Atmospheric Environment 39 (2005) 2399–2409
Establishing an air pollution monitoring network for intraurban
population exposure assessment: A location-allocation
approach
Pavlos S. Kanarogloua,Ã, Michael Jerrettb
, Jason Morrisonc
,
Bernardo Beckermanb
, M. Altaf Arainb
, Nicolas L. Gilbertd
, Jeffrey R. Brooke
a
Center for Spatial Analysis, School of Geography and Geology, McMaster University, Hamilton, Ont., Canada L8S 4K1
b
School of Geography and Geology and McMaster Institute of Environment and Health, McMaster University Hamilton, Ont., Canada
L8S 4K1
c
School of Computer Science, Carleton University, Ottawa, Ont., Canada
d
Health Canada, Air Health Effects Section, Ottawa, Ont., Canada
e
Meteorological Service of Canada, Toronto, Ont., Canada
Received 19 January 2004; received in revised form 21 May 2004; accepted 9 June 2004
Abstract
This study addresses two objectives: (1) to develop a formal method of optimally locating a dense network of air
pollution monitoring stations; and (2) to derive an exposure assessment model based on these monitoring data and
related land use, population, and biophysical information. Previous studies have located monitors in an ad hoc fashion,
favouring the placement of monitors in traffic ‘‘hot spots’’ or in areas deemed subjectively to be of interest. We apply
our methodology in locating 100 nitrogen dioxide monitors in Toronto, Canada. Locations identified by the method
represent land use, transportation infrastructure and the distribution of at-risk populations. Our exposure assessments
derived from the monitoring program produce reasonable estimates at the intra-urban scale. The method for optimally
locating monitors may have widespread applicability for the design of pollution monitoring networks, particularly for
measuring traffic pollutants with fine-scale spatial variability.
r 2005 Elsevier Ltd. All rights reserved.
Keywords: Air pollution; Traffic emissions; Monitoring networks; Location-allocation models; Health effects assessment
1. Introduction
Policymakers and scientists have shown growing
interest in the health effects of chronic exposure to
ambient air pollution. Recent epidemiological studies
have generated specific interest in traffic pollution. For
example, a cohort study from the Netherlands (Hoek et
al., 2002) reported that large health effects on cardiopulmonary
mortality were associated with living near a
major road (Relative risk ¼ 1.95, 95% CI: 1.09–3.51).
Yet uncertainties in exposure assessment methodologies
continue to raise questions about the reliability and
accuracy of risk estimates from intra-urban air pollution
studies (Briggs et al., 2000). In this context, we address
two research objectives: (1) to develop a formal method
of optimally locating a dense network of air pollution
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1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2004.06.049
ÃCorresponding author. Tel.: +1905 525 9140x24960 or
x23525.
E-mail address: pavlos@mcmaster.ca (P.S. Kanaroglou).
monitoring stations; and (2) to derive an exposure
assessment model based on these monitoring data and
related land use, population, and biophysical informa-
tion.
This paper represents the initial stage of a multi-year
research project funded by the Canadian Institutes of
Health Research. The ultimate goal of the project is to
assess the relation between traffic-generated air pollution
and health outcomes ranging from childhood
asthma to mortality from lung cancer. Some of the
health data are described elsewhere, and significant
associations with background particulate and sulfur
dioxide pollutants and elevated mortality have already
been reported (Finkelstein et al., 2003). A major
limitation of this first health effects study stemmed from
relatively crude exposure metrics, which relied exclusively
on interpolations from a sparse network of
government monitoring stations.
Because field-monitoring studies constitute one of the
most expensive components of an epidemiological study,
we were interested in developing a method that would
make maximal use of scarce air pollution monitoring
data. Previous studies have located monitors in an ad
hoc fashion, favouring the placement of monitors in
traffic ‘‘hot spots’’ or in areas deemed subjectively to be
of interest for land use and population characteristics
(Lebret et al., 2000; Kukkonen et al., 2001; Goswami et
al, 2002). We achieved this first step of our research plan
by developing a formal methodology for locating a fixed
number of air pollution monitors in Toronto, Canada.
Formal methods for the design of pollution monitoring
networks have been proposed before (Caselton and
Zidek, 1984; Caselton et al., 1992; Silva and Quiroz,
2003; Trujillo-Ventura and Ellis, 1991; Haas, 1992). Our
proposed approach is geared for urban applications for
air pollution measurements and is designed to capture
micro-scale variation, especially around major road-
ways.
After deriving the monitoring locations, we deployed
100 nitrogen dioxide (NO2) monitors throughout the
City of Toronto. We then implemented a land use
regression (LUR) model with the collected monitoring
data. For this study, we chose NO2 as a proxy for traffic
pollution because it is relatively inexpensive to measure
and has been used widely as a metric of exposure to
traffic emissions (Briggs et al., 1997).
2. Methods
2.1. Conceptual framework for locating monitoring
stations
The approach we use consists of two stages. First,
based on specified criteria, we determine a continuous
surface over the study area, which we term ‘‘the demand
surface’’. Higher values of the demand surface correspond
to increased need for monitoring. Second, we use
the demand surface as an input to an algorithm that
solves a constrained optimization problem from the
general family of location-allocation (L-A) problems.
The algorithm identifies the optimal locations for a
predefined number of air pollution monitors.
We use two criteria for the determination of the
demand surface. The first postulates that a larger
number of monitors should be located where the
pollution surface is expected to exhibit higher spatial
variability. To implement this criterion, we need an
initial estimate of the pollution surface, which would be
an approximation of the expected surface after actual
monitoring. Since this initial surface is not generally
available, we recommend obtaining a first estimate
either with available government monitoring data or
with a network of ground stations operated by
individual researchers. Both government and individual
monitoring networks are usually spatially sparse. In our
particular application, we obtained a first estimate of the
pollution surface through LUR with data from an area
wider than the City of Toronto to encompass a large
enough number of fixed-site monitoring stations (i.e.,
South-Central Ontario). The process is described in
more detail in the methodology implementation section
below.
