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Science of the Total Environm
Characterization of a spatial gradient of nitrogen dioxide across a
United States–Mexico border city during winter
Melissa Gonzalesa,*, Clifford Quallsb, Edward Hudgensc, Lucas Neasc
aDepartment of Internal Medicine, University of New Mexico School of Medicine, UNM-LRRI Environmental Health Sciences Center, MSC
10-5550, Albuquerque, NM 87131, USAbStatistics Laboratory, General Clinical Research Center, University of New Mexico School of Medicine, MSC 10 5540,
Albuquerque, NM 87131, USAcNational Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, MD 58,
Research Triangle Park, NC 27711, USA
Received 4 March 2004; accepted 2 July 2004
Abstract
A gradient of ambient nitrogen dioxide (NO2) concentration is demonstrated across metropolitan El Paso, Texas (USA), a
city located on the international border between the United States and Mexico. Integrated measurements of NO2 were collected
over 7 days at 20 elementary schools and 4 air quality monitoring stations located throughout the city during typical winter
atmospheric conditions. Replicate passive monitors were co-located with chemiluminescence analyzers at the monitoring
stations for two consecutive 7-day periods. The passive measurements correlated with the analyzer measurements (R2=0.74)
with precision of 2.5F2.2 ppb. Nitrogen dioxide concentrations ranged from 11.0 to 37.5 ppb (mean 20.6F7.1 ppb). In a
multivariate regression model, the site elevation and distances to a main highway and to an international port of entry from
Mexico explained 81% of the variance in the passive measurements. The results of this pilot study indicate that proximity to
vehicle-related sources of NO2 and site elevation are key predictors for future, more detailed assessments of vehicle-related air
pollution exposure in the El Paso region.
D 2004 Elsevier B.V. All rights reserved.
Keywords: Nitrogen dioxide; Passive diffusion tubes; Spatial gradient; GIS; GPS; Population exposure
1. Introduction studies. The spatial variability in emissions has been
Intra-urban gradients of traffic emissions have
recently been used in a number of children’s health
0048-9697/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.scitotenv.2004.07.010
* Corresponding author. Tel.: +1 505 272 9598; fax: +1 505
272 4186.
E-mail address: [email protected] (M. Gonzales).
associated with health effects such as asthmatic
symptoms and allergic sensitization in children
(Brunekreef et al., 1997, Kramer et al., 2000; Wyler
et al., 2000; Gehring et al., 2002, Janssen et al.,
2003). However, since air pollution monitoring is
generally conducted in only a few locations in a city,
the data from existing monitor networks provide
ent 337 (2005) 163–173
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173164
only limited spatial resolution of the exposure
distribution across a geographic region posing the
potential for exposure misclassification in epidemio-
logical analyses (Brauer et al., 2002; Levy et al.,
2003). In the absence of spatially detailed air
pollution measurements, exposure to traffic emis-
sions have been estimated with a variety of surrogate
measures including traffic density and the proximity
of schools and homes to major highways (Edwards
et al., 1994; Duhme et al., 1996; Brauer et al., 2003).
In addition, the use of passive diffusion samplers for
nitrogen dioxide (NO2) have expanded existing
networks to assess spatial variability in exposure to
traffic emissions (Roorda-Knape et al., 1998; Levy et
al., 2003; Singer et al., 2004).
El Paso, Texas (USA, population 0.56 million) is
located in the high altitude Chihuahuan desert at
location known as the Paso del Norte where the Rio
Grande River flows northwest to southeast through
a narrow mountain pass separating El Paso to the
north from Ciudad Juarez, Chihuahua (Mexico,
population 1.5 million) to the south. El Paso wraps
around the southern tip of the Franklin Mountains,
which run north to south through the Paso del Norte
and abruptly end just north of downtown sector of
the city. Ciudad Juarez extends westward to the
Juarez Mountains (Sierra Juarez), which lie imme-
diately southwest of the Franklins. The elevation in
the area ranges from 1428–2180 m peaks in the
Franklin Mountains to 1127–1188 m in the valley
below. The combination of meteorological and
geographic features in the region strongly influence
the mixing and dispersion of air pollutants emitted
from sources in the El Paso/Ciudad Juarez area
(MacDonald et al., 2001). Annually, more than 16
million private passenger vehicles and nearly one
million freight carriers enter the US via border
crossings bridges over the Rio Grande in El Paso.
