w , etc

. V p Vapo r pressure Indic ates vo latility
S Solu bility Indic ates m aximu m co ncen tration o f a chem ical that will dissol ve in wat er H Henry’s Law constant Ratio of vap or phase concen tration to the liquid phase con centra tion of a gas; high values in dicate tende ncy to volatil ize from w ater

sol uti on D a Diffusio n coe fficient in air Use d to c alculate rate of v olatilizatio n from air
D w Diffu sion coef fici ent i n water Used to calcu late rate of volat ili zati on from wat er K ow Octanol -wat er parti ti on

coefficient

(log K owis fre quent ly tabu lat ed) High value (> 1000, log K ow> 3) indica tes stron g tend ency to bioc onc entrate

K oc Organi c carbon part it ion coefficient

(log K oc often tabul ated) High value in dicate s strong tende ncy to b ind to so il/sedim ent; K oc , K owcan be est imated f rom each other

Kd soil Soil dissociati on constant Indic ates po tential of ino rganic ions/co mpo unds to bind to soil; varies for di ffe rent i onic speci es, pH, s oil t ypes Kd sed Sediment dissociation constant Indic ates po tential of ino rganic ions/co mpo unds to bind to se di men t ( si mi la r t o Kd soil )
t 1/2 soil Ha lf-life in soil Indicates pe rsistence in soil; generally soil type, conditions, and degradati on pathway (s) mus t be speci fied
t 1/2sed Half-li fe in sediment Indi cate s per sis tenc e in sedi ment; same c onsi derat ions as f or t 1/2 soil t 1/2sw Half- lif e in s urface water Indic ates pe rsistence in surface water; fo r mo dera te to hig h-v apor pre ssu re compou nds
BAF Bioaccu mulation f actor Indicat es accumul ation of a compound int o tis sues of an organis m from contac t wit h contami nated wat er, cont aminate d sedi ments , an d ing est ion of cont amina ted f

ood
BCF Bioconce ntr ati on fa ctor Indicat es accumul ation of a compound int o tis sues of an organi sm f

rom c ont act

wi th a con tam in ate d medi um. RCF Root conc entr ati on fa ctor Ratio of root to soil concentration, measures propensit y to take up pollut ant f

rom soil for def

ined plan t speci es B Biotransfer facto rs Describes propensity of pollutant to be transferr ed through food chain; defined for specif ic crops and consuming organis ms (e.g ., al falf

a => dairy ca ttl e); ge nerally correl ates with B CF , K ow ; used primarily for detailed pathway modelingApril 2004 Page 17-10

Fugacity as a Determinant in the Fate and Tran sport of a Pollutant
Once a contam

inant is emitted from

its source, the fate of the contaminant is d etermined by

a number of factors (e.g ., molecula r weig ht, sol ubility , partit

ion coe fficient s). I

n order to char acteriz e the fa

te of the p ollutan

t, mo delers have de

velop ed the c oncept of fug acity (20) to define the tendency of the gas to escape to another pha se in order to reach a steady-state equilibrium. This tend ency to m igrate then leads to partitioning of the pollutant across env ironmental m

edia based on its chem

ical and physical characteristics. Fugacity models are distribu

tion-based m

odels incorporating all environmental compartmen

ts (media) and are used to quantify stea dy-state flu

xes of pollutants across compartment interfaces. There exist three lev els at which fugac ity may be modeled. The Level I model depicts the distribution of a known quantity of a chemical in a c losed environm ent at equilibrium, in which no degrading reactio

n occurs and no adv

ective gain or loss is attained. A L evel II model describes a situation in wh

ich a chemica

l is con tinuous ly dis charg ed at a constan t rate an d achieves st

eady- state equilibrium, at w

hich the input and outp

ut rates are equal. I

n Level II mo dels, reaction and adv ection may occur. A Lev

el II I m odel is simila

r to a L evel I

I m odel in that the

mode led chemical

is continuously discharged at a constant rate and achieves a steady

state condition, in wh

ich the input rate equ als the output rate. Y et, a L evel I II mode l differs

in that fugac ity fo r the g iven chemic al is equal within a sing le compartm ent for all defined subcom partments, but is not equal between comp artme

nts. The refore, indiv

idual in puts for each m edium must be defi ned sep arately

in orde r to determine appropriate pollutant partitioning am ong environm

ental compartm

ents.

17.3 Evaluating Persistence and Bioaccumulation in Exposure Assessments Charac terizing

the movements of a

ir toxics tha

t persist a nd which a

lso may bioaccu mulate through various environmental media such as air, soils, water , sediments, and biota can be a highly complex task, and g enerally a vailable methods for evaluating these fate a nd transport pathways have

only rece ntly been deve loped. Specifically, EPA a nd other regulatory

agencies have developed a number of models that estimate the conc entrations of persistent and bioaccumulative compounds over time in the various environmental “compartments” subse quent to defined patterns of deposition. These models simulate both phy sical and chemical processes such as air deposition to soil, runoff, leaching (dissolution), soil/sediment adsorption, and chemica l specia tion (oxidat

ion/redu ction, pr ecipitat ion reactions). Some models a lso evalu ate the biodegradation of organic pollutants by bacteria a

nd other organisms in soil, sediment, and surface water. These models re quire a large number of site-spec ific inputs and many measur es of the phy sical and chemic al prope

rties of p ollutants. It is b eyond the sc ope of thi s chapte r to discuss in detail all the processes that may be modeled in the assessment of indirect exposure pathway s; howeve r, Macka y a nd his coll eague s provide a good overview

of the mult

imedia fa te and transport (20) and Conell and Emlay desc ribe the principles governing the bioac cumulation of pollutants in the env

ironment. (21) Also, Chapter 18 pr ovides a d

escripti on of ava

ilable multimedia models that are generally recommended for use in air toxics risk assessment. The documentation for these models provides detailed descriptions of the specific processes and methods us ed in eac

h model.April 2004 Page 17-11 1. Howard, P., Boethling, R., Jarvis, W.F., Mey lan, W. 1990. Handbook of Environmental Degradation Rates . Lewis Publishers, Michigan. 2. Mackay D ., Shiu W.Y., and Ma K.C. 1991-1997. Illustrate d Handboo k of Physi

calChemical Propertie

s and Envi

ronmenta l Fate for O

rganic Che

micals , (5 volume s). L ewis Publishers Inc., Boca Raton, F lorida. 3. Verschueren, K. 1996. Handbook of Environ mental Da ta on Organ ic Chemicals . 3rd ed. Van Nostrand Reinhold Co., New York, NY. 4. U.S. Environmental Protection Agency . 1997. Mercury Study Re port to Cong

ress . Office of Air Quality Planning a

nd Standards; Office of Research and De velopment, Washington, D.C., Dec ember 19 97. EPA/45 2/R-976/00 3. Availa ble at: http://www.epa.gov/ttn/atw/112nmerc/volume1.pdf

. 5. U.S. Environmental Protection Agency

. 2002. The Estimations Programs Interface for Windows (EPIW

IN) and SMILES . Update d June 20, 20 02. Avail able at: http://www.epa.gov /oppt/p2f ramewor k/docs/e piwin.htm. (Last accesse d April, 2004). 6. Stumm, W. and Morgan, J.J. 1996. Aquatic Ch emistry , 3rd Ed. John Wiley & Sons, I nc., New York. Langmuir, D. and K

lusman, R.L. 1997. Natural Backg round Concentrations of Metals That Exceed Drinking Water Standards: Defining Ba ckground and the Effect of Sample F iltration. Abstract. Geol. Soc. Am. Annual Meetings . Oct. 20-23. Salt Lake City UT: A-435. Langmuir, D., Chrostowski, P., Chaney , R., and Vigneault, C. 2003. Draft Issue Paper on the Envir onmental Chemistry

of Metals

. Submitted to EPA’s Risk Assessment Forum by ERG, Lexington, MA. August, 2003. 7. U.S. Environmental Protection Agency . 1999. Persiste nt Bioacc umulative

Toxic ( PBT) Chemicals (40 CFR Part 372 ). Final Rule. Federal Register 64:58665, October 29, 1999. U.S. Environmental Protection Agency . 1999. Notice of R eceipt o f Request s for Amen dments to Delete Uses in Certain Pesticide Registrations

. Federal R egister 64:60193, November 4, 1999. 8. Boethling, R.S., P.H. Howard, W. Mey lan, W. Stiteler, J. Beauman, and N. Tirado. 1994. Group contribution method for predicting probability and rate of aerobic biodegr adation. Environmental Science and Technology . 28:459-465. 9. U.S. Environmental Protection Agency . 1995. Great Lakes Water Quality Initiative Technica l Support D ocument for the Pro cedure t

o Determ ine Bioaccumulatio n Factors

. Office of Water, Washington, D.C., March 1995. EPA-820-B -95-005. ReferencesApril 2004 Page 17-12 10. U.S. EPA. 1990. Interim F inal Metho dology for

Assessing

Health Ris

ks Associ ated with Indirec t Exposure

to Combusto r Emissions

. Environmental Criteria and Assessment Office, Office of Research and De velopment, Washington, D.C., January 1990. EPA-600-90- 003. 11. U.S. Environmental Protection Agency . 1992. Classification Criteria for Environmental Toxicity and Fate o

f Industria l Chemica ls . Office of Pollution

Preventi on and Toxic s, Washingt on, D.C. 12. U.S. Environmental Protection Agency . 1995. Technical Support Document for the Hazardous Waste Identification Rule: Risk Assessment for Human and Ecological Re ceptors. Volume I . Office of Solid Waste, Washington, DC, August, 1995. 13. U.S. Environmental Protection Agency . 2003. Office of Water Shopping Cart Home Page

. Availabl e at: http://yosemite.epa.gov/wa ter/owrccatalog.nsf/

. 14. U.S. Environmental Protection Agency . 2003. AQUatic Information REtrieval (AQUIRE) database, a part of the

ECOTOX dat

abase

. Version 3.0. Office of Research and Development, National Health and Environmental Effects L aboratory , Mid-Continent Ecolog y D ivision, Du

luth, MN. A

vailable at: http: //www .epa .gov

/eco tox/ 15. Meylan, W. M., P. H. Howard, D. Ar onson, H. Printup, and S. Gouchie. 1997. Improved Method for Estimating Bioconcentration Factor (BCF) from Octanol-W ater Partition Coefficient. Third Update Report - August 1997. SRC-TR-97-006 (EPA Contract No. 68- D5-0012). Prepared for Robert S. Boethling, U.S. EPA, OPPT, Washington, D.C.; Prepared by SRC, Environmental Science Center, N orth Syracuse, N Y 13212. 16. U.S. Environmental Protection Agency . 1998. Methodol ogy for Ass essing He

alth Risks Associated with Multiple Pathways of Exposure to Combustor Emissions . Office of Research and Development, National Center for Environmental Assessment, Cincinnati, OH. EPA 600/R-98/137. 17. U.S. Environmental Protection Agency . 2000. Methodology for Deriving Ambient Water Quality Cri teria for th e Protec tion of Hum an Health . Office of Water, Washington, D.C., October 2000. EPA/ 822/B-0 0/004. Ava ilable at : http://www .epa.g ov/water science /humanhe alth/method/method .html 18. U.S. Environmental Protection Agency . 1998. Human Health Risk Assessment Protocol for Hazardous

Waste CombustionFacilitie

s, Peer R eview Dr

aft . Office of Solid Waste and Emerge ncy Response , Washing

ton, D.C., July 1 998. EOA/30/D-98/0 01A. Ava ilable at : http://www .epa.g ov/epaoswer/hazwaste/co mbust/risk .htm

. 19. U.S. Environmental Protection Agency . 2002. Risk-Screening E nvironme ntal Indicators User’s Manual RSEI Version 2.1 [1988-2000 TRI Data] . Office of Pollution Prevention and Toxics, Wash

ington, D .C., Dec ember, 2 002. Avai lable at: http://www.epa.gov/opptintr/rsei/docs/users_manual.pdf

.April 2004 Page 17-13 20. Mackay, D . 1991. Multimedia Environmental Models. The Fugacity Approach . Lew is Pu bl is her s, C hel sea , MI. Mackay D, Pater son S, and WY

Shiu. 1992. Generic

models for evalua

ting the regional fate of chemicals. Chemosphere 24(6):695-717. Mackay D, DiGua

rdo A, Pat

erson S, a nd CE Cowan . 1996. Eva luating the envir onmental fate of a variety

of types of che

micals using the EQC Model. Environmental Toxicology and Chemistry 15(9):1627-1637. 21. Connell, D.W. and D. Emlay. 1989. Bioaccumulation of Xenobiotic Compounds . CRC Press.

# Chapter 1 8 Quantificatio n of Exposure: Multimedia

Modeling

Table of Contents

18.1 Introduction

............ ............ ............ ............ ............ ...... 1
18.2 Multimedia Fate and Transport Modeling

............ ............ ............ ...... 1 18.2.1 Basis o f Multim

edia Mod els

............ ............ ............ .......... 1 18.2.2 Multimedia Ex

posure Models

............ ............ ............ ......... 2
18.3 Key Pa

ramet ers/I nputs fo r Multim edia Mod els

............ ............ ............ . . 5
18.4 Examples of Multim edia Modeling

............ ............ ............ ........... 8 References

............ ............ ............ ............ ............ ............ 10April 2004 Page 1 8-1 18.1 Introduction This chapter summa

rizes the c oncepts a

nd tools av ailable f

or multime dia model ing to sup port a multipathway

human hea

lth risk as sessment. The discu ssion is divi ded into th ree sections:

• Section 18.2 discusses multimedia fate and transport modeling used to estimate chemical concen trations i n abiotic and biotic

media tha

t indirectly r

esult from air emis sions;

• Section 18.3 discusses key par ameters used as inputs to multimedia models; and

• Section 18 .4 prese

nts example s of the us e of multimedia mode ls in air to xics risk assessme nts.
18.2 Multim edia Fat e and Transpo rt Model ing Although the primary

route of exposure to many a ir toxics is via inhalation, non-inhalation exposure t

hrough soil, wate

r, and fo od pathwa

ys c an be a p otential health concern f or those a ir toxics that persist and which also may bioaccumulate (see Chapter 4 for the list of persistent bioaccumulative hazardous air pollutants (PB-HAP) chemicals). The refore, risk assessments for these substances often include multimedia modeling to predict the movement of these air toxics in the environment. This section provides an overview of the multimedia fate and tra nsport models commonly used by EPA.
18.2.1 Basis of Mult

imedia M odels Multimedia fate and transport models take into account various phy sical and chemical processes to predict the move

ment of po llutants within and b etween environmental med

ia. Multime dia models ca

n be gr ouped into

the follow

ing basi c categories.

• Link ed mode ling syste ms are composed of several independent single-medium models. These systems ty

pically consist of a “one -way” process through a series of linked single

medium models or algorithms; that is, they ca lculate fate and transport byrunning a singlemediu m mode l (e. g., a

n atmo spheric model ) and using the output

as the input for t he next single-medium model (e.g., a soil or surfac e water model). One of the primary advantages of linked modeling systems is that they can incorporate several highly sophisticated singlemedium models into a single modeling sy stem. The primary dr awbacks of these ty

pes of models ar e (1) the y do not alway

s assure conserv ation of ma ss; (2) the y la ck dy namic “feedback” loops; and (3) seconda ry pollutant transfers are not treated in a fully coupled manner.

