Journal of Environmental Protection, 2013, 4, 49-62
Published Online December 2013 (http://www.scirp.org/journal/jep)
http://dx.doi.org/10.4236/jep.2013.412A1006
Open Access JEP
Traffic Impacts on Fine Particulate Matter Air Pollution at
the Urban Project Scale: A Quantitative Assessment
Chidsanuphong Chart-asa, Kenneth G. Sexton, Jacqueline MacDonald Gibson
University of North Carolina at Chapel Hill, Chapel Hill, USA.
Received September 9th, 2013; revised October 13th, 2013; accepted November 11th, 2013
Copyright © 2013 Chidsanuphong Chart-asa et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In accordance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the
intellectual property Chidsanuphong Chart-asa et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
ABSTRACT
Formal health impact assessment (HIA), currently underused in th e United States, is a relatively new process for assist-
ing decision-makers in non-health sectors by estimating the expected public health impacts of policy and planning deci-
sions. In this paper we quantify the expected air quality impacts of increased traffic due to a proposed new university
campus extension in Chapel Hill, North Carolina. In so doing, we build the evidence base for quantitative HIA in the
United States and develop an improved approach for forecasting traffic effects on exposure to ambient fine particulate
matter (PM2.5) in air. Very few previous US HIAs have quantified health impacts and instead have relied on stake-
holder intuition to decid e whether effects will be p ositiv e, neg ativ e, or neutral. Our method u ses an air dispersion model
known as CAL3QHCR to predict changes in exposure to airborne, traffic-related PM2.5 that could occur due to the
proposed new campus development. We emplo y CAL3QHCR in a new way to better represen t variability in road grade,
vehicle driving patterns (speed, acceleration, deceleration, and idling), and meteorology. In a comparison of model pre-
dictions to measured PM2.5 concentrations, we found that the model estimated PM2.5 dispersion to within a factor of
two for 75% of data points, which is within the typical benchmark used for model performance evaluation. Applying
the model to present-day conditions in the study area, we found that current traffic contributes a relatively small amount
to ambient PM2.5 concentrations: about 0.14 µg/m3 in the most exposed neighborhood—relatively low in comparison
to the current US National Ambient Air Quality Standard of 12 µg/m3. Notably, even though the new campus is ex-
pected to bring an additional 40,000 daily trips to the study community by the year 2025, vehicle-related PM2.5 emis-
sions are expected to decrease compared to current conditions due to anticip ated improvements in vehicle technologies
and cleaner fuels.
Keywords: PM2.5; Traffic; Health Impact Assessment
1. Introduction
The World Health Organization and other public health
advocates have long stressed the need for formal health
impact assessment (HIA) to inform decision-making in
sectors outside the health-care industry [1-3]. The ration -
ale is that chronic diseases that pose major health bur-
dens in the post-industrial world are driven largely by
policy, program, and planning decisions in transportation,
agriculture, urban planning, and other sectors that ordi-
narily do not includ e population health as an objective in
their decision processes. Commonly cited examples in-
clude the effects of government agricultural subsidies on
the availability of healthy foods and the effects of trans-
portation plans on population exposure to noise and air
pollution. HIA is intended to encourage decision-makers
in these and other sectors to make choices that minimize
negative and max imize positiv e impacts on p ublic health ,
within budgetary and other constraints. The intent of HIA
is to prevent the chronic, noninfectious diseases—in-
cluding heart disease, stroke, and diabetes—that have
replaced infectious diseases as the leading health con-
cerns in post-industrialized nations [4]. Health practitio-
ners have long recognized that exposures to risk factors
for these chronic diseases are driven by a wide range of
policy, planning, and program decisions in multiple sec-
tors and that prevention through better-informed deci-

Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 50
sion-making in all sectors is likely to be less costly than
treating the symptoms [2].
While the practice of HIA is well established in the
European Union and some other nations, in the United
States HIA practice is relatively new [2,5,6]. The first
U.S. HIA, which evaluated the health impacts of a pro-
posed policy to in crease the minimum wage in San Fran-
cisco, was completed in 1999 [2,7]. By the end of 2012,
at least 114 additional HIAs had been completed in the
United States [8]. However, only 14 of these HIAs pro-
vided quantitative estimates of the impacts of alternative
choices on health [9]. The rest are qualitative, relying on
the judgment of the HIA practitioner to determine
whether one choice will be more or less detrimental or
beneficial to population health, in comparison with other
options. In the US urban planning and transportation
sectors, such qualitative HIAs are of little use. In order to
prioritize urban planning and transportation projects,
state and local planning and transportation agencies em-
ploy cost-benefit analysis. To be able to include health
impacts in these cost-benefit analyses, quantitative esti-
mates of health impacts—in terms of numbers of ill-
nesses and premature deaths—are essential. Yet, a recent
review found that only four HIAs in the transportation
and urban planning sectors in the United States had em-
ployed quantitative methods, and all of these were con-
ducted in major metropolitan areas in California [9].
In order to expand the evidence base for the use of
quantitative HIA to support planning and transportation
decisions in the United States, this paper presents an im-
proved approach for quantifying the future air quality
effects of increased traffic brought by new urban or sub-
urban development projects. We focus specifically on
predicting exposure to airborne fine particulate matter
(i.e., particles with diameter less than or equal to 2.5 µm,
denoted as PM2.5), which often is used as a marker of
near-roadway air pollution to support health effects esti-
mates. We then demonstrate the modeling approach for a
case study site: a proposed extension to the campus of
the University of North Carolina (UNC) at Chapel Hill,
in the United States.
Our modeling approach improves on those in the pre-
vious four US transportation-related HIAs in several
ways. First, it accounts for the effects of acceleration,
deceleration, and idling on all roadway links in the study
corridor using an approach recommended by Ritner et al.
but not previously employed in an HIA [10]. Second, it
compares model predictions to measured pollutant con-
centrations along the roadway corridor. According to
Ritner et al., such a performance evaluation has not been
previous ly completed. Third, it improves on the Ritner et
al. approach by developing a new algorithm to incorpo-
rate daily temperature variability.