Vehicles remain idling in queues an average of 40
min as they wait for inspection. Earlier studies have
shown that motor vehicle emissions are the main
source of carbon monoxide, nitrogen oxides (NOx)
and hydrocarbon (VOCs) emissions in the central
Paso del Norte airshed (Einfeld and Church, 1995;
Funk et al., 2001). Stable meteorological conditions
during the winter, combined with complex local
terrain significantly limit the mixing and dilution of
air pollutants within the region and results in
exceedances of air quality regulations on both sides
of the border.
Previous studies in the Paso del Norte have
indicated that the concentration of traffic emissions
may vary considerably across the region depending on
the location of sources, atmospheric mixing height,
meteorological conditions and the topographic char-
acteristics of the terrain (Noble et al., 2003; Jeon et al.,
2001; Einfeld and Church, 1995). However, the intra-
urban gradient of traffic emissions has not been
studied across much of metropolitan El Paso because
the air quality monitoring stations operated by state
and local environmental agencies are located mainly
in the central El Paso with fewer stations located in
more distant residential areas.
Given the spatial distribution ofmajor highways, the
location of the international border crossings and the
complex river valley terrain of the Paso del Norte, it is
unlikely that data from existing pollution monitors are
sufficient to characterize the gradient of exposures
across the city, particularly in the school district region
where a children’s respiratory health study was
planned. We hypothesized that an intra-urban gradient
of NO2, as an indicator of mobile source influence,
could be measured across an El Paso school district that
spanned the central, northeastern and northwestern
sectors of the city and included the main international
border crossings. For this purpose, we established a
network of passive NO2 monitors deployed at twenty
elementary schools and four air pollution monitoring
stations during 1 week in February 1999, and a second
consecutive week at the monitoring stations alone. Our
main objectives for this pilot study are (1) to evaluate
the performance of passive diffusion tubes for measur-
ing NO2 during wintertime in El Paso; (2) to identify
whether an intra-urban spatial gradient of NO2 exists
across an El Paso school district; and (3) to evaluate
which of a set of easily obtainable geographic exposure
variables, chosen specifically for El Paso, were most
useful for predicting the spatial variation in NO2
concentrations across the school district.
2. Methods
Ambient NO2 was measured at 20 public elemen-
tary schools in the El Paso Independent School
District using a network passive diffusion tube
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173 165
monitors (Palmes, 1976). Integrated 7-day measure-
ments of NO2 where collected on February 11–18,
1999. Though requiring a long sample time (usually 7
days depending on location), Palmes tubes have been
used extensively in European NO2 exposure studies
(see Brunekreef et al., 1997 as example). To evaluate
the performance of the diffusion tube method, replicate
passive monitors were co-located with reference
chemiluminescent analyzers at four state-operated air
pollution monitoring stations over two consecutive 1-
week periods between February 11 and 25, 1999.
2.1. Study locations
Permission was obtained from the El Paso
Independent School District to monitor NO2 outside
Fig. 1. The spatial concentration gradient of nitrogen dioxide in El Paso
measurements were collected at 20 elementary schools and 4 continuous
district regions: 1=northeast; 2=north central; 3=south central; 4=northwest
CAMS 41=Chamizal.
of 20 public elementary schools. The schools were
selected from each of the school district’s four
regions (see Fig. 1): Region 1 (northeast), Region 2
(north central), Region 3 (south central) and Region 4
(northwest). The boundaries of the four administra-
tive regions were established by the school district
prior to this study. Five to six schools were selected
from Regions 1, 2 and 4 to represent the farthest
northern, southern, eastern, western and middle
sections of each region. Three schools were selected
in Region 3 due to its narrow north–south span and
because dense monitoring coverage was already
available from the existing air monitors located in
this region.
