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Assessing Population Exposures to Motor Vehicle Exhaust

Authors:

Abstract

The need is growing for a better assessment of population exposures to motor vehicle exhaust in proximity to major roads and highways. This need is driven in part by emerging scientific evidence of adverse health effects from such exposures and policy requirements for a more targeted assessment of localized public health impacts related to road expansions and increasing commercial transportation. The momentum for improved methods in measuring local exposures is also growing in the scientific community, as well as for discerning which constituents of the vehicle exhaust mixture may exert greater public health risks for those who are exposed to a disproportionate share of roadway pollution. To help elucidate the current state-of-the-science in exposure assessments along major roadways and to help inform decision makers of research needs and trends, we provide an overview of the emerging policy requirements, along with a conceptual framework for assessing exposure to motor-vehicle exhaust that can help inform policy decisions. The framework includes the pathway from the emission of a single vehicle, traffic emissions from multiple vehicles, atmospheric transformation of emissions and interaction with topographic and meteorologic features, and contact with humans resulting in exposure that can result in adverse health impacts. We describe the individual elements within the conceptual framework for exposure assessment and discuss the strengths and weaknesses of various approaches that have been used to assess public exposures to motor vehicle exhaust.
REVIEWS ON ENVIRONMENTAL HEALTH VOLUME 20, NO. 3, 2005
Assessing Population Exposures to Motor Vehicle Exhaust
Chris Van Atten,
1
Michael Brauer,
2
Tami Funk,
3‡
Nicolas L. Gilbert,
4
Lisa Graham,
5
Debra Kaden,
6
Paul J. Miller,
7
Leonora Rojas Bracho,
8
Amanda Wheeler,
4
and Ronald H. White
9
with input from
additional participants of the Workshop on Methodologies to Assess Vehicle Exhaust Exposure
§
1
M.J. Bradley and Associates, Concord, Massachusetts, USA;
2
University of British Columbia,
Vancouver, British Columbia, Canada;
3
Sonoma Technologies, Inc., Petaluma, California, USA;
4
Health Canada, Ottawa, Ontario, Canada;
5
Environment Canada, Ottawa, Ontario, Canada;
6
Health Effects Institute, Boston, Massachusetts, USA;
7
Commission for Environmental Cooperation,
Montreal, Quebec, Canada;
8
Instituto Nacional de Ecología, Mexico City, Mexico;
9
Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
Current address: GGF Ventures, Fairfax, CA;
§
Additional participants of the Workshop on Methodologies to Assess Vehicle
Exhaust Exposure held 29-30 September 2003 at the Commission for Environmental Cooperation, Montreal, Quebec: Jeffrey Brook,
Timothy Buckley, Verónica Garibay Bravo, Fernando Holguin, Hortencia Moreno-Macias, Alvaro R. Osornio Vargas, Matiana
Ramírez Aguilar, and Iris Xiaohong Xu
OUTLINE
Abstract
Background
Conceptual Framework
Surrogate Techniques
Modeling Techniques
Regression Modeling Approaches
Dispersion Modeling
Measurement Techniques
Within-city spatial variability in pollutant
concentrations
Components of the motor vehicle emissions
mixture: Diesel exhaust
Recommendations
KEYWORDS
air pollution, diesel, epidemiology, traffic, exposure
assessment, vehicle exhaust
_____________________________
ABSTRACT
The need is growing for a better assessment
of population exposures to motor vehicle
exhaust in proximity to major roads and high-
ways. This need is driven in part by emerging
scientific evidence of adverse health effects from
such exposures and policy requirements for a
more targeted assessment of localized public
health impacts related to road expansions and
increasing commercial transportation. The
momentum for improved methods in measuring
local exposures is also growing in the scientific
community, as well as for discerning which
constituents of the vehicle exhaust mixture may
exert greater public health risks for those who
are exposed to a disproportionate share of road-
way pollution. To help elucidate the current
state-of-the-science in exposure assessments
along major roadways and to help inform
decision makers of research needs and trends,
we provide an overview of the emerging policy
requirements, along with a conceptual frame-
work for assessing exposure to motor-vehicle
exhaust that can help inform policy decisions.
Reprint requests to: Michael Brauer, The University of
British Columbia, School of Occupational and Environmental
Hygiene, 2206 East Mall, Vancouver BC V6T1Z3 Canada; E-
mail: brauer@interchange.ubc.ca
© 2005 Freund Publishing House Ltd. 195
C. VAN ATTEN, M. BRAUER, ET AL.
196
The framework includes the pathway from the
emission of a single vehicle, traffic emissions
from multiple vehicles, atmospheric transfor-
mation of emissions and interaction with topo-
graphic and meteorologic features, and contact
with humans resulting in exposure that can
result in adverse health impacts. We describe
the individual elements within the conceptual
framework for exposure assessment and discuss
the strengths and weaknesses of various
approaches that have been used to assess public
exposures to motor vehicle exhaust.
BACKGROUND
Historically, air quality and transportation
planning typically evaluates emission impacts at
the urban air shed or the metropolitan area level
and does not directly address localized exposures
to high-volume traffic on specific roadways. Now
a need is emerging for gathering more information
within the public health policy arena on the local
health effects associated with air pollution in
communities adjacent to major traffic arteries. This
emphasis arises from a growing number of studies
that have raised concerns regarding the possible
associations between proximity to high-volume
motor vehicle traffic (and its associated emissions)
and increased risk of premature mortality, cardio-
vascular and respiratory diseases, and cancer health
endpoints /1–6/, and from studies describing the
increased concentrations of air pollutants measured
in proximity to major roads. These studies have led
regulators, health and environmental advocates,
and researchers to consider the implications of
these findings on the broader policy agenda.
In many metropolitan areas, residents living
close to major roadways are often low-income or
minority populations, raising concerns of environ-
mental justice and the role of air pollution and
socioeconomic conditions on health /7–8/. Such
population groups can (a) be exposed to other
health risks in the environment and in occupational
settings, (b) have poor nutritional status and
limited access to health care, or (c) have a high
prevalence of underlying diseases relevant to air-
pollution health effects. Factors such as these can
act as effect modifiers to air pollution exposures
from proximity to major traffic roadways, thus
increasing the potential for adverse health effects.
The increasing pressures of urban sprawl are
likely to promote the expansion of high-traffic road-
ways and a concomitant increase in vehicle miles
traveled (VMT), placing continued emphasis on
health concerns related to population exposures to
traffic exhaust /7/. The policy of promoting the
infilling of residential housing in urban central
core areas—in addition to being beneficial for the
economic revitalization of these areas and for re-
ducing urban sprawl and VMT—could increase the
size of the population potentially exposed to a high
level of motor vehicle emissions, especially from
heavy-duty vehicles.
In light of the emerging need for a better
understanding of the local health impact of vehicle
exhaust, and for framing the issues and assessing
the current state of the science, this article provides
a review of methodologies for assessing population
exposures to motor vehicle exhaust along major
transportation corridors. The review focuses on the
functional elements of methods to assess
population exposure. A number of other external
factors, such as socioeconomic status, behavioral
habits, and preexisting health conditions, can affect
health outcomes from exposures to vehicle
exhaust. Such factors, which are not discussed at
length here, have been the subject of other reviews
(for example, see /8/ and references therein).
CONCEPTUAL FRAMEWORK
To assist in providing a path forward in the
development of local population exposure assess-
ment for meeting emerging policy needs, we offer
a conceptual framework for understanding the
process of exposure assessment, while simultan-
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
197
eously attempting to convey the many challenges
involved in performing such an analysis. After
presenting this framework, we will describe certain
specific tools and techniques that can help estimate
population exposures to motor vehicle pollution in
proximity to major traffic corridors.
In general, the two reasons for conducting an
exposure assessment are
as part of epidemiologic studies linking obser-
vations of respiratory disease, cancer, and
other health endpoints with potential causes of
illness; and
for environmental risk assessment, in evaluating
and quantifying the risks to a population that
stem from a given source of pollution.
The objective of exposure assessment, whether
for an epidemiologic study or for a risk assessment,
and the magnitude of available resources will
influence the choice and rigor of the methodology
employed for assessing exposures.
Our conceptual framework, summarized in
Fig. 1, begins with the emissions generated by an
individual vehicle (Factor 1). A host of factors is
Emissions
Dispersion &
Transformation
Exposure
Health
Outcomes
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
p
pYp
ppY
& v
K
Individual
vehicle
emissions are
influenced by
a variety of
factors:
Vehicle load
Vehicle
temperature
(i.e., cold
starts)
Maintenance
Fuel
characteristics
Pollution
control
systems
Tampering
Speed
Collectively,
vehicle traffic
emissions in
a given
location will
depend on
the:
Number of
miles
traveled by
vehicles in
use
The types
and ages of
vehicles on
the road
Road grade
Congestion
Traffic
signals
Roadway
features
influence
pollution
transport and
dispersion:
Street
canyons
Sound
barriers
Tunnels
Wind breaks
Topography,
such as
valleys
Atmospheric
transformation and
decay will influence
the spatial and
temporal
concentrations of
pollution:
Sunlight
Temperature
Humidity
Wind speed and
direction
Mixing height
The mix of
chemicals in the
atmosphere and
their chemical
reactions
Deposition
inhaled concentration
The level of
exposure will
depend on the
activity patterns of
the individual and
the time spent in
different
microenvironments:
People are using
different types of
transportation and
moving between
different cities,
within cities, and
between work,
home, school, etc.