The planned future project used as the case study for
demonstrating the new modeling method is known as
“Carolina North,” which is plan ned as an extens ion to th e
current UNC campus. UNC-Chapel Hill is the oldest
public university in the United States and has a current
student population of more than 29,000 [11]. The campus
is located in the town of Chapel Hill, which has a popu-
lation just over 57,000 [12]. The planned new campus
will be located about 3 km (2 miles) north of the existing
campus (Figure 1). If constructed, it is expected to in-
crease the number of trips to the area by 10,000 per day
by 2015—half of those by private vehicle—and, accord-
ingly, to substantially increase traffic in the surrounding
neighborhoods [13]. By 2025, the number of additional
daily trips to the campus is expected to increase by as
many as 40,000 [13]. The main traffic effects are ex-
pected along Martin Luther King Jr. Boulevard, the main
thoroughfare connecting the new campus to both the ex-
isting campus (to the south) and the nearest highway in-
terchange (to the north).
UNC commissioned a transportation impact analysis
in 2009 in order to estimate the anticipated increases in
traffic volumes, but the air quality impacts of the in-
creased traffic were not evaluated. Hence, the transporta-
tion impact analysis cannot be used directly to support
decision-making about whether alternative transportation
network designs (including, for example, new or ex-
panded public transit routes) may be needed to prevent
traffic-related air quality degradation and associated
health impacts. By quantifying the air quality effects of
additional traffic generated by the future campus, this
paper can support a future quantitative HIA to inform
local transportation and planning decisions.
2. Materials and Methods
Our process for modeling population exposure to excess
PM2.5 attributable specifically to increased traffic from
the Carolina North campus builds on a new approach
recommended by Ritner et al. [10], who proposed an
algorithm to account for vehicle acceleration, decelera-
tion, and idling at intersections in modeling of near-
roadway pollutant concentrations. We improved on the
Ritner et al. approach by developing a new algorithm for
incorporating hourly temperature variability in the esti-
mation. We then tested our predictions against roadside
air quality measurements. We analyzed near-roadway air
quality for three different scenarios: 2009 conditions,
2025 conditions assuming the new campus is not built,
and 2025 conditions assuming the campus is built. In-
formation on traffic counts for all these scenarios came
from the previously completed transportation impact
analysis [14]. We modeled air quality effects only for
daytime traffic (6 a.m. to 7 p.m.), since we assume that
themajor impacts will occur during these hours.
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment
Open Access JEP
51
Site 2
Site 3
Site 1
Figure 1. The study corridor runs from the intersection of Martin Luther King Jr. Boulevard and Whitfield Road to the in-
tersection of South Columbia Street and Mt. Carmel Church Road, Chapel Hill, NC. This map also shows the locations of the
three selected study sites. Site 1 is on the east side of Martin Luther King Jr. Blvd., opposite the Rigsbee Mobile Home Park.
Site 2 is on the east side of Martin Luther King Jr. Blvd. near Ashley Forest Rd. Site 3 is on the west side of Martin Luther
King Jr. Blvd., opposite the entrance to Bolin Creek.
We modeled PM2.5 concentrations at each of the 160
census blocks located within 500 m of the study corridor
(following guidance from the Health Effects Institute
suggesting that key traffic-related pollution impacts oc-
cur within 300 - 500 m of major roadways) [15]. Ap-
proximately 16,000 people live within these census
blocks [16]. In this study, the population exposures in
each census block are represented by the estimated 24-
hour PM2.5 concentrations at each receptor.
2.1. Modeling Approach
Our modelin g framework includes nine Steps (Figure 2):
Step 1: Divide roadway into links for analysis. Air
emissions from any single vehicle depend substantially
on the vehicle speed, vehicle acceleration, time spent
idling, and road grade. To account for these effects, we
followed the approach of Ritner et al. by dividing the
study corridor roadway into very short links [10]. In total,
we modeled 1200 links along the 8.2 km (5.1 mile) study
corridor. Each link has a roughly constant road grade;
fraction of vehicle time spent decelerating, idling or ac-
celerating; and moving speed. We used ArcGIS 9.3.1
(ESRI, Redlands, CA) and 2010 aerial photos from the
Orange County Geographic Information Systems (GIS)
Division to draw the series of links [17]. Link-specific
traffic activities were determined based on the simula-
ted traffic data for 2009, 202 no-build, and 2025 build 5

Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 52
Figure 2. Flowchart showing the nine steps of our modeling framework.
scenarios from the transportation impact analysis [14].
Link-specific average speeds were assumed to be equal
to speed limits based on GIS street maps from the Town
of Chapel Hill [18]. The speed limit was 25 mph for 17%
of the links, 35 mph for 68% of the links, and 45 mph for
the remaining 15%. Link-specific grades were derived
from GIS contour maps from the Town of Chapel Hill
[19] and ranged from 0% - 10%.
Step 2: Estimate vehicle emissions factors for six dif-
ferent temperatures for each link using MOVES. As sug-
gested by both Ritner et al. [10] and the US Environ-
mental Protection Agency’s (EPA) “Guidance on Quan-
titative PM Hot-Spot Analyses for Transportation Con-
formity” [20], we used MOVES 2010b (Motor Vehicle
Emission Simulator, EPA, Washington, DC) to develop
2009 and 2025 link-specific emission rates of PM2.5
(grams/vehicle-mile), according to link-specific traffic
activities, average speeds, and grades. The MOVES
model was developed by the EPA based on laboratory
tests that measured emissions from different kinds of
vehicles under conditions designed to represent typical
driving behaviors. Unlike its predecessor, known as
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 53
MOBILE6, MOVES can provide separate emissions
factors for different vehicle operation modes: accelera-
tion, deceleration, idling, and cruising [10].
MOVES models emissions for 13 vehicle types: mo-
torcycle, passenger car, passenger truck, light commer-
cial truck, intercity bus, transit bus, school bus, refuse
truck, single unit short-haul truck, single unit long-haul
truck, motor home, combination short-haul truck, and
combination long-haul truck. It also considers three fuel
types: gasoline, diesel, and compressed natural gas.
Hence, in order for the model to provide accurate esti-
mates for any specific roadway segment, the fraction of
vehicles in each class and fuel type category must be
estimated. For this analysis, we used vehicle fleet distri-
bution data from Guilford County, NC [21] (county seat:
Greensboro), since data specific to Chapel Hill were un-
available. The fuel type distributions as well as fuel sup-
ply and formulation in the project areas were based on
national defaults. These data (fleet distributions and fuel
types) were fixed in all MOVES runs.
