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Assessing Population Exposures to Motor Vehicle Exhaust



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.
Assessing Population Exposures to Motor Vehicle Exhaust
Chris Van Atten,
Michael Brauer,
Tami Funk,
Nicolas L. Gilbert,
Lisa Graham,
Debra Kaden,
Paul J. Miller,
Leonora Rojas Bracho,
Amanda Wheeler,
and Ronald H. White
with input from
additional participants of the Workshop on Methodologies to Assess Vehicle Exhaust Exposure
M.J. Bradley and Associates, Concord, Massachusetts, USA;
University of British Columbia,
Vancouver, British Columbia, Canada;
Sonoma Technologies, Inc., Petaluma, California, USA;
Health Canada, Ottawa, Ontario, Canada;
Environment Canada, Ottawa, Ontario, Canada;
Health Effects Institute, Boston, Massachusetts, USA;
Commission for Environmental Cooperation,
Montreal, Quebec, Canada;
Instituto Nacional de Ecología, Mexico City, Mexico;
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
Conceptual Framework
Surrogate Techniques
Modeling Techniques
Regression Modeling Approaches
Dispersion Modeling
Measurement Techniques
Within-city spatial variability in pollutant
Components of the motor vehicle emissions
mixture: Diesel exhaust
air pollution, diesel, epidemiology, traffic, exposure
assessment, vehicle exhaust
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-
© 2005 Freund Publishing House Ltd. 195
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.
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).
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-
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
Dispersion &
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
& v
emissions are
influenced by
a variety of
Vehicle load
(i.e., cold
vehicle traffic
emissions in
a given
location will
depend on
Number of
traveled by
vehicles in
The types
and ages of
vehicles on
the road
Road grade
transport and
Wind breaks
such as
transformation and
decay will influence
the spatial and
concentrations of
Wind speed and
Mixing height
The mix of
chemicals in the
atmosphere and
their chemical
inhaled concentration
The level of
exposure will
depend on the
activity patterns of
the individual and
the time spent in
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-
pollutants can
concentrate at
higher than ambient
factors will
whether an
adverse health
results from
habits (e.g.,
conditions and
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.
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-
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
), and
nitric oxide (NO) (and to a lesser extent nitrogen
dioxide [NO
]) 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
single pollutants, such as certain gases (ozone,
nitrogen oxides, sulfur dioxide), particulate matter
, PM
), 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
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
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 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
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
concentration measurements at a limited number of
sites and prediction of the measured concentrations
using geographic data, such as nearby traffic
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
, PM
, 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
, by a
factor of three to four for ‘soot’, and by a factor of
four for NO
. 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
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
concentrations. As a part
of the Danish study, dispersion-modeling estimates
were compared with measured NO
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
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
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
and PM
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 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
, 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
3. Running averages of real-time continuous
measurements with sub-hourly resolution. For
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
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
were correlated with a
traffic index, based on the traffic density at the
home address (r = 0.70). Outdoor NO
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
, NO
, 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
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
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
/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
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
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
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
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).
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
Summary table of approaches to population exposure assessments for vehicle exhaust
Methodology Strengths Weaknesses
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
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.
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
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
Very feasible to perform a regression analysis
based on existing data and variables within a
GIS framework (e.g., distance to nearest
More rigorous analyses based on actual traffic
counts and spatial measurements can significantly
increase the required resources.
Best suited for model development and to
validate modeling approaches.
Best suited for epidemiologic studies.
Data can be collected for individual study
Depending on the pollutant, personal monitors
have the potential to provide greater temporal
resolution (but not always).
Passive samplers that can measure VOC, NO
, ozone, and aldehydes are available.
Feasible only for relatively small subsets of the
Because of the size of some continuous samplers
(e.g. CO, NO
, 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
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
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.
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.
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).
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.
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
TRAPCA, Traffic-Related Air Pollution on
Childhood Asthma
U.S. EPA, U.S. Environmental Protection Agency
VMT, Vehicle Miles Traveled
VOCs, Volatile Organic Compounds
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... Factors such as age, health, and genetic susceptibility make some individuals more sensitive to air pollution (Van Atten et al., 2004), thus the percentage of sensitive individuals in the SRKW population was determined. Chapter four summarizes the findings in the previous chapters, and concludes the study with suggestions for future research that would increase the confidence of the model predictions. ...