Passive nitrogen dioxide measurements were also
collected at four continuous air monitoring stations
, TX, measured February 11–18, 1999. Integrated nitrogen dioxide
air monitoring stations (CAMS) by passive diffusion tubes. School
. CAMS 6=Downtown; CAMS 12=UTEP; CAMS 30=El Paso East;
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173166
(CAMS) operated by the State of Texas Commission
on Environmental Quality (TCEQ). At the time of
the study, all of the CAMS sites were located in the
south central region of the school district, and were
sited to capture maximal population exposures to air
pollutants on the basis of proximity to emission
sources. Fig. 1 shows the site locations. The first
site (CAMS 6) is located in downtown El Paso, 1
km north of the Downtown El Paso/Juarez border
crossing immediately adjacent to an on-ramp ingress
to US Interstate Highway 10 (I-10), the major east
to west throughway in El Paso. The second site
(CAMS 41) is located in the Chamizal National
Monument, a park located 0.2 m north of the Bridge
of the Americas, the principal border crossing for
passenger vehicles and diesel trucks, and 0.2 m
south of I-10. The third site (CAMS 12) is located
adjacent to the campus of the University of Texas at
El Paso (UTEP), 2.1 km north of the downtown
border crossing and 0.2 km north of I-10. The
fourth site (CAMS 30) is located in Ascarte Park,
2.6 km northeast of the Bridge of the Americas and
0.9 km north of the Border Freeway, a local
thoroughfare that runs adjacent to the international
border in El Paso.
2.2. Passive diffusion tube measurements
Passive diffusion tube monitors consist of a
hollow 71.0�10.9 mm acrylic cylinder with two
closely fitting caps (Palmes, 1976). A stainless-steel
mesh, coated with tri-ethanolamine is placed at the
closed end of the tube to absorb NO2 and creates a
diffusion gradient inside the tube. The diffusion
tubes were obtained and analyzed by a single
laboratory. In the field, the diffusion tubes were
installed under protective, polyvinyl chloride caps.
The open ends of the tubes were level with the rim
of the cap to minimize turbulence from wind. The
caps were secured at least 1.0 m above the ground
or rooftop, and away from heating, ventilation and
air conditioning vents; objects that might alter the
mean wind flow, such as taller structures upwind;
and nearby sources that might influence the sam-
ples, such as vehicle emissions from parking lots. A
field blank and a replicate monitor were deployed at
one school in each of the four school district
regions.
The precision of the passive monitors was eval-
uated against TECO Model 42 gas-phase chemilumi-
nescent NOx analyzers (Thermo Environmental
Instruments, Franklin, MA), operated are operated
by the TCEQ as Federal Reference Method analyzers
according to US air quality regulations (Federal
Register, 1989). Each week, triplicate passive mon-
itors were collocated with reference analyzers at the
monitoring stations. Four additional replicate samples
were collocated at CAMS 41 during the second week.
One field blank was collected at each CAMS site each
week. Replicate passive measurements from each site
were then averaged by week. For comparison
purposes, hourly measurements of NO2 and total
nitrogen oxide (NOx) collected by the chemilumines-
cent analyzers were obtained from TCEQ and
combined into 1-week averages for the hours corre-
sponding to those when the collocated diffusion tube
measurements were collected.
2.3. Geographic data and geographic information
system (GIS) mapping
The latitude and longitude of each school and
monitoring station were measured with a hand-held
geographic positioning system (GPS) receiver
(March-II, Corvallis Microtechnology, Corvallis,
OR) and verified against data obtained from the El
Paso Independent School District, and the TCEQ
web site (http://www.tnrcc.state.tx.us/cgi-bin/monops/
site_info), as well as from GIS data available from the
Texas Natural Resource Information System (TNRIS,
1996). The elevation of each site was also measured
by GPS and verified against digital elevation model
(DEM) elevation projections (USGS 7.5-DEM Series,
scale 1:24,000). The DEM elevation projections
differed from the GPS elevation measurements by
+0.94±16.0 m (intercept 20.98, slope 0.98, R2=0.85).