• Fully co upled, ma ss-conse rving mo dels estimate the fate and tran sport of p ollutants betwee n and with

in media a

nd are a ble to full y a ccount f or the dis tribution o f pollutan t mass within a defined modeling region. I n these types of models, e ach of the included media (e.g., soil, air, biota) are modeled simultaneously (i.e., fully coupled) , and thus these models can simulate dynamic “fe edback” loops and secondary pollutant transfers. The primary drawback of these typ es of mode ls is that th ey typ ically involve some simplifi cation r elative t o sophistica ted sing le-mediu

m models due to the c omputation al deman ds associ ated with mo de li ng mu lt ip le me di a s im ul ta ne ou sl y.a Note that the MPE model and many of its variat ions are conceptual models used to describe fate and

transp ort, no t “read y-to-run” com puter mod els. Typ ically, users incorp orate these co ncep tual mo dels into sprea dshe ets or o ther co mpu ter fram ewo rks to c reate a usab le mo del. April 2004 Page 1 8-2 18.2.2 Multim edia Exposur e Models To date, EPA has used primarily the Multiple Pathways of Exposure (MPE) model and variations of the MPE approach to conduct multimedia fate and transport modeling for air toxics. More recent ly, EPA deve loped the

Fate, T ranspor t, and Ec ologica

l Exposure ( TRIM. FaTE) model as a component of the Total Risk Integr ated Methodology

(TRIM). (1) This sect

ion provid

es a summary of the MPE model, the var iations of the MPE approach, and the TRIM.F aTE model. During the development of the TRI M.FaTE model, EPA conducted a comprehensive re view of those multimedia fate and transport models that estimate exposures and risks from emissions of air toxics t

hat EPA an

d other or ganizat ions in the

United Sta

tes use. E

xhibit 18-1 provides a summary of the mod

els includ ed in this r

eview, w ith models g

rouped in to the two b asic categ ories de scribed

in the pre

vious section. The TRIM.F aTE docu mentation provides a descrip tion of ea

ch of the se models . Also pre sented a t the end o f this section is a mul timedia model developed by the

State of California called CalTOX. (2)

Exhibit 18-1. Multimedia Models Reviewed During TRIM.FaTE Develop ment Linked Modelin g Syste ms Fully Co upled, Mass-C onserv

ing Mod els

• Indirec t Expos ure Me thodol ogy (IEM)/Multipl e Pathways of Exposu re (MPE) , developed by

EPA’s National C enter for Environmen

tal Assessment

• Multimedia Env

ironmental Pollutant Assessment Sy

stem (MEPAS), developed by the U.S. Depart ment of En ergy • CalTOX, Califor nia Dep artme

nt of Toxic Substance Control’s Multim edia Risk Computerize d Model

• SimpleBOX, dev

eloped by the N etherlands National Institute of Public Health and the Environmen

t

• Modeling Multimed

ia Environm

ental Distribution for Toxics (Mend- Tox)/I SMCM), developed by

EPA’s Office of R

esearch and Development Multiple P athways of Expos ure Mode l (MPE) The Multiple Pathways of Exposure model, former ly known as the I

ndirect Exposure Methodolo gy

(IEM), prima

rily consists o f a set of

multimedia

fate an d exposure

algori thms developed by EPA’ s Office of Research and Deve lopment (ORD). (a) ORD issued an inter im document describing this methodology in 1990, a major addendum was issued in 1993, and an updat ed guid ance do cumen t was i ssued in 19 99 in respo nse to comme nts i t recei ved du ring a 1994 Scie nce Advi sory

Board r eview of the adde ndum. (3) The MPE documentation describes fate an d transport algorithms, expo sure pat hways, rece

ptor sce narios, a nd dose a lgorith ms. The MPE approach includes procedures for estimating human exposures and health risks resulting from the transfer of emitted pollutants from air to soil and surfa ce water bodies and theApril 2004 Page 1 8-3 The Draft Guidance on th e Development, Eva luation, and Application of Reg

ulatory Environmental Models recommen

ds best practices to help d etermine when a mod

el, despite its uncertainties, c an be appropriately used to inform a decision. The Knowled ge Ba se (KBas e) is a web-a ccessib le database of informa

tion on some o f EPA’s most frequently used m

odels. The draft guid

ance recom mend s what inform

ation ab out m

odels to document, w

hile the Knowledge B ase is the repository where this information is docu mented. Both pr oducts are av ailable at the C REM internet si te at http://www.epa.g ov/crem

. subsequent uptake by ve getation, animals, and humans. The methodology specifically a

ddresses exposures via inhalation; ingestion of food, water, and soil; and dermal contact. The MPE model was designed to predict long-term, stea dy-state impacts fr

om continuous sources, rather than short-term, time-series estimates. It consists of a “ one-way process” through a series of linked models an

d algor ithms, beginning with the mode ling of t he trans port of po llutant em

issions in a

ir and the subsequent deposition to soil and surface water and culminating in the uptake of the emitted pollutant(s) into biota. The aspects of the MPE model that address exposure estimation are described in more detail in Section 18.4 below. EPA desig

ned the MPE

model to a ssess human exposures to air toxics emitted from s tati onary comb usto rs, al thou gh analysts can a

pply most aspects of the approach to other ty

pes of stationary sources. One can apply

this model to one or more s ources a

t a singl e facili

ty simultaneously to estimate exposures within 50 kilometers of the facility. The MPE model will allow mo deling o f only one chemical at a time, and there is no trackin g (i.e. , carry

through the anal ysi s) of transformation products of the modeled chemical. To apply

the MPE approach, users must provide a significant number of site-specific inputs, such a s source emission rate, wind speed and direction, soil loss constant, and pollutant degradation rate. EPA modified the MPE approach to multimedia fate and transport modeling for use in two additiona l EPA model s and mode ling approache s. These models an

d approa ches ar e as follows.

• IEM2M . In 1997, Office of A ir Quality Planning and Standa rds (OAQPS) modified the then-current version of the I EM model to create IEM2M. This re vised version of IEM added the functionality nece ssary to model transfor mation between the three key species of mercury and trac k the conc entrations throug hout the mo deled sy

stem for e ach of these spec ies. This model was applied to estimate nationwide exposures to mercury for the Mercury Study Report to C

ongress . (4)

• Human Healt h Ris k Ass essmen t Protoc ol (HH RAP) . EPA’s Office of Solid Waste and Emergency

Response (OSWER) developed the HHRAP to provide guidance for c onducting multipathway

exposure a

nd risk as sessments

of emissions of air

toxics from

hazardou s waste combustion facilities. The suggested protocol for a ssessing multipathway e xposures was adapted from the MPE approach and the documentation of this protocol (5) compiles detailed informat ion on many

of MPE’s in

put para meters a nd algorithms.
Two addit

ional mode ls and approache s used by EPA to asse ss multipat

hway exposures

to air toxics use ma

ny o f the same

fate an d exposure

algori thms and me

thodolog ies used i

n the MPE model.April 2004 Page 1 8-4 • Dioxin Reassessment Methodology . Many of the a lgorithms used in the MPE model have been use d for ong oing EPA efforts to chara cterize e xposure an

d risks fr om dioxins, particu larly chlorina ted diben zodioxins and

dibenzofu

rans, as part of the Dioxin Reassessment proje ct. (6)

• Multimedia, Multipathway, Multi-receptor Exposure and Risk Assessment Model (3MRA) . The 3MRA

model is cu

rrently being d evelope d by

EPA’s Off

ice of Sol id Waste and Emergency

Response to support their Hazardous Waste Identification Rule (HWI R). Many of the fa

te and exposure algorithms used in 3MRA are similar to those used in MPE.
TRIM.F aTE EPA developed the TRIM Fate , Transport, and Ecological Exposure (TRI M.FaTE) model (7) to describe the movement and transformation of pollutants over time, throug h a user-defined, bound ed system of env ironm ental compa rtmen ts (i .e., a biot ic med ia and organi sms). The des ign of the compartmen

t sy stem can

encompa ss spatial

interconnection s (with some similar ities to grid-t ype Euleria n models)and eco logica

l exposure -relate d relationships. TRI M.FaTE i s designed to generate both media concentrations relevant to human pollutant exposures and exposure estimates relevant to ecological risk assessment primarily for air pollutants for which non-inhalation exposures are important.
In contrast to the I

EM/MPE approach, TRIM.FaTE is a fully coupled multimedia model that estimates the flow of pollutant through time among environmental compar tments. TRIM.FaTE offers the following important features that ar e not available using IEM/MPE.

• TRIM.FaTE is able to model mass-bala nced “feedback” loops betwee n media as well as secondary emissions (e.g., re-emission of deposited pollutants).

• TRIM.FaTE has the ability to provide detailed time-series estimates of pollutant concen trations i n the envi ronmenta l compar tments.

• TRIM.FaTE maintains a full mass balanc e of the pollutant mass in the system (i.e., a ll the pollutant introduced into the system is accounted f or among all the environmental compartments).

• TRIM.FaTE can mode l sensitivity of model results to variations in input parameters and perform probabilistic modeling such that uncertainty and variability in model results ca n be characterized.

• TRIM. FaTE is d

esigne d with the f

lexibility to allow f

or impleme

ntation of nearly limitless configurations (e.g., spatial re solution, types of biota), alg orithms, and approaches. Simulations

can ran ge from quite simp le analy ses of pol lutant dist

ribution a cross ab iotic media and biota to more complex, spatially-re fined assessments, with associated implications with reg ard to use

r requir ements. TRIM.FaTE can mode l multimedia fate and transport of air toxics from any type of stationary source. It can be

applied to multiple facilities, sources, and chemicals simultaneously to track the fate and transport of emitted pollutants as well as transformation products of the e mittedApril 2004 Page 1 8-5 pollutants. The amount of input data required by TRIM.FaTE is directly

related to the complexity

of the use

r-spec ified mode ling sy

stem; howe

ver, TRI M.FaTE a

naly ses ty pically require more inpu

t data tha n similar a

naly ses condu

cted usin g the MPE

approac h. As note d in Exhibit 18-2, TRIM.Expo is the exposure component of the TRIM modeling sy stem (see Section 18.2.2.2). California Total Exposure Model for Hazardous Waste Sites (CalTOX) The Department of Toxic Substances Control (DTSC), within the California Environmental Protection Agency , has the responsibility for mana ging the State’s hazardous-waste prog ram. As part of th is progr am, the DT SC funded t

he deve lopment of

the CalTO X progr

am. (2) CalTOX has been developed as a set of spreadshe et models and spreadsheet data sets to assist assessing human exposures and defining soil clean-up levels at uncontrolled hazardous wastes sites. More recently, Ca

lTOX has been modified for use in establishing waste cla ssification for landfills and hazardous waste facilities in California. CalTOX addresses contaminate d soils and the contamination of adjacent air, surface wa ter, sediments, and ground water. The modeling components of CalTOX include a multimedia transport and transformation model, exposure scenario models, and add-ins to quantify uncertainty a

nd variability. The multimedia transport and transformation model is a dy namic model that can assess time-vary ing concentrations of pollutants introduced initially to soil lay ers or for pollutants released continuously to air, soil, or water. This model assists the user in examining how chemical and landscape properties impact both the ultimate route and quantity of human c ontact. Multimedia, multiple pathway exposure models are used in CalTOX to estimate average daily doses within a human population. The exposure modeling part of CalTOX is described further in Chapter 20.
18.3 Key P arameters/Input s for Multim edia Mode ls For most air risk applications, multimedia modeling results are strongly dependent on the emission rate of pollutants emitted to the air from the facility . For the MPE framework and TRIM. FaTE mod

el, tran sport of mo deled pol lutants an d accumu lation in me dia of inte

rest res ult directly from the e

mission of the chemical into the air from the facility , the dispersion or advection of chemical through the air, a nd the subsequent deposition of the chemical onto land, water, or other surfaces in the modeled r egion. In addition to emission rate, seve ral other type s of data are often required by multimedia models to characterize the pollutants and site being modeled. Generally , the data requirements for multimedia fate and transport models fa ll into the following categ ories.

• Source characteristics for the sources that are modeled, such as loca tion, emission rates for the modeled pollutant(s), stack height, exit gas velocity, and exit gas temperature.

• Environmental setting characteristics for the abiotic media included in the modeling scenario, such as water body dimensions, surface soil characteristics (e.g ., organic carbon content, porosity), and da ta related to local meteorology and hydrolog

y (e.g ., precipitation, erosion, runoff rates).

• Abiotic chemical/physical data for the chemicals included in the modeling scenar io, such as Henry’ s law constant and soil-water partition coefficients. EPA’s draf t HHRAP provides default values f or many of these parameters. (5)April 2004 Page 1 8-6

Exhibit 18-2. Role of the TRIM Modelin g System

The Total Risk I

ntegrated Methodolog

y (TRIM) m

odeling system

can be used to asses s human inhalation, human

ingestion, and e cological risks. TRIM.FaTE accoun ts for move

ment of a chem

ical through a com

prehensive system of discrete com partments (e.g ., media and biota) that represent possible locations of the chemical in the physical and biological en vironments o f the modeled ecos yste m and p rovi des a n inve ntor y, over time, of a c hemic al th roug hout the e nti re sy ste m. In addition to providing

exposure estimates relevant to ecological risk assessm ent, TRIM.FaTE generates media concentrations relevant to hum an ingestion exposu

res that can be used as input to the ingestion component of the Exposure-E vent modu

le, TRIM.Expo. Measured concentrations also can be used as inputs to TRIM.E

xpo. In the inhalation comp onent of TRIM.E xpo, human exposures are evalua ted by tracking random

ly selected indiv

iduals that represent an area’s population and their inhalation and ingestion throug

h time and spac e. TRIM.Exp o Inh ala tion can accept amb ient air concentration estim ates from an external a

ir quality mo del or monitoring data. In the Risk Characterization module, TRIM.Risk, estimates of hum an exposures or doses are characterized with regard to potential risk using the correspond

ing exposure-

or dose-respo

nse relationships. The TRI M.Risk mo dule is also design ed to characteri ze eco logica l risks from mult imedi a expos

ures. Th e outpu

t from TRIM. Risk is intended to include docu

mentation of the in

put data, assum

ptions in the analysis, and measures of uncertainty/variability , as well as the results of risk calculations and exposure analysis. Inform ation on TRI M can be accesse d at: http://www.epa.g ov/ttn/fera/

.

• Non-chemical-specific characteristics of biota for any or

ganisms included in the modeling sc en ar io , s uc h a s f

ee di ng ra te s, bo dy w ei gh t, an d p op ul at io n d en si ty.