... A wide range of air quality dispersion models with different levels of complexity and combinations of parameters are available, and there are mobile-source models where the object emitting pollutants is mobile (as is the case with whale-watching vessels), and fixedsource models where the object emitting is stationary. All mobile-source models require the following parameters: the engine types in the fleet; the number of operating engines; the engine emission rates; the atmospheric conditions; and the geophysical characteristics (BCME, 2005;Van Atten et al., 2004). The British Columbia Ministry of Environment (BCME, 2006) recommends the use of extensively tested dispersion models; however, the recommended models are not appropriate for the whale-watching scenario because they are either: designed for terrestrial situations; designed for single point, area, or volume sources; designed for urban locations, highways, or industrial complexes; and/or require hour-by-hour meteorological data. ...
... Several factors influence the emissions produced by marine engines: the age of the engine, the engine type, the fuel characteristics, the engine maintenance, the performance of the engine's pollution control systems, the engine load, the engine temperature, the engine speed, and the RPM (Van Atten et al., 2004). However, Frey and Bammi (2003) found that engine exhaust emissions depend more on fuel type (diesel or gasoline), and engine technology (i.e. two or four-stroke) than engine size, age, or type of aspiration. ...
... Commuters using private or public transport are daily exposed to over 1000 different pollutants that have been associated with vehicular emissions (U.S. Environmental Protection Agency, 2006). The health risk of these pollutants depends on the proximity to the traffic, fleet characteristics, urban morphology, atmospheric condition and exposure time (Van Atten et al., 2005). The time spent in close proximity to motor vehicles is usually short but can contribute disproportionately to the total daily exposure to airborne toxics (Zuurbier et al., 2011). ...
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Transport microenvironments represent hotspots of personal exposure to airborne toxics, particularly of ultrafine particles. Thus, a large exposure may be experienced during daily commuting trips. Amongst these microenvironments, bus stops are critical because of the commuters' close proximity to fresh fumes rich in particles emitted by passing, idling and accelerating buses and motor vehicles, in general. Standing at a bus stop may represent a period of disproportionately high exposure and it is, therefore, essential to know the number, chemical composition and physical characteristics of such particles for a proper public health assessment and design of mobility strategies. On this account, a set of portable and battery operated sensors were used to evaluate a number of properties of the traffic particles to which thousands of citizens are daily exposed at bus stops of Singapore. In terms of fine particles, the exposure concentration was on average 1.5–3 times higher than the mean concentration at ambient level reported by the local authorities. On average 60% of those particles corresponded to black carbon. An important presence of particle-bound polycyclic aromatics was observed. The particle number concentration and active surface area were effective metrics to quantify ultrafine particles, as expected both showed strong correlations. The number of particles at bus stops was on average 3.5 times higher than at ambient level. The most alarming issue was probably the size of the particles. Assuming spherical particles, a median of 27 nm was estimated based on the active surface area and particle number data. Particles of this size form the nucleation mode, which is related to harmful health effects.
... In general, LUR models have performed as well as or better than more theoretically elegant, but empirically poor, dispersion models. 24,25 The LUR offers an improved level of detail at which pollution variability is observed. Identifying these smallarea variations in air pollution is potentially important for increased accuracy when conducting epidemiologic and health impact studies. ...
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This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at > 100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.