The EPA’s PM hot-spot guidance recommends that the
link-specific emission rates should be prepared based on
average temperatures for four different time periods in a
day for each season, meaning that each development
scenario would require 16 MOVES runs. However, this
approach does not fully account for daily temperature
variability within a given season. Previous studies have
shown that PM emission rates are highlight sensitive to
temperature, and hence omitting temperature variability
could decrease the accuracy of modeled emissions factors
[22,23]. Our new algorithm for representing intra-sea-
sonal variability in temperature and meteorological con-
ditions runs MOVES for six different temperatures: 10,
30˚F, 50˚F, 70˚F, 90˚F, and 110˚F [24]. Later steps of the
algorithm (described below) interpo late between these six
estimates to determine temperature-specific emissions
factors for each roadway link. For example, if a winter-
time simulation of any given hour yielded a temperature
of 40 degrees for that hour , we then esti mated the vehicle
emissions factors to be the average of the emissions fac-
tors for 30 and 50 degrees.
Step 3: Select an hourly temperature and meteorologi-
cal profile from empirical weather data. The meteoro-
logical data to estimate probability distributions of the
effects of weather on PM2.5 concentrations for each
season were obtained from the EPA’s Meteorological
Processor for Regulatory Models, using 2006-2012 sur-
face and upper air data at the national weather stations in
Chapel Hill and Greensbo ro respectively [24,25]. A total
of 2,100 days with complete required data were used in
the modeling, including 525 days for winter, 560 days for
spring, 532 days for summer, and 483 days for fall. Sea-
sonal temperature profiles are shown in Figure 3. Figure
4 shows the distributions of seasonal wind speed and
direction.
In this third step, we selected one day from th ese 2,100
days to support the modeling in steps 4 - 5 below, and
then we repeated this selection (step 6) without replace-
ment 2099 times until we had estimated PM2.5 concen-
trations in each census block for each day having a com-
plete weather record.
Step 4: Estimate the total emissions from vehicles
traveling on each roadway link. The MOVES model es-
timates average per-vehicle emissions in grams per vehi-
cle-mile, accounting for the specific distribution of vehi-
cle types, ages, and fuel sources at the study site. The
next step was to compute the total mass of PM2.5 emit-
ted from each vehicle on each roadway link. For this step,
vehicle counts were needed. The link-specific traffic
volumes were based on the simulated traffic data for
2009, 2025 no-build, and 2025 build scenarios from the
Carolina North Traffic Impact Analysis [14]. For the
temperature profile selected in step 3, we estimated emis-
sions factors by interpolating between the outputs of step
2 for the nearest two temperatures.
Step 5: Model dispersion of PM2.5 from roadway
emissions into the surrounding neighborhoods using
CAL3QHCR.The PM hot-spot gu idance suggests two air
pollution dispersion models—CAL3QHCR (EPA, Re-
search Triangle Park, NC) or AERMOD (EPA, Research
Triangle Park, NC)—for simulating PM2.5 pollution
dispersion from roadways. Both models are based on
Figure 3. Seasonal temperature profiles from 6 a.m. to 7 p.m., according to the meteorological data used in the CAL3QHCR
modeling.
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 54
Figure 4. Seasonal wind roses from 6 a.m. to 7 p.m., according to the meteorological data used in the CAL3QHCR modeling.
Gaussian plume dispersion. However, a recent model
comparison study suggested that CAL3QHCR requires
less meteorological data and user effort and appears to
perform better than AERMOD for analyses at the urban
project scale [26]. In this study, we tested and used
CAL3QHCR for estimating population exposure to
PM2.5 (g/m3) from the study corridor. As described
below under “model validation ap proach,” we tested two
different versions of CAL3QHCR: one dated 13196 and
the other dated 04244. We then used the best-performing
of the two in subsequent simulations. We ran C AL3QHC R
for each roadway link using the meteorological profile
from step 3 and the per-link total PM2.5 emissions from
step 4. We modeled concentrations at an elevation of 1.5
m, corresponding to the elevation of the adult breathing
zone.
Steps 6 - 9: Generate probability distribution of sea-
sonal average 24-hour PM2.5 concentration. As Figure 2
outlines, we first repeated steps 3-5 for each of the days
(2,100 in total) for which historical empirical weather
data were available. The result was 2,100 separate daily
estimates of the PM2.5 concentration at each of the 160
census block centroids: 525 winter day estimates and 560,
532, and 483 spring, summer, and fall estimates, respec-
tively. We then used a bootstrap technique to estimate a
probability distribution for the average daily PM2.5 con-
centration in each season. Specifically, for each season,
we resampled with replacement 91 days from the simu-
lated daily PM2.5 concentration estimates. We then
computed the mean value of these 91 daily estimates for
each receptor. Then, we repeated this process of com-
puting a seasonal mean 1999 times, in order to generate a
sample of 2000 seasonal mean 24-hour PM2.5 concen-
trations. This sample then served as the basis for devel-
oping a probability distribution of the seasonal mean
concentration for each season.
2.2. Model Validation Approach
This study tested the performance of the combined
MOVES-CAL3QHCR modeling approach by comparing
model predictions against roadside measurements at
three selected sites along the study corridor (Figure 1).
Furthermore, we compared the predictive validity of two
versions of CAL3QHCR (dated 04244 and dated 13196)
According to the model change bulletin, the mixed mode
rounding in the internal calculations of CAL3QHCR
dated 04244 was removed from CAL3QHCR dated
13196. Consequently, the simulated concentrations from
these two model versions are different in some cases.
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 55
We used a DustTrak DRX Aerosol Monitor Model
8534 (TSI, Shoreview, MN) to measure total PM2.5 con-
centrations at each of the three sites The DustTrak DRX
instrument or similar models have been used in roadside
measurements in several previous studies [27-29]. The
DustTrak can detect concentrations from 1 to 150,000
g/m3 with an error of 0.1% of the monitored concen-
tration [30]. All of these instruments are calibrated at the
factory with a known mass concentration of Arizona Test
Dust (ISO 12103-1, A1 test dust) [31]. In addition, in
each sampling period, we calibrated the instrument be-
fore taking measurements. During all sampling events,
the DustTrak was held about 1.5 m above the ground (the
adult breathing zone height) and programmed to record
the total concen tration every five seconds.