• Biotic chemical-specific datafor any or ganisms included in the modeling scenario, such as bioaccu mulation a

nd/or bioc oncentr

ation fa ctors or assimilati on effic iency

values.April 2004 Page 1 8-7 The Multip le Pathwa

ys o f Exposure

(MPE) mode l, (8) a commonly used model for multipathway analyses, re quires air concentrations, deposition rates, which are ty pically obtained via I

ndustrial Source Complex (e.g ., ISCS T3) model ing (se e Chapte r 9 for de scription s of these models). Risk assessors would execute the ISCST3 modeling for multipathway in a similar fashion to how they executed the modeling for inhalation. Specifically, the sources would be characterized in the same way ( e.g., vent height and diameter, r elease temperature and velocity , flow rate). The user would provide the inputs ne cess ary to ca lcula te the depo sitio n rates pr

oper ly (e.g ., pa rtic le siz e distributi on, scav enging

coeffi cient). Howeve

r, for mu ltipathwa y a naly ses, the u ser shoul d execute the ISCST3 model with the “depletion option” (i.e., telling the model to subtrac t out the mass of chemical deposited). The user would need to know the particulate/particle- bound/vapor fractions of the emissions for ISCST3 to calculate wet and dry deposition of vapors and particles. These would probably be considered source-related, since a lthough they a

re chemical-dependent, they also vary by

source (i.e., the industrial process affects the emissions profile). For dry deposition of particles, the user would supply the following inputs (in addition to the normal ISC inputs), including the:

• Array of partic le diamet

ers of th e emission s;

• Array of mass fractions corresponding to the differ ent particle diameters; and

• Array of partic le densiti es corr

esponding

to the diff

erent pa rticle di ameters

. For wet deposition of particles, the user would supply the following inputs (in addition to the normal ISC inputs), including the:

• Particle

scaveng ing coe fficien

ts for liqu id preci pitation c orresponding to t he diffe rent par ticle diameters; and

• Particle scavenging coe fficients for frozen precipitation corresponding to the different particle diameter s. For wet deposition of gases, the user would supply the following inputs (in addition to the normal ISC inputs), including the:

• Gaseous scavenging coe fficient for liquid precipitation; and

• Gaseous scavenging coe fficient for frozen precipitation. The ISC user’s guide (9) provides more detailed information on the deposition algorithms and required input data. There also is guidance for application of ISC for multipathway assessment in the latest MPE documentation. (8) The only fac ility-related/source term data points used by the TRIM Fa te, Trans port, and Ecologica

l Exposure ( TRIM.F aTE) model are chem ical e miss ion ra te, lo catio n (lat /lon g, UTM), and emission height, which are available f rom the inhalation modeling. TRIM.FaTE calculates all values internally for determining vapor/particle fra ctions and deposition rates based on chemical-specific ( not source- specific ) proper ties.April 2004 Page 1 8-8 Other multimedia models may requir e specific source charac

terization data and other documentation that were not obtained for the inhalation analy sis (the various user’s guides for these models should be consulted for appropriate inputs). It is important to note that the number and refineme nt of inputs to a multimedia model may vary depen ding o n the o utpu ts of i nteres t and l evel o f detai l enta iled i n the m odeli ng. 18.4 Example s of Multim edia Mode ling TRIM.FaTE Test Case Application . As a test case application, the TRI M.FaTE model was used to predict multimedia concentrations of mercury at a chlor-alkali facility

in the northeastern United States. Speciated mercury concentrations were calculated f or various abiotic media (e.g., surfac e soil, sur

face w ater, la

ke sedime

nt) and bi ota (e.g ., fish for variou s trophic levels, b irds, mammalia

n predat ors) for the ecosyst

em surro unding the facili

ty. A sensitiv

ity a nd uncer tainty analysis using TRI

M.FaTE tools and a model comparison involving the 3MRA modeling sy stem were also performed. The complete r eport on the test case will be available at the TRI M.FaTE page of EPA’s Fate, Exposure, and Risk Analy sis (FERA) website: http://www .epa.g ov/ttn/fe ra/trim_ fate.ht ml

. Paints Ha

zardous Wa ste Listin g Deter minatio n Analysis . On April 4, 2002, EPA issued a final determination not to list as hazardous certain wastes gener ated from the production of paint. EPA made this determination pursuant to the Resource Conservation and Recovery Act (RCRA), which directs EPA to determine whether certain wa stes from the paint production industry may present a substantial hazard to human health or the environment. EPA proposed concentration-based listings for certa in paint waste solids (K179) and liquids (K180) on February 13, 2001 (66 Federal R egister 10060). However, following a review of the public comments and supplemental analyse s based on public comments, EPA determined that the paint wastes identifie d in the Fe

bruary 13, 2001, proposal d

id not pre sent a sub stantial hazard to h uman hea lth or the en vironmen t. EPA cond

ucted a m ultipathw ay

risk asse ssment in su

pport of t his determination. (10) EPA used a series of models to estimate concentrations of Chemicals of Potential Concern (COPCs) in the environment with which human and ecological rec eptors may come into contact. The analysis used a source partioning model to estimate environmental releases of each COPC from a waste mana gement unit for each waste strea m, as appropriate. These e stimated e

nvironme ntal rele ases provided inpu t to the fa te and tr ansport models to estimate media concentra

tions in air, soil, sur

face w ater, a

nd grou ndwater . A farm f

ood chain model was

used to es timate COPC

concen trations i n produc e, beef

, and dair y pr oducts. A quatic bioconcentration factors were used to estimate c oncentrations in fish. Chlorinated Aliphatics Hazardous Waste Listing Determination . In support of a hazardous waste listing determination for wastewaters and wastewater treatment sludges g enerated from the production of certain chlorinated aliphatic chemica ls, EPA conducted a multipathway huma n health r isk asses sment. (11) EPA used the ISCST3 model to estimate dispersion and deposition of vapors emitted from wastewater treatment tanks and la ndfills, and vapors and particulates emitted from sludge land treatment units. EPA used a series of indirect exposure equations based on the MPE approach to quantify the concentrations of contaminants that pass from contaminated environmental media to the receptor indirectly . For example, EPA examined risks associated with contaminant transport in air; deposition onto plants and soil; accumulation in forage, gr ain,April 2004 Page 1 8-9 silage, and soil; subsequent ingestion by beef cattle and dairy

cattle; and human ingestion of contaminated bee f and dai ry products

. Hazardous

Waste Co mbustor

MACT Standa rd Analysis . A human health an d ecological risk asses sment was per forme d in s uppor t of de velop ing a Ma xim um Ach ievab le Con trol T echno logy (MACT) st

andard f or hazar dous wast e combust

or faci lities. (12) The risk analysis included a multimedia, multipathway assessment that addresse d direct exposures to constituents released into the atmosphere by ha zardous waste combustor units and indirect exposures due to the movement of air toxics in the food chain. The risk assessment addressed both human health risks (cancer effects and nonc ancer effects) as we ll as ecological risks. Constituents assessed were seven congeners of chlorinate d dioxin and 10 congeners of chlorinated furan; three species of mercur y; 1 4 metals ( antimony

, chromiu m II I, chromium VI , arseni c, lead, barium, n ickel, bery llium, sele

nium, cad mium, silve

r, thalliu m, cobalt , copper , and manganese) ; particu late matter; h ydr ochloric acid; an d chlorine gas. To the maximu m extent poss ible, this r

isk assessme nt followe d the late st risk g uideline s adopted

by E PA and use d the most r

ecent da ta available.
Columbus W aste-t o-Energy Study . A risk assessment study using f ate modeling was perfor med by EPA ’s NCE A for d iox in emi ssio ns at t he Col umbu s, Ohi o, Wa ste-t o-Ene rgy in ci ne ra to r f

ac il it y. (13) In 1994, EPA headquarters, the Office of Research and De velopment, and Region 5 conducted a screening assessment of indirect impacts, leading to the conclusion that continued emissions “may pose a n imminent endangerment to public health and the environment.” Fate modeling used to support EPA’s position utilized the air-to-beef model described in the draft Dioxin Exposure document (i.e., based on the principles included in the MP E f ra me wo rk ) a nd as su me d a su bs is te nc e f ar mi ng fa mi ly s ce na ri o. Ex po su re pa th wa ys considered beef, milk and vegetable ingestion; soil dermal contact and childhood soil ingestion; and breast milk ingestion. The exposure duration for adults was assumed to be seve nty y ears. Air concentrations used were the aver age from nine dairy

farms located between five and twelve miles from the incinerator. Overall exposure and cance r risk were estimated for each of the exposure pathways, with ca ncer risk being highest for be ef consumption (2×10 -4 ) and lowest for soil dermal contact (9×10 -9 ). Exposure from breast milk ingestion was determined to be higher by one order

of magnitude than exposure from beef and milk consumption, and highe r by two orders of magnitude than exposure from inhalation. Bre ast milk exposure near the incinerator site ranged between two and more tha n seven times the background dioxin levels. A TRIM.FaTE case

study has been de

veloped based on this analy sis, including a direct model compari son compon

ent on the air-soil outputs, a

nd will be a

vailable at: http://www .epa.g ov/ttn/fe ra/trim_ fate.ht ml

.April 2004 Page 18-10 1. A s um ma ry o f t hi s r ev ie w c an be fo un d i n: U. S. En vi ro nm en ta l P ro te ct io n A ge nc y. TRIM.FaTE Technical Support Document, Volume I (Chapter 2) . Office of Air Qu

ality Planning and Standards, Research Triangle Park, NC, September 2002. EPA/453/R-02/011a. Availabl e at: http://www .epa.g ov/ttnatw 01/urban /trim/trimp g.html 2. McKone, T.E. 1993. CalTOX, a Multimedia Total-Exposure Model for Hazardous wastes Sites Part I: Executive Sum mary . UCRL-CR-11456, Pt. I. 1993b. CalTOX, a M ultimedia Total-Exp osure Mod

el for Haza rdous W

astes Site s Part II: the Dyna mic Multim edia Transport an d Transform ation Mode l. UCRL-CR-111456, Pt. II . 1993c. CalTOX, aMultimedia Total-Exposure Model for Hazardous Wastes Sites Part III: The MultiplePathway Ex

posure Mo del. UCRL-CR-111456, Pt. II I. L ivermore, CA: Lawrenc e Livermore Na ti on al Lab or at or y. 3. U.S. Environmental Protection Agency . 1990. Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions

. Interim Final . Office of Healt h and Environmental Assessment, Washington, D.C., EPA/600/6-90/003. U.S. Environmental Protection Agency . 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions . National Center for Environme

ntal Asse ssment, Washington, D.C. EPA 60 0/R-98/137 . Both ar e availa ble at: http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=55525 4. U.S. Environmental Protection Agency . 1997. Mercury Study Re port to Cong

ress . Office of Air Quality Planning a

nd Standards, Research Triangle Park, N C and Office of Research and Development, Wash

ington, D C. EPA/452/

R97/003. A

vailable at: http://www .epa.g ov/ttn/atw /112nmer c/mercu ry .html 5. U.S. Environmental Protection Agency . 1998. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, Peer Review Draft. Office of Solid Waste, Washingt on, D.C. EPA -530-D- 98-001A. Availabl e at: http://www .epa.g ov/epaoswer/hazwaste/co mbust/risk .htm

. 6. EPA is progressing toward completion of its comprehensive r eassessment of dioxin exposure and human health effects entitled, Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds . The late st informat ion on the s tatus of the reass essment is availab le at: http://cf pub.epa .gov/nc ea/cfm/

dioxreass .cfm?ActTy

pe=def ault

. 7. U. S. Environmental Protection Agency . 2002. Total Risk Integrated Methodology. TRIM.FaTE

Technica l Support D ocument . Volume 1 : Descri

ption of Mo dule . EPA-453 /R02-011a ; Volume 2: Description of Chemical Transport and Transformation Algorithms

. EPA/453/R-02/011b. Evaluation of TRIM.FaTE. Volume 1: Approach and Initial Findings

. EPA/453/R- 02/012; TRIM.FaTE User’s Guide . Office of Air Quality Planning and Standard s, Resea

rch Tria ngle Pa rk, NC. These docu

ments and

informat ion are a vailable at: http://www.epa.gov/ttn/fera/trim_fate.html#current_user

. ReferencesApril 2004 Page 18-11 8. U.S. Environmental Protection Agency . 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions . Office of Research and Development, National Center for Environmental Assessment, Cincinnati, OH, 1998. EPA/600/R- 98/137. Available a t: http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=55525

. 9. U.S. Environmental Protection Agency . 1995. User’s Guide for the Industrial Source Complex (ISC) Dispersion Models. Volume 1 - User Instructions . Office of Air Qu

ality Planning and Standards, Research Triangle Park, NC, September 1995. EPA/454/B-95/003a. Availabl e at: http://www.epa.gov/scram001/userg/reg mod/isc3v1.pdf 10. U.S. Environmental Protection Agency . 2001. Risk Assessment Technical Background Document for the Paint and Coatings Hazardous Waste Listing Determination . Office of Solid Waste, Washington D.C., January 2001. (see also the F ebruary 28, 2002 addendum). Availabl e at http://www .epa.g ov/epaoswer/hazwaste/id/p aint/index.htm

. 11. U.S. Environmental Protection Agency . 1999. Risk Assessment Technical Background Document for the Chlorinated Aliphatics Listing Determination . Office of Solid Waste, Washingt on, D.C., July

1999. Ava

ilable at : http://www .epa.g ov/epaoswer/hazwaste/id/c hlorali/c lordoc1. htm . (see also the September 29, 2000 adde

ndum to the

risk asse ssment. Available a t: http://www.epa.gov/epaoswer/hazwaste/id/chlorali/ca_risk.pdf ). 12. U.S. Environmental Protection Agency . 1999. Human He alth and Ec ological R isk Assessment Support to the Development of Technical Standards for Emissions from CombustionUnits Burn ing Hazard ous W astes: Ba ckgroun d Docume nt, Final Re

port. Office of Solid Was te, Washin

gton, D. C., July

1999. Ava

ilable at : http://www .epa.g ov/epaoswer/hazwaste/co mbust/risk doc1.htm

. 13. Lorber, M. 2002. Comments on the Colu mbus W

aste-to- Energy S ite . Presentation at EPA Regional/ORD Workshop on Air Toxics Exposure Assessment. June 25 - 27, 2002. San Francisco, CA. National Center for Environmental Assessment. U.S. Environmenta l Pr ot ec ti on Ag en cy.

# Chapter 1 9 Quantificatio n of Exposure: Multimedia

Monitoring

Table of Contents

19.1 Introduction

............ ............ ............ ............ ............ ...... 1
19.2 Why Monitor?

............ ............ ............ ............ ............ .... 1
19.3 Planning and I

mplementin

g Issues

............ ............ ............ ............ 1
19.4 Monitori

ng and Sam

pling Methods , Techno logies and Co sts

............ ............ ... 3 19.4.1 Method Selection

............ ............ ............ ............ ....... 3 19.4.2 Available Methods

............ ............ ............ ............ ...... 7 References

............ ............ ............ ............ ............ ............ 12April 2004 Page 1 9-1 Monitoring for E valuati on of Mul timedia Modeling
For m ultipath way r isk as sessments, m

onitorin g is a valuable tool for evaluating m odel predictions because mult imedi a mod

eling is more complicated and in

volves m ore uncertainties than does air quality m

odeling. When u sing samples to eva luate m odel pre diction s, how ever, it is impor tant to realiz e that monitored concentrations may be greater than model predictions because sources other than those being mod

eled may have contributed to the contamination. 19.1 Introduction As noted earlier, modeling is gener ally the rec ommended approach for estimating exposure concentrations for air toxics risk assessments (for both inhalation and other pathway s). However, there may be

circumstances in which monitoring is requested or rec ommended for a particular mult ipath way risk as sessm ent. T his ch apter p rovid es an o vervi ew of m ulti media moni torin g, including the reasons for monitoring (Section 19.2), planning and implementation issues (Section 19.3), and available monitoring methods (Section 19.4).
19.2 Why Monitor? The reasons for monitoring for a multipathway

risk asse ssment ar

e identic al to those note d earlie

r for inh alation r

isk assessments (Chapter 10):

• Measuri

ng existin g conc entrations of air toxics in specific locations (e.g., soils in a schooly ard) an d/or food items (e. g., fish from a lake within the study a rea) for purposes of developing estimates of exposure;

• Develop ing or r

efining values f or speci fic parameters nee ded by

multimedia

models;

• Evaluating the predictions of a model in specified circ umstances (e.g., estimates of sediment concentrations resulting from deposition and runoff);

• Closing gaps that might be present in existing data (e.g ., gaps in emissions inventory) ; and

• Providing compliance/enforcement information as to whethe r a given facility

or set of sources is meeting

regula tory or permit require ments.
19.3 Planning and Implementing Issues The planning and implementation processes for multipathway risk assessment monitoring programs are similar to those for air monitoring pr ograms discussed in Chapter 10. The planning process involves a step-wise integration of data qua lity and data sampling a

nd analysis proce sses that are consistent with the study-spe cific conceptual model (CM), quality assurance project plan (QAPP), and data quality obje ctives (DQO) process. Many of the general planning a nd implement

ation issue s for air

monitoring

progra ms also ap

ply to multimed

ia modelin

g. Some additiona l conside rations a rise bec ause the

sampling and ana lys is progr

am might i

nclude so ils, surfac e waters, sedime

nts, fish, meat, ve getabl es, milk, a nd other h

uman food

items. The

scale and scope of monitoring could be much gre ater (e.g., multiple media could be sampled), and issues specific to ingestion need to be considered (e .g., what parts of plants and animals do people eat?).April 2004 Page 1 9-2 • Monitori ng or sampling methods should be ap propriate for the co mpounds and environmental media to be me asured . They must have the se nsitivity needed to monitor at the levels likely to be of he alth and/or regulatory concern.