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Introduction: Exposure assessment is a key challenge in environmental epidemiology. When modeling exposures for populations, one should consider (1) the applicability of the exposure model to the health effect of interest (i.e. chronic, acute), (2) the applicability of the model to the population of interest, (3) the extent to which modeled exposures account for individual factors and (4) the sources of variability within the model. Epidemiological studies of traffic-related air pollution and birth outcomes have used a variety of exposure models to estimate exposures for pregnant women. These models are rarely evaluated, let alone specifically for pregnant women. Methods: Measured and modeled personal exposures to air pollutants (nitric oxide: NO, nitrogen dioxide: NO2, filter absorbance and fine particles: PM2.5) were obtained for 62 pregnant women from 2005-2006 in Vancouver, Canada. Exposures were measured for 48-hours, 1-3 times over the pregnancy. Mobility was assessed using Global Positioning System monitoring and self-reported activity logs; individual factors (dwelling characteristics, socio-economic factors) were assessed using questionnaires. Results: Modeled home concentrations using a traffic-based land-use regression model were moderately predictive of personal samples for NO only (Pearson’s r=0.49). Models for NO including home and work locations explained more between subject variance than using home only (4% home only, 20% with home and work). Modeled exposures using ambient monitoring stations were predictive of personal samples for NO (Pearson’s r=0.54), absorbance (r=0.29) and PM2.5 (r=0.12) mainly due to temporal correlations (within subject variance: NO=37%, absorbance=11%, PM2.5=9%). Home gas stove was an important determinant of personal exposure for all pollutants. There was a significant (1 hour/day/trimester) increase in time spent at home with increased trimester of pregnancy. Conclusions: In this evaluation, based upon repeated 48-hour exposure measurements, models currently used in air pollution studies were moderately reflective of personal exposures, depending on the specific pollutant and model. Land-use regression shows promise for capturing spatial variability, especially when including mobility (work or school locations) in exposures, whereas monitor-based models are better for capturing temporal variability. Future models should include mobility, where possible, and consider the implications of increasing time at home over pregnancy in assessing exposures for pregnant women.
The formulation of motor vehicle fuels can alter the magnitude and composition of evaporative and exhaust emissions that are associated with environmental and health impacts. The goal of this research was to investigate consequences of using the new vehicle fuels, including bioethanol and biodiesel blends. Laboratory studies were used to characterize the composition of liquid and vapors of gasoline-ethanol and diesel-biodiesel blends; assess the collinearity of fuel profiles used in receptor modeling; evaluate permeation rates and permeant compositions through personal protective equipment (PPE) materials; and measure exhaust emissions from diesel engines. In an ambient study conducted in Detroit, daily volatile organic compound (VOC) levels were measured near a major highway, and VOC sources were apportioned using positive matrix factorization, a receptor model. The compositions of biofuel blends and conventional fuels differed significantly. Predictions of vapor concentrations were highly correlated to measurements, but activity coefficients are needed for ethanol blends. Petroleum diesel and biodiesel blends, and their vapors had similar compositions, which were distinct from those of gasoline. In permeation tests, breakthrough time and permeation rate strongly depended on the fuel-PPE material combination, and permeants were enriched in benzene and other VOCs. Recommendations are made regarding PPE appropriate for the current fuels. Diesel engine exhaust emissions depended on engine calibration, load, fuel and aftertreatment systems. Biodiesel blends generally reduced emissions of particulate matter, nonmethane hydrocarbons and VOCs, however, nitrogen oxides and formaldehyde emissions increased in certain conditions. In the ambient study, VOC concentrations were generally low and varied with both seasonal and weekly patterns. The major sources were identified as gasoline exhaust, diesel exhaust, fuel evaporation, industrial emissions, biomass burning, and others. The study provides information regarding VOC profiles of the new fuels, their vapors, diesel exhaust, and ambient levels near a highway site. These profiles can be incorporated into receptor modeling. The permeation study provides guidance for selecting PPE materials. Study results can be used to assess exposure and health impacts resulting from the use of new fuels and biofuel blends.
This study investigated the relationship between weather conditions and cycling ridership, as well as the hourly, daily, monthly, and yearly trends for use of urban bicycle facilities. A unique data set of cyclist ridership, collected at five automatic counting stations on primarily utilitarian bike facilities in the city of Montreal, Canada, was used. Absolute and relative ridership models were used to analyze the direct and lagging effects of weather variables and extreme weather conditions on hourly cyclist volumes. Precipitation, temperature, and humidity had significant effects on bicycle ridership. After other factors were controlled for, when the temperature doubled, a 43% to 50% increase in ridership could be expected; however, the temperature had a negative effect when it was higher than 28°C and humidity was greater than 60%. The results also showed that bicycle volumes in a given hour were significantly affected not only by the presence of rain in the same hour but also by the presence of rain in the previous 3 h or in the morning only. Daily bicycle volumes were 65% to 89% lower on weekend days than on Monday, the weekday with lowest ridership. This finding confirmed that the analyzed facilities were primarily utilitarian. Further, bicycle volumes peaked in the summer months, with an additional ridership of 32% to 39% with respect to April. Finally, bicycle volumes increased by approximately 20% to 27% in 2009 and 35% to 40% in 2010 with respect to 2008 in the cycling facilities under analysis.