This difference was not statistically significant (Stu-
dent’s t test, p=0.78). The locations of all the sites
were plotted on a TNRIS base map of El Paso using
ArcView 8.1 GIS software (ESRI, Eugene, OR).
Nitrogen dioxide concentrations measured by the
passive monitors were added to the GIS project file
and added to the base map. The two major divided
and restricted access highways in the El Paso area
are the east–west I-10 running approximately parallel
to the Rio Grande River through western and cen-
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173 167
tral El Paso, and the north–south Patriot Freeway
(Texas Highway 54) on the east side of the Franklin
Mountains (see Fig. 1). Each site’s distances to the
nearest highway and the nearest international border
crossing were calculated using the GIS software. Site
elevation (GPS and DEM), highway distance and
distance from a border crossing were used as predictor
variables in the subsequent regression analysis.
2.4. Statistical methods
The passive diffusion tube NO2 measurements
were natural-log-transformed for this analysis
because the distribution was right skewed. The
precision of the passive monitor, compared to the
reference analyzer was evaluated in linear mixed-
effects models using compound symmetry to account
for multiple measurements at each site (PROC
MIXED procedure, SAS, 1997). This statistical
procedure allows comparisons of means and corre-
lation structure accounting for multiple instruments
(that is, passive and active monitors) and replicate
measurements at the same site.
Univariate and multivariate mixed-effects regres-
sion models were used to predict the passive NO2
measurements from three site-specific geographic
variables: distance to the nearest international border
crossing, distance to the nearest highway and eleva-
tion above sea level. Potential bias in the modeled
results due to correlation among the predictor varia-
bles was evaluated using collinearity diagnostics.
3. Results
3.1. Performance of the passive samplers
The limit of detection for the diffusion tube
monitors was 0.5 ppb NO2 (v/v) (the mean plus three
times the standard deviation of the filed blanks using
the nominal sampling duration for the samplers, 168
h). All the diffusion tube NO2 samples were above the
limit of detection. The coefficient of variation
(CV=100�standard deviation/mean) of the replicate
samples was 5.9%.
The linear association between the passive and
analyzer NO2 measurements was evaluated by linear
regression (intercept 14.6, slope 0.50, R2=0.74). In a
linear, mixed-effects model, the passive diffusion tube
monitors were shown to overestimate the analyzer
measurements by 2.5F2.2 ppb NO2. However, this
difference did not significantly differ from zero
( p=0.34).
We evaluated the influence of total nitrogen oxides
[NOx] and ozone [O3] on the performance of the
passive monitor (Table 1) due to the differing ratios of
[passive NO2]/[analyzer NO2] measurements among
the four CAMS sites. Concurrent [NOx] and [O3]
measurements were obtained for each CAMS site and
combined into 1-week averages corresponding to the
hours when the collocated diffusion tube measure-
ments were collected. The overestimation in the
passive NO2 measurements compared to the analyzer
NO2 measurements increased as the ratio of [NO2]/
[NOx] increased. The 1-week [O3] concentrations did
not appear to influence the ratio of [passive NO2]/
[analyzer NO2] measurements.
3.2. NO2 concentration gradients
The mean concentrations of the 1-week NO2
measurements in each school district region are shown
in Table 2. The highest NO2 concentrations were
measured at the Chamizal monitoring station (37.7
ppb) located in Region 3 of the school district. The
lowest concentrations were measured at the schools
located in the far northeast (11.0 ppb) and northwest
(13.0 ppb) regions of the district. Two spatial
gradients in NO2 concentration across the school
district are shown in Fig. 1 and verified by the inverse
correlation with distance from a border crossing
(r=�0.74, pb0.001) and from a highway (r=�0.81,
pb0.001). Although elevation is not shown in this
figure, a third spatial gradient in NO2 concentration is
observed moving from central, downtown El Paso
(the area of the city with the lowest elevation) to the
northeast and the northwest regions as elevation
gradually increases (r=�0.78, pb0.001) (also see
Table 2). Note that in these correlations NO2 concen-
trations are natural-log-transformed and geographic
variables are on the linear scale. Addition correlations
between the geographic variables are: distance to a
border crossing and elevation 0.58 ( p=0.003); dis-
tance to a border crossing and distance to a highway
0.79 ( pb0.001) and elevation and distance to a
highway 0.62 ( p=0.01).