• Monitori ng sites and fr equency of m onitoring s hould be appro priate for the spatia

l and temporal variation of the chemical being measure d and the monitoring objective

. Typically , an exposure location (e.g., a water body , a property , an agricultural field) or source (e.g., milk from cows on a specific far m) is defined for the risk assessment. The monitoring program should be adequate to repre sent the spatial and temporal variation within the location or source, given the particular me asure(s) used to support the risk management decision to be made

(e.g. , avera

ge expos ure, maximu

m exposure ). However, seve ral aspe cts of spatial and temporal variation are unique to air toxics that persist and which also may bioaccumulate. For example:

– The temporal patterns of releases from sourc es may be less important be cause the chemicals may slowly

accumulate in media and biota over time;

– Spatial “

hot spots”

of conta mination ma

y oc cur (for example, if soils er ode and c ollect in low-lying a

reas);

– Chemicals generally

accumulate in different tissues at different ra tes; therefore, concentrations may

be higher in certain parts of the plant or a nimal (which may or ma

y not be the parts that people tend to eat, and vice versa) ;

– Certain s easonal

effects (e.g ., grow

ing sea son for pl ants, mig ratory movement s in animals) may be important sourc es of variation; and

– Age of the plant or animal being sampled ma y be important if it takes many months or ye ars for contaminants to re ach equ ilibrium in b iologic al tissues (or if eq uilibrium is never reached). For

example, mercury c oncentrations in fish tend to be higher in older, larger fish.

• The monitoring effor t should cons ider the r elative contribut

ions of the four

main sources of variability in measurements . As noted in Chapter

10, these are ana lyti cal, sampling , tempora

l, and spa tial.

• Standard operating procedures should be defined and f ollowed both in the field (during sample collection) and in the laboratory (during sample analysis). These include procedures related to sample c ollection , sample tr ansport a nd storag e (inclu

ding pre vention of

sample degradation), sample analy sis, “chain of custody,”

audits, data validation, and data reporting. These pr ocedure s may

be quite v aried du

e to the r ange o f possible

media an d biota tha

t could be sampled.

• Limits of quantitation or detection should be determined and compared against relevant decision needs, in cluding health be nchmarks and like ly e

nvironme ntal leve ls.

• Measurement processes should be properly calibrated to ensure the accuracy

of the method.

• Results must be adequately recorded and archived . The best monitoring program can be compromised by a fa

ilure to keep proper records that can be made part of the public record.a Air s ampli ng al so maybe conduc ted; howeve r, t hat is disc uss ed in Chapte r 10. April 2004 Page 1 9-3 A periodic, random check of the arc hived records (e.g. computer f iles) should be made against “hard copies” to ensure the integrity of

the process of recording the da ta.
Soil Depth: Issues for Sa mpling The depth over whic

h surface soils are sam

pled should reflect the ty pe of exposure expected

in the study area, the ty

pe of receptors expected in the study area, the depth of biolog ical activity and the depth of potential contam ination. Careful considera tion of the size, sh

ape, and orientation of sampling volume is important since they have an effect on

the reported mea sured contaminant concentration values. (1) Select

ion of s ampli

ng de sign a

nd me thods c an be ac comp lished by use of the D ata Qua lity Objectives (DQO) pro cess discussed in Chapte r 6. Additional soil s ampling g uidance that may

be consulted includes EPA ’s Preparation of Soil Sam pling Protocols: Sampling Techniques and Strategies and Guidance for Data U sability in Risk Assessmen t and Soil Screening Guidance , availa ble at: http://www.epa.g ov/superfund/prog rams/risk/tooltrad.h

tm#dbhh

. (2)
19.4 Monitoring and Sampling Methods, Technologies and Costs
19.4.1 Method Selection Method se lection f

or sample collecti on and analy sis prog rams that are app licable t

o multipathway

human hea

lth risk as sessments

are dep endent on

numerous

aspects of the pr oject. Factors such as media, sample ty pes, sample program designs, lead r egulatory authority, and concen tration r anges of conce rn all ca n impact the selection of the appropr iate methods. While it is not possible for this chapter to review all of the monitoring methods available for this broa d range of applications, several of the more important factors that genera lly influence dec isions on methods selected are discussed below. The prima

ry determin ing fac tor in sele ction of s ample co llection a nd analy sis methodo logies i s the sample

media to b e evalua

ted. Exhibit

19-1 pre sents sev eral exa mples of the ty

pes of me dia that migh t be sample d for a mu

ltipathwa y hu man heal th risk ass essment. (a) Other factors that affect selection of sample collection methods are sa mple type and sample pr

ogram design. Specific factors in selection of sample collection methods may also be construction material of the sampli

ng devi ces, its d

esign, d econtam ination, a nd prope r use, site -specif

ic condit ions, relative cost, and data qua lity limitations

. Sample collection methods may be c ategorized by sample type as discussed in Chapte r 10. However, the distinction is not alway s clear (e.g., a single fish tissue sample might be considered a grab sample because it is collecte d at a single location and time; however, beca use the contaminant concentrations in its tissues accumulate over time, the sample could also be consider ed a time- integr

ated sam ple). The more com

mon ty pes of sa mples use d for non- air sampling are provided below:

• Grab samples (also known as discrete samples) are collec ted at a specific location (and gener ally instantaneous) time . Any techniqu e where the sampli

ng cont ainer is f illed to repres ent a sna

pshot of the conce

ntration o f targ et conta

minants at

a single specific time isApril 2004 Page 1 9-4 considered a grab sample. Where the population to be represented is demonstrated to be homogenous or consistent, grab samples provide the ma ximum information.

• Time-integrated samplesare collected at a single loca tion but over an extended period of time. Similar to grab samples, analy sis of time-integrated samples provides a snapshot of that ran ge of ti me and loc ation as a

single v alue. On

ly the total po llutant co llected i s measured, and so only

the average conce

ntration during the sampling period can be determined.

Exhibit 19-1. Examples of Environ mental Media that May be Sampled for

Multipathway Human Health Risk Asse ssment (a) Medium Comment s Surface Water Generally sam pled only if used as a drinking water source Soils General ly sam pled wi thin the top few inches of the s

urface, where ingestion exposure o

r erosion may

occur, but other con siderations may require a differ ent soil samp ling d

epth Sedim ents General ly sam pled to support

assessm ents of bioaccu

mulat ion in aquatic syste ms (in more rare instances, sedim

ents m ight s upport a dermal assessm

ent of exposure to the sediments them selves) Fish Generally focused o n species and parts of the fish that people eat (although this m

ay vary regionally; e. g., som

e native cultures m

ay routinely eat the entire fish) Vegetables and other crops Generally focused o n the plants and parts o f plants that people eat and/or are fed to live stock Dairy products and other foods General ly foc uses on milk and other dairy

produc ts; eg gs and

meat are also sometim

es evaluated
(a) Note that this l ist is not exhau stive; additional ty pes of s ample s mig

ht be ap propria te for a give n risk assessm ent.

• Composite samples represent combinations of discrete samples, which may be collected either at different times or from different loca tions, that are combined into a single sample for analysis. Composite samples can be he lpful when the amount of material that can be obtained from a single sample is very small (e.g., fish tissue), and the analy tical quantitation limit can be lowered if the sample mass is increased (e.g ., by combining multiple samples into a single composite sample for analysis). Composite samples also can be helpful when resou rces fo r labo rator y analysis are limi ted, a s they pro vide a n esti mate o f avera ge concentration across multiple samples, with the analysis cost of only one sample. The greatest drawback to composite samples is that they do not allow for an understanding of the variation in concentration values among the individual samples collecte d.

• Continuous samples provide essentially re al-time measurements over time from a single, specific location . Continuou s measur ements ty pically involve r eal-time measure ments, because samples cannot be practica lly collected to provide true continuous data. Continuous monitoring data frequently are evaluated as a function of conc entration over the time periodApril 2004 Page 1 9-5 analyzed. Depe

nding on the application, maximum, median, time-weighted averag e, or distribution curves may be a pplied to reduce the large amount of re sults obtained from true continuous data to usable results which can be compared to dec ision criteria. Samp le col lecti on met hods m ay also be d eterm ined b y the samp le col lecti on des ign met hodol ogy (Exhibit 19-2). Sample design impacts method selection often by determining the number of samples being collected.

Exhibit 19-2. Common Types of Sampling

Purposive sam

pling focuses the sa

mpling effort in specific locations (in this ex ample, the area estimated to hav

e the highest conce

ntration). Grid sam

pling consists of reg

ularly-spaced samples in a pred

etermined grid . Random

sampling consists of samples in locations selected by chance.

• Purposive sampling involves focused sample collection based on previous knowledg e of release event locations. Purposive (also called biased) sampling is named such because the person taking the sample willfully takes that sample at a time or place where, ba sed on prior knowledge, it is expected that concentrations will gener ally be biased hig

h. Purposive sampling may be

desired in programs looking to verify expected model results. Purposive sampling often targets maximum contaminant conditions to evaluate maximally impacted areas. Howeve

r, it may be used f or reas ons such a s targe

ting spe cific spe cies to c alibrate bioaccumulation models or defining the spatial extent of contamination.April 2004 Page 1 9-6 • System aticsampling

consists o f collecting sam ples at loc ations an

d times ac cording

to specific pattern s (e.g. , grid sa

mpling) . Systematic s ampling may use prev ious knowl edge to set fr equen cy, densi ty, or cov erage of sampl ing.

• Random sampling involves collecting samples from locations in a manner such tha t each location has an equal probability of being sampled and analy zed. Random sample collection designs are an i mportant a spect of

certain statistica l data ev aluation s. The factors which primarily affect selection of prepara tion and analysis methods include target contaminants, required reporting limits (i.e., concentra tion range of decision criteria), number of sa mp le s, da ta qu al it y li mi ta ti on s, me th od /i ns tr um en t p or ta bi li ty, pr ev io us da ta co mp ar ab il it y, acceptance/approval by

regulators and stakeholders, and re lative cost and availability.

• Target contaminants . The specific contaminants being sampled may have a significant impact on both budget and overall approach. F or example, sampling and analy tical procedures for metals are diffe rent than those for organic chemica ls. Careful evaluation before inclusion of unwarranted para meters and establishment of a procedure for identification and removal of chemicals of potential concer n (COPCs) is critical to an effective monitoring progra m.

• Required reporting limits . Assessors should select analy tical methods so that the reporting limits (usually the estimated quantitation limits) are less than the effe cts concentrations of interest. If the assessor doe s not select an adequately sensitive analytical method, the quantitation limit for a given chemical could exceed the chemica l’s effects benchmark concentration of interest; in that case, monitoring information would not provide mea ningful input to the

risk asse ssment.

• Number of samples . A sampling program that involves screening -level assessment of a large number of

samples ma

y dr ive sele ction of c ertain me thods for the bulk of

samples in order to allocate limited re sources . In the opposite case, de terminat ion of low heterog eneity of sample media, a nd extreme ly low risk-ba sed conc entrations of inte rest as d ecision c riteria may require fewer s amples a

nd more hi ghly

sophistica

ted metho dologie s.

• Data qualit

y limita tions . High data quality

requirements imposed by hig h uncertainty or other factors may

influence the choice of sampling methods such as proc edures that are more stringe nt and mor

e costly than usua

l proced ures.

• Method/instrument portability . In -f ie ld or on -s it e a na lys is ha s b eg un to re pl ac e l ab or at or ybased an aly sis in many

monitoring

progra ms. Certa

in prepa ration a nd analy sis methodologies are more portable than others, in par t because of the sensitivity of the instrumen tation. However, consider able expe rtise in sa mpling a nd analy sis is need

ed to de ci de wh et he r i n- fi el d o r l ab or at or y-b as ed an al ysi s i s a pp ro pr ia te fo r t he st ud y.

• Previous data comparability . Previous data set s can af

fect se lection o f appropriate methods. All other factors being equal, data compa rability goa

ls and objectives are more easily met by use of consistent me

thods.April 2004 Page 1 9-7 • Stakeholde r input . Stakeholder preferences may influence method selection.

• Relative Cost/Availability . The reality of limited resources often impacts method selection. Certain monitoring methods are commonly performed and available at numerous labora tories or by rea dily available field instrumentation. Other more obscure methods may better meet th e ne eds of t he p roj ect but are onl y ava il abl e fr om hi ghl y spe cia li ze d l abo rat ori es. In addition to

cost impac

t, low ava ilability of some sp ecific m

onitoring methods c an impact data quality due to lac

k of prac tice, mar ket compe

tition, app

ropriate standar ds, or ce rtifica tions.
19.4.2 Available M ethods Hundred s of spec ific samp ling, te st, analy sis, and qu

ality assuran ce metho ds and pro cedure s exist for soil, water, sediment, and biota. The list of available methods chang es frequently as new methods are introduced and older methods are re tired. It is not possible for this chapter to review all of the monitoring methods available. I nstead, this section provides an overview of several key EPA resour

ces and provides a listing of web sites that serve as sour ces of additional information. Key EPA re sources include the EPA Test Methods Index ; the Contract Laboratory Progra m (CLP) ; and the F ish and Wildl ife Advis ories Pro gram.

• EPA Test Methods Index ( http://www .epa.g ov/epahome/Standa rds.html ). EPA has developed hundreds of specific sampling, test, ana lysis, and quality

assurance methods and procedures. In r

esponse to frequent requests for ag ency test methods, Region 1 Library staff developed a methods index as a tool to help locate copies. Confirming that there wa s no single volume containing all agenc y methods and no compre hensive list of them, the project commenc ed and in 1 988 printe d the first EPA Test Methods Index

. (3) It has been updated periodically to re flect new procedures and revoke d methods, and the current edition includes about 1,600 method references. The index includes only EPA methods, and its primary g oal remains as a reference

tool to identify a source f rom which the actual method can be obtained, either free or for a fe e.

• EPA Contract Laboratory Progr am . The Contract Laborator y Progr am (CLP) is a national network of EPA per

sonnel, c ommercia

l labora tories, a nd suppor t contra ctors who se fundamental mission is to provide data of known and documented quality , primarily for the Superfun d progr am ( http://www .epa.g ov/super fund/pro grams/

clp/abou t.htm ). The Analy

tical Operati ons/Data

Quality Center ( AOC) pro vides sev eral too ls to assist CLP cli ents, laborat ories, a nd sample

rs ( http://www .epa.g ov/super fund/pro grams/

clp/tools. htm ). These tools were designed to use the I nternet to facilitate many

of the essential functions of the CLP.April 2004 Page 1 9-8

Available Guidance

from EPA’s Contract Labor atory Program
Contra ct Labo ratory P

rogramNation al Func tional G uidelin es for L ow Con centrat ion Org anic Da ta Review EPA-54 0-R- 00-006 June 20 01 Contract Laboratory P

rogram National Fu nctional Guidelines for O rganic Data Review EPA-540/R

-99- 008 (PB99- 963506) October 199 9 Contract Laboratory P

rogram National Fu nctional Guidelines for Ino rganic Data Review EPA 540- R-01- 008 July 2002
Contra ct Labo ratory P

rogramNation al Func tional G uidelin es for C

hlorina ted Dio

xin/Fur an Data Review EPA-54 0-R- 02-003 Au

gust 2002
Contra ct Labo ratory P

rogramGuidan ce for F

ield Sam plers ( Draft-F inal) EPA- 540- R-0 0-00 3 April 2003 This informatio

n, as w ell as m

ethodol ogy

inform ation is avail able from the

CLP a t: http://w ww.ep a.gov

/superfu nd/prog rams/ clp/serv ices.ht m

• EPA’s Fish and Wildlife Advisories Program( http://www.epa.gov/waterscience/fish/ ). EPA’s Office of Science and Technolog y provides technic al and outreach material that support efforts by state , local, and tribal (S/L/T) gove rnments to protect their residents from the heal th risks of consuming

contaminated nonc ommercia lly c aught f

ish. S/L /T governments do this by issuing consumption advisories for the general population as well as for sp ecifi c vuln erable sub-p opul ation s. The se adv isori es tel l the p ubli c when h igh concentrations of chemical contaminants have bee n found in local fish. They a lso include recommendations to limit or avoid eating certain fish species fr om specific water bodies or water bo dy t

ype s. The pr

ogram a lso provid

es Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories ( http://www .epa.g ov/water science /fish/gu idance. html ), a set of four volumes that provides guidance for a ssessing health risks associated with the consumption of chemically c ontaminated non-commercial fish and wildlife. The set includes Third Edit