Within a vehicle and
other micro-
environments,
pollutants can
concentrate at
higher than ambient
levels
Personal
factors will
influence
whether an
adverse health
outcome
results from
pollutant
exposure:
Socioeconomic
position
Behavioral
habits (e.g.,
smoking,
nutrition)
Pre-existing
conditions and
illnesses
Genetic
susceptibility
Age
Fig. 1: Conceptual Framework for Assessing Population Exposures to Motor Vehicle Exhaust. A host of factors influences
population exposures to motor vehicle air pollution and their associated adverse health outcomes. Our conceptual
framework summarizes the key factors influencing the degree of exposure from the source of emissions (motor vehicles)
to the receptor population.
C. VAN ATTEN, M. BRAUER, ET AL.
198
known to influence an individual vehicle’s
emissions performance, such as the age of vehicle,
fuel burned, condition and performance of its
pollution control systems, engine load, driving cycle,
and other factors. Laboratory testing attempts to
capture such factors by simulating typical drive
cycles on a chassis or engine dynamometer for tail-
pipe emissions. Evaporative, running, and refueling
losses can be evaluated in laboratory settings, but
the methods are difficult, cumbersome, and do not
represent real-world conditions. Alternatively,
emissions can be measured in real-world situations
by fast-response monitoring of individual vehicles
while in use (for example /9/) or by on-road
emissions measurement systems (for example /10/).
Vehicle traffic emissions (Factor 2) reflect the
collective performance of hundreds or thousands of
vehicles traveling a given roadway under specific
driving conditions (for example, congestion).
Emission factor models, like the United States
Environmental Protection Agency’s (U.S. EPA)
MOBILE model, are designed to estimate motor
vehicle emissions based on a myriad of inputs and
assumptions, such as fleet characterization, vehicle
miles traveled, vehicle starts and stops, driving
speeds, the deterioration rates of pollution control
systems, and other factors. Real-world monitoring
such as remote sensing campaigns, tunnel studies,
or fuel-based emissions inventories (for example
/11–12/) are alternative approaches that can be used
to estimate emissions. Each alternative method
also has its own limitations, such as the limited
data available for specific locations and
uncertainties that should be considered when
developing a mobile source inventory.
For certain applications, however, the emission
factors generated by the MOBILE model might not
provide the detailed characteristics required for a
smaller scale, such as a 2-km stretch of road. The
latest version, however, MOBILE6, does include
emission factors specific to different roadway
types and congestion levels. In addition, the U.S.
EPA is developing its next-generation mobile
source emissions model, MOVES, which estimates
emissions based on modes of vehicle operation.
The MOVES model will allow for the calculation
of emission factors at a range of geospatial scales.
In addition to these efforts, other microscale
emission factor models have been developed (for
example /13–14/).
Vehicle count by type of vehicle can also be
logged and then used as model input. The Georgia
Tech Research Partnership has been developing the
Mobile Emission Assessment System for Urban
and Regional Evaluation
(MEASURE) model, a
research-grade motor vehicle emissions model
within a geographic information system (GIS)
framework /15/. The GIS framework of the model
allows the linkage of typical travel-demand model
outputs, simulation model outputs, or monitored
Advanced Traffic Management Systems (ATMS)
traffic-volume estimates. The MEASURE model
contains several ‘modal approaches’ to estimating
emissions as a function of vehicle fleet technology
and vehicle operating ‘mode’, representing a range
of vehicle operating conditions such as cruise,
acceleration, deceleration, idle, and power demands
leading to fuel enrichment.
Also recently introduced is the Comprehensive
Modal Emissions Model (CMEM), developed
jointly by the University of California-Riverside
and the University of Michigan. CMEM is a modal
model that estimates fuel consumption and gaseous
pollutant emissions based on physical principles,
and is calibrated with a data set of 300 vehicles
driven on a variety of driving cycles. CMEM has
recently been paired with the Transportation
Analysis Simulation System (TRANSIMS), a model
developed at the Los Alamos National Laboratory
that simulates the detailed travel behavior of an
urban population /16/. TRANSIMS determines
vehicle activities; the output provides the necessary
input data for CMEM-based emission calculations,
which are expressed in real-time. One has to keep
in mind, however, that these models require
significant amounts of input data.
Traffic count data can be useful for better
estimating vehicle emissions along specific road-
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
199
ways, problems can arise in finding data for a
relatively recent time period, or the data may be
limited to short periods (for example, 12 h), raising
concerns regarding their applicability for long-term
exposure assessments.
Once tailpipe or evaporative emissions enter
the atmosphere, geographic features such as street
canyons (Factor 3), as well as local weather and
atmospheric conditions (Factor 4), will influence
pollution chemistry, transport, and dispersion. For
example, Zhu et al. /17/ studied the impact of
seasonal meteorology on pollutant dispersion from
roadways. Line-source dispersion models such as
the California Line Source Dispersion Model
(CALINE) /18/ typically predict the fate and
transport of airborne pollutants by accounting for
such variables. These models are discussed in more
detail in the modeling section below.
Noteworthy are the varying characteristics of
the urban pollution mix. The spatial patterns
exhibited by ambient air pollutants will vary,
depending on the compound in question.
Secondary pollutants, which form in the
atmosphere from precursor pollutants, can be more
evenly distributed across a city. An important
exception is ozone in the immediate vicinity of
major roadways, where it will exhibit lower
concentrations relative to a more even distribution
further away. The near roadway ozone deficit is
due to its rapid destruction by short-lived nitric
oxide present in fresh vehicle emissions. Under the
assumption of an even distribution, spatially-
averaged ambient concentrations of secondary
pollutants can provide a reasonably accurate
estimate of individual exposures to these types of
pollutants. Primary pollutants, which are directly
emitted by local sources, such as elemental carbon,
carbon monoxide (CO), sulfur dioxide (SO
2
), and
nitric oxide (NO) (and to a lesser extent nitrogen
dioxide [NO
2
]) from motor vehicles, will show
wide spatial variability across a city. Because of
such spatial variability, spatial average ambient
concentrations of primary emissions will be far less
reliable than secondary pollutants for estimating the
actual magnitude of individual exposures.
Ambient air pollution will also exhibit temporal
variability, influencing individual exposures. The
sources of variability include long-term trends in air
quality, seasonal variations in air pollution
concentrations, day-to-day variability, and diurnal
variations in air pollution levels. Depending on the
nature of the exposure assessment, accounting for
these different categories of temporal variability and
averaging times may or may not be necessary. For
example, long-term variability in air pollution
exposures can be significant in a longitudinal study
that would be able to capture chronic health effects,
but for a study assessing acute health effects—such
as emergency room visits, exacerbation of asthmatic
symptoms, or daily mortality—the daily variation in
air pollution levels would be the exposure time-
frame of interest.
Personal exposures to motor-vehicle related air
pollution (Factor 5) will depend on the activity
patterns of the individual in question, the inter-
action between these activities and traffic sources,
and the contribution of indoor sources to personal
exposures of the pollutants in question. Throughout
a given day, individuals can be exposed to very
different levels of air pollutants, depending on the
different microenvironments in which they spend
their time, their proximity to pollution sources, their
smoking habits, and their occupational exposures.
Such microenvironmental exposures compose an
individual’s integrated personal exposure. As
emphasized above, such variability can be more or
less pronounced, depending on the pollutant in
question. Extending the timeframe of the inquiry to
encompass a greater portion of an individual’s life
introduces additional levels of complexity, due to
the mobility of the individual and to long-term
changes in factors 1–3. The longer the duration of
inquiry, the higher the probability that study
subjects will have moved from one city to another
or within a city, with the potential for significant
variations in the level of exposure.
Personal-exposure monitors that can directly
measure individual exposures are available for
C. VAN ATTEN, M. BRAUER, ET AL.
200
single pollutants, such as certain gases (ozone,
nitrogen oxides, sulfur dioxide), particulate matter
(PM
2.5
, PM
10
), elemental/organic carbon (EC/OC)
/19–21/, and for multiple pollutants simultaneously
(particulate matter, criteria gases, and EC/OC) /22/.
Time-activity diaries have been used to ‘track’
individual activity patterns /23/. By combining the
individual diary information with measurements or
estimates of ambient and micro-environmental
pollution concentrations, researchers can assess
individual exposures. In addition, time-activity
diaries and microenvironmental measurements
combined with personal exposure monitoring data
can be used to evaluate the main determinants of
personal exposures /24–25/. To assist in estimating
exposures, researchers have also made use of GPS
tracking devices fitted to study participants /26/.