Table 1
Comparison of collocated passive diffusion tube and chemiluminescent analyzer measurements from four Texas Commission on Environmental
Quality (TCEQ) continuous air monitoring stations (CAMS) in El Paso, TX, over 2 consecutive weeks in February 1999
Date and site Passive diffusion
tube 7-day [NO2]
measurements
[mean, ppb (S.D.)]
Chemiluminescent
analyzer [NO2]/[NOx]
ratio [mean (S.D.)]
Passive-to-analyzer
[NO2]/[NO2] ratio
[mean (S.D.)]
Eight-hour
ozone
(mean, ppb)
February 11–18, 1999
CAMS 6 37.7 (2.9) 0.54 (0.27) 0.98 (0.07) 17.7
CAMS 41 30.9 (1.0) 0.67 (0.27) 1.07 (0.03) 38.1
CAMS 12 28.1 (1.0) 0.72 (0.43) 1.26 (0.04) 23.7
CAMS 30 25.6 (1.0) 0.81 (0.51) 1.42 (0.05) 45.0
February 18–25, 1999
CAMS 6 34.2 (3.0) 0.56 (0.28) 0.95 (0.08) 15.3
CAMS 41a 37.6 (2.7) 0.69 (0.29) 1.03 (0.11) 38.1
CAMS 12 27.7 (1.1) 0.75 (0.37) 1.40 (0.06) 22.8
CAMS 30 23.9 (1.7) 0.87 (0.74) 1.33 (0.09) 44.6
Triplicate, 1-week NO2 measurements were collected at each CAMS site by passive diffusion tube monitor. [NO2]/[NOx] is the ratio of nitrogen
dioxide (NO2) and total nitrogen oxides (NOx) measurements collected by TCEQ chemiluminescent analyzers. [NO2]/[NO2] is the ratio of NO2
measurements collected by the passive monitors and chemiluminescent analyzers. Ozone measurements were collected by TCEQ.a Four replicate samples were collocated at CAMS 41 during the second sampling week.
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173168
3.3. Regression models
Univariate regression models were fit with both
log-transformed and non-transformed dependent and
independent variables (see Fig. 2a–c) to visually
Table 2
Summary statistics of explanatory geographic variables and passive diffusi
and 4 TCEQ CAMS sites in El Paso, TX, February 11–18, 1999
Regiona Elevation m
(1) Northeast, six sites Mean 1207.7
S.D. 27.4
Range 1170.7–1258.9
(2) North central, five sites Mean 1200.6
S.D. 25.1
Range 1157.0–1219.5
(3) South central, seven sitesb Mean 1143.1
S.D. 7.5
Range 1136.9–1157.9
(4) Northwest, six sites Mean 1198.7
S.D. 44.0
Range 1146.4–1248.7
a Region indicates the school district region where the monitoring sitb Includes four TCEQ CAMS sites.c Includes triplicate collocated samples at CAMS sites.
evaluate the model fit. GPS elevation measurements
were used in these analyses. The linear model
(untransformed NO2 and independent variables) pre-
dicted monotonic decreases in NO2 regardless of
proximity to main highways and the border (Fig. 2a
on tube nitrogen dioxide measurements from 20 elementary schools
Distance to border
crossing, km
Distance to
highway, km
Passive NO2
7-day measurements
[NO2], ppb
13.8 2.0 15.5
5.3 1.1 3.1
7.4–20.4 0.4–2.9 11.0–20.5
(n=6)
7.5 1.1 16.6
3.9 0.7 2.9
2.7–12.9 0.4–1.9 11.8–18.8
(n=7)
1.6 0.4 30.4
0.9 0.4 4.1
0.2–2.6 0.01–0.9 25.6–37.7
(n=19c)
11.8 1.4 17.7
2.5 0.4 3.0
8.8–14.5 1.0–2.1 13.0–21.5
(n=9)
es were located.