ions of Vo lume 1: Fish Sampl ing and Ana lysis and Volume 2: Risk Assessment and Fish Con

sumption Lim its

. Exhibit 19-3 provides

links to inf

ormation o

n specif ic samplin

g and a naly sis methods , summarized from key EPA compendia of methods. Methods are divided into four categories (General, Analy

tical Method Index, Sample Collection, and Quality Assurance). Key

words are added to help readers get to the ar ea they a re concerned with. Additional effort may be required to “drill i nto” eac h site to vie

w the rel evant inf

ormation. These lin

ks gene rally are limite d to gover nment site s. Some non-EPA sites are inc luded (e .g., Oc cupational Safet y a nd Health Administration (OSHA), National Institute of Standards and Te chnology (NIST), and National Insti tute for O ccupati onal Safe ty a nd Health (NI OSH)) to help fill sp ecific i nformati on gaps
.April 2004 Page 1 9-9 Exhibit 19-3. Sources for Information on Sp ecific Sampling and Analysis Methods Keywords Description and U RL Link General References Samp le colle ction, analy sis method, criteria, w

ater General EPA Water page with links to an alytical method s, sampling g

uidance, and criteria for ass essment of contam ination. http://www.epa.g ov/waterscience/ Analysis m

ethods EPA’ s Office of Grou

nd Water and Dr inking Water (O GWDW) links to analys is methods. htt p:/ /www .epa .gov/ OGWD W/me thod s/met hods .html Samp le colle ction, analy sis methods, reference NIOSH pocket guide to chemical haz ards contains inform ation by analyte which can support field sample collection, analysis, and determination of relev

ant criteria. htt p:/ /www .cdc .gov/ nios h/np g/np g.ht ml Samp le colle ction, analy sis methods, reference NIST web b ook contains inform ation by analy

te which can support field sample

collection, analysis, and basic chem ical parameters from

thermodyn

amic constants to reference mass spectra. http: //web book.ni st.gov/c hemistr y/ Samp le colle ction, analy sis methods, reference Genera l EPA envi ronment al tes t method s and guid eline s page with numerous links to other areas o f information throug hout EPA web sites. htt p:/ /www .epa .gov/ epah ome/S tan dard s.ht ml Analysis Method I

ndex Analy sis m ethods, samp le collection Region I l ist of m ethods availa

ble as hardcopy

and pa rtial lin ks to analysis meth

ods. http://www.epa.g ov/epahome /index/ Analy sis m ethods, samp le collection Searchable online database of analysis m ethods. NEMI

is a project of the National Methods an d Data Com

parability Board, a partnership of water quality experts from Fe deral agencies, States, Tribes, municipalities, ind ustry, and priv

ate organizations suppor ted by EPA a nd the U. S. Geolo gical Su rvey. http://www.nem

i.gov Analy sis m ethods, samp le collection National Exposure Res earch Laboratory (N ERL) formerly

EMSL, Manual of Manuals links to information abou t analysis methods; summ aries an d order ing informa

tion for eight laborat ory analytical chem

istry methods manuals published by the form

er Envir onme ntal Mon itoring Syste ms La borator y- Cincinn

ati (EMSL-Cincinn

ati) between 1988 and 1995. htt p:/ /www .epa .gov/ nerl cwww /met hmans .html Analysis Methods Analysis m

ethods, water EPA’s Office o f Water l ink to analy sis m ethods. Labor atory analytical metho ds that are used by industries and m

unicipalities to analy ze the chem ical and biological com

ponents of was tewater , drinking wate

r, sediment, an

d other environm

ental samples that are required by reg

ulations under the authority of the Clean Water Act (CW A) and the Safe

Drink ing Wa ter Act (SDWA)

. http://www.epa.g ov/waterscience/m ethods/ Analysis m

ethods, water, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 624, 625, 1624, 1625 Methods for organic chemical analysis under the authority of the Clean Water Act (CWA) an d the Safe Drink

ing Water Act (SDWA)

. htt p:/ /www .epa .gov/ ost wate r/me thod s/gu ide /met hods .htmlExhibit 19-3. Sources for Information on Sp ecific Sampling and Analysis Methods Keywords Description and U RL Link

April 2004 Page 19-10 Analysis m

ethods, drinking

water Recent drinking water methods from EPA’s O ffice of Research and Develop ment, National Expo sure Re search L aboratory (NER L), f

ormer ly th e Envi ronme nta l Mon ito rin g Syst ems Laboratory (EMSL). http://w ww.ep a.gov

/nerlcw ww/ord meth. htm Organic, analy sis methods, drinking wate

r Organ ic method index wit

h hyp erlink

to me thod by analy te in drinking wate

r as maintained b

y Office of Grou

nd Water and Drink ing Wa ter. htt p:/ /www .epa .gov/ OGWD W/me thod s/o rch_ tbl .html Inor ganic , met

al, ana lysis methods, drink

ing water Inorganic and metal analy sis methods in d rinking water as maint ained by Offi ce of G

round W ater and

Drink ing Wa ter. htt p:/ /www .epa .gov/ OGWD W/me thod s/i nch_ tbl .html Analysis m

ethods, drinking water, radionuclides Radionuclides in drink ing water as m

aintained by Office of Ground Water an d Drink ing Wa ter. htt p:/ /www .epa .gov/ OGWD W/me thod s/r ads. html (EPA) http :// www.e pa.go v/OGW DW/me thod s/i ndra ds.h tml (non-EPA) Analysis m

ethods, drinking water, Approved m ethods for unregulated

contaminants in drinking water as maintained

by Office of G

round Water and Drink

ing Water. htt p:/ /www .epa .gov/ OGWD W/me thod s/u nreg tbl .html Analysis m

ethods, drinking water, Secondary contam inants in drinking

water as maintain

ed by Office o f Groun

d Water a nd Drin king Water. htt p:/ /www .epa .gov/ OGWD W/me thod s/2 nd_t bl.h tml Analysis m

ethods, imm

unoassay Region 1 g

uidance on imm

unoassay m

ethods. htt p:/ /www .epa .gov/ regi on1/ measu re/ ia/ iagu ide .html Analysis m

ethods, CLP, organic, dioxin, inorg anic, water, so il Contract Laboratory Program (C LP) methods fo r organics, inorganics, and d

ioxins/furans. http://w ww.ep a.gov

/superfu nd/prog rams/ clp/methods. htm Analy sis m ethods, air EPA Emission s Measurement Cen ter (EMC) for metho ds related to determination of airborne pollutants. http://www.epa.g ov/ttn/emc/ Analy sis m ethods, pestici de, soi l, water EPA’ Office of Pesticide Programs (OPP) database of enviro nmen tal chem istry, residua

l, and antim

icrobial analy sis methods. http://www.epa.g ov/oppbead1/m ethods/ Analy sis m ethods, water, soil, sediment, wa ste, air EPA’s OSWER provides online updated SW-846 waste sampling and analysis m

ethods manual w

hich is the source of m

any related methods used in

environme

ntal sampling and analysis. http://w ww.ep a.gov

/epaoswer/haz

waste/test/main.htm Samp le colle ction, analy sis metho ds, air Occupa tional S afety and He alth ind ex of sa mplin g and analy sis methods alphabetically

by param

eter and general inform

ation on selection of methods and laboratories. http://www.oshaslc .gov/ dts /sl tc/ metho ds/ inde x.ht ml Samp le colle ction, analy sis metho ds, air EPA's Organic (TO) Com pendium of m

ethods for air toxics and EPA's I norganic (I O) Compend

ium meth ods. htt p:/ /www .epa .gov/ ttn /amt ic/ air tox. html Sample Collection Sample collection, analysis, fish, shellfish, biot

a Methods for sampling and analyzing contam

inants in fish and shellfish tissue. htt p:/ /www .epa .gov/ wate rsc ien ce/ fis hadvi ce/ volu me1/i ndex .htmlExhibit 19-3. Sources for Information on Sp ecific Sampling and Analysis Methods Keywords Description and U RL Link

April 2004 Page 19-11 Sample collection Current manu als and protoco ls prepa

red by NERL -Cin

cinnati scientists. NERL is the EPA’s scientific lead fo r the following stream and source monitoring in dicators: fish, macroin

vertebrates, periphyton, z ooplankton, functional ecosystem indicators, water and sediment toxicity and fish tissue co ntaminants. A s part of their indicator lead res

ponsibi lities N

ERL- Cincinn

ati scien tists prepare and update field a nd laboratory protoco l and methods manuals for these indicators. http://w ww.ep a.gov

/nerleer d/met hman .htm Sample collection, monitoring wells, low stress Guidance for RCRA /Superfund ground

water sample collection methodolog ies and the logical p rocess for determining

an approach fit to site specifics. http://www.epa.g ov/tio/tsp/download/g w_sampling

_guide.pdf Sample collection, monitoring wells, low stress Generally well accepted low stress (low flow ) ground water sample collection g

uidance from EP

A Region I . Several versions exist across EPA reg ions and withi n other gov ernme

ntal and State guidelines. htt p:/ /www .epa .gov/ regi on1/ measu re/ well /wel lmon. html Samp le colle ction, field analysis EPA Environm ental Response Team

provides num

erous sampling and field analysis Sta

ndard Operating P

rocedures (SOPs) often encountered in env

ironmental responses including otherwise atypic al sam

ple coll ections

SOPs s uch as d rum, wipe, and wa ste pile sampling techniques. http://www.ertresponse.com/sops.asp Sample collection, field analysis, progra m design EPA’s Office o f Techno logy Innovation prov

ides a web site with information on prope r sampling program design, QA/QC concerns, and use of field methodologies to expedite inform ation collection without loss of d ata quality. http://c lu-in .org Quality Assurance Quality assurance EPA Agency -wide quality

system d ocuments for EP

A and nonEPA or ganiz ations p lus ge

neral g uidance . Docu

ments are available as PDFs

. htt p:/ /www .epa .gov/ qual ity/ qa_d ocs. html Quality assurance Region I guidance includes quality assurance doc uments. htt p:/ /www .epa .gov/ regi on1/ lab /qa /qua lsys .htmlApril 2004 Page 19-12 1. U.S. Environmental Protection Agency . 2000. Draft Ecological Soil Screening Level Guidance . Office of Emergency and Remedial Response. July 10, 2000. 2. U.S. Environmental Protection Agency 1992. Guidance

for Data Us eability i

n Risk Assessment (Part A) . Office of Emerg ency

and Reme dial Response, Washi ngton, D .C., April 1992. Publi cation 92 85.7-09A , PB92-96 3356. Ava ilable at : http://www .epa.g ov/super fund/pro grams/

risk/data use/par ta.htm U.S. Environmental Protection Agency 1992. Preparati

on of Soil Sa mpling Pro tocols: Sampling Techniques and Strategies . Office of Resea rch and D evelopme

nt. EPA/600/R- 92/128. Available a t: http://www.epa.gov/superfund/programs/risk/tooltrad.htm#dbh U.S. Environmental Protection Agency 1996. Soil Screening Guidance: User’s Guide . Office of Solid Waste and Emergency Response. Washington, D.C., July 1996. EPA540/R-96/018. See especially

Attachment B, Soil Scre ening DQO s for Surfac e Soils and

Subsurfac e Soils

. Availabl e at: http://www.epa.gov/superfund/resources/soil/index.htm#user U.S. Environmental Protection Agency . 2002. Suppleme ntal Guidan ce For De

veloping Soil Screening Levels for Superfund Sites . Office of Solid Waste and Emergency Response, Washingt on, D.C., D ecembe

r 2002. OSWE

R 9355.4-2 4. Availa

ble at: http://www.epa.gov/superfund/programs/risk/tooltrad.htm#dbh 3. U.S. Environmental Protection Agency . 1988. EPA Test Methods Index . EPA 901/388/001. References

# Chapter 2 0 Exposure Metrics f or Multimedia Assessment

Table of Contents

20.1 Introduction

............ ............ ............ ............ ............ ...... 1
20.2 Generi c Equat ion for Dietar y Intake

............ ............ ............ ........... 3
20.3 Estimating E

xposure Concentrations

............ ............ ............ .......... 4
20.4 Calculating I

ntake Variable Values

............ ............ ............ ........... 6 20.4.1 Consum

ption R ate

............ ............ ............ ............ ...... 7 20.4.2 Exposure Frequency

............ ............ ............ ............ ... 10 20.4.3 Exposure Duration

............ ............ ............ ............ ..... 10 20.4.4 Body Weight

............ ............ ............ ............ ......... 12
20.5 Calculating Averaging Tim e Value

............ ............ ............ .......... 12
20.6 Combinin g Exposur e Estima tes Acr oss Pat hways

............ ............ ........... 13
20.7 Exposu

re Model s
............ ............ ............ ............ ............ 14 References

............ ............ ............ ............ ............ ............ 16April 2004 Page 2 0-1 20.1 Introduction This chapter concludes the exposure assessment component of the multipathway risk assessment by describing

how to develop estimates of intake (i.e., the metric of exposure) for the ing estion pathways sele cted for analy sis. Estimates of chemical intake via the inhalation pathway were presented in Chapter 11. Exhibits 14-2 and 20-1 provide an overview of the potential multimedia

exposure p athway

s by which air toxics that p ersist an

d potentia lly b ioaccum

ulate may reach e cologic al and human rece ptors, re spective ly. Determi nation of chemica l intake via the inge stion exposu

re route combines

the estima

tes of chemical of

potentia l concer n (COPC) levels in food items and drinking water (discussed in Chapter 7) with estimates of c onsumption rates (food, water), exposure freque ncy and dura

tion, averaging time, and body weight to derive estimates of the chemical intake rate (expressed generally

as mg/kg-day

). (1)

Exhibit 20-1. Potential Multimedia Expo sure Pathways of Concern

This gr aphic il lustrate s man

y of the poten

tial m ultim edia pa

thway s of con

cern for air tox ics. Air toxics r eleased from a sourc e disperse throu

gh the air and event ually fall to t

he earth (atmo spheric deposit ion) v ia settli ng and/or pre cipitati on. A ir toxic s depos ited to soil m ay be absorbe d by p lants that are then harvested

for human co

nsumption. H

umans m ay be exposed via ingestion o f contaminated plants and soils, or by consuming contaminated terrestrial an imals (e.g. , beef, for those air toxics that bioaccum

ulate and transfer up the te rrestrial food web). Air toxics deposited to water may be diss olved

in the w ater col umn a nd/or m ay set tle and be absorbed int

o aquat ic sedim ents. A ir toxics i n sedim ents and the wa ter colu mn m ay be absorbe d by a

quatic p lants (u

ptake ). Aqu

atic organisms (e. g., fish) m

ay be exposed directly to air toxics in the water column and/or by consu

ming other contaminated aquatic organism s (for those air toxics that b ioaccumulate and transfer up the aquatic food w eb) or s edime

nts. P eople m

ay be exposed

to air t oxics b y eati

ng co ntami

nated aquatic plants, fish, or shellfish an d/or by drink

ing contam

inated water. Note a

lso that, while in the atmosphere, air tox

ics may also

have direct im

pacts to humans via inhalation.April 2004 Page 2 0-2 Exposure and

Intake via Ingestion The proc ess of a

chem ical ent ering the bod

y can be described in

two ste ps: exposure

(contact), follo wed by entry (crossing the boun dary). Intake invol

ves ph ysical ly m

oving the che mical in question through an

opening in the outer boundary (us ually the m

outh), typically via eating or drinki ng. Normall y the che mical is contai ned in a medium that comes int o conta ct with the bod y, such as food or water, and the concentration o f the chemical at th is point of contact is called the exposure concentration . The estimate of how much o f the chemical enters into the body is based on how much o f the carrier medium enters the body. The chemical intake rateis the amount of chemical crossing

the outer boundary

per unit time, and is the product of th e exposure concentration times the ing estion rate. Ingesti on rate is the a moun t of the carrier mediu

m cro ssing the bou

ndary per unit time, su

ch as the num

ber of kilogram

s of food ingested/d

ay or liters of water consumed/day

. Ingestion rates typically are not constant over tim e (they can vary over tim e and among

individuals) and are usually g

iven (for determ

inistic analyses) as an average intak e rate over som

e period of time. In a ddition , the in

take r ates are usually normalized

to bod y wei ght. Thus, a

comm on intak e rate would take the fo

rm of m illigrams of po llutant ingested per k ilogram of b ody weigh

t per day (or mg/kg -d). A different ingestion rate would be developed for each type of person in the population under s tudy. For ex ample , one i ntake rate could be d

evelo ped to r epresen t the av erage adult (m

ale and female) while a separate intake rate c ould be developed to represent children between the ages of birth to four years o ld. Chapter 7 described two genera l approaches for deriving the exposure conc entration (EC) for an inhalation risk assessment: (1) use of ambient air concentrations as a sur rogate for the EC, and (2) exposure modeling that combines estimates of ambient air concentr ations with information about the population of interest, including the ty pes of people present (e.g., ethnicity , age, sex), time spent in different microenvironments, and microenvironment concentr ations. The first approach (i.e., use of ambient concentrations in abiotic media such as soil, water, or sediments) generally is not used for multipathway air toxics risk assessments. Instead, a multipathway exposur e assessm ent must involve som e type of exposure m odeling(e.g. , at a minimum simple scenarios to characterize persons who are exposed and the amount and dur ation of their contact with the abiotic and biotic media). Note that EPA has derived some human health screening -level concentration benchmarks for surface water and soil (i.e., the Off ice of Water’s Ambient Water Quality Criteria for the Protection of Human Health, (2) and the Su perfund Progra m’s soil screening

levels (3) ). However, these human health benchmarks are based on spe cific scenarios (e.g., how much wa ter a person drinks ea ch day, how much

they weigh) that wer e selected to mee

t differ ent prog rammatic goals and statutory requir

ements. Therefore, the scenarios on whic h these benchmarks are based may not be app

ropriate for a spe cific ai

r toxics ris k assessment. The way a chemic al enters the body and eve ntually reache s the target orga

n is a comp lex proce ss (see box below). For most chemicals, however, it is not necessary to quantify any

thing beyond the chem ical intake rate , because the dose-response value (e .g., Reference Dose [RfD] or Cancer Slope Factor [CSF]) is also based only on the amount of chemical ingested and not the amount of chemical that has been absorbed into the bloodstream.