Again, long-term exposure assessments face a
greater challenge, requiring information on the
subjects’ residential history, as well as on the
pollution characteristics of the different micro-
environments where subjects spend their time /27/.
As personal monitoring is usually labor intensive
and requires subject participation and effort, such
monitoring is usually conducted only for relatively
short periods and from small samples of the
population of interest. Although this limitation
may be a weakness of this approach, the exposure
assessment of representative samples of the popu-
lation can provide accurate estimates of the mean
and variability of population exposures. Further,
personal monitoring can be used to test the
assumptions of exposure models and to identify
important determinants of exposure, such as in-
vehicle exposure /28–30/.
Factor 6, the variables influencing whether an
adverse health outcome is triggered by the
exposure, extends beyond the realm of exposure
assessment. Nevertheless, we include Factor 6
within the conceptual framework to provide a more
complete model and to acknowledge that the
components of Factor 5 can be systematically
different for individuals of different ages and
underlying disease status. The occurrence of an
adverse health outcome will vary, depending on
the age, nutrition, and genetic makeup of an
individual exposed to the pollution.
Many different approaches have been used to
estimate exposure to traffic-related air pollution for
epidemiologic studies and for environmental risk
assessment, often with tradeoffs between the
specificity of the exposure assessment and the
ability to extend the study to large populations. We
now turn our focus to the specific tools and
techniques for estimating exposure to motor
vehicle pollution, which we broadly categorize as
(a) surrogate techniques, (b) modeling techniques,
and (c) measurement techniques. In many cases, an
exposure assessment will rely on more than one
approach as an integral part of the study, or use
several approaches as a separate sub-study assessing
the distribution of error in the primary exposure
estimates.
SURROGATE TECHNIQUES
Perhaps the most straightforward of the
exposure assessment methodologies is what we
have termed the surrogate approach: indicators of
the relative concentrations of pollution to which an
individual or population is exposed. In studies with
relatively large sample sizes, the surrogate approach
can be useful as a proxy for exposure assessment
to vehicle emissions.
Examples of surrogate techniques include both
subjective and objective measures of nearby traffic
intensity. Several subjective approaches used self-
reported measures of nearby traffic intensity or
local knowledge of congested roads to gauge the
statistical relations between illness and proximity
to high volumes of motor vehicle traffic /31–33/.
Some studies asked participants to report the
distance from their home to the nearest major
roadway, the occurrence of traffic congestion near
their home, estimates of truck or bus traffic at their
home address, speed limit on street of home
address, traffic annoyance scores, and perception
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
201
of traffic exhaust. For instance, in a survey of
approximately 39,000 subjects, Ciccone et al. /31/
found a strong association between childhood
respiratory disorders and high truck traffic density
in the area of residence. Recently, Heinrich and
colleagues /34/ reported that subjective assessments
of traffic intensity were only weakly associated
with the estimated concentrations of traffic-related
air pollution derived from the type of regression
models described in the following section. Other
studies have used objectively determined exposure
measures, such as traffic density on the residential
street /35/, the distance between the residence and
the nearest highway or busy road /36–38/, total
traffic within a certain radius /39–40/, and
distance-weighted traffic density /41/.
These examples focus exclusively on a subset
of the Factor 2 variables, specifically the number
of vehicles in use and the level of congestion. As
suggested by our conceptual framework, many more
variables are available that will ultimately
determine the level of individual exposures that are
not reflected in these metrics, highlighting their
potential limitations. Depending on the objective
of the exposure assessment and study, however,
Factor 2 variables can be adequate for their intended
use. For example, surrogate techniques and many
of the modeling approaches described below are
likely to misrepresent differences in the dispersion
characteristics between specific components of the
vehicle exhaust mixture /42–44/.
In terms of identifying areas likely to have
higher levels of air pollution within a city, it is
likely that local transportation planners would be
capable of identifying the most congested areas of
the city. Such congested areas would then be areas
of special concern to determine if exposure is also
high. Despite its ‘low-tech’ approach, this technique
can be sufficiently suited to the purpose at hand
(for example, when targeting air quality improve-
ment projects or screening areas for a more
detailed study).
If the objective is to evaluate alternative
transportation projects rather than existing con-
ditions, then the surrogate approach may not be
helpful. For assessing a set of alternative scenarios
or future conditions, other techniques are needed,
including modeling potential vehicle emissions
within the various scenarios and their dispersion
into surrounding communities.
MODELING TECHNIQUES
Modeling techniques can be divided into two
basic categories: regression or GIS modeling
approaches and dispersion modeling.
Regression Modeling Approaches
Researchers are increasingly relying on regres-
sion modeling to estimate individual exposures for
epidemiologic studies. In some cases, GIS is used
to compute independent variables for inclusion in
such regression models. Two examples of such
approaches are the TRAPCA (Traffic-Related Air
Pollution on Childhood Asthma) and SAVIAH
(Small Area Variations in Air Pollution and Health)
studies. TRAPCA /1, 45–46/ and SAVIAH /47–49/
exposure-assessment approaches were developed
for use in large epidemiologic studies estimating
individual exposures to air pollutants. Both
approaches allow individual exposures to be
modeled based on the regression of measured air
pollutant concentrations against surrogate variables
in a GIS framework. The specific use of traffic-
related surrogate variables allows these methods to
develop exposure estimates that are specific to
traffic-related pollutants.
The SAVIAH study found significant variation
in NO
2
concentrations within individual European
cities, largely related to traffic proximity /49/. The
study relied on regression modeling to develop
individual estimates of exposures based on NO
2
concentration measurements at a limited number of
sites and prediction of the measured concentrations
using geographic data, such as nearby traffic
C. VAN ATTEN, M. BRAUER, ET AL.
202
intensity, population density, and altitude. Regres-
sion models relating the measured concentrations
to the geographic variables were then used to
generate estimates of exposures at locations where
no measurements were made.
Using a similar approach, but extending the
methodology to particles, the TRAPCA study found
substantial variability in the measured annual
average concentrations of NO
2
, PM
2.5
, and ‘soot’
(an elemental carbon surrogate) at forty sites in
each of three study locations. Pollutant concen-
trations varied by a factor of two for PM
2.5
, by a
factor of three to four for ‘soot’, and by a factor of
four for NO
2
. In all study areas, a major fraction of
the variability was explained by available
geographic variables, such as population density
and proximity to major roadways.
The basic approach employed in the SAVIAH
and TRAPCA studies involves the measurement of
long-term average air-pollution concentrations at
monitoring sites specifically selected to characterize
the complete range of within-city variability in air
pollution concentrations. At each selected moni-
toring location, geographic variables like traffic
and population densities are calculated. A regres-
sion model then relates the measured air pollutant
concentrations with the geographic data to enable
the prediction of air pollutant concentrations for
additional locations where no monitoring data are
available, such as the home addresses of study
participants. The address locations of the study
participants are input into the regression model,
and exposure estimates are calculated for each
individual address within a GIS framework. Life-
time exposure histories for study participants can
be calculated for those who move by computing
new exposure estimates for each new address.
In a cohort mortality study, a related approach
used a combination of regression modeling and
surrogate techniques to take into account local air
pollution (proximity to major roads) and back-
ground air pollution /3, 50/. In this multivariate
analysis, the surrogate variable (living near a major
road) was associated with a significantly increased
risk of cardiopulmonary mortality, whereas the
modeled exposure variable (background air
pollution) was not.
Despite the common use of GIS, especially in
epidemiologic studies, their use for estimating
exposures involves several challenges. For instance,
many geocoding services do not accurately or
consistently place addresses in their actual physical
location. Because of the near-field pollutant
distribution observed along roadways, it was
suggested that the address locations of study
participants be accurately geocoded to within 20 to
30 meters. Moreover, the road network used for
modeling traffic exposures must be consistent with
the database used for geocoding addresses.
Dispersion Modeling
In dispersion modeling, emissions parameters
are input into dispersion or other types of atmos-
pheric models to predict the concentrations of
pollutants at individual ‘receptor’ points. For
example, CALINE 4, built on Gaussian dispersion
models, can predict the concentration of an air
pollutant downwind of a road segment using
emission factors (emissions/length of road) and
meteorologic data /18/.
Dispersion models require a large amount of
location-specific input data, such as detailed infor-
mation on the specific makeup of the motor vehicle
fleet, specific emissions of representative vehicle
types, traffic volumes, and detailed meteorologic
and topographic information /51/. For a variety of
air pollutants, the presence of ‘high emitters’
(typically vehicles that may be older, poorly main-
tained, or tampered with) and the emergence of
new technology vehicles, complicates estimating
vehicle emissions under variable driving conditions.
Dispersion models are commonly used in the
evaluation of air-quality-management programs and
for environmental risk assessment. Such models
have not been used with great frequency in
epidemiologic studies. The LUCAS study in
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
203
Stockholm, Sweden /52/ and a study of traffic
pollution and childhood cancer in Denmark /53/
are exceptions. Both studies used dispersion
modeling to estimate NO
2
concentrations. As a part
of the Danish study, dispersion-modeling estimates
were compared with measured NO
2
concentrations
at 200 addresses in Copenhagen and in several
rural areas in Denmark. The analysis suggested
that the model calculations based on traffic data
and physical characteristics for each address were
good estimates of the measured concentrations.