Fig. 2. Linear, log-linear and log-log regression model results of univariate analysis for predicting nitrogen dioxide concentrations at 24 sites in
El Paso based on (a) distance to highway; (b) site elevation; and (c) distance to the nearest border crossing.
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173 169
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173170
and c) and negative NO2 estimates at extreme values
of elevation. The log-linear model (natural-log-trans-
formed NO2 measurements predicted by the untrans-
Table 3
Log-Log regression models for predicting passive nitrogen dioxide
measurements using geographic explanatory variables
Model Log-log regression
model results
Parameter
estimate
p value R2
Univariate Intercept 2.92 b0.001 0.62
Highway �0.20 b0.001
Intercept 3.36 b0.001 0.62
Border �0.23 b0.001
Intercept 4.31 b0.001 0.60
Elevation (GPS) �7.94 b0.001
Intercept 4.23 b0.001 0.61
Elevation (DEM) �7.45 b0.001
Bivariate Intercept 4.01 b0.001 0.75
Elevation (GPS) �4.70 0.004
Border �0.14 0.002
Intercept 3.90 b0.001 0.71
Elevation (DEM) �4.13 0.02
Border �0.14 0.01
Intercept 3.17 b0.001 0.72
Border �0.13 0.01
Highway �0.12 0.01
Intercept 3.83 b0.001 0.79
Elevation (GPS) �5.02 b0.001
Highway �0.13 b0.001
Intercept 3.75 b0.001 0.80
Elevation (DEM) �4.82 b0.001
Highway �0.13 b0.001
Trivariate Intercept 3.78 b0.001 0.81
Elevation (GPS) �4.12 0.005
Border �0.07 0.10
Highway �0.10 0.02
Intercept 3.72 b0.001 0.81
Elevation (DEM) �4.21 0.007
Border �0.04 0.48
Highway �0.13 0.004
Passive measurements were collected at 24 sites in El Paso, TX, on
February 11–18, 1999.
Nitrogen dioxide measurements and geographic explanatory varia-
bles (Border, Highway, Elevation) are natural-log-transformed in the
model. Border=kilometers to the nearest international border cross-
ing between El Paso (USA) and Ciudad Juarez (Mexico). High-
way=kilometers to the either US Interstate Highway 10 or Texas
State Highway 54. Elevation of site in kilometers, as measured by
geographic positioning system (GPS) or obtained from a DEM.
formed independent variables) estimated faster initial
decline in NO2 followed by constant, proportional
decline with increasing site elevation, and fit approx-
imately similar to the linear model with regard to
distance from highways and the border. Log-log linear
model results (natural log-transformed NO2 measure-
ments predicted by the natural log-transformed
independent variables) estimated greater initial
declines in NO2 followed by constant, proportional
decline with increases in all independent variables.
The log-log construct was chosen for further evalua-
tion in multivariate regression models.
Using the log-log regression model construct,
individual geographic variables explained 60% of
the variation in the NO2 measurements collected
across El Paso (Table 3). The most predictive bivariate
model included elevation and distance to a highway as
independent predictors (R2=0.79). The inclusion of
distance to the border improved the fit (R2=0.81) but
was not a significant addition to the model. Colli-
nearity did not significantly influence the results of
the models. Substituting DEM elevation projections
for GPS elevation measurements did not significantly
change the fit of the regression models (Table 3).
Cross-validation of the bivariate elevation–high-
way model was further evaluated using a jackknife-
like estimation method. In this analysis, the NO2
measurement from each site was sequentially dropped
and estimated by the model. The residual plot (jack-
knife-residual versus predicted value of NO2) showed
no structure except possibly one influential observa-
tion at the Downtown TCEQ site (Cooks D=0.22,
next highest 0.13). The omission of the Downtown
site did not result in significant change in the model
results except to slightly reduce the model fit from
R2=0.79 to R2=0.77. (The fit of the trivariate model
was likewise reduced from R2=0.81 to R2=0.77.)