We collected samples on two separate days at Site 1
and on one day at Sites 2 and 3 for a total of four sam-
pling days in the study corridor. During three of the four
sampling days, we monitored PM2.5 concentrations dur-
ing the morning and evening peak traffic periods and also
in the middle of the day four an hour at a time (roughly
8:00 - 9:00 a.m., noon-1:00 p.m., and 5:00 - 6:00 p.m.).
At Site 2, the property owner requested that we not col-
lect samples in the evening, so we only sampled during
the morning and noon hours. Table 1 shows sample col-
lection dates and measured PM2.5 concentrations.
During each sampling event, we drew continuous air
samples for three minutes at 10 m from the roadway and
then repeated the three-minute sampling at locations of
30 m and 50 m from the roadway (except at Site 2, where
obstructions prevented sampling at 50 m). Then, we re-
peated this process over the co urse of ab out on e hour. As
a result, at each site and during each sampling event, we
collected PM2.5 concentrations for six three-minute in-
tervals at 10 m, 30 m, and 50 m perpendicular distances
from the roadway, as Figure 5 illustrates. For each event,
we then computed the average PM2.5 concentration mea-
sured during these three-minute intervals; Table 1 shows
the resulting estimated one-hour average concentrations.
During each sampling event, we simultaneously col-
lected traffic counts and meteorological data. Traffic was
monitored with a hand-held counter, and the counts were
confirmed by viewing digital video recordings from a
portable video recorder positioned on a tripod to film the
roadway during sampling. We measured wind speed us-
ing a Skymate model SM-18 wind meter with accuracy
within 3% (Campbell Scientific, Inc, Logan Utah); wind
Figure 5. Diagram of sampling points along the study cor-
idor. r
Table 1. Measured and modeled PM2.5 concentrations (μg/m3).
Site Date Time
period
Measured
concentrations*
Measured
concentration
difference**
Predicted concentration
differences: CAL3QHCR
(04244)
Predicted concentration
differences: CAL3QHCR
(13196)
10
m
30
m
50
m
10 vs. 30
m
10 vs. 50
m
30 vs. 50
m
10 vs. 30
m
10 vs. 50
m
30 vs. 50
m
10 vs. 30
m
10 vs. 50
m
30 vs. 50
m
1 16-May Morning 14.9 13.8 13.9 1.1 1.0 NEG 0.7 0.9 0.2 0.7 1.0 0.3
Noon 9.0 8.7 8.3 0.3 0.7 0.4 0.5 0.8 0.3 0.4 0.6 0.1
Evening 9.7 10.0 9.7 NEG NEG 0.3 1.0 1.3 0.3 0.9 1.3 0.3
31-May Morning 5.1 5.1 4.9 0.0 0.2 0.2 0.7 0.8 0.1 0.7 0.9 0.2
Noon 2.6 2.2 1.6 0.4 1.0 0.6 0.5 0.8 0.3 0.5 0.6 0.2
Evening 3.0 2.6 2.4 0.4 0.6 0.2 1.1 1.4 0.3 1.0 1.3 0.4
2 24-Apr Morning 21.4 20.8 NA WD NA NA 0.5 NA NA 0.5 NA NA
Noon 10.5 10.4 NA WD NA NA 0.7 NA NA 0.6 NA NA
3 16-Apr Morning 10.8 10.8 10.5 NEG 0.3 0.3 0.7 0.9 0.2 0.6 0.8 0.2
Noon 9.7 9.2 9.0 0.5 0.7 0.2 0.6 1.0 0.4 0.5 0.7 0.2
Evening 9.2 8.9 8.5 WD WD WD 0.0 0.0 0.0 0.1 0.1 0.0
*NA indicates PM2.5 could not be measured at this location due to a physical obstruction; **Negative values excluded during data cleaning are labeled as
NEG”; those excluded due to unfavorable wind direction are labeled as WD. “
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 56
direction using a windsock and compass; and tempera-
ture, dewpoint, and relative humidity using an Extech
model 445814 thermometer-psychrometer with tempera-
ture accuracy of ±1.8˚F and relative humidity accuracy of
±4%. Data on atmospheric stability class and mixing
height were estimated using EPA’s Meteorological Pro-
cessor for Regulatory Models [36]. Table 2 shows the
traffic counts and meteorological conditions for each
sampling event.
The measured concentrations at each sampling point
represent the sum of background concentrations, PM2.5
contributions from other nearby sources, and traffic-re-
lated PM2.5. Therefore, in order to evaluate the per-
formance of the CAL3QHCR model, concentrations of
PM2.5 attributable to background and other sources must
be subtracted from the monitored concentrations, in order
to determine how much of the measured PM2.5 comes
from the roadway. In testing model performance, other
studies have used background concentrations measured
at an upwind location or central air quality monitor
[26,32,33]. However, Ch apel Hill does no t have an active
PM2.5 monitor; the nearest PM2.5 monitor is about 45
km away, in Raleigh. Furthermore, due to resource limi-
tations, we were able to use only one DustTrak monitor
and hence were unable to capture background concentra-
tions while simultaneously measuring near-road concen-
trations. Hence, we accounted for the effect of back-
ground PM2.5 by character izin g the differentials be tween
the measured concentrations at pairs of sampling points
at distances 10 m and 30 m, 10 m and 50 m, and 30 m
and 50 m from the roadway. Table 1 shows these differ-
entials, as computed from the measured concentrations.
A factor-of-two plot has been commonly used to
evaluate the performances of the CALINE series of dis-
persion models (e.g., CALINE3, CAL3QHC/CAL3QCHR,
and CALINE4) [26,32-35]. That is, modeled PM con-
centrations are plotted against measured concentrations
to see whether the model estimates are within a factor of
two of measured concentrations. Typically, the model is
considered valid in predicting the traffic-related concen-
trations if at least 75% of the comparing pairs are within
a factor-of-two envelope. This criterion was also applied
in this study. We adopted this approach, comparing mea-
sured PM2.5 concentration differences between pairs of
points with differences predicted by the two different
CAL3QHCR model versions.
2.3. Data Cleaning
In total, the sampling events shown in Table 1 yielded 29
data points. Of these, five points had to be eliminated
because the wind direction was outside of a 120˚ degree
arc from a line drawn perpendicular to the roadway (see
Figure 5). In such conditions, the monitoring locations
were not downwind of the roadway and therefore could
not capture roadway contributions to PM2.5 [37]. Four
additional data points were eliminated because they in-
dicated negative dispersion (that is, PM2.5 concentra-
tions increased rather than decreased with distance from
the roadway). This data cleaning process left 20 data
points for comparing measured PM2.5 concentrations to
modeled concentrations.
