Focusing on Children's Inhalation Dosimetry and Health Effects for Risk Assessment: An Introduction

Abstract

Substantial effort has been invested in improving children's health risk assessment in recent years. However, the body of scientific evidence in support of children's health assessment is constantly advancing, indicating the need for continual updating of risk assessment methods. Children's inhalation dosimetry and child-specific adverse health effects are of particular concern for risk assessment. When focusing on this topic within children's health, key issues for consideration include (1) epidemiological evidence of adverse effects following children's exposure to air pollution, (2) ontogeny of the lungs and effects on dosimetry, (3) estimation and variability of children's inhalation rates, and (4) current risk assessment methodologies for addressing children. In this article, existing and emerging information relating to these key issues are introduced and discussed in an effort to better understand children's inhalation dosimetry and adverse health effects for risk assessment. While much useful evidence is currently available, additional research and methods are warranted for improved children's health risk assessment.

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Focusing on Children's Inhalation Dosimetry and Health Effects for Risk
Assessment: An Introduction
Brenda Foos
a
; Melanie Marty
b
; Joel Schwartz
c
; William Bennett
d
; Jacqueline Moya
e
; Annie M. Jarabek
ef
;
Andrew G. Salmon
b
a
Office of Children's Health Protection and Environmental Education, U.S. Environmental Protection Agency,
Washington, DC, USA
b
Office of Environmental Health Hazard Assessment, California Environmental
Protection Agency, Oakland, California, USA
c
Department of Environmental Health, Harvard University,
Boston, Massachusetts, USA
d
Center for Environmental Medicine, Asthma, and Lung Biology, University of
North Carolina, Chapel Hill, North Carolina, USA
e
National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA
f
National Health
and Environmental Effects Laboratory, Office of Research and Development, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, USA
Online Publication Date: 01 January 2008
To cite this Article Foos, Brenda, Marty, Melanie, Schwartz, Joel, Bennett, William, Moya, Jacqueline, Jarabek, Annie M. and Salmon,
Andrew G.(2008)'Focusing on Children's Inhalation Dosimetry and Health Effects for Risk Assessment: An Introduction',Journal of
Toxicology and Environmental Health, Part A,71:3,149 — 165
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Journal of Toxicology and Environmental Health, Part A, 71: 149–165, 2008
Copyright © Taylor & Francis Group, LLC
ISSN: 1528-7394 print / 1087-2620 online
DOI: 10.1080/15287390701597871
149
UTEH
Focusing on Children’s Inhalation Dosimetry and Health
Effects for Risk Assessment: An Introduction
Children’s Inhalation and Risk Assessment
Brenda Foos
1
, Melanie Marty
2
, Joel Schwartz
3
, William Bennett
4
,
Jacqueline Moya
5
, Annie M. Jarabek
5,6
, and Andrew G. Salmon
2
1
Office of Children’s Health Protection and Environmental Education, U.S. Environmental Protection
Agency, Washington, DC, USA,
2
Office of Environmental Health Hazard Assessment, California
Environmental Protection Agency, Oakland, California, USA,
3
Department of Environmental Health,
Harvard University, Boston, Massachusetts, USA,
4
Center for Environmental Medicine, Asthma, and
Lung Biology, University of North Carolina, Chapel Hill, North Carolina, USA,
5
National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental Protection
Agency, Washington, DC, USA, and
6
National Health and Environmental Effects Laboratory, Office
of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina, USA
Substantial effort has been invested in improving children’s
health risk assessment in recent years. However, the body of
scientific evidence in support of children’s health assessment is
constantly advancing, indicating the need for continual updating
of risk assessment methods. Children’s inhalation dosimetry and
child-specific adverse health effects are of particular concern for
risk assessment. When focusing on this topic within children’s
health, key issues for consideration include (1) epidemiological
evidence of adverse effects following children’s exposure to air
pollution, (2) ontogeny of the lungs and effects on dosimetry,
(3) estimation and variability of children’s inhalation rates, and
(4) current risk assessment methodologies for addressing children.
In this article, existing and emerging information relating to these
key issues are introduced and discussed in an effort to better
understand children’s inhalation dosimetry and adverse health
effects for risk assessment. While much useful evidence is cur-
rently available, additional research and methods are warranted
for improved children’s health risk assessment.
In recent years, regulatory agencies in the United States
have focused more attention on the health risks to children
from environmental exposures. Why focus on children’s inha-
lation dosimetry and health effects for risk assessment? The
simple answer is that children are not small adults. In order to
assess whether traditional risk assessment paradigms adequately
address risk to children from airborne pollutants, one must
consider the epidemiology literature regarding children’s
inherent susceptibility to air pollutants, and age-related differ-
ences in exposure and dosimetry. The U.S. Environmental Pro-
tection Agency (EPA) held a workshop in June 2006 with
experts from academia and state and federal environmental
agencies to discuss these topics, describe current risk assess-
ment practices of state and federal agencies with respect to air-
borne pollutants, and discuss future steps to further encompass
infants and children in assessing risks. This article provides a
summary of the presentation and discussion of these topics that
took place during the first session of the workshop on
Children’s Inhalation Dosimetry and Health Effects for Risk
Assessment. This introductory session examined the epidemio-
logical evidence of adverse health effects of air pollution in
children, respiratory system development and its impact on dep-
osition and dose, new methods for estimating inhalation rates,
and various current risk assessment practices for addressing chil-
dren’s risks from inhalation of airborne pollutants. This article is
not a comprehensive review of these topics; the reader is referred
to the citations throughout the text for further information.
A number of adverse health effects in children attributed to
air pollution have been described in the epidemiological
literature, with studies showing elevated risks of adverse birth
outcomes, infant mortality, and in children respiratory diseases
The authors wish to acknowledge the contributions of Jessica
Sanford and Bob Lordo from Battelle who, under contract with the
U.S. EPA, conducted the analysis of ventilation rates presented in
section III of this article, and Laurie Schuda, who served as the task
manager for the analysis.
The views and opinions expressed in this article are those of the
authors and are not necessarily representative of an official position of
the U.S. Environmental Protection Agency, the California
Environmental Protection Agency, or the Office of Environmental
Health Hazard Assessment.
Address correspondence to Brenda Foos, U.S. EPA Office of
Children’s Health Protection and Environmental Education, MC
1107A, 1200 Pennsylvania Avenue, NW, Washington, DC 20460.
E-mail: foos.brenda@epa.gov
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150 B. FOOS ET AL.
such as bronchitis and asthma. In order to adequately assess
risks of air pollutants to children, relative differences between
infants, children, and adults in exposure, toxicokinetics, toxicody-
namics, and underlying prevalence and severity of disease such
as asthma need to be considered. Furthermore, consideration of
the ontogeny of lung structure and function, breathing patterns,
and the implications for dose and deposition is key to address-
ing children’s health risks from inhalation exposures. For
example, factors that may contribute to enhanced lung deposi-
tion in children include higher ventilation rates, less contribu-
tion from nasal breathing, less efficient uptake of particles in
the nasal airways, and greater deposition efficiency of particle
and some vapor phase chemicals in the lower respiratory tract.
New methods for estimating ventilation rates are an important
factor in assessing children’s health risks, as ventilation rate,
which differs by age, is a physiological parameter that may be
used in estimating risk.
The U.S. Environmental Protection Agency (U.S. EPA) has
a mandate to consider risks to children through Executive
Order 13045 (1997) and the Agency Policy on Evaluating
Health Risks to Children (U.S. EPA, 1995). The Food Quality
Protection Act and the Safe Drinking Water Act Amendments
also provide legislative mandates to the U.S. EPA to consider
children’s health risks. Similarly, the State of California is
mandated by the Children’s Environmental Health Protection
Act to (1) review all health-based California Ambient Air
Quality Standards (CaAAQS) to determine whether they
adequately protect public health, including infants and chil-
dren, and revise those that are found inadequate; (2) include
differential exposure patterns and susceptibility of infants and
children in evaluating CaAAQS and health effects of Toxic Air
Contaminants; and (3) review the state’s list of Toxic Air
Contaminants to identify those that might cause infants and
children to be especially susceptible to illness, and institute air
toxic control measures to reduce exposures. Current methods
of inhalation risk assessment at the U.S. EPA and California
EPA address children’s risk via slightly different but comparable
approaches.
The methods for addressing children’s inhalation dosimetry
and health effects in risk assessments are evolving, which was
the primary motivation for the workshop. This summary of the
introductory session was intended to prepare the reader for the
accompanying summary articles describing new and emerging
scientific data and approaches for children’s inhalation risk
assessment.