The remainder of this chapter focuses on how to qua ntify ingestion exposure (intake ) for multipathway air toxics risk assessments. The corr esponding chapter for inhalation analy sesApril 2004 Page 2 0-3 (Chapter 11) discu sses how to

evaluat e uncer tainty in the expos

ure asse ssment an

d how to present the exposure assessment results; this applies to all exposure evaluations (i.e., inhalation and ingestion).
20.2 Generi c Equation for Dietary Intake Equation 20-1 is the generic equation used to calcula te dietary c

hemical intake: (4) (Equation 20- 1)

where
I = Chemical intake rate, or the amount of pollutant ingested per unit time per body

weight (mass), e xpressed i

n units of mg

/kg-da y. F or evalu

ating exposure to noncarcinogens, the intake is refe rred to as Average Da

ily Dose ( ADD ); for evaluating expo sure t o carci nogeni c comp ounds , the i ntake is ref erred t o as Lifet ime Av erage Daily Dose (

LADD ). Chemical -related variable : EC = Exposure c oncentr ation of the chemic al in the me dium of concern f or the time period being analy

zed, expressed in units of mg/kg for soil and food or mg/L for surface water or bever

ages (including milk). Variables that describe the e

xposed p opulation (

also terme

d “intake v

ariables”) : CR = Consumption rate, the amount of contaminated medium consumed per unit of time or

event (e.g., kg/day

for soil and L/day

for water). EF = Exposure frequency (number of day s exposed per year ). ED = Exposure duration (number of ye ars exposed). BW = Average body weight of the receptor over the exposure period (kg).
Assessme nt-dete rmined v ariable : AT = Averag

ing time, the peri od over w hich exposu

re is ave raged (days). For c arcinog ens, the aver aging time is 25,5 50 days, based on an assumed lifeti me exposur

e of 70 y ears; for nonc arcinog ens, ave

raging time equa

ls ED (year

s) multiplied by 365 day

s per year. The values of some exposure factors depend on site conditions as well as the c haracteristics of the po tenti ally exp osed p opul ation (e.g., child vs. ad ult). Becaus e of di fferen ces in physiol ogy and behavior, exposures among children are expected to be different than exposures among adults. For example, body we ight and consumption rate differ for children a nd adults. For the evaluat ion of non- carcinogenic

effects, intake s for chi ldren g eneral ly a re estima ted sepa rately (often for ages 0-6) than f or adults (often from ages 6-bey ond). For the evaluation of carcinogenic effe cts, intake estimates are averag ed over the assumed lifetime (70 y ears).April 2004 Page 2 0-4 20.3 Estimat

ing Exposure

Concentr ations The exposure concentration for a chemical is ca lculated separately

for each food item and environmental medium of concern. The value of these variables may

be determined by

modeling (Chapter 18), monitoring (Chapter 19), or a combination of both. The specific algorithms for determining these concentrations will depend on the spec ific models and/or sampling and analysis techniques use d. For example, EPA has developed methodologies for estimating E C values in soil, wate

r, sedime nt, and va rious foo d items for

releas es from hazardous

waste combustion facilities (see Appendix L). (5) For ing estion pa

thways, the spe cific me dia conc entration values o btained f rom a multi

media modeling simulation for use in deriving exposure concentrations depends on se veral important decis ions made d uring p roble m form ulati on, i nclud ing:

• Choice of modeling duration for a model run;

• Choice of the ye ar or ye ars of the model run on which to base the EC; and

• Choice of a specific ED. Exhibit 20-2 presents several different examples relevant to different pur poses/objectives for an assessme nt.

Exhibit 20-2. Example Decision s in Assessing Ex posures Resulting From Distrib ution o f Air Tox

ics into Other Me dia

time (y ears) In this hy pothetical example, a modeling analysis was use d to predict the concentration s of a persistent bioaccu mulat ive ha zardous air p ollutan

t (PB- HAP) i

n fish t issue du ring a 100-year e

missi ons sce

nario (annual average

was estimated e ach year and is p lotted here using a logarithmic scale ). As discussed below, the exp osure scenar io asse ssed wi ll ref lect s everal key choice s incl uding: (1) choice of mo deling duration for m odel run; (2) choice of year or years of m odel run on which to

base EC (i.e., the model outpu ts); and (3) choice of spe cific ED.April 2004 Page 2 0-5 Exhibit 20-2 (co ntinued) The modeling duration is a separate decision from the ED and is not related to

the average human lifespan. Note that in this exam

ple, the analyst assu

med that the starting

concentration was z ero (i.e., the tissue concentrations reflect only the sources being modeled). Some multim edia models (e.g ., TRIM.FaTE) can start with an initial con centration. Modeling Duration . The analyst can choose to run a m ultimedia m

odel for any period

of time. Duration will usually

be chosen to reflect the expected duration of em issions from the so

urce(s) being evaluated or, perha

ps, that duration expected in order to reach steady

-state conditions. A comm on duratio n is 30 or 40 y

ears (e. g., t

he expe cted life span of

many faciliti es or pr

ocesses ). For

this example, a 10 0-year d

uration was selected.
Selectio n of Mode l Output s . Usua lly th e mod

eling duratio n will have be en chos en with conside ration o f the m

odel ou tputs on which the exp osure sc enario i s to be based a nd the exposure duration. Some common examples follow:

• Year of maximum concentration . Screening- level analyses o ften use the max imum concent ration r eached during

the m odeling period which, for a co

nstant e missi ons sce

nario, will usually be the final year of the m odeling simu

lation. For this exam

ple (see figure), it would be the 100 th year (at such

time as the f

ish concentrati on was approxi matel y 2,0 00 ng

/kg)

.

– Exposure Du ration. With u se of th e maximum model resul t, the a nalysi s presu mes no cha nge in fis h conce ntrat ion over the exp osure durati on (i.e ., in thi s example EC = 2,000 ng/kg throug hout th e expos

ure peri od).

C Initial years of simulation. In this cas e the ex posure being

assesse d is tha

t begi nning with initiation of em

issions and ext

ending throug h the du ration s elected for asse ssmen t.

– 30-year Exposu re Duration . In this case, the analyst is basing the expo sure duration near the 95 percentile of how

long people liv

e in the same h

ome. (6) If the ana

lyst chose to exam

ine chang ing concentra

tions ov er time, the E Cs wou

ld var y, ref lecting the concentra

tion ou tputs from the first 30 y

ears of the mode ling duration.

– 70-year Exposu re Duration. In this cas e, the a nalyst is usin g a lif etime exposu re assumption. The e

xposure scenario then m

ay be based on

the model ou

tputs from the first 7 0 years of the m

odeling duration.

C Last years of simulation. In this ca se, the exposure being assessed is that which occurs during the end ing y ears of the sim

ulation , with the num

ber of y ears inv olved equal t o the ex posure duratio n select

ed for a ssessm

ent.

– 30 -year Expo sure Duration. For this ED, the ECs would vary reflecting the predicted concentrations from the last 30 years o f the model sim ulation.

– 70-year Exposu re Duration. In this cas e, the a nalyst is usin g a lif etime exposu re assumption. The e

xposure scenario m

ay then em

ploy vary ing ECs reflecting

the predicted concentrations from the last 70 years o f the model sim ulation.
Note: When using varying exposu re concentrations for the exp osure scenario, other v ariables included in the calculation of ing estion exposure estim ates (pollutant intake, mg/kg -day) for the population(s) of interest may

also vary. For example, if the exposure scenario includes exposure for coh orts aging from birth -

30 years, other exposure factors (e.g ., body we ight, consum ption rate) will also vary over time.a NC EA recen tly publish ed a n ew co mpila tion of c onsu mptio n data from th e 19 94-1 996 CSF II. Th is data

upd ates C SFII data in the 19 97 E xpo sure F actor s Ha ndb ook

. See : htt p:/ /cf pub.epa .gov /nce a/cf

m/re cordi splay. cfm?dei d=56610

.
April 2004 Page 2 0-6 20.4 Calculating Intake Variable Values Each intake variable in Equation 20-1 (e.g ., consumption rate, body we ight) has a range of potential values. Intake varia ble values for a given pathway may be selec

ted so that the combination of all intake variables results in an estimate for an individual at the “hig h-end” of potentia l exposure

levels. Alternati

vely , the inta

ke varia bles may be selected to re

present a “central tendency

” individual expected to receive an averag e exposure. In doing this, the assessor needs to avoid combinations of parameter values that a re inconsistent (e.g., low body weight used in combination w

ith high d ietary intake r ates), a nd must kee

p in mind the

ultimate objective of being within the distribution of actual expected exposures and doses, and not bey ond it. Commonly, both the central tendency and high end intakes are quantified. I n some cases, the distribution of intake rates in the population may be de scribed using probabilistic risk assessment methods (discussed in Part VI).
EPA recommends values for intake variables for the U.S. population in the Exposure Factors Handbook, (7) the Child-Specific Exposure Factors Handbook , (8) and the Consolidated Human Activity Database

. (9) (a) EPA also r ecently published

draft g uidance on selecting the

appropr iate age g roups for assessin g childhood exposur

es. (10) Note, however, that there are likely to be differences between rec ommended default, and regional and site-spec ific, exposure parameter values. This may be e specially true f or consumption rate (see below).
For central tendency

estimates, risk assessors commonly set all of the exposure factors in the Equation 20-1 at central tendency values. If only

limited information on the distribution of the exposure or dose factors is available, risk assessors commonly approach the high-end

estimates by i dentify

ing the most sensiti

ve varia bles and u sing hig

h-end va lues for a subset o f these variabl es, leav

ing othe rs at thei r centr al value s. As menti oned ea rlier, the assess or needs

to avoid combinations of parameter values that are inconsistent (e.g ., low body weig ht with high dietary intake rates) and must keep in mind the ultimate objective of being within the distribution of actual e xpected exposures a

nd doses. Maximizing all variables will in virtually all cases result in an estimate that is above the actual values seen in the population. When the principal parameters of the dose e quation (e.g., concen tration [a ppropria tely integr ated ove r time], inta

ke rate , and dur

ation) a re broke n out into sub-comp onents, it

may be nece ssary

to use maximu

m values f

or more than two of

these sub-comp onent par

ameters , depend

ing on a sensitivit y a naly sis.
For probabilistic analyses , values for exposure factors are commonly allowed to vary a

ccording to specif ic assume d distribut

ions of pot

ential va lues.
Note that the

high-end inta

ke estimate

is a plausible

estimate of

intake for

those persons at the upper end of the exposure distribution. This descriptor is intended to estim ate the exposures that are expected to occur in small but definable high-end segments of the subject populat ionApril 2004 Page 2 0-7 (but not higher than the highest person in the population), but may not be appropriate for estimating ex

posure for

the populati

on as a whole

. (1)
20.4.1 Consumption Rate Consumption rate is the amount of contaminated food or medium consumed per event or unit of time (e.g., amount of fish consumed per meal or per day). The

consumption rate is multiplied by a fraction of the total dietary intake for this type of f

ood or medium, representing the amount consumed

from the study

area. T he speci fic fra

ction app lied depe

nds on the a

naly sis.

C For screening-level a

nalyses, it is common to assume that the person obtains 100 per cent of the food type fr

om the study area

(e.g., farm, water body

) being evaluated. This assumption also mig ht be used

for a sub sistence -ty

pe rece ptor (e. g., a lo cal fish c onsumer w ho only eats fish caught from the study area).

C For higher tiers of analy ses, it is common to assume that the person obtains some of the food type from the study

area (i.e., the contaminated frac tion) and some of the food type from other sources (e.g., at the g rocery store). This latter fraction genera lly is assumed to be uncontam inated by the sourc e(s) und

er asse ssment. Th

us, if a pe rson is as sumed to e at 1⁄2 pound of fish per day , but only 25 percent is ca ught within the study are a, the assumed consumption of contaminated fish would be 1/8 pound per day

. The following pathway

-specific considerations are important for estimating consumption rate.

C Food Ingestion . Plants and animals may acc umulate COPCs that were deposited onto soil or water. H umans may

be exposed

to these c ompounds via the fo od chain w hen they consume these plants (and animals that consume these plants) as a food source . Human intake of COPCs is quantified on the basis of the concentration of COPC in the food (Section 20.3) and:

– The types of f

oods consumed, which vary with age (e.g., children a nd adults often eat different things), geog

raphical region, and sociocultural fa ctors (e.g., ethnicity , cultural factors);

– The amou

nt of food consumed

per day , which c an vary

with ag e, sex, and

geog raphic region , and also

within the se cate gories ;

– The fraction of the diet contaminated by COPCs (which can vary

by food ty

pe); and

– The effect of food prepara tion techniques on concentrations of COPCs in the food itself.

C Soil Ingestion . Children and adults may re ceive direct exposure to COPCs in soil when they consume soil that has adhered to their hands (called incidental soil inge stion). Factors that influence exposure by soil ing estion include concentration of the COPC in soil, the rate of soil ingestion during the time of exposure, and the length of time spent in the vicinity of contaminated soil. Soil ingestion rates in children are based on studies that measure the quantities of nonabsorbable tracer minerals in the fec es of young children. Inge stion rates for adults ar e based on assumptions about exposed surf ace ar ea and f requenc y of hand-to-mouth transfer. Indoor dust and outdoor soil may both contribute to the total daily incidental ingestion of soil (indoor dust is partially made up of outdoor soil that has been tracked inside).April 2004 Page 2 0-8 In addition, some y

oung children – referred to a s “pica” children – may intentionally eat soil. The typical medica

l and scientific use of the term “pica” ref ers to the ingestion of nonfood items, suc h as soil, c

halk, and cray ons. (10) Such behavior is considered a temporary part of a child’s development. For risk assessment purposes, pica is ty pically defined a s “an abnormally hig

h soil ingestion rate” and is believed to be uncommon in the gene ral population. If available information indicate s that there are children exhibiting pica behavior in the ass essment a

rea, it ma y be appropr iate to inc lude thes e childr en as a se parate group in the exposur

e assessment. EPA’ s Exposure Factors Handbook provides quantitative data on soil ingestion rates related to pica. (11)
Inha lation of s oil result

ing from dust resu spension by wind erosi on gene rally is not a signific ant pathway

of conce rn for a ir toxics. (5) However, it may be an issue for locations at which there is little vegetative cover. Methodologies have been developed to assess the exposure t

o pollutan

ts resusp ended by

wind ero sion for la

ndfills an d Superfu nd sites. (12) The exposure e stimate f

rom resu spended

soil would d

epend on moisture c ontent of

the soil, fraction of veg etation c over, wi nd veloci ty, soil parti cle size, C OPC conce ntration i n the soil, and size of the contaminated area.