Table 2. Traffic and meteorological data used in CAL3QHCR modeling.
Site Date Period
Average
Traffic
Count
(veh/min)
Average
Wind
Direction
(deg)
Average Wind
Direction within
120˚ Arc from
Study Corridor?
Average
Wind Speed
(m/s)
Average
Temperature
(˚F)
Stability
Class*
Mixing
Height
(m)*
1 16-May Morning 34 80 Yes 0.8 73.7 Slightly unstable 678
Noon 26 83* Yes 1.4 85.9 Unstable 1315
Evening 42 91 Yes 0.7 80.5 Slightly unstable 1395
31-May Morning 34 91 Yes 0.9 77.5 Slightly unstable 878
Noon 29 55 Yes 1.5 88.6 Unstable 1676
Evening 38 41 Yes 0.8 99.4 Slightly unstable 1776
2 24-Apr Morning 32 349 No 0.6 56.9 Slightly unstable 670
Noon 27 37 No 1.1 74.8 Unstable 1360
3 16-Apr Morning 24 252 Yes 0.2 68.5 Neutral 1869
Noon 22 264 Yes 0.7 80.0 Very unstable 1939
Evening 31 20* No 0.7 77.8 Neutral 1944
N
OTE: Wind speeds below 1 m/s were reset to 1 m/s in CAL3QHCR, as suggested by the US EPA [36]. *Data obtained fr om MPRM.
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 57
3. Results
3.1. Vehicle Emission Rates
The output from MOVES can provide useful insights
about the vehicle classes contributing most to roadside
pollution, the effects of meteorological and road charac-
teristics on per-vehicle emissions, and the effects of fu-
ture vehicle technologies.
To identify the vehicle classes contributing most to
roadway emissions, we ran MOVES for a study corridor
link with 0% grade, a 35 mph speed limit, and an ambi-
ent temperature of 90˚F. Figure 6 show s the results. Th is
analysis reveals that trucks are the major contributors to
roadside emissions for this corridor. In total, trucks of all
categories contribute 79% of emissions: 19% from pas-
senger trucks (e.g., sport utility vehicles) and the re-
maining 60% from various kinds of commercial trucks.
Consistent with this result, diesel-fueled vehicles account
for nearly two-thirds (64%) of emissions whereas gaso-
line-fueled vehicles account for 36%. As well, vehicles
more than 10 years old account for half of the roadside
Figure 6. Example of 2009 link-specific emission rate frac-
tions (%) at 35 mph average speed, 0% grade, and 90˚F by
fuel types, age groups, and vehicle types.
emissions. Hence, improving emissions controls or en-
gine efficiency in diesel-fueled trucks, plus retiring older
vehicles, could greatly reduce roadside emissions in the
study corridor.
MOVES output also shows the important effects of
temperature, road grade, and vehicle speed on roadway
emissions. As Figure 7 shows, emissions decrease as
temperature increases, increase as road grade increases,
and decrease as vehicle speed increases. These results
illustrate the importance for modeling of accurately cap-
turing temperature, vehicle speed, and especially road
grade—hence the importance of dividing a study corridor
into short links as in ou r study.
Interestingly, the results show that 2009 link-specific
emission rates (ranging from 0.02 - 0.50 g/veh-mile) are
higher than 2025 link-specific emission rates (ranging
from 0.01 - 0.26 g/veh-mile). The differences result from
the assumption, built into MOVES, that future vehicles
will have more efficient engines that reduce emissions
and will use cleaner fuels.
3.2. Model Performance Evaluation
Figure 8 compares the predictions of the two CAL3QHCR
model versions to measurements of pollutant dispersion
along the roadway corridor. The figure also shows the
“factor-of-two envelope:” th at is, the range of predictions
that are within a factor of two of the measured dispersion.
As shown, the models contain both under-predictions of
Figure 7. Examples of 2009 link-specific emission rate (g/veh-
mile) changes by average speeds, grades, and temperatures.
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 58
the amount of dispersion (i.e., data points below the fac-
tor-of-two envelope) and over-predictions (data points
more than twice the measured amount). However, both
models are more likely to over-predict than to under-
predict dispersion: that is, to predict greater concentra-
tion differences as one moves away from the roadway
than were actually measured. Possible reasons for this
prediction error include physical obstacles to dispersion
(for example, at site 3, a large rock outcropping may in-
terfere with dispersion) and intermittent winds. Previous
model evaluations also have observed that the predeces-
sor to CAL3QHCR did not perform well in the presence
of street canyons or other physical obstacles or when
winds are intermittent [32].
Of the two models, model 1 (the version dated 04244)
performs better than model 2 (the version dated 1196).
For model 1, 15 modeled estimates (75%) were within a
factor of two of the measured value. Previous studies hav e
suggested that a 75%, factor-of-two prediction capability
indicates reasonable model performance, and model 1
achieves this metric [32]. For model 2, 13 observations
(65%) were within a factor of two of observed values.
Because model 1 better predicted the observed data than
model 2, we used model 1 for our exposure predictions.
3.3. Estimated PM2.5 Exposure under Current
and Future Scenarios
Our modeling approach can be used to predict the effects
of the Carolina North campus on ambient PM2.5 con-
centrations in census blocks in the study corridor if the
campus is built.
Even if the new campus is built, the roadway con tribu-
tion to ambient PM2.5 levels in the stud y corridor is pre-
dicted to be very low by 2025. The maximum contribu-
tion the new campus contributes to any one census block
occurs in winter and is predicted to be 0.11 µg /m3, which
is quite low in comparison with the ambient air quality
standard (12 µg/m3 annual average PM2.5 concentration).
In comparison, if the new campus is not built, the maxi-
mum PM2.5 concentration in any one census block is
0.085 µg/m3, which is 24% lower than if the campus is
built. In both cases, though, the maximum concentration
is higher under current conditions than under future con-
ditions, despite the an ticipated traffic growth. Under cur-
rent conditions, the model predicts that the maximum
roadway contribution to seasonal PM2.5 in any one cen-
sus block is 0.14 µg/m3, which is 24% higher than ex-
pected in 2025, even if the new campus is built. These
future emissions reductions reflect the built-in assump-
tions of MOVES that the future vehicle fleet will become
more efficient (less polluting) and that fuels will be
cleaner. The results thus illustrate the value of ensuring
continued improvements in vehicle fuel economy and
emissions standards.