AIR POLLUTION AND CHILDREN’S
HEALTH—EPIDEMIOLOGICAL EVIDENCE
Background
The adverse health effects attributed to air pollution expo-
sure have become an area of increasing focus in the last 30
years. A growing body of evidence demonstrated that there are
serious health consequences to community air pollution, and
that these consequences are not spread equally among the pop-
ulation. Relative to adults, children spend a greater portion of
their days outside and at greater exertion levels, both of which
may increase their exposures to pollution from traffic, power
plants, and other combustion sources, pollution that is generally
higher outdoors. For ozone, in particular, exposure is generally
outdoors (as ozone disappears quickly indoors) and in the
afternoon on sunny days (U.S. EPA, 2006a). Children’s expo-
sure to air pollution is also a special concern because their
immune system and lungs are not fully developed when expo-
sure begins, raising the possibility of different responses than
seen in adults (Dietert et al., 2000).
The lung is not mature at birth, and development of full
architecture and functionality does not occur until about 18 yr
of age (Dietert et al., 2000; Harding et al., 2004). During early
childhood, the bronchial tree is still developing. For example,
the number of alveoli in the human lung increases from 24 mil-
lion at birth to 257 million at age 4 yr (Dunnill, 1962), and the
lung epithelium is not fully developed. Children also have a
larger lung surface area per kilogram of body weight than
adults, and under normal breathing, breathe a greater amount
of air per kilogram of body weight than adults, with the highest
levels in the youngest children. This process of early growth
and development, whose outcome is important for the future
health of the child, suggests that there is a critical exposure
time where air pollution may have persistent effects on respira-
tory health.
At the same time the child’s lung is developing, the child’s
immune system, immature at birth, is also beginning to
develop. Much attention in asthma research has focused on
immune development and factors that influence the development
of TH-2 (humoral immunity dominant) versus TH-1 (cellular
immunity dominant) phenotypes (Holt, 1998).
The following paragraphs provide a brief overview of find-
ings to date from epidemiological studies of the association of
air pollution with a number of children’s health outcomes
important for risk assessment, and are not meant to be a compre-
hensive review, which is beyond the scope of this proceeding. In
the literature, topics of children and air pollution have been
reviewed with differing perspectives (Transande & Thurston,
2005; Schwartz, 2004; Mathieu-Nolf, 2002). While most of
these studies do not compare children’s risk relative to those of
adults, the findings are important because they reinforce the fact
that the health of children is adversely affected by air pollution.
Air Pollution Health Effects Observed in Children
Prenatal effects. There is now considerable evidence that
maternal exposure to air pollution during pregnancy is associ-
ated with adverse birth outcomes. Perhaps the most unexpected
results have been papers reporting that prenatal exposure of
populations to prevailing levels of air pollution is associated
with early fetal loss (Pereira et al., 1998), preterm delivery
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CHILDREN’S INHALATION AND RISK ASSESSMENT 151
(Xu et al., 1995; Ritz et al., 2000; Lin et al., 2001; Wilhelm &
Ritz, 2005; Sagiv et al., 2005), and lower birth weight (Wang
et al., 1997; Ritz & Yu, 1999; Dejmek et al., 1999; Bobak &
Leon, 1999a; Ha et al., 2001; Bobak, 2000; Bobak et al., 2001;
Parker et al., 2005). These studies are generally well controlled
because birth certificates in most areas have extensive informa-
tion on maternal medical conditions that may affect the preg-
nancy, as well as maternal age, education level, and cigarette
smoking before and during pregnancy. Particulate ambient air
pollution from combustion sources shares many characteristics
with sidestream tobacco smoke, which is rich in particles and
polycyclic aromatic hydrocarbons (PAH). Environmental
tobacco smoke has been associated with low birth weight,
small-for-gestational age, and preterm delivery (Ahluwalia et
al., 1997; Dejin-Karlsson et al., 1998; Windham et al., 1999;
Dejmek et al., 2002; Kharrazzi et al., 2004). These findings
provide support for the plausibility of the reported association.
Infant mortality. Studies reported that air pollution is
associated with infant morality. In considering air pollution
and death, one is inevitably led to the great air pollution epi-
sode of December 1–5 in London in 1952. A low-level thermal
inversion that trapped coal smoke in the Thames valley, coupled
with a stationary front that dropped wind speed to 0, resulted in
a rapid buildup of pollution to extremely high levels. Approxi-
mately 4000 excess deaths occurred in London in that week
(HMPHS, 1954), and elevated death rates continued for weeks
afterward (Anderson, 1999), indicating that there were delayed
as well as early-onset effects. While most of the deaths were in
adults, infant mortality doubled during that period (Schwartz,
1994). This episode is important because it implies causality.
More recently, Woodruff and coworkers (1997, 2006) exam-
ined infant deaths in the United States and levels of PM
10
in the
air and found that PM
10
was associated with higher infant
death rates at ages 2–12 mo (the first month after birth was
excluded as likely to reflect complications of pregnancy and
delivery). This excess risk seemed to be principally from respi-
ratory illness, although sudden infant death syndrome deaths
were also elevated.
Bobak and Leon (1992) examined the cross–sectional asso-
ciation between air pollution and infant mortality rates across
towns in the Czech Republic. A significant association was
seen between infant death rates and particle (TSP-10) and SO
2
concentration. More recently, this group conducted a popula-
tion-based case-control study and found significant associa-
tions between infant mortality from respiratory causes and total
TSP and SO
2
(Bobak & Leon, 1999b). Other studies examined
day-to-day changes in air pollution and day-to-day changes in
infant deaths. Saldiva and coworkers (1994) reported that
infant death from respiratory disease was associated with air
pollution, particularly from traffic. Loomis and coworkers
(1999) and Ha et al. (2003) similarly found respiratory deaths
in infants associated with air pollution.
Respiratory effects: Chronic cough and bronchitis. Dockery
and coworkers (1989) reported that chronic bronchitis and chest
illness in children were associated with exposure to particulate
air pollution. Subsequent study confirmed the association of
particulate exposure with higher rates of chronic cough and
bronchitis symptoms in children (McConnell et al., 1999).
A similar large study (n=4470) comparing school children in
10 communities in Switzerland reported an adjusted odds ratio for
bronchitis of 2.88 (95% CI 1.69–4.89) for PM
10
exposure between
the most and least polluted community (Braun-Fahrlander
et al., 1997). The largest study examined 13,369 children in
24 communities in the United States and Canada (Dockery
et al., 1996). Again, particulate air pollution was associated
with bronchitis episodes across these communities. Chronic
cough in children was also associated with air pollution in a
number of studies (van Vliet et al., 1997; Titanen et al., 1999;
Mar et al., 2004)
A recent study looking at eastern Germany, where there has
been a reduction in pollution since the reunification of
Germany, shows that this reduction was related to reductions
in the rates of chronic cough and bronchitis symptoms in a
cohort of children (Heinrich et al., 2000). This demonstrates
not merely an association, but that an intervention produces
improvements in health. A similar result was found in Switzerland
(Bayer-Oglesby et al., 2005).
Respiratory effects: Asthma. Air pollution, including par-
ticulate matter, ozone, NO
2
, and sulfur oxides, has long been
known to exacerbate asthma, including increased symptoms,
need for bronchodilator medication, decreased lung function,
and emergency visits for attacks (Sheppard et al., 1999; Ostro
et al., 2001; Lin et al., 2001; Gent et al., 2004; Mar et al., 2004).
More recently, studies suggested that air pollution, particularly
traffic-related pollution, was associated with the development
of asthma and atopy. For example, Studnicka and coworkers
(1997) examined 8 small rural communities and found a strong
association between asthma prevalence and NO
2
levels, with
odds ratios reaching 5.81 (95% CI1.27–26.5) when contrasting
the highest and lowest exposures. Kramer and coworkers
(2000) examined 317 children in three German communities.
NO
2
measurements outside each child’s home were significant
predictors of hay fever, symptoms of allergic rhinitis, wheez-
ing, and sensitization against pollen, house dust mites or cats,
while indoor NO
2
was not, indicating that outdoor NO
2
was
serving as a surrogate for other traffic-related pollutants. In the
Netherlands, residence on a high-traffic street was associated
with more than a twofold increase in the risk of wheezing after
control for confounders (Oosterlee et al., 1996). Similar results
were seen in other studies of traffic-related pollution
(Brunekreef et al., 1997; Kim et al., 2004; Gauderman et al.,
2005). Overall, diesel exhaust was shown to be a potent adju-
vant for atopic sensitization (Diaz-Sanchez et al., 1999), and
also seems the most important traffic indicator for prediction of
asthma and atopy.