Depth of Conta minated Soils: A Key V ariable When exposures to CO PCs in soils are m odeled for huma

n health risk assessm

ent, an importan

t factor affecting the exposure estimate is the depth of contaminated soils used to calculate soil co ncentrations. The same deposition rate will result in different soil co ncentrations depending

on how deeply

the COPCs are assum

ed to mix or m

igrate into the soil. Mixing depth also m ay affect exposure e stimates via specific pathways. For exam ple, in calculations of exp osures resulting from uptake throug

h plant roots, the averag e concentration of COP Cs over the depth of the plant root determ ines plant uptake. However, calcula

tions that assess soil ing estion through hand -to-m outh activity commonly

focus on only t he top f ew cent imete rs of so il.
COPCs deposi ted onto undistu rbed so ils ge

nerally are assumed

to rem ain in t he shall ow, up

per soil layer. However, CO PCs depo sited onto so il sur faces may be moved int o lower soil pr ofile s by till ing, whether manually

in a garden or mechanically in a large field. Other factors such as soil disturbance by do mesti c anim

als (e.g ., catt

le in an enclosu re) also may need to be con sidered . Som

e chem icals ar e also highly so luble in water and m ay be carried deepe

r into soil along w

ith infiltrating rainwater. The key questions

to ask therefore inc lude:

• Are soils tilled, or is it reaso nable to assume they are undisturbed?

• If soils are tilled, what mixing depth is reasonable to a ssume?

• What other factors mig ht affect how deeply

COPCs will be moved into soils? EPA guidance and other references (5) (13) provide a m ore detailed discussion of d epth of contamina

ted soils, along with recommen ded values.b No te that inge stion o f conta minate d gro undw ater ge nerally is no t a significan t pathw ay of co ncern for air

toxi cs r isk

ass essme nts becaus e most air toxi cs t hat persi st and maybioac cumul ate tend to ge t boun d up in soi l an d, therefore, t end not to move readily into groundwater. However, if the groundwater pathway were a concern for a specific study, it would be ev aluated in genera lly the same way as the ingestion of surface w ater pathway (i.e., as a drinking water s ource; however, depending on the circumstances, groundwater may or may not be tr eated to remove particles pri or to consumption). April 2004 Page 2 0-9 • Ingestion of Drinking Water . In air toxics assessments, assessors only evaluate the ingestion of drinking water when an a ffected surface water

body or collecte d precipitation (e.g., a cistern) is used as a drinking water source. (b) Important factors af fecting the concentration of COPCs in a surface water body include the location of the surface water body or prec ipitation collection apparatus relative to emissions sources; concentrations of COPCs in and characteristics of the soils (which affects runoff and leachate concentrations); and the si ze and loc ation of the watershed. For drinkin g wate r, the expo sure est

imate is af fe ct ed by:

– The concentration of the COPC in the water;

– The daily amount of drinka ble water ingested; and

– The fra

ction of ti me that the

individua

l spends in

the are a servic ed by

that wate r supply system. (Note that for sc

reening level analy

ses, 100% of drinking water may be presumed to come from the contaminated source.) Note that in estimated exposures associated with drinking water supplies, risk assessors commonly

assume th at the dr

inking w ater und

ergoe s at least

a minimum le

vel of tre atment to remove solids (i.e., particles in the water which are PB-HAPs or onto which PB-HAPs may be absorbed). Therefore, the r isk assessment commonly focuses on the dissolved concen trations o f PB-HA

Ps in drinki

ng wate r source s.

Groundwater as a Sourc e of Drinking Water If site- specific circumstances s uggest that g

roundwater may

represent a potential concern (e.g., the presenc e of ext

remel y shal low aqu

ifers us ed for d

rinkin g water purp oses or a kars t envi ronme nt in which the local surface water significantly affects the quality of g round water used as a

drinking water source), the TRIM. FaTE library includes a groundwater com partment that can b e used to assess the groundwater pathway. EPA’s HumanHealthRisk A ssessme nt Prot ocol for Hazard ous W aste Combustion Facilities (14) and Draft Technical Backgro und Document for Soil Screening Guidance (15) discu ss the met hods fo r evalua ting the groundwa ter pa thway.

• Ingestion o f Fish . Factors that affect human exposure by ingestion of fish from a surface water body inc lude:

– Sediment a

nd water COPC conce

ntrations ;

– The types of f

ish and shellfish consumed;

– The portion of fish eaten (e.g., fillet only , fillet plus skin, whole body);

– The effect of food prepara tion techniques on concentrations of COPCs in the fish;

– Ingestion rates f

or the various fish and shellfish groups; and

– The fraction of dietary fish caught in the surface water body or bodies being

evaluated. (Note tha t for scr eening level analy ses, 100 p ercent of fish/shellfish is

presume d to come from the contaminated water body .)April 2004 Page 20-10 The ty pes of fi sh consume d will aff ect exposu

re beca use diff erent ty pes of fi sh and she llfish accumul ate COPCs a

t differ ent rate s. For example, fa tty f ish tend to

accumul ate lipophilic organ ic compou

nds more r

eadily than lea n fish. The

amount of

fish cons umed also

affects exposure because people who eat larg e amounts of fish will tend to have higher exposures. Fi sh co ns um pt io n r at es an d t he pa rt s o f t he fi sh th at ar e c on su me d c an va ry g re at ly, depending on geogr

aphic region and social or cultural fa ctors. Also, because all of a person’s dietary fish may not orig inate fr

om the sur

face w ater bod

y ne ar the so

urce of the PB-H AP, the fraction of locally

caught fish is also a variable for exposure.
20.4.2 Exposure Frequency The specific exposure frequency will depend on how the exposure analy sis is set up. For example, a scenario-based analy sis would specify one or mor e exposure frequencies for each defined scenario. A ty

pical screening-level exposure fre quency is 350 day s per yea r; this number is based on the assumption that all people spend a minimum of two weeks at a location other than the exposure scenario location selected for ana lysis (e.g., on va

cation). (1) (5) However, many activities vary on a

weekly a nd/or seasonal basis. For example, recreational fishing is more likely to occur on we ekends than on weekdays, and most areas in the U.S. have limited fishing and huntin

g seasons. 20.4.3 Exposure Duration Exposure duration is the length of time over which exposure occurs (e.g ., a lifetime or a particular residence time). As noted in Section 20.3 above, c hoice of ED will depend on many factor s, includi

ng the p urpose of the asse ssment or r

isk manag ement de cision, th e tier of analy sis, and the pa

rticular effect(s) of c oncern. There a re no univ ersally

established ED val

ues for r isk assessme nts beca use diff erent ED s may

be appro priate in differe nt situations. Some commonly used EDs include:

• Life time (70 y

ears) – gener ally used for screen ing-le vel anal yse s;

• High-end number of y ears a person resides in a single loca tion (about 30 years);

• Median number of y

ears a person resides in a single loca tion (about 9-10 yea

rs); and

• Seven y

ears (te n perce nt of an a ssumed lif

etime) – s ometimes u sed for n oncanc er eff

ects. Although a source may

remain in the same location for more than 70 y ears, and a person may have a lifetime of exposure to emissions from that source, U.S. Bureau of the Census data on population mobility indicate that many Americans do not alway s remain in the same area for their assumed 70-ye ar lifetime. (16) An estima te of the number of

ye ars that a

person is likely to spend in one area can be derived f rom information about mobility rate and media n time in a residence. Analysts may

use long EDs when conducting simple scree ning analy ses performed to determine if more complex analyses a re necessary . The rationale for use of such EDs is that if risks are not of concern when the exposure duration is long, then they would not be of concern given other, shorter, exposure durations. (Ty pically analy

sts also make other conservative or “healthprotect ive” ass umptions when condu

cting th is ty pe of scr eening analy sis.) Ana lys ts may use specific EDs part icular to the leg al frame

work for the asse ssment. F or example , the res idual risk section of the Clean Air Act (CAA) refer ences an Agency

rulemaking for which one prominentc The central tendency esti mate of adult exposure duration commonly used in ris k assessments i s 9 years

(Section 1 5.4.3 and Tab le 15-17 4 in EP A’s Exposu re Factors Han dbook , Part III ). (10) This est imate i s a median value b ased on na tional re siden tial occ upa ncy da ta for the gener al po pulatio n. Th is estimate may no t be ap pro priate in c ert ain sit

uati ons, suchas wh en popul ati on-s pecif ic da ta e xis t or when t

he ana ly st is eval uating a s pecif ic s ubpopulation that i s expected to differ f rom the general population (e.g. , farm famil ies).

d As described in Section 20.4, estimati on of high-end exposure will sometimes involve set ting exposure dura tion at its high -end v alue. T he high -end e stimate o f adult ex pos ure d uration typically use d in risk a ssessm ents is 30 years (S ection 15 .4.3 and T able 15 -174 in E PA’s Exposu re Factors Han dbook , Part III ), (10) although thi s may vary for specific sub-po pulations. April 2004 Page 20-11 risk metric considered a 70-y ear exposure duration (see CAA section 112(f)(2) and 54 Federal Register 38044). The type of r

isk metric being derived also influences the consider ation of exposure duration. For example, w

hen the a naly st wants to

describ e centr

al tende ncy risk base d on a dete

rministic analysis, s/he ty

pically will use mean or me dian exposure assumptions to calculate risk. (c) Similarly , when the

analy st wants to

describ e high- end risk b ased on a determin istic ana lys is, s/he may use hig

h-end exposure assumptions or a combination of central tendency and high-end exposure assumptions that provide a reasonable estimate of the individual risk for those persons at the upp er end of

the risk d istribution. As expla

ined in EPA ’s Policy onRisk Characterization : (17) “Conceptually , high-e nd exposure

means expo sure above about t he 90th percentile of the population distribution, but not higher than the individual in the population who has the highest exposure.” (d) When the analyst wants to conduct a probabilistic analysis of risk, s/he ty pically will use or

develop a

distributi on of exposu

re dura tions from

the avai lable da

ta (e.g. , see EPA’ s Exposure Factors Handbook, Part III ; Tables 15-164, 15-166, 15-167, and 15- 168). (10) The areal extent of the impacted area(s) may also be a c onsideration. If a source of concern occurs in the majority of

communities, then it is possible that individuals may be exposed to the source for a longer per

iod of time than one might predict using standard estimates of exposure duration. In this case, the ana lyst might assume that even thoug h an individual changes residence, the individual still would be exposed to the source of concern, and thus the individua l’s exposur

e durati on would be

great er than t ypi cally anticipa ted. Such an analy sis must consider whether the concentration of the pollutant at the multiple locations of exposure would be e quivalen

t. Beca use loca tion-spe cific pa

rameter s such as meteorol ogical

condition s, distance from the source, and the presenc e of certain pathway s of exposure (e.g., surface wa ter, home-g rown pro duce) ma

y va ry consider ably by geogra

phic are a, the analy st likely will have to estimate exposure concentrations for each g eographic location or community of interest. Similarly, if a single sourc e impacts a large ge ographic area, then it is possible that national estimates of population mobility will not adequately capture an individual’s potential duration of exposure. That is, an individual may move from one point of exposure associated with a particular source to another point of exposure associated with that same sour ce. For example, data indicate 29 percent of home buy ers move less than five miles to a new home (Table 15-171 in EPA’s Exposure Factors Handbook, Part III (10) ). Similar to the caution expressed above, the concentrations of pollutants within an area impacted by a single source may

vary conside

rably. The ana lys is should r

easonab ly a

ccount f or such si tuations.April 2004 Page 20-12 The persistence of the source-assoc iated contamination may a lso be an important consideration in the expos

ure dura tion for in

gestion pathway exposure a ssessmen

t. For exa

mple, the analy st should not automatically assume that the exposure dura tion can be no greater than the ope rating life of the source . Persiste nt pollutan

ts may remain in

the envir onment (e .g., soi ls and sed iments) for yea rs after the primary

source is discontinued. Nevertheless, in certain case s, once the source of exposur

e stops, the polluta

nt conce ntrations in the af fected media may

diminish. Pa

rticular ly in more re fined as sessments , the exposu re conc entration may

reflect any

expected variations in media or food concentrations over time. When evaluating the risk of noncancer he alth effects from ingestion exposures (i.e., calcula ting hazard quotients for ingestion exposures), we do not averag e pollutant dose over the lifetime of an individual as we do when calculating car cinogenic risk. Rather, when calcula ting hazard quotients for ing estion expo sures, w e avera ge the d ose over an aver aging time equivalent to only the pe riod o f exp osure (i.e. , we cal culat e an ave rage dai ly dose ra ther t han a l ifeti me aver age daily dose). Consequently , the values for exposure duration and averaging time are the same, and mathematically c ancel each other out. Neverthele ss, when calculating averag e daily dose, the anal yst must still consider exposure dur ation whe n select ing and computing

food and media intakes for use in the dose equation. EPA ty pically considers e

xposures of seven yea

rs or greater as chronic exposures. Food and media intakes that repre sent time-weighted averag es over a seven-ye ar period are reasonable f or evaluating chronic non-canc er health effects. Durations as short as one year are also comm only used, particularly in screening assessments, and for childhood evaluations where intake on a per body weigh t basis may rapidly change from year to year

. 20.4.4 Body Weight The choice of body

weight for use in the exposure assessment depends on the definition of the population group at potential risk. Because childre n have lower body

weights, typica

l ingestion exposures per unit of body weig ht, such as for soil, milk, and fruits, tend to be higher for childre n. If a lifetime

exposure d

uration ( or an expos

ure dura tion over the child hood and a

dult years ) is bei ng e val uat ed, it nee ds t o be bas ed o n di ffe rin g va lu es f or t he d iff ere nt age gro ups . If a less than a lifetime exposure estimate is being evaluated, it is important to include the childre n’s age group in the spec ific sce

narios or cohorts u sed. EPA’ s Exposure Factors Handbook

(6) and Child-Specific Exposure Factors Handbook (7) provide age-specific value s for body weight and cons umption ra

te per un it body weight

. 20.5 Calculati ng Averag ing Time V alue When evaluating exposure for the purposes of assessing hazard (vs. predicting cancer r isk), intakes are calculated by averaging intakes over

the period of exposure (i.e., subchronic or chronic durations) and result in averag e daily doses or AD Ds for the duration of interest. For evaluat ion of ca ncer ri sks, poten

tial dose i s calcul ated as t he aver age da ily d ose over a lifetime (i.e., chronic daily

intakes, also called lifetime average daily doses or L

ADDs). The approach for carcin ogens i s base d on t he prem ise th at ris k is p ropor tion al to t otal l ifeti me dos e (i.e. , a high dose received over a short period of time is equivale nt to a corresponding low dose spread over a lifetime). (18) The basis for this approach becomes less strong a s the exposures in question become more intense but less frequent, especially when there is evidence that the ag ent has shown age-related varia

tions in carcinogenic potency, or a nonlinear dose-response relationship.April 2004 Page 20-13 In some cases, there fore, it may be necessary

to consult a toxicologist to assess the level of uncerta inty associa ted with the exposure

assessme nt for ca rcinog ens. Note that , even when the expo sure of i nterest i s a full lif

etime, ch ronic ha zards ar e gene rally calcula ted sepa rately for chr onic exposu

res to ag e grou

ps that dif

fer subs tantially

with reg ard to pertine nt exposure

factor s (e.g. , ingest

ion rate or body weight ) and ar e not comb ined (i.e ., usually the oral route ha zards ca lculated for child ren are not added

to the haza

rds posed to adults to repres ent a “lif

etime haza

rd”). Ra ther, bot h hazard q

uotients/i ndices a re pres ented as

chronic hazard me

trics re levant to

the two g roups. Whe

n assessi ng car cinoge nic risks f or a life time exposure, on the othe

r hand, c ancer r

isk estima

tes are usually added a cross dif ferent age g roups, since the risk received over discrete pe riods of time (e.g., as a child, as a y oung adult, as an older adult) are each considered to be f ractions of the risk associated with a full lifetime of exposure. Note that in calculating LA DDs, it is essential to account for differences in the value s of different intake va riables ( e.g., b ody

weight , consumpt

ion rate ) at diff

erent a ges. 20.6 Combining Exposure Estimates Across Pathways A given population may

receive exposure to an individual chemical from severa l different exposure pathways. F or example, individuals may rece ive exposure via inhalation of the chemical in the air and via ingestion of surfac e water and fish that have become conta minated through deposition. The specific exposure scenarios or cohor ts defined for the analy sis may include more than one pathway . The corresponding intake variables use d in the analysis may need to account for the number of pathway s over which exposure will be combined. For example, to

develop a

high-e nd estimat

e for a s cenari o that inc ludes inha lation, ingestion of

soil, and ing estion of

fish, it ma

y be necessa ry to combine

high-e nd exposure

assumption s for all pathways. I

n other cases, it may

be more appropriate combine high-e nd exposure assumptions for particular pathway

s with more central-tendency assumptions for others. Otherwise, the estimate may repres ent an extr

eme situa tion in whic

h the simula

ted beha vior is ass umed to re sult in high exposures vi a all path way s. Two steps

are re quired to

determine whethe

r intake estimate s should be

combined

for a sin gle scenario:

• Identify re asonable e xposure pathway combi nations . Identify exposure pathways that have the potential to expose the same individual, cohort, or subpopulation at the key e xposure areas evaluated in the exposure assessment, making sur e to consider areas of hi

ghest exposure for each pathway . For each pathway, the intake estimates have been developed for a particular exposure area and time period; they do not necessarily a

pply to other locations or time periods. Hence, if two pathways do not affect the same individual, cohort, or subpopula

tion, neither pathway ’s exposur e estimat e affe cts the other, and e

xposures should not be combined.

• Examine whether it is likely that the same individuals would consistently face a reasonable central tendency or high-end exposure by more than one pathway . Once reasonable exposure pathway combinations have been identified, it is necessary to examine whether it is likely that the same indiv iduals would consiste

ntly face central tendency

or highend exposure conditions. As noted in Section 20.4 above, the exposure estimate for each exposure pathway include s many conserva tive estimates. Also, some of the exposureApril 2004 Page 20-14 parameters are not completely predictable in space and/or time (e.g ., the maximum downwind concentration may shift compass direction). For real-world situations in which contaminant concentrations vary over time and space, the same individual or cohort may or may not experience c entral-tendency or high-end exposure conditions for more than one pathway ove

r the same period of time. Thus, it is important to clearly explain why the key assumptions chosen for more than one pathway for an individual, subpopulation, or cohort are set at central tendency and/or high-end exposure estimates. (Note that an important g oal in the analysis of hig

h-end receptors is to identify exposures that are in the high-end of the range - usually

higher than the 90 th percen tile exposur

e - but not

higher than the h

ighest exposure in the population.)
20.7 Exposure M odels Exposure models have been developed that automate the calcula tion of chemical intake. They may simply calcula te exposur

e for a s et of indiv

idual sce narios, or they

may draw upon activity pattern and/or dietary

survey data

bases to characterize cohort exposure within a population. Three exposure models are described below. California Total Exposure Model for Hazardous Waste Sites (CalTOX) As described previously

in Part II , Chapter 9, the California Environmental Protection Agency funded the develo

pment of th

e CalTOX

progra m. (19) CalTOX has been developed as a set of spreadsheet models and spreadsheet data se ts to assist in assessing human exposures and defining soil clean-up levels at uncontrolled hazardous wastes sites. CalTOX addresse s contaminated soils and the contamination of adjacent air, surface wa ter, sediments, and ground water. The modeling componen

ts of CalTOX include

exposure s cenari o models. T he exposur

e models encompass twenty- three exposure pathway s. The exposure assessment process consists of relating pollutant concentrations in the multimedia model compartments to pollutant concentrations in the media with which a human population has contact (e.g ., personal air, tap water, foods, household dusts, soils). The temporal resolution is either daily for inhalation and dermal exposure or annual for ingestion. The ag gregation period is variable, de pending on the duration of residence at a single location. The spatial resolution and modeling domain are userspecified, but generally

encompass some vicinity ar ound the waste site of interest. Activity data, such as in halation , ingest

ion, and d ermal co ntact ra tes, are derived from EPA’ s Exposure

Factors Handbook. (6) TRIM.Expo As discussed in Chapter 18, TRIM.Expo is the exposure component of the TRI M modeling system. The inge stion component of TRIM.Expo (TRIM.Expo Ingestion ) is designed to take input values from TRIM.FaTE, but may also be operated independently with inputs from measurement studies or alternative models. TRIM.Expo Ingestion will employ a scenar

io-based approach, based on that used in the 3MRA modeling system, in its initial version. I nformation about the ingestion component of TRIM.Expo is available on EPA’s Fate, Exposure and Risk Analy sis (FERA) web site: http://www.epa.gov/ttn/fera

.April 2004 Page 20-15 Stochasti c Human Ex

posure and D ose Simul ation Model (SHEDS) The Stochastic Human Exposure and Dose Simulation (SHEDS) Model (20) is a probabilistic, physically

-based model that simulates aggreg ate exposure and dose for population cohorts and multimedia pollutants of interest. It is being developed by EPA’s National Exposure Research Lab or at or y ( http://www.epa.gov/nerlpage/ ). At present the model is applied to assess children’s exposures to pesticides (SHEDS-Pesticides) and population exposures to particulate matter (SHEDS-PM).
SHEDS-Pes ticides f

ocuses on childre n’s agg regat e populat ion exposur

e to pestic ides. Ac tivity data ar e selected from d

aily sequenti al time/loc ation/activity diaries f rom surve

ys c ontained

in EPA’s Consolidated Human Activity Databa se (CHAD). (8) For ea ch individ ual, SHED SPesticides constructs daily exposure and dose time profiles for the inhalation, dietaryand nondietary ing estion, and dermal contact exposure routes, and then ag gregates the dose prof iles across routes. A pharmacokinetic component has bee n incorporated to predict pollutant or metabolit e conce ntrations in the bloo d compar

tment or e liminated

urine. Expo sure and

dose metrics of interest (e.g., peak, time-ave raged, time-integrated)

are extracted from the individual’s profiles. Two-stage Monte-Carlo sampling is applied to pre dict the range and distribution of aggr egate doses with

in the spe cified p opulation and ident ify the unce rtaintie s associa

ted with percen tiles of in terest. SHEDS-Pes ticides is

curren tly b eing re fined to c

haract erize both aggr egate and cumul ative dos e associated with human exposure (i.e. , for both adults and children) to a variety of environmental pollutants in addition to pesticides. SHEDS-Pesticides will eventually be expanded to include source- to-conc entration (i.e., f ate and t ranspor t) models a

nd more c omplete exposure-t o-dose models (i.e., pharmacokinetic or dosimetric models). SHEDS-PM estimates the population distribution of particulate matter (PM) exposure by sampling from distributions of ambient PM concentrations, distributions of emission strengths for indoor source s of PM (e. g., cig arette smoking a nd cookin g), and distribut

ions of ma ssbalance parame ters (e. g., air

exchang e rate, penetra

tion rate , depositi on rate) . A stead y- state mass balance equation is used to calculate PM concentra tions for the residential and other microenvironments. Additional model inputs include demographic and human activity pattern data fr om the Nat

ional Huma

n Activity Pattern Su

rvey (NHAPS). Output fr

om the SHED S-PM model includes distributions of PM exposures in various microenvironments (e.g., in the home, in vehicles, outdoors) and the relative contributions of these various microenvironme nts to the total exposure.April 2004 Page 20-16 1. U.S. Environmental Protection Agency . 1992. Guidelines for Exposure Assessment . 1992. National Center f or Environmental A ssessmen t, Risk Asse ssment Forum. Washin gton, D. C. EPA 600Z -92/001. 1 70 pp. Ava ilable at : http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=15263

. U.S. Environmental Protection Agency . 2001. Risk Assessment Guidance for Superfund: Volume I. Human Health Evaluation Manual (Part E, Supplemental Guidance for Derm al Assessme nt, Inter im - Rev iew Draft fo r Public Co mment) . Office of Emergency and Remedial Response . Washing

ton, D.C., EPA/540/R/9 9/005, av ailable a t: http://www .epa.g ov/super fund/pro grams/

risk/rag se/index.h

tm

. 2. U.S. Environmental Protection Agency . 1999. National Recommen ded W ater Quali ty Criteria - Correction . Office of Water. Washingt

on, D.C. EPA

822-Z- 99-001. A

pril. 3. U.S. Environmental Protection Agency . 1996. Soil Screening Guidance: Technical Background Document . Office of Superfund Remediation and Technolog y I nnovation, formerly the O ffice of Office of Emerg ency & Remedial Response. EPA/540/R-95/128. Availabl e at: http://www .epa.g ov/super fund/re sources

/soil/introtbd.htm

. 4. U.S. Environmental Protection Agency . 1989. Risk Assessment Guidance for Superfund: Volume I. Human Health Evaluation Manual (Part A) . Office of Emergency and Remedial Response . Washing

ton, D.C., EPA/541/1- 89/002, a vailable at: http://www .epa.g ov/super fund/pro grams/

risk/rag sa/index.h

tm 5. U.S. Environmental Protection Agency . 1990. Interim Final Methodology for Assessing Health Ris

ks Associ ated with I

ndirect E xposure t o Combustor

Emissions. Environmental Criteria and Assessment Office, Office of Research and Development, January, 1990. EPA-600-90-003. U.S. Environmental Protection Agency . 1993. Review Draft Addendum to the Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions

. OHEA, Office of Research a nd Development, November 1993. EPA/600-AP-93-003. U.S. Environmental Protection Agency . 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions . Update to EPA/600/6-90/003 Methodology for Assessing Health Risks Associated With Indirect Exposure to Combustor Emissions . National Center for Environmental Assessment. EPA600/R-98- 137; avai lable at: http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=55525

. 6. U.S. Environmental Protection Agency . 1997. Exposure Factors Handbook, Volume III. Activity Factors . Office of Research and De velopment, Washington, D.C., August 1997. EPA/600/P- 95/0002F c, avail able at: http://www.epa.gov/ordntrnt/ORD/WebPubs/exposure/

. 7. U.S. Environmental Protection Agency . 1989. Exposure Factors Handbook . Office of Research and Development, National Center for Environmental Assessment, Washington, D.C., May

1989. EPA/600/8-89/

043, ava ilable at : http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=12464

. ReferencesApril 2004 Page 20-17 8. U.S. Environmental Protection Agency . 2002. Child-Specific Exposure Factors Handbook (Inte rim Repor

t) . Office of Research and De velopment, National Center for Environmental Assessment , Washingt on, DC, Sep tember 2002 . EPA-600-P -00-002B. 9. The Consol idated Hu man Activ

ity D atabase

(CHAD) i s availa

ble at: http://www.epa.gov/chadnet1/

. 10. United States Environmental Protection Agency . 2003. Guidance on Selecting the Appropria te Age Gr

oups for Ass essing Chil

dhood Exp osures to E

nvironme ntal Contam inants (Exte rnal Revi ew Draft) . Risk Assessment Forum, Washington, D.C. September, 2003. EPA/630/P- 03/003A; a

vailable at www.epa.gov/ncea

. 11. U.S. Environmental Protection Agency . 1997. Exposure Factors Handbook, Volume II. Food Inge

stion Fact

ors . Office

of Resea rch and D evelopme

nt, Washing

ton, D.C., August 1997. EPA/600/P-95/0 002Fb, a vailable at: http://www.epa.gov/ordntrnt/ORD/WebPubs/exposure/

. 12. U.S. Environmental Protection Agency . 1985. Rapid Asse ssment of E

xposure t o Particul

ate Emissions from Surface Contamination Sites . Office of Health and Environmental Assessment (OHEA). Washington, D.C. EPA/600/8-85/002. NTIS PB 85-192219. U.S. Environmental Protection Agency . 1988. Superfund Exposure

Assessme

nt Manual. Off ice of E mer gen cy an d R eme di al R esp ons e. W ash in gto n, D .C . EP A/5 40/ 1-8 8/ 001 . NT IS PB 89-167985. U.S. Environmental Protection Agency . 1994. Draft Technical Background Document for Soil Screening Guidance. EPA/540/R-94/106. OSWER. Washington, D.C. December. 13. U.S. Environmental Protection Agency . 1990. Interim Final Methodology for Assessing Health Ris

ks Associ ated with I

ndirect E xposure t o Combustor

Emissions. Environmental Criteria and Assessment Office, January 1990. ORD. EPA-600-90-003. U.S. Environmental Protection Agency . 1992. Estimating Exposures to Dioxin-Like Compounds . Draft Re

port . OHEA, Washington, D.C. EPA/600/6-88/005B, August 1992. Brzusy, L .P. and Hites, R.A. 1995. Estimating the atmospheric deposition of poly chlorinated dibenzo-p-dioxins and dibenzofurans from soils. Environ. Sci. Tech nol. 29: 2090-2098. 14. U.S. Environmental Protection Agency . 1998. Human Health Risk Assessment Protocol for Hazardous

Waste CombustionFacilitie

s, Peer R eview Dr

aft . Office of Solid Waste and Emerge ncy Response , Washing

ton, D.C., July 1 998. EOA/30/D-98/0 01A. Ava ilable at : http://www .epa.g ov/epaoswer/hazwaste/co mbust/risk .htm 15. U.S. Environmental Protection Agency . 1994. Draft Technical Background Document for Soil Screening Guidance . Office of Solid Waste and Emergency Response, Washington, D.C., December 1994. EPA/540/R-94/106.April 2004 Page 20-18 16. U.S. Bureau of the Census. 1986. Geographical Mobility: March 1983 to March 1984

. Current Population Reports. Series P-20. Number 407. U.S. Government Printing Office. Washingt on, D.C. 17. U.S. Environmental Protection Agency . 1995. Policy for Risk Characterization (“Browner Memoran dum”). Sc ience Pol icy

Council, Wa shington , DC. Avai lable at: http://64.2.134.196/committees/aqph/rcpolicy.pdf 18. U.S. Environmental Protection Agency . 1999. Guidelines for Carcinogen Risk Assessment (INTERIM) , Risk Assessment Forum, Washington, DC NCEA-F-0644. 19. McKone, T.E. 1993a. CalTOX, a Multimedia Total-Exposure Model for Hazardous wastes Sites Part I: Executive Sum mary . UCRL-CR-11456, Pt. I. 1993b. CalTOX, a M ultimedia Total-Exp osure Mod

el for Haza rdous W

astes Site s Part II: the Dyna mic Multim edia Transport an d Transform ation Mode l. UCRL-CR-111456, Pt. II . 1993c. CalTOX, aMultimedia Total-Exposure Model for Hazardous Wastes Sites Part III: The MultiplePathway Ex

posure Mo del. UCRL-CR-111456, Pt. II I. L ivermore, CA: Lawrenc e Livermore Na ti on al Lab or at or y. 20. Zartarian V.G., Ozkay nak H., Burke J.M., Zufall M.J., Rigas M.L., a nd Furtaw Jr. E.J. 2000. A modeling

framework for e stimating childre n’s resid ential expo sure and

dose to chlorpyrifos via de

rmal residue contact and non-dietary ingestion. Environm ental Hea lth Perspectives 108:505-514.