We measured outdoor fine particulate matter (PM(2.5)) concentrations in a low- and a nearby middle-income neighborhood in Bangalore, India. Each neighborhood included sampling locations near and not near a major road. One-minute average concentrations were recorded for 168 days during September 2008 to May 2009 using a gravimetric-corrected nephelometer. We also measured wind speed and direction, and PM(2.5) concentration as a function of distance from road. Average concentrations are 21-46% higher in the low- than in the middle-income neighborhood, and exhibit differing spatiotemporal patterns. For example, in the middle-income neighborhood, median concentrations are higher near-road than not near-road (56 versus 50 μg m(-3)); in the low-income neighborhood, the reverse holds (68 μg m(-3) near-road, 74 μg m(-3) not near-road), likely because of within-neighborhood residential emissions (e.g., cooking; trash combustion). A moving-average subtraction method used to infer local- versus urban-scale emissions confirms that local emissions are greater in the low-income neighborhood than in the middle-income neighborhood; however, relative contributions from local sources vary by time-of-day. Real-time relative humidity correction factors are important for accurately interpreting real-time nephelometer data.
Thesis (Ph. D.)--University of Washington, 2008. Air pollution is associated with adverse health outcomes, and changes in the immune system may be intermediate steps between exposure and a clinically relevant adverse health outcome. We analyzed the associations between three different types of measures of air pollution exposure and five biomarkers of immune function among 115 overweight and obese postmenopausal women whose immunity was assessed as part of a year-long moderate exercise intervention trial. For air pollution metrics, we assessed: (1) residential proximity to major roads (freeways, major arterials and truck routes), (2) fine particulate matter(PM2.5) at the nearest monitor to the residence averaged over three time windows (3-days, 30-days and 60-days), and (3) nitrogen dioxide (NO2) modeled based on land use characteristics. Our immune biomarkers included three measures of inflammation---C-reactive protein, serum amyloid A and interleukin-6---and two measures of cellular immunity---natural killer cell cytotoxicity and T lymphocyte proliferation.We hypothesized that living near a major road, increased exposure to PM2.5 and increased exposure to NO2 would each be independently associated with increased inflammation and decreased immune function. We observed a 21% lower average natural killer cell cytotoxicity among women living within 150 meters of a major arterial road compared to other women. For PM2.5 , we observed changes in 3 of 4 indicators of lymphocyte proliferation stimulated by anti-CD3---an antibody to the T cell receptor associated with increases in 3-day averaged PM2.5. For 30-day averaged PM 2.5 and 60-day averaged PM2.5 we did not observe any statistically significant associations. We observed an increase in lymphocyte proliferation index stimulated by the plant protein phytohemagglutinin (PHA) at 1 of 2 PHA concentrations in association with modeled NO2. For the three inflammatory markers, we observed no notable associations with any of our measures of air pollution.If confirmed, our results provide preliminary evidence to support the biologic plausibility of previously observed associations between traffic and colds and infections, suggest immune function should be considered as part of the assessment of regulatory standards for PM2.5 and indicate that living in close proximity to major roads may have adverse health impacts.