Respiratory effects: Lung development. Finally, there is
increasing evidence that air pollution negatively impacts lung
growth. Chronic ozone exposure during childhood was associated
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152 B. FOOS ET AL.
with decreased lung function and increased risk of wheeze and
chronic respiratory symptoms in college freshman (Kunzli
et al., 1997; Galizia & Kinney, 1999). Results from the
California Children’s Study cohort also demonstrate that
chronic exposure to air pollutants including nitrogen dioxide,
acid vapors, PM
2.5
, and elemental carbon during childhood
decreases lung function growth, producing significant decre-
ments at age 18 yr, and that decrease may be permanent with
implications for long-term adverse respiratory health as adults
(Gauderman et al., 2004).
ONTOGENY OF LUNG STRUCTURE AND FUNCTION,
BREATHING PATTERNS, AND IMPLICATIONS FOR
DOSE AND DEPOSITION
As discussed in the previous section, epidemiological evi-
dence of childhood susceptibility is substantial, and of particu-
lar importance is that epidemiological studies suggest children
may be more susceptible than adults to lower respiratory tract
effects of inhaled particulate matter (Pope & Dockery, 1992;
Schwartz et al., 1994; Ostro et al., 1995; Gauderman et al.,
2000; Conceicao et al., 2001). Clearly adverse effects on devel-
opmental processes, such lung function growth, occur only in
children and not in adults. While children may be especially
predisposed to adverse effects from airborne particulates, they
may also receive an increased dose of particles to their lungs
compared to adults. At least three factors may contribute to
enhanced lung deposition in children versus adults: (1) lesser
nasal contribution to breathing at rest and during exercise in
children, (2) less efficient uptake of particles in the nasal air-
ways of children, and (3) greater efficiency of particle deposi-
tion within the lower respiratory tract of children. The nose is a
more effective filter than the mouth for preventing penetration
of particles to the lower respiratory tract. Thus, the route of
breathing, oral versus nasal, is an important determinant of par-
ticulate dose to the lung. The efficiency of the nose itself for
filtering particles is dependent on the anatomy of the nasal pas-
sage, which changes with age. Lung growth and changes in
breathing patterns with age (from child to adult) may affect the
fractional deposition of inhaled particles in the lower respira-
tory tract. Furthermore, among children there are several risk
factors that may contribute to enhanced pulmonary deposition
of inhaled particles, including race, obesity, and presence of
allergic rhinitis or asthma.
Very large (>5 μm aerodynamic diameter) and very small
(<0.01 μm) particles are deposited very efficiently in the nose
by inertial impaction and diffusion, respectively, during nasal
breathing (Yu et al., 1981; Cheng et al., 1996). The ability of
the nose to filter particulate matter (as well as soluble or reac-
tive gases) serves as a protective mechanism against toxicity to
the lower respiratory tract. Thus, the pattern of breathing (nose
or mouth) due to different activities is a critical factor in deter-
mining the location and amount of deposition of inhaled parti-
cles in the respiratory tract (ICRP, 1994; U.S. EPA, 1996).
Only a couple of studies attempted to measure oronasal
breathing in children as compared to adults (James et al., 1997;
Becquemin et al., 1999). In both cases only a limited number
of children were studied. James et al. (1997) found that chil-
dren (age 7–16, n=10) displayed more variability than adults
with respect to their oronasal pattern of breathing with exer-
cise. However, it was not possible to predict the pattern of the
partitioning of ventilation during exercise based on age, gen-
der, or nasal airway resistance. Further, in a limited number of
children (age 8−16, n=10), Becquemin et al (1999) found that
the children tended to display more oral breathing both at rest
and during exercise than the adults. The highest oral fractions
were also found in the youngest children. None of these stud-
ies, however, was able to show a relationship between nasal
resistance and the relative contribution of nasal breathing in
their children. Furthermore, a recent study in adults (Bennett
et al., 2003) found that nasal ventilation during exercise varied
as a function of both race and gender. African-Americans pos-
sessed a greater nasal contribution to breathing during exercise
than Caucasians. At similar exercise efforts (i.e., normalized as
% maximum work capacity) the females also had a greater
nasal contribution to breathing during exercise than males.
Whether these race and gender effects occur in children as well
has not been investigated. While a number of investigators
measured the oronasal pattern of ventilation in adults
(Niinimaa et al., 1981), few addressed this parameter in chil-
dren. Additionally, those that have done so, in either adults or
children, did not fully characterize nasal anatomy/physiology
in their study subjects, such that the mechanisms/ determinants
of oronasal breathing during exercise are not fully understood.
Bennett et al. (2007) made preliminary measurements of the
relative contributions of oral versus nasal breathing at rest and
during incrementally graded submaximal exercise on the cycle
ergometer for adults and children (10% increments from 0–60%
maximum physical work capacity (PWCmax) for each sub-
ject), as shown in Figure 1. At first analysis, these data show
the children have significantly lesser nasal contribution to
breathing than adults at rest, 20%, and 40% PWCmax. However,
FIG. 1. Nasal contribution to breathing with exercise: children versus adults
(Bennett et al., 2007).
0
20
40
60
80
100
% Nasal
contribution
Work load (%max)
Adults (n
= 22)
Children
< age 11(n = 16)
Rest 604020
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CHILDREN’S INHALATION AND RISK ASSESSMENT 153
only 3 of the 16 children studied were African-Americans, so
that when percent nasal contribution is determined as a func-
tion of race and child versus adult, by analysis of variance,
only race significantly predicts nasal breathing at rest and the
two exercise levels. Among the children the maximum nasal
ventilation (L/min) that occurred during exercise for each child
was negatively correlated with nasal resistance and positively
correlated with maximal inspiratory flow through the nose
(MIFnose) (r=−.72 and .61, respectively).
The concentration of inhaled particles entering the lower
respiratory tract is diminished by nasal filtering. While the
uptake of particles was compared for oral versus nasal breath-
ing in adults for various particle sizes (Anderson & Proctor,
1982), there is little comparable data for children. Model anal-
yses suggest that when properly scaled physiological flows are
used in the calculation of nasal deposition, children, who have
higher nasal resistance than adults, should have higher nasal
deposition compared to adults (Phalen et al., 1989). Only one
study reported in vivo particle deposition measures for nasal
breathing in children (Becquemin et al., 1991). Surprisingly,
these investigators found lower nasal deposition efficiencies
for fine particles (1−3 μm mass median aerodynamic diameter
[MMAD]) in the children, despite their higher nasal resis-
tances. Their technique for measuring deposition fractions
(DF), however, required collection of exhaled particles in a
spirometer for subsequent analysis. In light of their methodologies
and surprising findings, these data need to be verified using
real-time DF measures by light scattering photometry at the
mouth and nose (Bennett & Zeman, 1998). It was shown that
nasal anatomy is an important variable for the efficiency of
particle uptake in the noses of adults (Kesavanthan et al.,
1998); however, these studies were not extended to a character-
ization of children’s nasal anatomy/physiology that may pre-
dict nasal efficiency for uptake of particles.
Bennett et al. (2007) measured DF of fine particles (1 and 2
μm MMAD; GSD < 1.2) for oral and nasal breathing using
individual total ventilation breathing patterns by respiratory
inductance plethysmography during their exercise protocol
(rest and 20% PWCmax breathing). DF values for both nasal
and mouth breathing were measured separately by laser photome-
try at the same tidal volume and breathing rate. From these DF
measures, nasal deposition efficiency (NDE) was calculated
for each condition (Becquemin et al., 1991). For light exercise
breathing conditions in adults, NDE for both 1- and 2-μm par-
ticles were significantly less in African-Americans versus Cau-
casians, 0.15 ± 0.07 versus 0.24 ± 0.11 for 1μm and 0.29 ±
0.13 versus 0.44 ± 0.11 for 2μm. Preliminary data show that
NDE for 2-μm particles is less in the children versus adults for
their light exercise (20% PWCmax) ventilation patterns, 0.25 ±
0.14 versus 0.37 ± 0.14, respectively. The fact that few Afri-
can-American children were studied (n=3) did not affect the
difference found between children and adults. If only Caucasian
children and adults are compared, the difference is even
greater, 0.25 ± 0.16 and 0.44 ± 0.10, respectively. These results
are consistent with previous findings of Becquemin et al.