4. Discussion
This pilot study demonstrates an intra-urban
gradient of NO2 concentration across El Paso as a
prerequisite for conducting a study of the health
effects of vehicle emissions on children’s respiratory
health. In a log-log regression model, distance to the
nearest major highway and site elevation predicted
79% of the variation in the passive measurements
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173 171
indicating that the El Paso NO2 gradient is associated
with proximity to areas of localized high traffic
density and lower elevation (a feature of reduced
atmospheric mixing height during winter). The log-
log regression model was selected for this analysis
because it estimated greater initial declines in NO2
followed by constant, proportional decline with
increasing altitude and distance from emission sources
(highways and border crossings) than did linear and
log-linear models. The pattern of steep initial declines
followed by more constant decline is similar features
previously noted by Gilbert et al. (2003) for NO2 and
by Zhu et al. (2002a,b) for ultrafine particulate matter
with increased distance from roadways. The results of
this study indicate that distance to a major highway
and site elevation are the main predictors of relative
intra-community NO2 variability across central, north-
western and northeastern El Paso.
In this pilot study, NO2 concentrations were
inversely correlated with increasing distance from
international border crossings, main highways and
elevation. This result correspond to previous studies,
which suggested spatial gradients of vehicle emis-
sions in the Paso del Norte region. Temporal
variations in carbon monoxide, nitrogen oxides
(NOx), particulate matter (PM10), fine particulate
matter (PM2.5) and ultrafine particulate matter have
also been shown to coincide with the morning and
evening commute hours at monitoring sites near
emission source regions of central El Paso and
Ciudad Juarez (Jeon et al., 2001; Noble et al., 2003;
Estado de Chihuahua, Mexico, 1998, MacDonald et
al., 2001). Einfeld and Church (1995) and Jeon et al.
(2001) reported an increase in PM10 concentrations in
El Paso as one moves southward towards the
international border, with the highest concentrations
in the Ciudad Juarez/El Paso downtown areas. In the
current study, the highest NO2 concentrations were
measured in central El Paso at sites closest in
proximity to main traffic emission sources and at
the lowest elevation in the city. The NO2 concen-
trations at schools located farthest to the northeast and
northwest of central El Paso were approximately 3.4
times lower.
Several investigators cite wind speed and direction
as potential influences (Singer et al., 2004; Levy et al.,
2003; Roorda-Knape et al., 1998) on NO2 concen-
trations. The 1996 Paso del Norte Ozone Study
demonstrated how both the mixed layer growth rate
and height, as well as wind speed critically influence
NOx concentrations in El Paso (MacDonald et al.,
2001; Brown et al., 2001). On days when the mixed
layer growth rate is slow and the winds light, NOx and
CO remain confined near central El Paso emission
sources, while on days with moderate wind, the
precursor cloud is dispersed and NOx concentrations
are lower but more widely and evenly dispersed
across the city. Low winds (3.0±2.2 m/s) and the
predominant meteorological inversions during the
current winter study period resulted in similar
dispersion pattern of NO2 across the city and provides
an explanation for the significance of site elevation for
predicting NO2 in El Paso. Although stable meteoro-
logical conditions are a hallmark of winter in El Paso,
measurements of wind speed and direction in addition
to estimates of mixed layer growth rate and height
would be needed to expand the current analysis to
other seasons.
Geographic positioning systems are useful for
measuring latitude, longitude and elevation in areas
where these data are not readily available from other
sources. In this study, there was a statistically non-
significant difference between GPS elevation meas-
urements and DEM elevation projections. A DEM is
an approximated representation of topography, which
relies on manually collected data and automated
calculations to predict elevation. The elevation values
in USGS DEMs are subject to three types of errors:
(1) blunders, (2) systematic errors and (3) random
errors (USGS, 1998). Random errors remain in the
data after blunders and systematic errors are removed
and result in uncertainty in the DEM estimates.
Weschler (1999) points out that it is the responsibility
of the DEM user to determine whether uncertainty in
the DEM will affect results from specific analyses that
utilize data derived from a particular DEM. In the
current analysis, substituting the DEM elevation
projections for GPS measurements did not signifi-
cantly change the fit of the models.