Our modeling approach included a new method for
representing meteorological variability. Our results illus-
trate that variability can be important in some locations.
Overall, the daily meteorological variability caused little
change in seasonal daily mean PM2.5 concentrations. For
example, in the 2025 scenario in which the Carolina
North campus is built, the average coefficient of varia-
tion (standard deviation of the predicted seasonal mean
divided by average of the seasonal mean) is 0.06, mean-
ing that seasonal variability on average has a relatively
small effect on model predictions. The maximum coeffi-
cient of variation in this scenario was less than 0.5,
which means that 95% of the time, meteorological vari-
ability will change the predicted seasonal mean by less
than a factor of 2. (According to the Central Limit Theo-
rem, the seasonal mean converges to a normal distribu-
tion, and hence 95% of the time, the seasonal mean
should be within two standard deviations of the actual
mean, and in this case the standard deviation is about half
the mean.) Thus, this meteorological variability is less
important than the model uncertainty shown in Figure 8.
The modeling approach can be used to characterize
spatial variability in roadway emissions effects on sur-
rounding neighborhoods. Figure 9 shows the resulting
spatial variability for current conditions, and Figure 10
shows the spatial variability for future conditions. In both
instances (because both models rely on the same set of
meteorological data), the census block with the maxi-
mum concentration is in the same location and also (de-
spite changes in wind directions) does not vary season-
ally. The most affected census block (shown with arrows
in Figures 9 and 10) is located on the east side of Martin
Figure 8. Factor-of-two plots of concentration differences
(mg/m3) observed during roadside measurements and pre-
icted by CAL3QHCR (dated 13196 and dated 04244). d
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59
Figure 9. PM2.5 concentrations attributable to roadway emissions from the study corridor, as predicted by the combined
MOVES-CAL3QHCR approach (g/m3) by season for the year 2009.
Figure 10. PM2.5 concentrations attributable to roadway emissions, as predicted by the combined MOVES-CAL3QHCR
approach (g/m3) by season for the year 2025, assuming the Carolina North Campus is built.
Luther King Jr. Boulevard at Blossom Lane. Such infor-
mation could be useful for zoning decisions (e.g., deci-sions about locations for schools, retirement homes, or other land uses attracting sensitive p opulations).

Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 60
4. Discussion
Our results are consistent with the few empirical evalua-
tions of the accuracy in predicting roadway PM2.5 con-
centrations of CAL3QHCR and its predecessor, known
as CALINE. Yura et al. compared CALINE predictions
of PM2.5 to measured PM2.5 concentrations at a busy
intersection in a suburban community in Sacramento,
California, and an urban site along a six-lane road in
London, England [32]. They found that 80% of model
predictions were within a factor-of-two envelope of meas-
ured concentrations at the suburban site but that only
56% of predictions were within the factor-of-two enve-
lope for the urban site. They attributed the poor per-
formance at the urban site to limitations of the emissions
factors they used (they relied on scaling United Kingdom
PM10 emissions factors) and to street canyon effects.
Chen et al. extended Yura’s work by comparing the per-
formance of the CAL3QHC model to that of the
CALINE model for the same two sites (although during a
different time period) as Yura used [26]. Chen et al.
found that predicted PM2.5 concentrations were within
the factor-of-two envelope for 69% of the Sacramento
data points and for 59% of the London data points. In
both cities, CAL3QHC outperformed CALINE. Gokhale
and Raokhande compared the CALINE and CAL3QHC
models’ ability to predict roadside PM2.5 concentrations
at a busy intersection in Guwahati, India [38]. They
found that the CAL3QHC mode l predictions were within
a factor-of-two envelope for 65% of (66 of 102) hourly
PM2.5 observations during winter and that the CAL3QHC
model outperformed the CALINE model (the latter of
which produced predictions within the factor-of-two en-
velope for 46 of 1 02 data po int s) .
Our findings about the amount of PM2.5 contributed
to a given location by a single busy roadway also are
consistent with findings of the few modeling studies and
quantitative HIAs of local effects of traffic in the United
States. In a modeling study, Zhang and Batterman used
CALINE along with the predecessor to MOVES, known
as MOBILE6.2, to estimate the amount of PM2.5 pollu-
tion contributed by a busy roadway in Detroit, Michigan
[33]. They found that the lo cal roadway contributed only
a small amount of the measured PM2.5: total measured
PM2.5 concentrations averaged 16.8 µg/m3, but Zhang
and Batterman attributed “no more th an 0.5 µg/m3” to the
roadway. They attributed the majority of observed PM2.5
“to long range transport of sulfate and other aerosols
from the Ohio River Valley.” Chen et al. also found that
roadways in Sacramento and London contributed rela-
tively small fractions to observed PM2.5 concentrations
at the study sites [26].
Of the four transportation-related quantitative HIAs
identified in the comprehensive review by Bhatia and
Seto et al., three predicted PM2.5 concentrations attrib-
utable to vehicles on roadways (the fourth predicted
PM10 concentrations) [9]. All of these HIAs (including
the HIA that estimated PM10 concen trations) focused on
proposed new development projects in or near Oakland,
California, and all used CAL3QHCR to support their
predictions. The first, an HIA of a proposed residential
development to be constructed near a highway (with an
average daily traffic volume of about 119,000 vehicles)
in Pittsburg, California, used CAL3QHCR to estimate
that traffic-attributable exposures adjacent to the high-
way are about 2 µg/m3 but that these exposures decline
rapidly with distance to about 0.2 µg/m3 [39]; this esti-
mate assumed a constant emissions factor of 0.15 g/ve-
hicle-mile travelled, whereas our estimate employed
MOVES to estimate link-specific emissions factors, re-
sulting in a range of emissions factors of 0.02 - 0.5
g/vehicle-mile travelled. The second of these three quan-
titative HIAs con sidered the poten tial traffic-rela ted he al th
effects of potential affordable housing sites in Oakland,
California; this HIA used CAL3QHCR to estimate that
two major roadways with combined annual average daily
traffic counts of ab out 225,000 vehicles would co ntribute
about 0.4 - 0.5 µg/m3 to PM2.5 exposu res at the location s
under consideration, all of which were within meters of
the roadways [40]. The third HIA concerned a potential
new residential development near a transit station in
Oakland; it estimated that alongside a major highway
(with daily traffic counts averaging 144,000 vehicles)
neighboring the proposed development site, about 0.3
µg/m3 of PM2.5 could be attributed to traffic but that this
traffic-related contribution decreased to 0.1 µg/m3 at a
distance of 150 m from the highway [41]. In summary,
these HIAs estimate that directly adjacent to highways
running through the Oakland area, traffic contributes
anywhere from about 0.3 - 2 µg/m3. All of these high-
ways have daily traffic counts at least five times as high
as the current traffic along the roadway corridor analyzed
in the present study. The estimated roadway contributions
that our modeling approach yielded (with the maximum
roadway-contributed concentration of 0.14 µg/m3 under
current conditions) hence are quite consistent with these
previous estimates when traffic volumes and distances of
census block centroids to the roadway are considered.