(1991), and, similar to that study, no relationship between
nasal resistance and NDE was found.
Few data are available on DF of inhaled aerosols in children.
Both lung growth with age (from child to adult) and changes in
breathing patterns may affect DF of inhaled particles (Bennett
& Smaldone, 1987; Bennett et al., 1996; Heyder et al., 1988).
From age 6 yr to adult, it appears that a constant number of res-
piratory units is maintained while both the smallest bronchioles
and alveoli expand in size to produce the increased lung vol-
ume with increased age and height (Zeman & Bennett, 2006).
Likewise, the tracheobronchial airways grow in length and
diameter from birth to adulthood (Thurlbeck, 1997). Breathing
patterns also change with increasing age; i.e., tidal volumes
increase and respiratory rates decrease (Tabachnik et al., 1981;
Tobin et al., 1983). Schiller-Scotland et al. (1994) found higher
deposition (about 50%) of monodisperse, fine particles (1−3
μm MMAD) in children (age 3−14) compared to adults for
spontaneous breathing on a mouthpiece. The reported minute
ventilations in these children were much higher than might be
expected (Tabachnik et al., 1981), suggesting these children
may have been breathing more deeply than normal on the
mouthpiece apparatus (Tabachnik et al., 1981; Gilbert et al.,
1972). This would have contributed to an increased DF in these
children (Bennett et al., 1996). The DF of monodisperse, fine
particles (2 μm MMAD) in children (age 7−14), adolescents
(age 14−18), and adults (age 19−35) were compared for mouth
breathing conditions (Bennett & Zeman, 1998). In contrast to
previous deposition studies in children (Schiller-Scotland et
al., 1994; Becquemin et al., 1987), Bennett and Zeman (1998)
measured DF of inhaled monodisperse, fine (2 μm MMAD)
particles in all subjects as they breathed the aerosol with a pat-
tern previously determined by respiratory inductance plethys-
mography in each individual at rest (Bennett et al., 1996), i.e.,
that subject’s “real” resting breathing pattern. Breath-by-
breath DF (ratio of particles not exhaled/total particles
inhaled) was determined by photometry at the mouth. Among
the children, variation in DF was not dependent on subject
age or height, but DF was highly dependent on intersubject
variation in tidal volume (Vt). Unlike the Schiller-Scotland
study (1994), this study found no difference in DF for the
children versus adults for these fine particles. On the other
hand, the rate of deposition normalized to lung surface area,
nD
rate
, tended to be greater (35%) in children versus the com-
bined group of adolescents and adults for resting inhalation of
these particles.
Data on 2-μm particle deposition for mouth breathing at rest
in children was combined with that of a previous study on fine
particle deposition in children (Bennett & Zeman, 1998) to bet-
ter discern the factors contributing to interchild variability in
fine particle deposition. Both studies measured DF of fine par-
ticles (2 μm MMAD monodisperse (GSD ≤ 1.2), carnauba wax
particles) in healthy children, age 7–13 (n=36) while they fol-
lowed a breathing pattern previously determined by respiratory
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154 B. FOOS ET AL.
inductance plethysmography (i.e., that child’s spontaneous
pattern at rest). Among all variables that might predict deposi-
tion in children, tidal volume (Vt) (r =.79) markedly predicted
the variation in DF among children (Figure 2). Multiple regres-
sion analysis further showed that Vt is predicted by age, height,
and body mass index (BMI); at any given age and height, Vt
rose significantly with increasing BMI. Table 1 shows that the
overweight children (>90th percentile BMI) had twice the DF
of those in the lowest BMI quartile (<25th percentile). In the
same groups, resting minute ventilation (Ve) was also signifi-
cantly higher in the heaviest children. Consequently, the rate of
deposition, D
rate
(i.e., particles depositing/time), in the over-
weight children (proportional to the product of DF and Ve) was
2.7 times that of the leanest children. Among all children, D
rate
was significantly correlated with BMI (r=.46). These results
suggest that children’s weight may be a risk factor for respira-
tory morbidity associated with the inhalation of pollutant parti-
cles in ambient air (Bennett & Zeman, 2004). These
preliminary data have only been determined for fine particles
under resting breathing conditions. Furthermore, the children
recruited for study have not included extremely obese children.
Further experimental studies on the factors affecting parti-
cle deposition in children are required to improve and expand
on particle dosimetry and risk assessment models. For exam-
ple, current particle dosimetry models do not consider variabil-
ity in oronasal breathing among either adults or children. In
addition, a wider range of particle sizes might help characterize
typical ambient exposures. Due to a lack of experimental data
on nasal deposition efficiencies in children, extrapolation from
a limited data set for fine particle deposition in children under
resting breathing conditions is currently used to predict nasal
filtering efficiency for children over a wide range of particle
sizes. Finally, among the existing lung deposition data in chil-
dren there is limited information on relationships between dep-
osition and physiological parameters in children, i.e., variables
that may be used to better predict deposition in the lower respi-
ratory tract in children.
THE U.S. EPA ANALYSIS OF AGE-SPECIFIC
VENTILATION RATES USING BASAL METABOLIC
RATES AND ACTIVITY PATTERN DATA
The exposure to a chemical from the inhalation pathway is
not a simple function of the ventilation rate and body weight.
As has been discussed in the preceding sections, there are
many factors that affect the exposure to airborne contaminants
and the inhaled dose in the respiratory tract. These factors
include (1) exposure factors such as exposure concentration
and duration, (2) physicochemical properties of the contami-
nant such as, solubility, and (3) factors that influence the inter-
nal dose and disposition of the inhaled agents, including the
anatomy and physiology of the airways and ventilation rates.
Ventilation rates are a key physiological parameter that differ
across age groups, gender, and physical activity. Currently rec-
ommended ventilation rates given in the U.S. Environmental
Protection Agency (EPA) Exposure Factors Handbook (U.S.
EPA, 1997) rely on the methodology derived by Layton
(1993). This methodology is limited in that “ventilatory equiv-
alent” varies from individual to individual due to factors such
as differences in oxygen uptake efficiency, lung physiology,
and metabolic efficiency (Layton, 1993). The relationship
between oxygen consumption and ventilation rate is non-linear
(Hebestreit et al., 1998, 2000).
The intensity of physical activity is measured by the meta-
bolic equivalent of task (MET). The harder a body works dur-
ing an activity, the higher is the MET value. The MET is
fundamental in deriving ventilation rates for individuals while
performing various activities. Distributions of MET values
have been developed using data from the exercise physiology
and clinical nutrition literatures (McCurdy, 2000). The MET
represents the ratio of the energy needed for the activity
performed to the energy needed to sustain life (i.e., basal
metabolism) (Graham & McCurdy, 2005).
The U.S. EPA National Exposure Research Laboratory of
the Office of Research and Development has developed an
FIG. 2. Deposition fraction versus tidal volume in children (mouth
breathing) (Bennett & Zeman, 2004). Reprinted with permission from the
J
ournal of Applied Physiology.
R = 0.79
0
0.1
0.2
0.3
Deposition Fraction
0.4
0.5
0.6
Tidal Volume (Liters)
0 0.80.60.40.2
TABLE 1
Total Deposition Rate (D
rate
) of Inhaled 2-μm (MMAD,
GSD < 1.2) Particles as a Function of Body Mass Index
(BMI) in Children
Parameter
BMI percentile
<25, n=9
BMI percentile
25–90, n=17
BMI percentile
>90, n=10
Mean BMI
(kg/m
2
)
15.2 ± 0.8 18.4 ± 1.9 24.0 ± 3.1
DF 0.15 ± 0.06 0.22 ± 0.09 0.28 ± 0.11
V
e
(L/min) 5.9 ± 1.1 7.2 ± 1.8 8.1 ± 2.1
D
rate
0.9 ± 0.4 1.7 ± 1.0 2.4 ± 1.6
Note. Values are means ± SD; adapted from Bennett and Zeman
(2004).