Briggs and colleagues /48/ compared the
estimated NO
2
concentrations from the regression
modeling approach developed for the SAVIAH
study with several other methods, including two
dispersion models—CAR and CALINE. In that
comparison, the regression model estimates
explained more of the variability in measured NO
2
concentrations than did any other modeling method.
The authors concluded that the regression-
modeling approach was of equal or better accuracy
relative to dispersion model approaches, including
highly advanced models, such as ADMS
(Atmospheric-Dispersion Models). Recently, Cyrys
and colleagues /54/ conducted a similar comparison
between dispersion and regression model estimates
of NO
2
and PM
2.5
levels for TRAPCA study
locations in Munich, Germany and concluded that
both methods performed equally well in estimating
exposures of their study population.
MEASUREMENT TECHNIQUES
Measurement techniques rely on the actual
measurement of traffic-related air pollution, with
data collected from air quality monitoring networks
or from personal samplers. By working directly
with the exact concentration of pollution, measure-
ment techniques essentially bypass the many com-
plexities involved in estimating motor vehicle
emissions and subsequent transport and dispersion
of pollutants. Nevertheless, several important
challenges remain.
Pollutants that are generated by motor vehicles
are also produced by a variety of other sources.
Consequently, accurately resolving the fraction of
ambient concentrations or population exposures
that are due to vehicle emissions versus other pre-
dominant sources is not possible with monitoring
data alone. This limitation also applies to regres-
sion modeling approaches. To differentiate motor
vehicle impacts from those of other sources, one
can use such data as emissions inventories and
meteorologic measurements. In addition, receptor-
based methods (‘receptor models’), which typically
require the detailed chemical characterization of
PM
2.5
, PM
10
, or volatile organic compounds (VOCs),
are useful tools for source apportionment /55/.
Combining all the available information and
methods for both particulate and gaseous pollutants
is expected to lead to the greatest degree of
understanding. This approach is difficult, however,
requiring considerable effort, resources, and
experience. Furthermore, although certain aspects
of such an effort will be similar from location to
location, detailed interpretations can be expected to
be site-specific and potentially time-specific as
well (namely, valid only for the period during
which the measurements were collected).
Some of the options for apportioning ambient
pollutant concentrations to motor vehicles include
the following:
1. Comparison of simultaneous measurements of
pollutants from multiple sites where at least
one site is located to be affected maximally by
known traffic sources with other sites that are
not as heavily influenced by these sources.
This approach could include upwind versus
downwind sites, or near source sites versus
sites representing regional or urban background.
2. Comparison of different periods of time at a
single site known to be influenced by traffic
(for example, rush-hour versus non-rush hour,
weekday versus weekend, daytime versus
nighttime).
3. Running averages of real-time continuous
measurements with sub-hourly resolution. For
C. VAN ATTEN, M. BRAUER, ET AL.
204
example, concentrations from hourly running
averages represent a larger ‘footprint’ of the
source area than would instantaneous measure-
ment. Subtracting one from the other indicates
the impact of the local traffic source on the
ambient concentration;
4. Variation in concentrations as a function of
wind direction can also lead to valuable
inferences regarding the contribution from a
source of concern. For nearby sources, a
simple pollution schematic (‘rose’) that bins
hourly data by wind-direction sector can reveal
higher concentrations from specific directions,
including point sources or major roadways.
This approach requires hourly resolution (or
better) for pollutant and wind direction
measurements.
Numerous epidemiologic studies have relied
on ambient monitoring data to determine average
exposure levels. The American Cancer Society
(ACS) /56/ and Harvard 6-cities studies /57/ are
two of the most widely cited studies of the effects
of air pollution exposure on human health because
of their cohort designs and very large sample sizes.
Both studies used single long-term average
pollution-concentration values measured at fixed
ambient monitoring sites for each urban area to
characterize the exposure of study populations.
Cross-sectional studies or studies having smaller
populations, however, have taken a more targeted
approach. For example, in a study of children living
near major roads in two urban areas and one
suburban area, Kramer et al. /58/ measured personal
and outdoor pollutant concentrations. Outdoor
concentrations of NO
2
were correlated with a
traffic index, based on the traffic density at the
home address (r = 0.70). Outdoor NO
2
concen-
trations at the front of the children’s homes were
associated with atopy and allergic symptoms.
Janssen et al. /59/ conducted a study involving
children from 24 schools situated within 400
meters of 22 different motorway stretches. The
pollutants PM
2.5
, NO
2
, and benzene were measured
inside and outside all 24 schools. The study, based
on a measurements approach, found that the
concentration of air pollutants inside and outside
schools near motorways was significantly associated
with distance, traffic density/composition, and the
percentage of time downwind, suggesting that
these variables can be used as surrogates for
traffic-related air pollution exposure assessments.
A limited number of studies have assessed
exposures by conducting extensive ambient moni-
toring throughout the entire region of interest
(namely, at multiple grid locations or at the home
address of all study subjects) /58, 60/. Short of such
an extensive monitoring effort, researchers have
interpolated ambient concentrations based on
measurements collected by air quality monitoring
sites or networks /61–62/. The interpolation of
monitoring data cannot identify small-scale
variations in concentration, given the density of
most typical monitoring networks and given the
spatial distribution of traffic sources.
Within-city Spatial Variability in Pollutant
Concentration
The more refined measurement programs to
support epidemiologic research are based upon a
growing appreciation for spatial variability in air
pollution concentrations within urbanized areas
/49, 53, 63–64/. Recent information has suggested
greater than expected levels of variation in ambient
air pollutant concentrations within a city.
Previously, ambient concentrations for ozone and
particles were assumed to be relatively
homogeneous within urban areas /65/. Several
studies have documented within-city variability in
ozone concentrations /66/, mainly resulting from
the variability in nitric oxide (NO) levels—an
ozone quenching substance when its concentration
is relatively higher than that of reactive hydro-
carbons. Additional studies have documented
important variations in the concentration of a
variety of gaseous and particle species within
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
205
cities, especially those related to the location of
motorized traffic—for example, city center versus
suburb /46, 53, 63, 64, 67–68/.
Recent research has revealed that certain types
of vehicle-related air pollution are likely to be
localized (within a few hundred meters) near
heavily traveled roadways. Studies conducted by
Levy et al. /26/ and Zhu et al. /43/ found that
concentrations of ultrafine particles and CO
dropped to background levels within 200 to 300
meters downwind of a freeway; another study
found a similar pattern for NO
2
/67/. A subsequent
study by Zhu et al. /17/ compared downwind
measurements from two highways having different
amounts of diesel vehicle traffic. Although both
highways displayed a decrease in particle concen-
trations with increasing distance from the road, the
elemental carbon levels were substantially higher
proximal to the highway having a higher amount of
heavy-duty diesel traffic. In a study by Hitchins et
al. /69/, the concentration of submicron particles
dropped by approximately 50% at locations 150
meters away from a road.
Despite the general trends of distance-related
decay in concentration, meteorologic factors will
influence the extent of the roadway proximity
effect. Zhu et al /17/ measured larger decay rates
for CO and elemental carbon in summer than in
winter and found particle number concentrations to
be significantly higher in winter than in summer.
The results suggest that winter conditions favor
greater particle formation, possibly due to a
combination of increased condensation of organic
vapors and lower atmospheric mixing.
The results of these studies suggest that the
concentration of at least some pollutants from motor
vehicle exhaust decline substantially with increasing
distance. Consequently, fixed-site monitoring stations
for these types of pollutants might not accurately
represent near-field pollutant concentrations from
motor vehicle exhaust. In addition, researchers are
finding elevated concentrations of pollution in
smaller micro-environments. For example, studies
conducted on roadways in California and Mexico
City have measured pollution levels several times
higher within a car or a public transit vehicle than in
the air outside of the vehicle, ranging from 2 to 10
times greater /70–71/. The studies in California
found that cars driven during peak traffic periods
contained nearly twice the pollution found in cars
driven during less congested times /71/. Studies
conducted in the Mexico City metropolitan area
found that personal PM
2.5
and CO exposures in
commuters using public transportation were highest
during morning rather than evening peak hours, in
agreement with higher morning than evening fixed-
site monitoring station peak levels /72–73/.
Recently, elevated concentrations of traffic-related
pollutants have been measured inside school buses
/74/, due to the infiltration of exhaust from other
vehicles on the road as well as to self-pollution from
the school bus itself. Such measurements suggest
that exposures encountered inside school buses are
major contributors to the total exposure for a
number of pollutants /75/.
By relying on air quality monitoring data,
measurement techniques avoid the many
complexities involved with estimating source-
specific emissions and pollutant dispersion. On the
other hand, relying on ambient monitoring data
provides a limited ability to attribute pollutants to
specific sources. In formulating strategies to
address the risks to human health, knowing the
sources of pollution is important.