Increasing utilitarian bicycling in urban areas is a means to reduce air and noise pollution, increase physical activity, and reduce the risk of chronic diseases. We investigated the impact of individual- and city-level characteristics on bicycling in Canadian cities to inform transportation and public health policies. The study population included 59,899 respondents to the 2003 Canadian Community Health Survey (CCHS) living in cities with populations greater than 50,000. In 2005, data on individual characteristics were drawn from the CCHS, and city-level climate data from Environment Canada records. Separate multilevel logistic regression models were developed for the general (nonstudent) and student populations. The proportion of the urban population reporting bicycling in a typical week was 7.9%, with students cycling more than nonstudents (17.2% vs 6.0%). In the general population, older age, female gender, lower education, and higher income were associated with lower likelihood of cycling. More days of precipitation per year and more days of freezing temperatures per year were both associated with lower levels of utilitarian cycling (odds ratios [ORs] for every 30-day increase in precipitation=0.84, 95% confidence interval [CI]=0.74-0.94, and for every 30-day increase in freezing temperatures OR=0.91, 95% CI=0.86-0.97). There was less variation in the proportion of students who cycled by age and income, and only the number of days with freezing temperatures influenced bicycling. Bicycling patterns are associated with individual demographic characteristics and the climate where one lives. This evidence might be useful to guide policy initiatives for targeted health promotion and transportation infrastructure.
In many urban areas, residential wood burning is a significant wintertime source of PM2.5. In this study, we used a combination of fixed and mobile monitoring along with a novel spatial buffering procedure to estimate the spatial patterns of woodsmoke. Two-week average PM2.5 and levoglucosan (a marker for wood smoke) concentrations were concurrently measured at upto seven sites in the study region. In addition, pre-selected routes spanning the major population areas in and around Vancouver, B.C. were traversed during 19 cold, clear winter evenings from November, 2004 to March, 2005 by a vehicle equipped with GPS receiver and a nephelometer. Fifteen-second-average values of light scattering coefficient (bsp) were adjusted for variations between evenings and then combined into a single, highly resolved map of nighttime winter bsp levels. A relatively simple but robust (R(2) = 0.64) land use regression model was developed using selected spatial covariates to predict these temporally adjusted bsp values. The bsp values predicted by this model were also correlated with the measured average levoglucosan concentrations at our fixed site locations (R(2) = 0.66). This model, the first application of land use regression for woodsmoke, enabled the identification and prediction of previously unrecognized high woodsmoke regions within an urban airshed.
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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.
As part of on-going research at the Georgia Institute of Technology, three algorithms have been recently estimated for predicting mobile source emissions of carbon monoxide, hydrocarbons, and oxides of nitrogen from light duty passenger vehicles. The Georgia Tech Mobile Emissions Assessment System for Urban and Regional Evaluation (MEASURE) is one of several new modal emissions models designed to improve predictions of CO, HC, and NOx for the on-road vehicle fleet. Three statistical criteria are used to assess the relative predictive performance of the MEASURE and Mobile5a models on an external data set of observed emission factors of CO, HC, and NOx. These statistical comparisons clearly indicate that the MEASURE model provides significant improvements in both average emissions estimates and explanatory power over MOBILE5a for all three pollutants across almost every operating cycle under which the emission factors were measured. The most significant improvements arise in the CO and HC estimates, but even NOx emissions, which are largely a function of average speed, are improved under the MEASURE modeling regime. The paper compares and contrasts various prediction performance measures for the two models, and illustrates the importance of such prediction biases with a simple hypothetical scenario.
Epidemiological studies of health effects of air pollution often must rely on only one monitoring site to characterize the exposure of a large population. This is reasonable if ambient air pollution concentrations have limited spatial variability within the study area. To verify this assumption in an area with many local sources of pollutants, the spatial variability of particle strong acidity (PSA), aerosol sulfate, and PM10 was studied in Erfurt, Eastern Germany. Fine-particles (PM2.5) and coarse particles (PM10) were collected using Teflon membrane filters. The fine particles were analyzed for PSA and sulfate. Samples were collected simultaneously at four sites: one located downtown (D), one at the border of the downtown area (ERF), and one each at a northern (N) and at a southeastern (SE) suburban locations. The distance between the downtown area and the suburban stations was about 4-5 km. Because the two suburban locations were not affected by local emissions, the measured concentrations were highly correlated and did not differ appreciably from each other. In contrast, the inner-city sites showed more variability since these locations may be affected by local sources. Statistically significant differences in levels between the downtown site (D) and the two suburban sites (N and SE) for sulfate (up to 17%) and PM10 (30-40%) were observed. With respect to cross-sectional epidemiological studies, for which the knowledge of the absolute pollutant concentration levels is of importance, the results suggest that population exposure estimated by the use of only one monitoring site may be inaccurate. This is especially possible in communities with substantial sources of pollutants and during periods of low wind speed. Epidemiological studies evaluating acute effects, for which the demarcation of episodic periods may be sufficient, are not as adversely affected by the use of a single monitoring site in a study region. The relatively high inter-site correlations for sulfate and PM10 (r ≥ 0.9) between SE, D, and N would indicate that regional episodes of this pollutant can be identified with only one monitoring site. The correlations between the ERF site and the other sites were weaker, especially on days with low wind speed since the ERF site may be more strongly influenced by local sources and local traffic due to its short distance from a major road. This indicates that even time series studies may suffer from misclassified exposures depending upon the particular choice of monitoring site and the coincidence of air pollution episodes with periods of low wind speed. Our data constellation shows that the slope estimate in a univariate model would be underestimated by 35% for PM10 and 22% for sulfate when using the ERF site compared to using the average of all sites for the analysis of epidemiological time series data. Since sulfate shows less spatial variability than PM10 they may serve as better marker for episodes.