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CHILDREN’S INHALATION AND RISK ASSESSMENT 155
approach to estimate the “ventilatory equivalent” directly
from the oxygen consumption rate. The National Center for
Environmental Assessment (NCEA) of the U.S. EPA has
used this approach to conduct an analysis of ventilation rates
and study their variability with regard to age, gender, and
basal metabolic rate. This approach is described in more
detailed in the external review draft report entitled Revision
of the Metabolically-Derived Ventilation Rates within the
Exposure Factors Handbook (U.S. EPA, 2006b). Briefly, in
this approach the oxygen consumption rate is estimated as a
function of metabolic equivalent task (MET) and the basal
metabolic rate (BMR). BMR is estimated for each age and
gender categories using equations derived by Schofield
(1985) (Table 2). This analysis uses data provided from more
recent sources such as the 1999–2002 National Health and
Nutrition Examination Survey (NHANES) for body weight
and the U.S. EPA Consolidated Human Activity Database
(CHAD). The NHANES 1999–2002 database contains
records of body weight, age and gender for a total of 19,022
individuals (quality control and quality assurance procedures
for the NHANES data are available in the Interviewers Proce-
dures Manual and Laboratory Procedures Manual; CDC,
2001). The CHAD database contains nearly 23,000 person-
days of time–location–activity data representing all ages and
gender (data quality flags are used in the CHAD database to
indicate missing data, data outside the allowable range, and
data of poor quality; U.S. EPA 2002). MET values were
assigned to each activity within the CHAD database. Daily
ventilation rates as well as ventilation rates associated with
various activity categories, including, sleep or nap, passive,
low intensity, medium intensity, and vigorous intensity, were
derived for various age cohorts. Adults from 21 to 80 yr were
divided into 6 groups broken into 10-yr intervals (21–30, 31–
40, etc.). Adults above 80 yr were placed in a single group.
Children <21 yr were divided into 8 age categories according
to the U.S. EPA guidance entitled Selecting Age Groups for
Monitoring and Assessing Childhood Exposures to Environ-
mental Contaminants, with the exception of children <1 yr,
who were lumped together due to sample size limitations
(U.S. EPA, 2005a).
Table 3 summarizes daily average ventilation rates adjusted
for body weight, by gender and age categories based on the
method already described. These results represent an average
rate taken over a 24-h period among individuals in the speci-
fied category. Mean daily ventilation rates ranged from 0.17 to
1.18 m
3
/min-kg, with the lowest value corresponding to
females 71<81 yr old and the highest value to females 1 yr of
age. Results of the analysis showed that children have higher
ventilation rates per unit of body weight than adults. Ventila-
tion rates change most dramatically from age 5 to 15 yr, as
noted by a steady decline to levels that are consistent through
most of adulthood.
Average ventilation rates were also calculated for each of
the five groups of activities (such as sleep or nap, sedentary/
passive, low intensity, medium intensity, and vigorous inten-
sity) according to specified ranges of METS values. These are
presented in Table 4 for each age category and gender. Young
children have higher ventilation rates per unit of body weight
than adults. For children <1 yr old this difference can be up to a
factor of 6. The values estimated using the methodology
described earlier were compared to the values estimated by
Layton (1993) and the values used by the International Com-
mission on Radiological Protection (ICRP, 1995). These com-
parisons are presented in Table 5. In general, for children < 6
yr of age, the ventilation rates estimated using the U.S. EPA
methodology were higher than the values estimated by both
Layton (1993) and ICRP (1995). This difference can be up to a
factor of three for children <1 yr of age. For children >6 yr of
age, the U.S. EPA analysis was consistent with the Layton
(1993) estimates.
The primary advantage of the approach used in this analysis
is that it accounts for differences in ventilation rate that occur
naturally with activity level, the effect of age and gender, and
variation both between and within individuals. The approach
allows for the direct estimation of ventilation rate from oxygen
consumption rate.
TABLE 2
Equations That Predict Basal Metabolic Rate (BMR in MJ/d) as a Function of Body Weight (BW, kg)
Age range
Sample size
Male FemaleMale Female
<3 yr 162(M) 137(F) BMR=0.249*BW − 0.127 BMR=0.244*BW − 0.130
3–9 yr 338(M) 413(F) BMR=0.095*BW +2.110 BMR=0.085*BW+2.033
10–17 yr 734(M) 575(F) BMR=0.074*BW+2.754 BMR=0.056*BW+2.898
18–29 yr 2,879(M) 829(F) BMR=0.063*BW+2.896 BMR=0.062*BW+2.036
30–59 yr 646(M) 373(F) BMR=0.048*BW+3.653 BMR=0.034*BW+3.538
>60 yr 50(M) 38(F) BMR=0.049*BW+2.459 BMR=0.038*BW+2.755
Note. From Schofield (1985).
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156 B. FOOS ET AL.
TABLE 3
Descriptive Statistics for Daily Average Ventilation Rate (m
3
/d-kg), by Age and Gender Categories
Age
category (yr)
Daily average ventilation rate for males (m
3
/d-kg) Daily average ventilation rate for females (m
3
/d-kg)
Mean 5th%tile Median 95th%tile Mean 5th %tile Median 95th %tile
<1 1.08 0.92 1.08 1.24 1.11 0.94 1.10 1.33
1 1.17 1.01 1.15 1.37 1.18 1.01 1.16 1.40
2 0.93 0.80 0.93 1.07 0.95 0.84 0.94 1.10
3–<6 0.70 0.52 0.70 0.90 0.69 0.51 0.67 0.90
6–<11 0.44 0.31 0.43 0.59 0.42 0.29 0.42 0.58
11–<16 0.28 0.21 0.28 0.38 0.25 0.19 0.24 0.33
16–<18 0.24 0.19 0.24 0.30 0.23 0.18 0.23 0.28
18–<21 0.22 0.16 0.22 0.28 0.20 0.15 0.20 0.25
21–<31 0.23 0.16 0.22 0.31 0.20 0.15 0.20 0.27
31–<41 0.23 0.16 0.23 0.32 0.21 0.14 0.20 0.29
41–<51 0.24 0.17 0.24 0.34 0.22 0.15 0.21 0.30
51–<61 0.23 0.17 0.23 0.31 0.21 0.15 0.21 0.29
61–<71 0.21 0.17 0.20 0.25 0.18 0.14 0.17 0.22
71–<81 0.20 0.17 0.20 0.23 0.17 0.14 0.17 0.22
>81 0.20 0.17 0.20 0.24 0.18 0.14 0.17 0.22
Note. Individual daily averages are weighted by their 4-yr sampling weights as assigned within NHANES 1999–2002 when calculating the
statistics in this table (U.S. EPA, 2006b).
TABLE 4
Daily Average Ventilation Rates (m
3
/h-kg) Within Specified Intensity Categories for Males and Females
According to Age Category
Age
category (yr)
Nμmber of
individuals
Mean ventilation rate (m
3
/h-kg)
a
Sleep Sedentary Low intensity
Medium
intensity
Vigorous
intensity
MFMFMFMFMFMF
<1 419 415 0.023 0.023 0.024 0.024 0.059 0.059 0.108 0.112 0.209 0.196
1 308 245 0.023 0.024 0.024 0.025 0.061 0.063 0.113 0.114 0.211 0.203
2 261 255 0.019 0.020 0.020 0.021 0.050 0.054 0.093 0.096 0.173 0.168
3–<6 540 543 0.014 0.014 0.015 0.015 0.038 0.037 0.070 0.068 0.130 0.119
6–<11 940 894 0.009 0.009 0.009 0.009 0.023 0.023 0.044 0.043 0.085 0.080
11–<16 1,337 1,451 0.006 0.005 0.006 0.006 0.015 0.014 0.029 0.026 0.057 0.053
16–<21 618 554 0.005 0.005 0.005 0.005 0.011 0.010 0.023 0.022 0.043 0.042
21–<31 701 1,023 0.003 0.003 0.004 0.004 0.009 0.009 0.021 0.020 0.040 0.039
31–<41 728 869 0.004 0.003 0.004 0.004 0.010 0.009 0.021 0.019 0.039 0.037
41–<51 753 763 0.004 0.004 0.004 0.004 0.010 0.010 0.022 0.020 0.039 0.039
51–<61 627 622 0.004 0.004 0.004 0.004 0.010 0.010 0.023 0.020 0.041 0.038
61–<71 678 700 0.004 0.004 0.005 0.004 0.010 0.009 0.021 0.018 0.037 0.033
71–<81 496 470 0.004 0.004 0.005 0.004 0.010 0.009 0.022 0.018 0.039 0.036
>81 255 306 0.005 0.004 0.005 0.005 0.011 0.010 0.023 0.020 0.043 0.040
a
METS, metabolic equivalent Task; Sedentary refers to METS ≤ 1.5; low intensity, 1.5 < METS ≤ 3; medium intensity, 3 < METS ≤ 6;
vigorous intensity, METS > 6 (U.S. EPA, 2006b).