The NO2 measurements collected by the passive
diffusion tubes at the four monitoring stations were
highly correlated with reference analyzer measure-
ments (R2=0.74). Although the passive moni-
tors overestimated the analyzer measurements by
2.5±2.2 ppb, the +16% bias was not significant in
this sample and is similar the +18% to +35% biases
M. Gonzales et al. / Science of the Total Environment 337 (2005) 163–173172
previously reported for passive NO2 diffusion tube
samplers (Hamilton and Heal, 2004; Heal et al.,
1999). Heal et al. (1999) reported that the positive bias
in 1 week NO2 samples reached a maximum as the
proportion of NO to NO2 increases, but declined when
NO was in large excess of NO2 and ambient air
outside the sampler is likely to be depleted in ozone
and the concentration of ozone becomes the limiting
factor for generation of extra NO2 inside the tube. In
the current study, the positive bias in the passive
[NO2] measurements declined when NO was in
excess of NO2 (decreasing [NO2]/[NOx] ratio), but
was unrelated to ozone concentrations measured by
collocated analyzers. The discrepancy in results may
be a result of different NO2 photolysis and ozone
formation rates due to high intensity sunlight in El
Paso or other local El Paso conditions under which the
passive diffusion tubes have not been fully charac-
terized. Although this finding is based on a small
sample size (eight sets of replicate 1-week measure-
ments), the differences between the current El Paso
results and previous studies indicate that an accurate
quantitative correction for NO2 passive diffusion tube
measurements may not be possible, as suggested by
Heal et al. (1999). Nonetheless, given that the passive
NO2 measurements were well correlated with analyzer
NO2, our results indicate that passive diffusion tube
measurements provide an accurate qualitative measure
of NO2 variation across the monitored region.
A limitation of this pilot study is that only one week
of NO2 measurements were collected. Expansion of
the current analysis to include additional sampling
periods, predictor variables and air pollutants is needed
in order to reliably characterize vehicle-related expo-
sures at an additional 35 unmonitored schools in the El
Paso area targeted for a respiratory health study. For
example, Brauer et al. (2003) estimated PM2.5 in terms
of traffic intensity, distance to a major road and
population density using data from the Netherlands,
Munich, Germany and Stockholm, Sweden. Elevation,
high density housing and industrial use areas were
used by Briggs et al. (2000) to model NO2. In addition,
the contribution of diesel emissions from on-road
vehicles to ultrafine particulate exposure gradients,
as suggested by Levy et al. (2003) and Zhu et al.
(2002b), should also be examined in El Paso in light of
the significant interstate and international freight
traffic in the region.
Our findings suggest significant variability in
intra-community NO2 concentrations across El Paso,
Texas, during winter based on a network of passive
monitors located at schools and local air quality
monitoring sites. Regression models indicate that
elevation and distance to a major highway explained
79% of the variance in the NO2 measurements.
This study is another example of how surrogate
geographic exposure variables can be used in a
regression analysis approach to develop practical
techniques for mapping air pollution on an urban
scale.
5. Disclaimer
The U.S. Environmental Protection Agency
(EPA) through its Office of Research and Develop-
ment has partially funded and collaborated in the
research described in this paper through contract QT-
RT-02-000665 to the University of New Mexico.
The views expressed in this article are those of the
authors and do not necessarily reflect the views or
policies of the U.S. Environmental Protection
Agency. Mention of trade names or commercial
products does not constitute endorsement or recom-
mendation for use.
Acknowledgements
The authors would like to acknowledge the support
and cooperation of the Facilities and Maintenance
Department of the El Paso Independent School
District; the technical staff of El Paso TCEQ, Kuenja
Chung and Alison Siwik for field assistance; Shaibal
Mukerjee for reviewing the manuscript; Casson
Stallings for providing the GIS elevation projections;
Jose Barrios and Jennifer Slotnick for assistance with
GIS data sets and mapping, and Gina Terrell for
developing Fig. 1.
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