Ultrafine particles (diameter Articles that cite this article? Document Type: Research Article DOI: Affiliations: 1: University of California Los Angeles, Department of Environmental Health Sciences, Southern California Particle Center and Supersite, Center for Occupational and Environmental Health, Los Angeles, California 2: University of Southern California, Department of Civil and Environmental Engineering, Southern California Particle Center and Supersite, Los Angeles, California Publication date: January 1, 2004 More about this publication? Information for Authors Subscribe to this Title ingentaconnect is not responsible for the content or availability of external websites $(document).ready(function() { var shortdescription = $(".originaldescription").text().replace(/\\&/g, '&').replace(/\\, '<').replace(/\\>/g, '>').replace(/\\t/g, ' ').replace(/\\n/g, ''); if (shortdescription.length > 350){ shortdescription = "" + shortdescription.substring(0,250) + "... more"; } $(".descriptionitem").prepend(shortdescription); $(".shortdescription a").click(function() { $(".shortdescription").hide(); $(".originaldescription").slideDown(); return false; }); }); Related content In this: publication By this: publisher In this Subject: Chemical Engineering By this author: Zhu, Yifang ; Hinds, William ; Shen, Si ; Sioutas, Constantinos GA_googleFillSlot("Horizontal_banner_bottom");
A survey was carried out to measure commuters' exposure to PM 2.5, CO, benzene, and the chemical composition of PM 2.5 on different routes and modes of transport in Mexico City. PM 2.5 ( n=62), CO ( n=54) and benzene ( n=22) are presented from morning (6:30-9:30 a.m.) and evening (17:30-20:30) rush hours on minibuses, buses and Metro (underground or subway system). Three routes were selected from a previous commuters' exposure study covering some of the most important thoroughfares of the valley. For PM 2.5, mass concentration was determined for all the samples. Nitrates, sulphates, inorganic elements and carbon fraction were analysed. CO was sampled using electrochemical sensors and 6-l canisters with flow controller devices were used to collect integrated samples for benzene. Minibuses had a slightly higher geometric mean PM 2.5 concentration in the morning than other modes of transport, but the ranking of geometric mean PM 2.5 by mode of transport is opposite in the evening and the variability within modes is approximately double the difference between modes. The highest single measurement was a concentration of 137 μg m -3 on a bus during an evening rush hour. The main component identified in PM 2.5 was carbon. Carbon monoxide levels in this study were approximately 3 times lower than those found in a commuter exposure study conducted in 1991. A strong association was shown between wind speed and PM 2.5 exposure in minibuses ( r2=0.50) and buses ( r2=0.54). The relationship between wind speed and CO exposure was strong only in minibuses ( r2=0.52).