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CHILDREN’S INHALATION AND RISK ASSESSMENT 157
This analysis shows that gender does not seem to have a
significant effect on ventilation rates. However, children have
higher ventilation rates than adults when results are adjusted
for body weight. For example, average daily ventilation rate
for children 1 yr of age is 13.3 m
3
/d, while adults 41 to <51 yr
old have a ventilation rate of 21.1 m
3
/d. The average daily ven-
tilation rates adjusted for body weight for the same age groups
are 1.2 and 0.24 m
3
/kg-d, respectively. This represents a five-
fold difference. Incorporating this variability in ventilation
rates into inhalation modeling applications is important for a
more accurate prediction of risk.
CHILDREN’S INHALATION RISK ASSESSMENT
PRACTICE
This article has discussed several key factors in understand-
ing children’s risks due to inhalation exposure to airborne pol-
lutants, and now the focus changes to the application of this
type of information in risk assessment. The evaluation of chil-
dren’s health risks, and the practices used to do so, are essential
to ensure public health protection.
U.S. Environmental Protection Agency
In assessing risk to public health, the U.S. EPA has a man-
date to explicitly evaluate risks to children through Executive
Order 13045 (1997) and the Agency Policy on Evaluating
Health Risks to Children (U.S. EPA, 1995). The Food Quality
Protection Act and the Safe Drinking Water Act Amendments
also provide a legislative mandate to U.S. EPA to explicitly
consider children’s health. Prior to these mandates, the agency
performed risk assessment that assessed risk to children in
situations where they were considered the most sensitive sub-
population.
Approaches applied in risk assessment by the U.S. EPA
Office of Air and Radiation (OAR) provide a broad range of
examples for inhalation risk assessment. These approaches fall
into two broad categories based on legal authority granted to
the Agency under the Clean Air Act Amendments (CAAA) of
1990. Under CAAA § 108 and § 109, National Ambient Air
Quality Standards (NAAQS) are developed for “criteria pollut-
ants.” Criteria pollutants represent a category of ambient air
pollutants that are ubiquitous in the environment, as they result
from numerous or diverse mobile or stationary sources. These
pollutants have been relatively well studied and encompass
large databases with an abundance of human data. Criteria pol-
lutants include the following: ozone, particulate matter, carbon
monoxide, lead, nitrogen dioxide, and sulfur oxides. As
required under the CAAA, the scientific basis and adequacy of
each of the NAAQS must be reviewed and revised if necessary
every 5 yr. The Clean Air Scientific Advisory Committee
(CASAC), part of the U.S. EPA Science Advisory Board,
reviews draft criteria documents prepared by the Office of
Research and Development and exposure and risk assessments
prepared by OAR, as well as a staff paper that provides recom-
mendations concerning the adequacy of existing standards and,
if appropriate, provides a range of alternatives for the Adminis-
trator to consider.
Hazardous air pollutants (HAP) or air toxics are regulated
under § 112 and 202 of the CAAA, respectively, with 112
being focused on stationary sources such as dry cleaners or
refineries, and 202 being particular to mobile-source related air
toxics (e.g., benzene). The § 112-specified list of HAP encom-
passes a large category of pollutants and includes reactive
gases (e.g., hydrogen chloride, phosgene, chlorine, aldehydes),
solvents, and metals. The health effects database available for
an individual HAP varies; some have significant databases,
while other HAP databases are sparse. Dose-response esti-
mates for many HAP were developed within the Integrated
Risk Information System (IRIS), a process that includes exter-
nal scientific peer review.
Because of the different mandates and databases, the opera-
tional risk assessment approaches for the NAAQS and HAPs
are also somewhat different. In general, approaches to both the
NAAQS and the HAPs include a review of the entirety of the
health effects database to establish the weight-of-evidence for
the nature and severity of potential health effects. Consider-
ation is given to all types of data, including epidemiological
studies of exposures to ambient air, controlled clinical studies,
animal lab studies, the consistency and coherency across the
different types of studies, exposure profiles, and population
demographics if available.
The NAAQS process for the criteria pollutants is much
more comprehensive in scope than that of the HAP because of
the wealth of information that has been compiled over the years.
Separate modeling efforts are devoted to each of the following
TABLE 5
Comparison of Average Ventilation Rates Based on ICRP,
a
Layton (1993), and U.S. EPA Analysis (m
3
/d)
Age category ICRP
b
Layton (1993)
b
U.S. EPA
analysis
<1 yr 2.86 4.5 8.5
1 yr — — 13.2
2 yr — — 12.8
1–<3 yr 5.16 (1–2 yr) 6.8 13.0
3–<6 yr 8.72 (2–7 yr) 8.3 12.4
6–<11 yr 15.3 (7–12 yr) 12.3 12.8
11–<16 yr 20.1 (12–17 yr) 13.5 (12–14 yr) 14.3
16–<18 yr 22.2 (>17 yr) 14.5 (15–18 yr) 15.8
18–<21 yr — 13.3 (>19 yr) 14.7
a
International Commission on Radiological Protection.
b
Some of the age groups were not entirely consistent with the U.S.
EPA recommended age groups in U.S. EPA (2005). The values in
parentheses indicate the age groups represented in the estimated venti-
lation rate.
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158 B. FOOS ET AL.
components, depending on the type of information available:
(1) air quality monitoring/modeling; (2) inhalation exposure
modeling; (3) dosimetry modeling; and (4) risk assessment/
characterization. Depending on the available data and nature of
the endpoint, an assessment may be based (1) on an ambient
concentration-response relationship (e.g., particulate matter
and ozone adverse health effects based on epidemiological
studies where the relationship is with ambient concentrations
measured at fixed-site monitors), (2) on an inhalation expo-
sure-response relationship (e.g., ozone health effects based on
controlled human exposure studies where the relationship is
with the exposure concentration in the breathing zone), or (3)
on an internal dose-response relationship (e.g., carbon monox-
ide and lead where the health effects are related to internal
measures of dose). To date, the bases for all of the NAAQS
have relied most heavily on human data to characterize the
exposure- or dose-response relationship, with supporting
mechanistic information from animal lab studies.
NAAQS exposure assessment considers air concentrations
of the pollutant across microenvironments, as well as the
amount of time spent in the different microenvironments and
the associated ventilation rates. Data from ambient air monitor-
ing and/or modeling are used as input to the Air Pollutant
Exposure (APEX) model (also referred to as the inhalation
exposure module of the Total Risk Integrated Methodology or
TRIM.Expo; U.S. EPA, 2006c, 2006d), which is derived from
a previous version called the probabilistic NAAQS Exposure
Model (pNEM). This modeling is population based and simu-
lates individuals moving through time and space. To calculate
exposure and internal dose, the amount of time in different
microenvironments and associated ventilation rates are based
on the Consolidated Human Activity Database (CHAD)
described previously. This approach provides the ability to
identify and characterize the groups that are most exposed, or
groups which are indicated by the adverse response data to be
susceptible, either of which could be children. Thus, children
are often addressed in the NAAQS process. For example, in the
ozone exposure assessment, assessed groups included the fol-
lowing: general population and active general population,
school age children (5 to 18 yr), active school age children
(defined based on physical activity level), and asthmatic school
age children (U.S. EPA, 2006e, 2006f). The NAAQS assess-
ments also characterize variability and uncertainty using a
Monte Carlo approach for exposure event modeling. The risk
characterization for these assessments includes both quantita-
tive assessments for those health endpoints with sufficient data
to estimate concentration- or exposure-response relationships,
as well as qualitative assessments of population exposures of
concern for those health endpoints where there is insufficient
data to estimate these relationship. An uncertainty factor
approach is not applied in the NAAQS reviews.
The scope of HAP assessments varies with pollutant and
source characteristic, and may include chronic and acute time
scales for inhalation exposure, as well as other routes of exposure.
The general approach to HAPs mirrors that of the NAAQS,
with estimation of emissions, air quality monitoring and mod-
eling, personal monitoring, and inhalation exposure modeling
to the extent the data allow. A tiered approach is used for effi-
ciency, whereby lower tiers use simpler, more conservative
tools and assumptions to identify important sources and pollut-
ants, and higher tiers use more refined tools and assumptions are
replaced by data. Risk assessment activities for HAP may be for
nonregulatory, programmatic purposes such as the triennial
national-scale assessment performed as part of the National Air
Toxics Assessment (NATA) activities, or they may be for regu-
latory purposes such as residual risk standards and listing/delist-
ing HAP or source categories from CAA § 112.