Components of the Motor Vehicle Emissions
Mixture: Diesel Exhaust
Because diesel exhaust exposure is an issue of
particular concern, a great deal of current research
is focused on developing techniques to assess and
characterize specific exposures to diesel exhaust
(see /76/). In the past, elemental carbon (EC) was
used as a marker of vehicular diesel fuel com-
bustion. When diesel engines are the dominant
source of particles, elemental carbon can be a
C. VAN ATTEN, M. BRAUER, ET AL.
206
useful marker for occupational exposures to diesel
exhaust, but this method lacks the sensitivity and
specificity needed for a signature of diesel exhaust
in ambient exposure settings that typically include
elemental carbon from other combustion sources.
For example, because gasoline combustion and
many industrial and non-vehicle combustion
processes produce EC emissions, EC is not a
reliable ‘unique’ identifier to distinguish diesel-
powered vehicle emissions from other vehicle and
non-vehicle sources.
In 2002, the Health Effects Institute /77/ held a
workshop addressing the topic of Improving
Estimates of Diesel and Other Emissions for Epi-
demiologic Studies. Workshop attendees and
speakers included experts involved in developing
methodologies to assess human exposures to
vehicle exhaust and the limitations associated with
the various exposure assessment methods. One key
area of focus was the development of validated
markers or a set of markers (signature) to
distinguish diesel exhaust from gasoline exhaust
and other air pollution types.
The concept for determining a vehicle exhaust
signature implies the identification of compounds
found in ambient air that, when measured in com-
bination, can act as a unique set of markers for
vehicle fuel combustion. To date, the accurate
assessment of an individual’s exposure to vehicle
exhaust in ambient air containing pollutants from
several sources is not possible. As such, it is
desirable to identify compounds found in ambient
air that, although individually may not be specific
for a particular pollution source, taken together can
act as a signature of vehicle fuel combustion with a
high degree of confidence.
An ideal signature or marker for diesel and
gasoline vehicle exhaust would have the following
properties:
specific to the vehicle-related combustion source,
feasible to measure,
can be generated from routinely collected data,
has an appropriate cost, and
relatively insensitive to engine technology and
fuel characteristics.
In an effort to develop vehicle exhaust
signatures or individual markers, researchers are
investigating a number of promising research
avenues, although none is presently ready for
general use. Instruments such as the aerosol mass
spectrometer (AMS) provide detailed information
about the chemical composition and physico-
chemical properties of particulate matter (size
distribution, positive or negative ion mass spectra)
/78–80/. Transmission electron microscopy (TEM)
has been used to characterize the morphology of
particles emitted by vehicle engines /81/. Data
analysis (statistical) methods applicable to using
chemical markers as a proxy for inferring exposures
to vehicle emissions are also available /82/.
Ideally, if a chemical marker is used to estimate
human exposures to vehicle exhaust, this ‘inferred’
estimate should be accompanied by an associated
estimate of measurement error, and a number of
factors contribute to potential measurement errors.
Such factors include spatial and temporal variation
in ambient particulate matter and their component
levels, variable engine operating conditions /83/,
and limited spatial and temporal scales of the
collected data sets.
Recent advancements have been made in the
development of chemical signatures or markers for
vehicle exhaust. Hopanes and steranes found in
motor vehicle engine lubricating oil can be useful
as unique marker constituents in vehicle-derived
particulate matter from combustion /84/. Researchers
have demonstrated the utility of the molecular
marker method in collecting field samples for
source apportionment in epidemiologic studies,
supplemented with EC measurement data /84–85/.
Confidence in this exposure estimate might be
increased by including other measurements of
particle characteristics, such as particle number,
concentration, and size distribution.
Although signature or marker approaches are
advancing, none of the methods currently satisfies
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
207
all five of the previously listed criteria for useful
exhaust signatures or markers. The remaining
challenges include the feasibility of measurements
(complex instrumentation and experimental set-up,
operational expertise), data analysis capabilities
(specialized skills required for analysis and
interpretation of key dataset values), and appro-
priate cost (lengthy experimental set-up, analysis
time, skilled worker salaries).
RECOMMENDATIONS FOR FUTURE
RESEARCH
Each technique for assessing population ex-
posures to motor vehicle pollution has its own sets
of strengths and weaknesses. Such parameters
include (a) feasibility, defined in terms of cost and
data availability, (b) accuracy, (c) temporal reso-
lution, (d) spatial resolution, (e) pollutants available
for analysis, and (f) sensitivity (ability to detect a
response over noise or variability of measure-
ments). In Table 1, we summarize the different
approaches discussed in this article according to
these key criteria. Ultimately, the objective of the
study is what will influence the choice of the
methodology employed for assessing exposures.
Based on the current state-of-the-science in
local population exposure assessments to vehicle
exhaust, a number of general recommendations can
be made for future research. Continuing the work
on developing a diesel vehicle-exhaust signature is
important, particularly in light of the generally
expanded truck traffic in major trade corridors. In
addition, improved information on idling emissions
from vehicles is needed, which is a salient point
with regard to locations having frequent traffic
congestion and idling trucks.
For mobile source emission models, the need
continues to improve vehicle emission factors,
vehicle fleet composition data, and driving cycle
parameters. Further improvement can be made by
collecting location-specific traffic count data from
traffic planners or other relevant authorities, or as
part of the exposure assessment study if no
relatively recent traffic count data exist.
Evaluating the feasibility of applying a stan-
dardized exposure-assessment methodology across
different and widely separated locations will be
informative. A standardized approach can help
identify differing impacts at locations of concern
that could arise from differences in local ambient
pollution composition, rather than arising as an
artifact of different assessment approaches. For
example, differences in the sulfur content of diesel
fuel or in diesel engine emission standards across
different regions can be reflected in local ambient
air concentrations where trucks frequently idle or
travel. Additional work should be done to invest-
igate the importance of time-resolved ambient air
moni-toring for such traffic-related pollutants as
EC, PAHs, or potential markers of diesel exhaust
that can help to reveal different health effects or to
reinforce existing studies. Specific health effects
from short-term peak exposures may not appear in
exposure assessments using long-term ambient air
concentration averages. The importance of time-
resolved air monitoring data, however, can depend
on the relevant health effect being investigated. For
example, for studies of asthma exacerbation, short-
term temporal resolution may be more important,
whereas for cancer studies, annual averages may
be sufficient.
Also relevant to the potential usefulness of
time-resolved air monitoring data is a recommen-
dation to develop siting criteria for air monitors to
be located in special areas of concern, such as near
schools or adjacent to high-traffic roadways. In such
locations, time-resolved data could be important,
depending on where individuals are spending their
time in relation to the ebb and flow of local traffic
patterns. Having monitors located in representative
sites for population exposures to vehicle exhaust is
clearly an important assessment need. For personal
monitoring, efforts to improve their capabilities in
terms of pollutants measured, temporal resolution,
and reduced weight are important and should
continue.
C. VAN ATTEN, M. BRAUER, ET AL.
208
TABLE 1
Summary table of approaches to population exposure assessments for vehicle exhaust
Methodology Strengths Weaknesses
Surrogate
Approaches
Generally the least resource intensive, therefore
rank high in terms of feasibility
Applicable for urban-wide assessment
Best suited for analysis of existing conditions
Focused by design on long-term concentrations
Not appropriate for assessment of individual roads.
Do not always account for changes in existing
conditions
Generally deficient in terms of short-term variability
Do not address individual pollutants, which can be
a serious shortcoming for researchers seeking to
link a specific pollutant to a health risk.
Surrogate techniques that incorporate subjective
assessments suffer from potential for bias.
Dispersion
Models
May be most appropriate for the modeling of
specific scenarios (forecasting) and a limited
number of roads
Useful for transportation planning agencies that
may already possess much the input data
required
Have the ability to evaluate short-term changes
in pollutant concentrations (e.g., hourly,
seasonal, day of week profiles) as long as
appropriate temporally-resolved input data
(traffic counts, emissions factors, meteorology)
are available
Resource intensive, requiring large amounts of
location-specific input data such as detailed
information on the specific makeup of the motor
vehicle fleet, the specific emissions of
representative vehicle types, traffic volumes, and
detailed meteorologic and topographic information
Difficult to apply across entire metropolitan areas
Regression or GIS
Modeling
Very feasible to perform a regression analysis
based on existing data and variables within a
GIS framework (e.g., distance to nearest
highway)
More rigorous analyses based on actual traffic
counts and spatial measurements can significantly
increase the required resources.
Personal
Monitoring
Best suited for model development and to
validate modeling approaches.
Best suited for epidemiologic studies.
Data can be collected for individual study
participants.
Depending on the pollutant, personal monitors
have the potential to provide greater temporal
resolution (but not always).
Passive samplers that can measure VOC, NO
2
,
SO
2
, ozone, and aldehydes are available.