That is, if one multiplies the maximum estimate from our
modeling approach by 5, then the estimated maximum
predicted concentration in any census block in the study
corridor is 0.7 µg/m3. This is within the range of concen-
trations predicted in the California studies.
5. Conclusions
In this study, a new modeling framework to quantify the
project traffic growth impacts on population exposure to
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Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment 61
PM2.5 air pollution was proposed and then demonstrated
by quantifying exposure to roadway PM2.5 emissions
that may occur in the future due to the Carolina North
development in Chapel Hill, North Carolina. This mod-
eling framework should benefit others conducting quan-
titative HIAs of the built environment and transportation
projects. Whereas previous HIAs employing air disper-
sion models have used average meteorological data and
have assumed that vehicles move at a constant cruising
speed along roadway links, our approach considers link-
by-link variation in vehicle behavior and hourly mete-
orological variability.
Our results reveal that improvements in vehicle tech-
nologies and fuels will b e a key factor in protecting pub-
lic health from the air pollution generated by increases in
traffic expected to occur due to local and regional devel-
opments in the future. In fact, the models we employed
predict that traffic-related PM2.5 in the study corridor
may actually decrease in the future, even if traffic in-
creases, due to improved vehicle technologies and fuels.
Our results also reveal the need for improve models
to predict near-road PM2.5 concentrations. While the
CAL3QHCR dispersion model was able to predict dis-
persion reasonably well, about 25% of model pr edictions
over-estimated dispersion. This overestimation bias re-
sults in under-estimates of pollutant exposure. Hence,
reducing model bias is critical to ensuring that deci-
sion-makers are adequately informed about air quality
and health risks associated with roadway traffic.
REFERENCES
[1] J. Kemm, “Perspectives on Health Impact Assessment,”
Bulleting of the World Health Organization, Vol. 81, No.
6, 2003, p. 387.
[2] National Research Council, “Improving Health in the
United States: The Role of Health Impact Assessment,”
National Academy Press, Washington DC, 2011.
[3] A. Wernham, “Health Impact Assessments Are Needed in
Decision Making about Environmental and Land-Use Po-
licy,” Health Affairs (Project Hope), Vol. 30, No. 5, 2011,
pp. 947-956. http://dx.doi.org/10.1377/hlthaff.2011.0050
[4] R. Lozano, M. Naghavi, K. Foreman, S. Lim, K. Shibuy a,
V. Aboyans and C. J. L. Murray, “Global and Regional
Mortality from 235 Causes of Death for 20 Age Groups
in 1990 and 2010: A Systematic Analysis for the Global
Burden of Disease Study 2010,” Lancet, Vol. 380, No.
9859, 2012, pp. 2095-2128.
http://dx.doi.org/10.1016/S0140-6736(12)61728-0
[5] A. L. Dannenberg, R. Bhatia, B. L. Cole, S. K. Heaton, J.
D. Feldman and C. D. Rutt, “Use of Health Impact As-
sessment in the U.S.: 27 Case Studies, 1999-2007,” Ame-
rican Journal of Preventive Medicine, Vol. 34, No. 3,
2008, pp. 241-256.
http://dx.doi.org/10.1016/j.amepre.2007.11.015
[6] M. Wismar, J. Blau, K. Ernst and J. Figueras, “The Effec-
tiveness of Health Impact Assessment: Scope and Limita-
tions of Supporting Decision-Making in Europe,” Politi-
cal Science, Copenha gen WHO R egional Office fo r E u r o p e ,
2007.
[7] R. Bhatia and M. Katz, “Estimation of Health Benefits
from a Local Living Wage Ordinance,” American Journal
of Public Health, Vol. 91, No. 9, 2001, pp. 1398-1402.
http://dx.doi.org/10.2105/AJPH.91.9.1398
[8] L. Singleton-Baldrey, “The Impacts of Health Impact As-
sessment: A Review of 54 Health Impact Assessments,
2007-2012,” University of North Carolina at Chapel Hill,
Chapel Hill, 2012.
[9] R. Bhatia and E. Seto, “Quantitative Estimation in Health
Impact Assessment: Opportunities and Challenges,” En-
vironmental Impact Assessment Review, Vol. 31, No. 3,
2011, pp. 20301-20309.
http://dx.doi.org/10.1016/j.eiar.2010.08.003
[10] M. Ritner, K. K. Westerlund, C. D. Cooper and M. Clag-
gett, “Accounting for Acceleration and Deceleration Em is-
sions in Intersection Dispersion Modeling Using MOVES
and CAL3QHC,” Journal of the Air and Waste Manage-
ment Association, Vol. 63, No. 6, 2013, pp. 724-736.
http://dx.doi.org/10.1080/10962247.2013.778220
[11] University of North Carolina at Chapel Hill, “About
UNC,” 2013. http://www.unc.edu/about/
[12] Town of Chapel Hill, “Snapshot of the Town of Chapel
Hill,” 2012.
http://www.townofchapelhill.org/Modules/ShowDocume
nt.aspx?documentid=12177
[13] K. Ross, “Carolina North Hearing Highlights Traffic Im-
pact,” The Carrboro Citizen, 2009.
http://www.carrborocitizen.com/main/2009/05/14/ca r olin
a-north-hearing-highlights-traffic-impact/
[14] Vanasse Hangen Brustlin, Inc., “Transportation Impact
Analysis for the Carolina North Development,” Water-
town, 2009.