Most assessments for HAP are based on an inhalation expo-
sure-response relationship, as a function of the available data
and nature of the chemical and endpoint. Depending on the
HAP, the basis for the exposure-response relationship may be
human or animal data. For noncancer effects the reference con-
centration (RfC) is used as the dose-response estimate, whereas
lifetime cancer risk is calculated based on an inhalation unit
risk (risk per microgram per cubic meter). Approaches for
dosimetry adjustment generally follow Methods for the Deri-
vation of Inhalation Reference Concentrations and Application
of Inhalation Dosimetry (RfC Methods; U.S. EPA, 1994). This
method considers particles and different types, or categories, of
gasses separately. Category 1 gasses are highly reactive and
water soluble, and interact with the respiratory tract at the por-
tal of entry; Category 2 gasses are water soluble, but some
blood accumulation may occur, and may demonstrate both res-
piratory and remote effects; and Category 3 gasses are poorly
soluble, thereby demonstrating remote (systemic) effects. The
RfC Methods provides a hierarchical choice of model structure
to describe different dose metrics used in the dose-response
analysis. The purpose of the hierarchy is to provide the flexi-
bility to address varying data on different dose metrics that
might be most relevant to the mode of action. The RfC Meth-
ods’ default approach utilizes a dosimetric adjustment factor in
the derivation of the RfC, and the approach for this adjustment
is based on the nature of the pollutant (i.e., particle, gas/category)
and a dose metric calculated for the respiratory tract region of
interest (e.g., extrathoracic, tracheobronchial, pulmonary). The
normalizing factor may vary for different effects, such as sur-
face area for portal-of-entry effects, body weight for remote
effects, or other factors relevant to the dose metric. A more
refined, or optimal, model structure as discussed in the RfC
Methods would describe all significant mechanistic determi-
nants of chemical disposition, toxicant-target interaction, and
tissue response; uses chemical-specific and species-specific
parameters; and describes dose metric(s) at a level of detail
commensurate to the toxicity data. Refined models are data
intensive and developed on a case-by-case basis.
HAP exposure-response assessments may consider chil-
dren’s risk in several different ways. Noncancer assessments
typically apply an uncertainty factor to address intrahuman
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CHILDREN’S INHALATION AND RISK ASSESSMENT 159
variability. Additionally, the critical effect from which the RfC
is derived may be an effect in the young (e.g., neurodevelopmental
toxicity), and another uncertainty factor for database insufficiency
is generally applied when developmental data are lacking. In
cancer risk assessment when there is no mode of action (MOA)
established, the linear default extrapolation to low doses is con-
sidered to provide adequate public health conservatism (U.S.
EPA, 2005b). When chemical-specific data pertaining to poten-
tial susceptibility in early life are available, they are considered.
Additionally, when a mutagenic MOA is established for carcino-
genicity and chemical-specific data for early life are not avail-
able, then age-dependent adjustment factors are applied per the
Supplemental Guidance for Assessing Susceptibility from Early
Life Exposure to Carcinogens (U.S. EPA, 2005c).
California Environmental Protection Agency
Inhalation risk assessment is a long-time activity of the Cal-
ifornia Environmental Protection Agency (Cal/EPA), and
updating the practice of children’s inhalation risk assessment
(in support of the California Children’s Environmental Health
Act) is a current activity. In 1983, the California legislature
established the Toxic Air Contaminant program requiring
establishment of a list of toxic air contaminants (TACs) and a
regulatory program to reduce risks associated with exposures
to TACs. The California Air Resources Board is the lead
agency for implementing this program. The Office of Environ-
mental Health Hazard Assessment (OEHHA) within the Cal/
EPA is responsible for conducting health effects assessments
for TACs. In addition, the legislature created the Air Toxics
Hot Spots Act in 1987 which established an emissions inven-
tory program for air toxics and other air contaminants emitted
from stationary sources and a requirement for site-specific
health risk assessments characterizing public health impacts of
these emissions.
As part of the Air Toxics Hot Spots program, OEHHA
developed site-specific risk assessment guidelines. Included in
the guidelines were reference exposure levels (RELs) to be
used to evaluate impacts of peak 1-h exposures (acute RELs),
and annual average exposures (chronic RELs), using a hazard
index approach. In addition, OEHHA was mandated to evaluate
risk from carcinogens emitted from stationary sources. These
guidelines were developed as four technical support documents
describing development of: (1) acute reference exposure levels
applicable to infrequent short-term (1 h) exposures (OEHHA,
1999); (2) chronic reference exposure levels applicable to
long-term exposures (OEHHA, 2000a); (3) a document that
describes cancer risk assessment and compilation of available
cancer potency factors (OEHHA, 2005); (4) exposure algo-
rithms and exposure factors to apply to specific sites for risk
assessment including a stochastic exposure assessment option
(OEHHA, 2000b); and (5) a guidance document that combines
key information from the technical support documents
(OEHHA, 2003).
The risk assessment guidelines for inhalation exposure follow
U.S. EPA guidance closely. California’s chronic reference
exposure levels are similar to the RfC and in some cases are
equivalent to the RfC. Where new data were available at the
time of developing the REL, OEHHA used the new data and
this resulted in some cases in differences between the RfC and
the REL. In addition, the database deficiency uncertainty factor
that the U.S. EPA uses was not used in some cases, and there-
fore some of the RELs are actually higher than the RfC. None-
theless, overall the existing methods are quite similar. It should be
noted that OEHHA is in the process of revising these documents
to more explicitly account for infants and children. A database
deficiency factor may be used in future assessments, particu-
larly where developmental toxicity data are lacking.
In the late 1990s OEHHA was mandated to develop a risk
assessment approach that considered explicitly variability in
assessing risks. As part of this effort, breathing rate distribu-
tions were developed (Marty et al., 2002) that combined data
on activity patterns of California residents and ventilation rates
measured in individuals at specific activities. Separate breath-
ing rates were generated for children and adults as well as a
simulated 0–70 yr distribution, which incorporated children’s
breathing rates, to use in lifetime carcinogen risk assessment.
These breathing rate distributions allow the risk assessor to
adjust cancer risk for the higher breathing rates and exposures
of children. In particular, the dose algorithm for inhalation
utilizes the 95th percentile, mean, or other chosen percentile or
the entire distribution of children’s breathing rates or the simu-
lated lifetime breathing rate to characterize dose. The dose is
then multiplied by the cancer potency factor to obtain a central
tendency and high-end estimate of risk, or a distribution based
on the distribution of breathing rates. Thus, a short-term 9-yr
exposure scenario (similar to RCRA/CERCLA assessments)
utilizes a child’s breathing rate to account for higher exposures
in childhood. Similarly, when estimating a lifetime risk, the
simulated 0–70 yr breathing rate distribution may be utilized
and incorporate the higher breathing rates of children.
There are a number of issues related to differences between
children and adults that are now the focus of attention to
improve California’s risk assessment methodologies, and bet-
ter characterize risk to children. OEHHA originally utilized the
U.S. EPA (1994) default dosimetric methods to calculate
human equivalent concentration (HEC) to address some of the
uncertainty in interspecies extrapolation in developing RELs.
However, the HEC adjustments for both regional gas and parti-
cle deposition are based on parameters in adult humans, not
those of children of various ages. In addition, particle deposi-
tion is dependent somewhat on physiological status; for
instance, individuals with asthma have greater fine particle
deposition (1 μm uniform diameter) than those without asthma
(Kim & Kang, 1997). Thus, this HEC methodology is not par-
ticularly quantitative, as currently applied, for addressing the
intraspecies uncertainty associated with the differences in
dosimetry for infants and children; instead an intrahuman
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160 B. FOOS ET AL.
uncertainty factor is used. OEHHA will be reevaluating vari-
ous possible approaches to accommodate human variability in
dosimetry and toxicokinetics for risk assessments of infants
and children, including the use of the default RfC method for
HEC adjustment with ventilation rates and regional surface
areas specific to infants and children.
It has become customary to regard the original default
10-fold uncertainty factors for inter- and intraspecies extrapo-
lation as both being composed of two threefold factors (half-
log=3.16), one allowing for toxicokinetic differences and one
for toxicodynamics (Jarabek, 1995; Renwick & Lazarus,
1998). In effect, in the HEC method as currently practiced, the
risk assessor replaces the threefold uncertainty factor for toxi-
cokinetic differences between species with the HEC adjust-
ment. However, the default RfC method for HEC estimation is
not a full toxicokinetic adjustment, but rather, depending on
the chosen metric, may be a partial dosimetric adjustment and
not fully account for other dispositional differences such as
those for distribution, metabolism, and elimination. Thus,
OEHHA is considering retaining a partial toxicokinetic uncer-
tainty factor when the default RfC method for HEC estimation
is used in interspecies extrapolation, to account for remaining
toxicokinetic differences.