Feasible only for relatively small subsets of the
population.
Because of the size of some continuous samplers
(e.g. CO, NO
2
, and PM), subjects may not follow a
regular daily routine when wearing a personal
monitor, biasing the resulting data.
Less temporal resolution with passive samplers
that require longer integration periods (e.g. 24
hours).
Ambient
Monitoring
Established monitoring networks can contain
consistent information on long-term air pollution
trends at specific locations.
Capable of high-temporal resolution for a large
number of air pollutants.
Where pre-existing monitoring already done for
regulatory purposes, it can be a low-cost source
of monitoring information for exposure
assessment studies.
Typically lacks sufficient spatial coverage on its
own to capture within city variability of air pollution
levels.
Relevance of ambient air monitoring data for
measuring exposures to motor vehicle pollution
varies depending on the location of the site(s), the
temporal and chemical resolution of the data, and
the amount of data available.
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
209
Researchers should work with transportation
planners to identify potential ‘hot spots’ along
existing major routes or at sites of proposed high-
way expansion projects as candidates for exposure
assessment. Investigators can use dispersion or GIS
modeling approaches to help identify populations
that may be affected vehicle emissions. Exposure
assessment can be aided by efforts to develop
hybrid strategies incorporating both spatial and
temporal variability and specific exhaust
components to improve their accuracy. Verifying
and improving road spatial accuracy for inputs into
GIS modeling techniques will be important to
reduce uncertainties from roadways located
incorrectly. Mislocated roadways of only a few
hundred meters can significantly change estimated
population exposures in the local area.
Because of the complexities of modeling
techniques and their inputs, some level of un-
certainty in their use for exposure assessments will
always remain. As an aid to decision makers and
transportation planners in light of the continuing
uncertainties, providing in any assessment a
general background that includes a description of
toxics and other air pollutants in the environment
will be useful, as well as a general description of
sensitive populations to these contaminants.
Locally, specific information may already be
available in terms of monitoring or modeling and
should be included in the general background.
Within this context, the background can also
include a discussion of reasonably foreseeable
changes in traffic volume or congestion that can
alter the amount of air pollutants emitted from
local traffic. Specific examples of these types of
exhaust-related contaminants are benzene and
diesel particulate matter. By providing this general
context, decision makers and trans-portation
planners will have an improved under-standing of
the local context in which to evaluate the results of
location-specific exposure assess-ments and their
uncertainties in relation to sensitive populations
exposed to vehicle exhaust.
DISCLAIMER
Some material in this article was prepared for
the Secretariat of the Commission for Environ-
mental Cooperation (CEC) as a product of
workshop discussions. The opinions, views or other
information contained herein do not necessarily
reflect the views and policies of the CEC, the
governments of Canada, Mexico or the United
States, or the Health Effects Institute and its
sponsors (U.S. Environmental Protection Agency,
and motor vehicle and engine manufacturers).
ACKNOWLEDGMENTS
Additional helpful contributions to this article
were made by Richard Baldauf of the U.S.
Environmental Protection Agency, Barry Jessiman
of Health Canada, and Anne-Marie Baribeau.
ABBREVIATIONS
ATMS, Advanced Traffic Management Systems
CALINE, California Line Source Dispersion Model
CEC, Commission for Environment Cooperation
CMEM, Comprehensive Modal Emissions Model
EC, Elemental Carbon
FHWA, U.S. Federal Highway Administration
GIS, Geographic Information System
MEASURE, Mobile Emission Assessment System
for Urban and Regional Evaluation
NAAQS, National Ambient Air Quality Standard
PAH, Polycyclic aromatic hydrocarbons
SAVIAH, Small Area Variations in Air Pollution
and Health
TEM, Transmission Electron Microscopy
TRANSIMS, Transportation Analysis Simulation
System
TRAPCA, Traffic-Related Air Pollution on
Childhood Asthma
U.S. EPA, U.S. Environmental Protection Agency
VMT, Vehicle Miles Traveled
VOCs, Volatile Organic Compounds
C. VAN ATTEN, M. BRAUER, ET AL.
210
REFERENCES
1. Brauer M, Hoek G, van Vliet P, Meliefste K,
Fischer PH, Wijga A, et al. Air pollution from
traffic and the development of respiratory infec-
tions and asthmatic and allergic symptoms in
children. Am J Respir Crit Care Med 2002; 166:
1092–1098.
2. Creason J, Neas L, Walsh D, Williams R, Sheldon
L, Liao D, et al. Particulate matter and heart rate
variability among elderly retirees: the Baltimore
1998 PM study. J Expo Anal Environ Epidemiol
2001; 11: 116–122.
3. Hoek G, Brunekreef B, Goldbohm S, Fischer P, van
den Brandt PA. Association between mortality and
indicators of traffic-related air pollution in the
Netherlands: a cohort study. Lancet 2002; 360:
1203–1209.
4. Lin S, Munsie JP, Hwang SA, Fitzgerald E, Cayo
MR. Childhood asthma hospitalization and resi-
dential exposure to state route traffic. Environ Res
2002; 88: 73–81.
5. Schwartz J, Morris R. Air pollution and hospital
admissions for cardiovascular disease in Detroit,
Michigan. Am J Epidemiol 1995; 142: 23–35.
6. Zanobetti A, Schwartz J, Dockery DW. Airborne
particles are a risk factor for hospital admissions
for heart and lung disease. Environ Health Perspect
2000; 108: 1071–1077.
7. Frumkin H.. Urban sprawl and public health. Public
Health Rep 2002; 117: 201–217.
8. O’Neill MS, Jerrett M, Kawachi I, Levy JI, Cohen
AJ, Gouveia N, et al. Health, wealth, and air
pollution: advancing theory and methods. Environ
Health Perspect 2003; 111: 1861–1870.
9. Pokharel SS, Bishop GA, Stedman DH, Slott R.
Emissions reductions as a result of automobile
improvement. Environ Sci Technol 2003; 37:
5097–5101.
10. Norbeck JM, Miller JW, Welch WA, Smith M,
Johnson K, Pankratz D. 2001. Develop On-Road
System for Emissions Measurement from Heavy-
Duty Trucks. Final Report, South Coast Air Quality
Management District Contract 20906.
http://www.
cert.ucr.edu/research/pubs/trailer_build_fr_20906b.
pdf
[accessed 28 July 2005].
11. Gertler AW, Gillies JA, Pierson WR, Rogers CF,
Sagebiel JC, Abu-Allaban M, et al. Real-world
particulate matter and gaseous emissions from
motor vehicles in a highway tunnel. Res Rep
Health Eff Inst 2002; 107: 5–56; discussion 79–92.
12. Singer BC, Harley RA.. A fuel-based inventory of
motor vehicle exhaust emissions in the Los Angeles
area during summer 1997. Atmos Environ 2000;
34: 1783–1795.
13. Singh RB, Colls JJ. Development and preliminary
evaluation of a particulate matter emission factor
model (PMFAC) for European motor vehicle
emission. J Air Waste Manage 2000; 50: 1805–1817.
14. Singh RB, Huber AH, Braddock JN. Development
of a microscale emission factor model for partic-
ulate matter for predicting real-time motor vehicle
emissions. J Air Waste Manage 2003; 53: 1204–
1217.
15. Fomuning I, Washington S, Guensler R. Compari-
son of MEASURE and MOBILE5a predictions
using laboratory measurements of vehicle emis-
sion factors. In: Chatterjee A, ed, Transportation
Planning and Air Quality IV; American Society of
Civil Engineers; Reston, Virginia, USA, 2000.
16. Los Alamos National Laboratory. TRANSIMS.
LANL, 2004. Available at:
http://transims.tsasa.
lanl.gov/
[accessed 28 July 2005].
17. Zhu YF, Hinds WC, Shen S, Sioutas C. Seasonal
trends of concentration and size distribution of
ultrafine particles near major highways in Los
Angeles. Aerosol Sci Technol 2004; 38 Suppl 1:
5–13.
18. Benson P. CALINE4 - a dispersion model for pre-
dicting air pollution concentrations near roadways.
California Department of Transportation, Report No.
FHWA/CA/TL-84/15, November 1984 (Revised
November 1986, June 1989).
19. Clayton CA, Pellizari ED, Rodes CE, Mason RE,
Piper LL. Estimating distributions of long-term
particulate matter and manganese exposures for
residents of Toronto, Canada. Atmos Environ
1999; 33: 2515–2526.
20. Howard-Reed C, Rea AW, Zufall MJ, Burke JM,
Williams RW, Suggs JC, et al. Use of a contin-
uous nephelometer to measure personal exposure
to particles during the U.S. Environmental Protec-
tion Agency Baltimore and Fresno Panel studies. J
Air Waste Manag 2000; 50: 1125–1132.
21. Thomas KW, Pellizzari ED, Clayton C, Whitaker
DA, Shores RC, Spengler JD, et al. Particle total
exposure assessment methodology (PTEAM) 1990
study: method performance and data quality for
personal, indoor and outdoor monitoring. J Expo
Anal Environ Epidemiol 1993; 3: 203–226.