[15] Health Effects Institute, “Traffic-Related Air Pollution: A
Critical Review of the Literature on Emissions, Exposure,
and Health Effects,” HEI Special Report 17, Boston,
2010.
[16] US Census Bureau, “Census Block Shapefiles with 2010
Census Population and Housing Unit Counts,” 2011.
[17] Orange County, “Aerials 2010,” 2012.
[18] Town of Chapel Hill, “Street Centerline,” 2009.
[19] Town of Chapel Hill, “2ft Elevation Contours,” 2009.
[20] US Environ ment al Protecti on Age ncy , “Gui dance on Qu an -
titative PM Hot-Spot Analyses for Transportation Con-
formity,” 2011.
http://www.epa.gov/otaq/stateresources/transconf/policy/
pm-hotspot-guide.pdf
[21] N.C. Division of Air Quality, “MOVES Input and Output
Giles: Hickory and Triad PM2.5 Redesignation Demon-
stration and Maintenance Plan,” 2011.
[22] P. Mulawa, S. Cadle, H. Knapp, R. Zweidinger, R. Snow,
R. Lucas and J. Goldbach, “Effect of Ambient Tempera-
ture and E-10 Fuel on Primary Exhaust Particulate Matter
Emissions from Light-Duty Vehicles,” Environmental
Open Access JEP

Traffic Impacts on Fine Particulate Matter Air Pollution at the Urban Project Scale: A Quantitative Assessment
Open Access JEP
62
Science & Technology, Vo. 31, 1997, pp. 1302-1307.
http://dx.doi.org/10.1021/es960514r
[23] D. Choi, M. Beardsley, D. Brzezinski, J. Koupal and J.
Warila, “MOVES Sensitivity Analysis: The Impacts of
Temperature and Humidity on Emissions,” The MOVES
Workshop 2011, US Environmental Protection Agency,
Ann Arbor, 2011.
[24] National Climatic Data Center, “Quality Controlled Local
Climatological Data (QCLCD),” 2013.
[25] National Oceanic and At mospheric Administration,
“NOAA/ESRL Radiosonde Database,” 2013.
[26] H. Chen, S. Bai, D. Eisinger, D. Niemeier and M.
Claggett, “Predicting Near-Road PM2.5 Concentrations,”
Transportation Research Record: Journal of the Trans-
portation Research Board, Vol. 2123, 2009, pp. 26-37.
http://dx.doi.org/10.3141/2123-04
[27] M. G. Boarnet, D. Houston, R. Edwards, M. Princevac, G.
Ferguson, H. Pan and C. Bartolome, “Fine Particulate
Concentrations on Sidewalks in Five Southern California
Cities,” Atmospheric Environment, Vol. 45, No. 24, 2011,
pp. 4025-4033.
http://dx.doi.org/10.1016/j.atmosenv.2011.04.047
[28] J. S. Wang, T. L. Chan, Z. Ning, C. W. Leung, C. S.
Cheung and W. T. Hung, “Roadside Measurement and
Prediction of CO and PM 2.5 Dispersion from On-Road
Vehicles in Hong Kong,” Transportation Research Part
D: Transport and Environment, Vol. 11, No. 4, 2006, pp.
242-249. http://dx.doi.org/10.1016/j.trd.2006.04.002
[29] Y. Wu, J. Hao, L. Fu, Z. Wang and U. Tang, “Vertical
and Horizontal Profiles of Airborne Particulate Matter
near Major Roads in Macao, China,” Atmospheric Envi-
ronment, Vol. 36, No. 31, 2002, pp. 4907-4918.
http://dx.doi.org/10.1016/S1352-2310(02)00467-3
[30] TSI, Inc., “Spec Sheet Model 8533/8534 DustTrak DRX
Aerosol Monitor, 2012.
[31] TSI, Inc., “DustTrak DRX Aerosol Monitor Theory of
Operation (EXPMN-002),” 2012.
[32] E. A. Yura, T. Kear and D. Niemeier, “Using CALINE
Dispersion to Assess Vehicular PM 2.5 Emissions,” At-
mospheric Environment, Vol. 41, No. 38, 2007, pp. 8747-
8757. http://dx.doi.org/10.1016/j.atmosenv.2007.07.045
[33] K. Zhang and S. Batterman, “Near-Road Air Pollutant
Concentrations of CO and PM2.5: A Comparison of
MOBILE6.2/CALINE4 and Generalized Additive Mod-
els,” Atmospheric Environment, Vol. 44, No. 14, 2010, pp.
1740-1748.
http://dx.doi.org/10.1016/j.atmosenv.2010.02.008
[34] P. E. Benson, “CALINE3, A Versatile Dispersion Model
for Predicting Air Pollutant Levels Near Highways and
Arterial Streets,” Sacramento, 1979.
[35] P. E. Benson, “CALINE4, A Dispersion Model for Pre-
dicting Air Pollutant Concentrations near Roadways,”
Sacramento, 1989.
[36] US Environmental Protection Agency, “Meteorological
Monitoring Guidance for Regulatory Modeling Applica-
tio n s,” EP A - 4 5 4 /R -99-00 5 , R e s e ar c h T r i a ngl e P a r k, 2000.
[37] Battelle, “Detailed Monitoring Protocol for U.S. 95 Set-
tlement Agreement,” Columbus, 2006.
[38] S. Gokhale and N. Raokhande, “Performance Evaluation
of Air Quality Models for Predicting PM10 and PM2.5
Concentrations at Urban Traffic Intersection during Win-
ter Period,” Science of the Total Environment, Vol. 394,
No. 1, 2008, pp. 9-24.
http://dx.doi.org/10.1016/j.scitotenv.2008.01.020
[39] Human Impact Partners, “Pittsburg Railroad Avenue
Specific Plan Health Impact Assessment,” Oakland, 2008.
[40] Human Impact Partners, “Pathways to Community Health:
Evaluating the Healthfulness of Affordable Housing Op-
portunity Sites along the San Pablo Avenue Corridor Us-
ing Health Impact Assessment,” Oakland, 2009.
[41] UC Berkeley Health Impact Group, “MacArthur BART
Health Impact Assessment,” Berkeley, 2007.