Although OEHHA developed breathing rate distributions
for children and a simulated 70-yr breathing rate distribution
incorporating childhood for use in cancer risk assessment, the
data available at the time were limited for young children. Spe-
cifically, while activity data for young children were available,
the measured breathing rates for specific activities incorpo-
rated into the breathing rate distribution did not include mea-
surements in children younger than 3 yr. Thus breathing rates
for newborns to age 2 yr were assigned based on measurements
in children 3 yr and older, and are therefore not adequately
reflected in the breathing rate distribution. This data gap likely
results in an underestimate of the mean and upper percentiles
of the distribution. OEHHA is developing breathing rate distri-
butions for smaller age groups starting with newborns based on
energy intake data. These will be useful to apply to cancer risk
assessment and in particular for weighting by age-at-exposure
as described by U.S. EPA (2005c).
DISCUSSION
The role of genotype in affecting risk was raised as a
concern. Some researchers have observed a modification of the
physiological effect by genotype, which may be dramatic.
Based on these observations, understanding the distribution of
risk, identifying genetic polymorphisms, and determining how
to incorporate the unidentified polymorphisms are consider-
ations for risk modeling. The observation of responders and
nonresponders in ozone chamber studies was noted as an
example of a possible genetic basis for variability in response
(Romieu et al., 2004; Yang et al., 2005). In chamber studies
measuring lung function in response to ozone exposure, it was
found that certain individuals consistently respond with signif-
icant lung function decrement whereas others do not (McDon-
nell et al., 1985; Bedi et al., 1988).
With respect to infant mortality and particulate matter, it
was noted that in the association of infant mortality and partic-
ulate matter a plausible causal factor is that children are sus-
ceptible to respiratory infections in the first year of life, which
may rise and be exacerbated due to air pollution. Furthermore,
it was noted that direct comparison of the susceptibility to air
pollution of adults and children is complicated by the various
methods and health endpoints measured, which tend to differ
for children and adults. It is clear that there are susceptible
adult populations such as the elderly for cardiovascular mor-
bidity and mortality, and that there are adverse health effects of
air pollution measured in children, such as adverse birth out-
come and respiratory morbidity and infant mortality. It was
noted that asthma is generally a more serious disease in chil-
dren because they have smaller airways and higher prevalence
rates of the disease, with the youngest children having higher
hospitalization rates for asthma than older children and adults.
Further, effects of air pollution on development and birth out-
come such as low birth weight are health consequences spe-
cific to infants and children and not adults.
Concerns were raised for the lack of longitudinal studies for
children’s health, although it was pointed out that the Southern
California Children’s Study was a well-conducted longitudinal
study with a number of published papers (Gauderman et al.,
2000). While most asthma studies have been cross-sectional
and assessed prevalence, birth cohorts to study asthma devel-
opment have been established and data will be available in sev-
eral years.
A question was raised regarding emerging research on air
pollution and cognitive development, as well as latency in the
manifestation of effects. Cognitive development, particularly
with regard to nasal deposition of manganese and other neuro-
toxicants and subsequent translocation via the olfactory nerves
to the brain, is an area of emerging interest, but it may be some
time before adequate data are available. A few studies of envi-
ronmental tobacco smoke exposure showed a relationship with
altered cognitive function in children (Yolton et al., 2005;
Rauh et al., 2004); however, studies of cognitive development
are generally difficult and there are many confounding factors.
It is helpful when epidemiological data are reported as the
change in health outcome distribution between exposed and
control populations, for the purpose of applying these values in
risk assessment. Further, it is a concern that central site moni-
tors are used for determining exposure in epidemiological stud-
ies because this approach does not provide data for personal
microenvironments where concentrations may be higher or
lower. Use of central site monitors produces exposure mea-
surement error, exposure misclassification and tends to bias the
results towards the null (Jerrett et al., 2005; Zeger et al., 2000).
The examination of BMI and particle deposition in children
was identified as a potential epidemiological research area, as
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CHILDREN’S INHALATION AND RISK ASSESSMENT 161
was the interaction of air pollutants and allergens. There are
more data on the influence of BMI available in adults than for
children. There is limited epidemiological data on the interac-
tion of allergens and pollutants, except that substantial work
indicated that exposure to diesel exhaust increased the response
to allergens in human subjects (Diaz-Sanchez et al., 1999,
2000, 2003; Nel et al., 1998), and pollen in conjunction with
particulates may alter the immune system’s response (Ishizaki
et al., 1987; Maejima et al., 2001).
Breathing rates are also an area that would benefit from fur-
ther experimental data, as models are only as useful as the data
that goes into them. Limited data are available to verify the
current models for breathing rates. A recent development
called a “life shirt” was identified as a tool; it is worn for 24 h
to measure not only breathing rate (or minute ventilation), but
also the tidal volumes and breathing frequencies associated
with those rates throughout the entire day. An alternative
method for validating breathing rates has been evaluated by the
California EPA. This validation exercise (OEHHA, 2000,
Appendix K) examined data from energy expenditure studies
using doubly labeled water to determine how much oxygen
subjects consumed. From these data it is possible to determine
how much air the individual subjects inhaled over a few weeks.
Such data can be compared to estimates of inhalation rates
obtained through other approaches.
The consideration of premature infants is a concern, as these
infants have underdeveloped lungs and other organs and are
predisposed to a number of adverse health outcomes including
mortality (generally, this group would be included in the age
group of less than 1 yr old in the breathing rate distributions
based on activity patterns). However, the activity pattern data
that are currently available record information for children 0 to
1 yr old, such that no distinction can be made, for example,
between a 2-mo-old versus a 9-mo-old. Activity pattern data
for premature infants are not available. It was noted that there
are different activity profiles derived from NHAPS for each
age group that are considered in the evaluation of inhalation
rates, as discussed already in the third section of this article.
A concern was raised about focusing on breathing rate when
other kinetic parameters, such as metabolism and blood flow,
may be important factors. Current assessment methods involve
PBPK models (when available), and these models take into
account more factors than breathing rate; moreover, breathing
rate may be applied as a variable parameter in the exposure
assessment. Quantitative allowances are made for variability
due to age (insofar as suitable age-dependent parameter values
can be identified) and for issues of internal dosimetry. Under
the California Air Toxics Hot Spots Program, the air pollution
control districts in California have a number of emissions
sources for which public health impacts are assessed via emis-
sions inventories and subsequent health risk assessment. The
reporting is somewhat similar to the U.S. EPA Toxic Release
Inventory but with lower reporting triggers (lb/yr) and a more
comprehensive accounting for small facilities. Facilities are
ranked based on a prioritization procedure, and for high priority
facilities, a risk assessment is completed using the guidelines
already described briefly in the fourth section.
Other issues identified for further examination included the
risk assessment of nanoparticles on children’s health, the
effects of in utero exposures, and the mode of breathing (nasal
versus mouth breathing). A broad concern is that the applica-
tion of available knowledge in current risk assessment practice
sometimes lags behind, for children’s health risk assessment
and in general, and updating needs to become more routine.
CONCLUSIONS
The scientific evidence presented in this review is an intro-
duction that helps to illustrate the scientific basis for concern
about children’s exposure to air pollutants.
• The available epidemiological evidence indicates
adverse health effects in children from exposure to
current levels of ambient air pollution.
• A review of the ontogeny of lung structure and func-
tion, breathing patterns, and the resulting implications
for dose and deposition reveals age-related differ-
ences that increase risk for children.
• New methods for estimation of inhalation rate illus-
trate variability with age, which is greater than was
previously estimated.
• Reviewing the current risk assessment methods illus-
trates how children are considered, as well as areas
where additional analysis may be warranted to ade-
quately characterize risk.
Focusing on children’s inhalation dosimetry and health
effects will result in application of the best available scientific
data and methods in future risk assessments of air pollution to
adequately protect children’s health. A number of research
interests and data needs were identified for children’s inhala-
tion dosimetry and risk assessment; however, these needs were
not prioritized as a part of the workshop, and that is a potential
future activity. Further research on the impacts of airborne
pollutants on infants and children, and on the differences in
dosimetry by age, is clearly warranted for the improvement of
risk assessment and public health.
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