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
211
22. Demokritou P, Kavouras IG, Ferguson ST, Petroukis
P. Development and laboratory performance eval-
uation of a personal multipollutant sampler for
simultaneous measurements of particulate and
gaseous pollutants. Aerosol Sci Technol 2001; 35:
741–752.
23. Zmirou D, Gauvin S, Pin I, Momas I, Just J,
Sahraoui F, et al. Five epidemiological studies on
transport and asthma: objectives, design and des-
criptive results. J Expo Anal Environ Epidemiol
2002; 12: 186–196.
24. Rojas-Bracho L, Suh HH, Catalano PJ, Koutrakis
P. Personal PM2.5 and PM10 exposures and their
relationships with personal activities for chronic
obstructive pulmonary disease patients living in
Boston. J Air Waste Manage 2004; 54: 207–217.
25. Sarnat JA, Schwartz J, Catalano PJ, Suh HH.
Gaseous pollutants in particulate matter epidemi-
ology: confounders or surrogates? Environ Health
Perspect 2004; 109: 1053–1061.
26. Levy J, Bennett D, Melly S, Spengler J. Influence
of traffic patterns on particulate matter and poly-
cyclic aromatic hydrocarbon concentrations in
Roxbury, Massachusetts. J Expo Anal Environ
Epidemiol 2003; 13: 364–371.
27. Kunzli N, Tager IB. Long-term health effects of
particulate and other ambient air pollution: research
can progress faster if we want it to. Environ
Health Perspect 2000; 108: 915–918.
28. Park JH, Spengler JD, Yoon DW, Dumyahn T,
Lee K, Ozkaynak H. Measurement of air exchange
rate of stationary vehicles and estimation of in-
vehicle exposure. J Expo Anal Environ Epidemiol
1998; 8: 65–78.
29. Alm S, Jantunen MJ, Vartiainen M. Urban
commuter exposure to particle matter and carbon
monoxide inside an automobile. J Expo Anal
Environ Epidemiol 1999; 9: 237–244.
30. Riediker M, Williams R, Devlin R, Griggs T,
Bromberg P. Exposure to particulate matter, vola-
tile organic compounds, and other air pollutants
inside patrol cars. Environ Sci Technol 2003; 37:
2084–2093.
31. Ciccone G, Forastiere F, Agabiti N, Biggeri A,
Bisanti L, Chellini E, et al. Road traffic and
adverse respiratory effects in children. Occup
Environ Med 1998; 55: 771–778.
32. Duhme H, Weiland SK, Keil U, Kraemer B,
Schmid M, Stender M, et al. The association
between self-reported symptoms of asthma and
allergic rhinitis and self reported traffic density on
street of residence in adolescents. Epidemiology
1996; 7: 578–582.
33. Weiland SK, Mundt KA, Rückmann A, Keil U.
Self-reported wheezing and allergic rhinitis in
children and traffic density on street of residence.
Ann Epidemiol 1994; 4: 243–247.
34. Heinrich J, Gehring U, Cyrys J, Brauer M, Hoek G,
Fischer P, Bellander T, Brunekreef B. Exposure to
traffic-related air pollutants: subjective versus GIS
modelled assessment. Occup Environ Med. 2005;
62: 517–23.
35. Savitz DA, Feingold L. Association of childhood
cancer with residential traffic density. Scand J
Work Environ Health 1989; 15: 360–363.
36. Brunekreef B, Janssen NA, de Hartog J, Harssema
H, Knape M, van Vliet P. Air pollution from truck
traffic and lung function in children living near
motorways. Epidemiology 1997; 8: 298–303.
37. Livingstone AE, Shaddick G, Grundy C, Elliott P.
Do people living near inner city main roads have
more asthma needing treatment? Case control
study. BMJ 1996; 312: 676–677.
38. van Vliet P, Knape M, de Hartog J, Janssen N,
Harssema H, Brunekreef B. Motor vehicle exhaust
and chronic respiratory symptoms in children living
near freeways. Environ Res 1997; 74: 122–132.
39. English P, Neutra R, Scalf R, Sullivan M, Waller L,
Zhu L.. Examining associations between childhood
asthma and traffic flow using a geographic infor-
mation system. Environ Health Perspect 1999;
107: 761–767.
40. Wilkinson P, Elliott P, Grundy C, Shaddick G,
Thakrar B, Walls P, et al. Case-control study of
hospital admission with asthma in children aged
5–14 years: relation with road traffic in northwest
London. Thorax 1999; 54: 1070–1074.
41. Langholz B, Ebi KL, Thomas DC, Peters JM,
London SJ. Traffic density and the risk of child-
hood leukemia in a Los Angeles case-control
study. Ann Epidemiol 2002; 12: 482–487.
42. Zhu Y, Hinds WC, Kim S, Sioutas C. Concen-
tration and size distribution of ultrafine particles
near a major highway. J Air Waste Manage 2002;
52: 1032–1042.
43.
Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C.
Study on ultrafine particles and other vehicular
pollutants near a busy highway. Atmos Environ
2002; 36: 4375–4383.
44. Zhang KM, Wexler AS, Zhu YF, Hinds WC,
C. VAN ATTEN, M. BRAUER, ET AL.
212
Sioutas C. Evolution of particle number distribution
near roadways. Part II: The 'road-to-ambient' pro-
cess. Atmos Environ 2004; 38: 6655–6665.
45. Brauer M, Hoek G, van Vliet P, Meliefste K,
Fischer P, Gehring U, et al. Prediction of long
term average particulate air pollution concentrations
by traffic indicators for epidemiological studies.
Epidemiology 2003; 14: 228–239.
46. Hoek G, Meliefste K, Cyrys J, Lewné M, Brauer
M, Fischer P, et al. Spatial variability of fine
particle concentrations in three European countries.
Atmos Environ 2002; 36: 4077–4088.
47. Briggs DJ, Collins S, Elliott P, Fischer P, Kingham
S, Lebret E, et al. Mapping urban air pollution
using GIS: a regression-based approach. Int J
Geogr Inf Sci 1997;11: 699–718.
48. Briggs DJ, de Hoogh C, Gulliver J, Wills J, Elliott
P, Kingham S, et al. A regression-based method
for mapping traffic-related air pollution: applica-
tion and testing in four contrasting urban environ-
ments. Sci Total Environ 2000; 253: 151–167.
49. Lebret E, Briggs DJ, Collins S, van Reeuwijk H,
Fischer P, Smallbone K, et al. Small area variations
in ambient NO
2
concentrations in four European
areas. Atmos Environ 2000; 34: 177–185.
50. Hoek G, Fischer P, van den Brandt P, Goldbohm
S, Brunekreef B. Estimation of long-term average
exposure to outdoor air pollution for a cohort
study on mortality. J Expo Anal Environ Epidemiol
2001; 11: 459–469.
51. National Research Council. Modeling Mobile
Source Emissions. Washington, DC, USA:
National Academy Press, 2000; 151–152.
52. Bellander T, Berglind N, Gustavsson P, Jonson T,
Nyberg F, Pershagen G, et al. Using geographic
information systems to assess individual historical
exposure to air pollution from traffic and house
heating in Stockholm. Environ Health Perspect
2001; 109: 633–639.
53. Raaschou-Nielsen O, Hertel O, Vignati E, Berko-
wicz R, Jensen SS, Larsen B, et al. An air pollution
model for use in epidemiological studies: evalu-
ation with measured levels of nitrogen dioxide and
benzene. J Expo Anal Environ Epidemiol 2000;
10: 4–14.
54. Cyrys J, Hochadel M, Gehring U, Hoek G,
Diegmann V, Brunekreef B, Heinrich J. GIS based
estimation of exposure to particulate matter and
NO
2
in an urban area: stochastic versus dispersion
modeling. Environ Health Perpect 2005; 113:
987–992.
55. Watson JG, Zhu T, Chow JC, Engelbrecht J,
Fujita EM, Wilson WE. Receptor modeling
application framework for particle source appor-
tionment. Chemosphere 2002; 49: 1093–136.
56. Pope CA, Burnett RT, Thun MJ, Calle EE, Krewski
D, Ito K, et al. Lung cancer, cardiopulmonary
mortality and long-term exposure to fine particulate
air pollution. JAMA 2002; 287: 1132–1141.
57. Dockery DW, Pope AC, Xu X, Spengler JD, Ware
JH, Fay ME, et al. An association between air
pollution and mortality in six U.S. cities. N Engl J
Med 1993; 329: 1753–1759.
58. Krämer U, Koch T, Ranft U, Ring J, Behrendt H.
Traffic-related air pollution is associated with atopy
in children living in urban areas. Epidemiology
2000; 11: 64–70.
59. Janssen NA, Brunekreef B, van Vliet P, Aarts F,
Meliefste K, Harssema H et al. The relationship
between air pollution from heavy traffic and
allergic sensitization, bronchial hyperresponsive-
ness, and respiratory symptoms in Dutch school
children. Environ Health Perspect 2003; 111:
1512–1518.