During the summers of 1992 and 1993, particle mass concentrations (PM2.5 and PM10) were measured at eight sites located within metropolitan Philadelphia. Particle sampling was performed simultaneously at these sites on alternate days during the summer of 1992 and every day at seven of these sites during the summer of 1993. Sampling was conducted over 24-h periods beginning at 9 am (EDT) during both summers. All PM2.5 and PM10 samples were collected using 10 L/min inertial impactors with particle cutpoints of 2.5 and 10 μm, respectively. In this paper, we examine the relationship among PM2.5, coarse particulate (2.5 < da < 10 μm), and PM10 concentrations. In addition, we analyze their spatial variation and compare our findings with those made in an earlier study of sulfate (SO42-) concentrations. PM2.5 and PM10 concentrations were found to be relatively uniform across Philadelphia, suggesting that concentrations measured at a single monitoring site are able to characterize particulate concentrations across Philadelphia and other similar urban areas well. Coarse particulate concentrations were found to vary spatially within Philadelphia, with its variation related to population density. Coarse particulate levels were also shown to vary by day of week as weekday levels were higher than weekend levels. Variability in PM10 concentrations was driven primarily by variability in PM2.5 concentrations, which in Philadelphia comprised approximately 75% of PM10. SO42- related species in Philadelphia were, in turn, responsible for variability in PM2.5 and, as a result, in PM10 as well. SO42--associated species were the largest component of both PM2.5 and PM10 concentrations, comprising approximately 65 and 50% of their concentrations, respectively.
In this article we develop and compare several space–time models for hourly ambient PM10 collected in the Vancouver area. The models consist of a deterministic trend plus stochastic residuals. We find that the ambient PM10 field over the area has an essentially constant temporal pattern across its monitoring sites; the spatial variation is relatively small. Our comparison leads us in the end to adopt a model with a common temporal correlation structure for all the sites. The residuals after eliminating serial correlation prove to have at most small-scale spatial correlations, suggesting that dense monitoring networks would be necessary to make widespread spatial interpolation meaningful. Copyright © 1999 John Wiley & Sons, Ltd.
A personal multipollutant sampler has been developed. This sampler can beused for measuring exposurestoparticulate matter andcriteriagases.Thesystemusesasinglepersonalsamplingpump that operates at a èow rate of 5.2 l/min. The basic unit consists of two impaction-based samplers for PM2:5 and PM10 attached to a single elutriator. Two mini PM2:5 samplers are also attached to the elutriator for organic carbon (OC),elementalcarbon (EC),sulfate, and nitrate measurements. For the collection ofnitrate and sulfate, theminisamplerincludes aminiaturized honeycomb glass denuder that isplaced upstreamof the é lter toremove nitric acid and sulfur dioxide and tominimizeartifacts.Twopassive samplers can also be attachedtotheelutriatorformeasurementsofgaseouscopollutants such as O3, SO2, and NO2. The performance of the multipollutant sampler was examined through a series of laboratory chamber tests. The results showed a good agreement between the multipollutant sampler and the refer- ence methods. The overall sampler performance demonstrates its suitability for personal exposure assessment studies.
This project measured 2-hour integrated concentrations of PM10, PM2.5, metals and a number of organic chemicals including benzene and MTBE inside vehicles on California roadways. Using continuous samplers, particle counts, black carbon, and CO were also measured. In addition to measuring in-vehicle levels, the investigators measured pollutant levels just outside the vehicle, at roadside stations, and ambient air monitoring stations. Different driving scenarios were designed to assess the effects of a number of factors on in-vehicle pollutant levels. These factors included roadway type, carpool lanes, traffic conditions, geographical locations, vehicle type, and vehicle ventilation conditions. The statewide average in-vehicle concentrations of benzene, MTBE, and formaldehyde ranged from 3--22 {micro}g/m{sup 3}, 3--90 {micro}g/m{sup 3}, and 0---22 {micro}g/m{sup 3}, respectively. The ranges of mean PM10 and PM2.5 in-vehicle levels in Sacramento were 20--40 {micro}g/m{sup 3} and 6--22 {micro}g/m{sup 3}, respectively. In general, pollutant levels inside or just outside the vehicles were higher than those measured at the roadside stations or the ambient air stations. In-vehicle pollutant levels were consistently higher in Los Angeles than Sacramento. Pollutant levels measured inside vehicles traveling in a carpool lane were much lower than those in the right-hand, slower lanes. Under the study conditions, factors such as vehicle type and ventilation and little effect on in-vehicle pollutant levels. Other factors, such as roadway type, freeway congestion level, and time-of-day had some influence on in-vehicle pollution levels.