60. Hirsch T, Weiland SK, von Mutius E, Safeca AF,
Grafe H, Csaplovics E, et al. Inner city air
pollution and respiratory health and atopy in
children. Eur Respir J 1999; 14: 669–677.
61. Brown PJ, Le ND, Zidek JV.. Multivariate spatial
interpolation and exposure to air-pollutants. Can J
Stat 1994; 22: 489–509.
62. Li KH, Le ND, Sun L, Zidek JV.. Spatial-temporal
models for ambient hourly PM
10
in Vancouver.
Environmetrics 1999; 10: 321–338.
63. Bernard NL, Astre CM, Vuillot B, Saintot MJ,
Gerber MJ. Measurement of background urban
nitrogen dioxide pollution levels with passive
samplers in Montpellier, France. J Expo Anal
Environ Epidemiol. 1997; 7: 165–178.
64. Cyrys J, Heinrich J, Brauer M, Wichmann HE.
Spatial variability of acid aerosols, sulfate and
PM10 in Erfurt, Eastern Germany. J Expo Anal
Environ Epidemiol 1998; 8: 447–464.
65. Burton RM, Suh HH, Koutrakis P. Spatial
variation in particulate concentrations within met-
ropolitan Philadelphia. Environ Sci Technol 1996;
30: 400–407
66. Liu LJS, Rossini AJ. Use of kriging models to
predict 12-h mean ozone concentrations in metro-
politan Toronto—a pilot study. Environ Int 1996.;
22: 677–692.
67. Gilbert NL, Woodhouse S, Stieb DM, Brook JR.
ASSESSING POPULATION EXPOSURES TO MOTOR VEHICLE EXHAUST
213
Ambient nitrogen dioxide and distance from a
major highway. Sci Total Environ 2003; 312: 43–46.
68. Lewne M, Cyrys J, Meliefste K, Hoek G, Brauer
M, Fischer P, et al. Spatial variation in nitrogen
dioxide in three European areas. Sci Total Environ
2004; 332: 217–230.
69. Hitchins J, Morawska L, Wolff L, Gilbert D. Con-
centration of submicrometer particles from vehicle
emissions near a major road. Atmos Environ
2000; 34: 51–59.
70. Fernández-Bremauntz A, Ashmore MR. Exposure
of commuters to carbon monoxide in Mexico City
II. Comparison of in-vehicle and fixed-site concen-
trations. J Expo Anal Environ Epidemiol 1995; 5:
447–464.
71. Rodes C, Sheldon L, Whitaker D, Clayton A,
Fitzgerald K, Flanagan J, et al. Measuring concen-
trations of selected air pollutants inside California
vehicles. Final Report. Contract 95–339, 1998.
http://www.arb.ca.gov/research/abstracts/95-
339.htm
[accessed 28 July 2005].
72. Gómez-Perales JE, Colvile RN, Nieuwenhuijsen
MJ, Fernández-Bremauntz A, Gutiérrez-Avedoy
VJ, Páramo-Figueroa VH, et al. Commuters'
exposure to PM2.5, CO, and benzene in public
transport in the metropolitan area of Mexico City.
Atmos Environ 2004; 38: 1219–1229.
73. Instituto Nacional de Ecología. Segundo alman-
aque de datos y tendencias de la calidad del aire en
seis ciudades mexicanas [in Spanish]. Secretaría de
Medio Ambiente y Recursos Naturales, INE,
México, 2003.
74. Sabin LD, Behrentz E, Winer AM, Jeong S, Fitz
DR, Pankratz DV, et al. Characterizing the range
of children's air pollutant exposure during school
bus commutes. J Expo Anal Environ Epidemiol
2004; [Epub ahead of print]
75. Marshall JD, Behrentz E. Vehicle self-pollution
intake fraction: children's exposure to school bus
emissions. Environ Sci Technol 2005; 39: 2559–
2563.
76. Health Effects Institute Diesel Epidemiology
Working Group. Research Directions to Improve
Estimates of Human Exposure and Risk from Diesel
Exhaust, Special Report, Boston, Massachusetts,
USA: HEI, 2002.
http://www. healtheffects.org/
Pubs/DieselSpecialReport02.pdf
[accessed 28 July
2005]
77. Health Effects Institute. Improving Estimates of
Diesel and Other Emissions for Epidemiologic
Studies. Boston Massachusetts, USA: HEI, 2003.
http://www.healtheffects.org/Pubs/Comm10Full.pdf
[accessed 28 July 2005]
78. Guazzotti SA, Prather KA. Using individual
particle signatures to discriminate between HDV
and LDV emissions. In: Health Effects Institute
Communication 10: Improving Estimates of
Diesel and Other Emissions for Epidemiologic
Studies. Boston, Massachusetts, USA: HEI, 2003.
http://www.healtheffects.org/Pubs/Comm10Full.p
df
[accessed 28 July 2005]
79. Worsnop DR, Canagaratna M, Jayne J, Jimenez J.
Characterization of vehicle emissions and urban
aerosols by an aerosol mass spectrometer (AMS).
In: Health Effects Institute Communication 10:
Improving Estimates of Diesel and Other Emis-
sions for Epidemiologic Studies. Boston, Massa-
chusetts, USA: HEI, 2003.
http://www.healtheffects.
org/Pubs/Comm10Full.pdf
[accessed 28 July 2005]
80. Ziemann PJ, Tobias HJ, Sakurai H, McMurry PH,
Kittelson DB. On-line mass spectral analysis of
thermally evaporated diesel exhaust particles. In:
Health Effects Institute Communication 10: Im-
proving Estimates of Diesel and Other Emissions
for Epidemiologic Studies. Boston, Massachusetts,
USA: HEI, 2003.
http://www.healtheffects.org/
Pubs/Comm10Full.pdf
[accessed 28 July 2005]
81. Blom DA, Storey JME, Graves RL. Morpho-
logical aspects of combustion particles. In: Health
Effects Institute Communication 10: Improving
Estimates of Diesel and Other Emissions for Epi-
demiologic Studies. Boston, Massachusetts, USA:
HEI, 2003.
http://www.healtheffects.org/Pubs/
Comm10Full.pdf
[accessed 28 July 2005]
82. Smith RL. Data analytic procedures for moni-
toring specific pollutants in epidemiological
studies. In: Health Effects Institute Communica-
tion 10: Improving Estimates of Diesel and Other
Emissions for Epidemiologic Studies. Boston,
Massachusetts, USA: HEI, 2003.
http://www.
healtheffects.org/Pubs/Comm10Full.pdf
[accessed
28 July 2005]
83. Kittelson DB. Some characteristics of diesel and
gasoline particulate emissions. In: Health Effects
Institute Communication 10: Improving Estimates
of Diesel and Other Emissions for Epidemiologic
Studies. Boston, Massachusetts, USA: HEI, 2003.
http://www.healtheffects.org/Pubs/Comm10Full.p
df
[accessed 28 July 2005]
84. Fujita E, Zielinska B.. Chemical characterization
of on-road motor vehicle PM emissions. In:
Health Effects Institute Communication 10:
C. VAN ATTEN, M. BRAUER, ET AL.
214
Improving Estimates of Diesel and Other
Emissions for Epidemiologic Studies. Boston,
Massachusetts, USA: HEI, 2003.
http://www.
healtheffects.org/ Pubs/Comm10Full.pdf
[accessed
28 July 2005]
85. Schauer JJ. Diesel exhaust signatures for source
attribution: parts 1 and 2. In: Health Effects Insti-
tute Communication 10: Improving Estimates of
Diesel and Other Emissions for Epidemiologic
Studies. Boston, Massachusetts, USA: HEI, 2003.
http://www.healtheffects.org/Pubs/Comm10Full.p
df
[accessed 28 July 2005]
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BACKGROUND Evidence for an association between road traffic pollution and asthma is inconclusive. We report a case-control study of hospital admissions for asthma and respiratory illness among children aged 5–14 in relation to proxy markers of traffic related pollution.METHODS The study was based on routine hospital admissions data in 1992/3 and 1993/4 for North Thames (West) health region within the M25 motorway. Cases were defined as emergency admissions for asthma (n = 1380) or all respiratory illness including asthma (n = 2131), and controls (n = 5703) were other emergency admissions excluding accidents. Cases and controls were compared with respect to distance of residence from nearest main road or roads with peak hour traffic >1000 vehicles and traffic volume within 150 m of residence, obtained by Geographical Information System techniques. Statistical analysis included adjustment for age, sex, admitting hospital, and a deprivation score for the census enumeration district of residence.RESULTSAdjusted odds ratios of hospital admission for asthma and respiratory illness for children living within 150 m of a main road compared with those living further away were, respectively, 0.93 (95% CI 0.82 to 1.06) and 1.02 (95% CI 0.92 to 1.14).CONCLUSIONS This study showed no association between risk of hospital admission for asthma or respiratory illness among children aged 5–14 and proxy markers of road traffic pollution.
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