Given a first estimate of the pollution surface, spatial
variability of pollution g(x
*
; h
*
) at location x
*
is
determined by the following equation, inspired by the
semivariogram equation that is often used in geostatistics
(Cressie, 1993, p. 58):
^gðx
*
; h
*
Þ ¼
1
2
X
h
zðx
*
Þ À z x
*
þ h
*
 & '2
" #
. (1)
In practice, a grid is imposed on the study area and g
_
is calculated at all the centroids of the zones that make
up the grid. If Zðx
*
Þ represents a random variable of the
pollution estimate at location x
*
; then zðx
*
Þ is a specific
pollution estimate at the same location. The right-side
summation of Eq. (1) occurs over all possible pairs of
locations that are formed between x
*
and a location
within distance h
*
from x
*
: The determination of g
_
at all
locations x
*
provides a surface of variability in the
pollution surface. This creates the demand surface that
satisfies the first criterion noted above.
To satisfy the second criterion, we appropriately
modify the demand surface achieved through Eq. (1).
More specifically it might be desirable to intensify
pollution monitoring in areas where the density of a
population of interest is high. Here, we intensify demand
for pollution monitors in areas with high densities of atrisk
populations. That population group might have
demographic characteristics of importance for the
intended health study, such as children or the elderly.
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P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–24092400
To achieve this effect, a weighting scheme is implemented
according to the equation
WR ¼
PR=PT
^gR=^gT
. (2)
Here PR is the population of interest in region R
within the study area and PT is the same population for
the entire study area. Thus, the numerator represents the
proportion of the total population of interest in the
study area that resides in region R. Similarly, the
denominator represents the proportion of the total
variability for the entire study area that can be
attributed to region R. The weight WR is applied to
g
_
ð~x; ~hÞfor each location x
*
that belongs to region R. The
weighting scheme is designed so that the ratio of
variability estimates for any two locations within region
R remains unchanged after weighting. At the same time,
the variability for all locations within region R is
augmented by the proportion of the population of
interest within region R. Furthermore, the total variability
for the entire study area is maintained at the
unweighted level.
The approaches used in the literature can be classified
into two main groups. The first makes use of information
theory and devices optimization methods that
locate a given number of monitors in a network so that
the information content in the collected data is
maximized or the uncertainty is minimized (Caselton
and Zidek, 1984; Caselton et al., 1992; Silva and Quiroz,
2003). The same idea is applied to evaluating the
effectiveness of a government monitoring network of
stations or optimizing the information gain by locating
an additional fixed number of new monitors. The second
group uses kriging, a geostatistical spatial interpolation
technique. The idea is to locate the monitors of a
network so that the mean square prediction error (or
kriging variance) is minimized (Trujillo-Ventura and
Ellis, 1991; Haas, 1992). This method ensures adequate
coverage of the study area, since it is well known that the
kriging variance is higher in places of sparse sampling.
The basic idea for our approach comes from the
statistical stratified sampling theory, whereby strata
associated with higher variability are sampled more
intensively. The method relies on the identification of the
pollution surface variability over the study area. It is
designed for capturing the small-scale spatial variability
of pollution, especially around expressways in urban
areas. As with the other methods, additional objectives
in the optimization problem can be added, such as
increased sampling in areas of high population density.
An advantage of our method is that it relies on welldeveloped
location theory and the associated optimization
algorithms, known as L-A.
2.2. The study area and data
For the derivation of an initial estimate surface of
pollution over the city of Toronto we used available data
from a network of fixed monitoring stations in SouthCentral
Ontario; a densely populated conurbation
extending from St. Catherines to Oshawa (roughly
200 km, see Fig. 1) and encompasing Toronto.
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Fig. 1. Study area in South-Central Ontario.
P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–2409 2401
Predictors of NO2 pollution in a LUR model were
derived from land use and transportation data acquired
from a commercial source, Desktop Mapping Technologies
Incorporated (DMTI), Markham, Ont., Canada.
Population data at the census tract (CT) level were
obtained from the standard tabulations of census data
provided by Statistics Canada.
Toronto, the study area, is Canada’s largest city with
an estimated population of 4.7 million people (Statistics
Canada, 2001) and an approximate area of 633 km2
.
Located on the north shore of Lake Ontario (431 390
N,
791 230
W), it is classified as being in the ‘‘Humid East’’
region of temperate North America (Getis and Getis,
1995). Similar to other large cities in North America,
many expressways traverse the Toronto landscape,
including some of the busiest in North America. For
example, Highway 401 has an estimated flow of about
400,000 vehicles per day.
Monitor deployment in Toronto, based on the
locations derived with the method we describe in this
paper, occurred in the time period 9–25 September 2002,
at 100 locations. OgawaTM
passive samplers were used
to measure concentrations of NO2. We deployed twosided
samplers in pairs of two (yielding four observations
per location) at a height of 2.5 m. All samplers
were removed 14 days after their installation. We used
ion chromatography to determine the nitrite content on
collection pads (Gilbert et al., 2003). This field sampling
yielded 95 valid measurements. Five samples were lost
due to vandalism or equipment malfunction.
2.3. Estimating the initial pollution surface as input for
the location-allocation model
To estimate the initial pollution surface for determining
the spatial variability, we employed a LUR. LUR
uses pollution concentrations as the dependent variable
and proximate land use, traffic, and physical environmental
variables as independent predictors. This methodology
thus seeks to predict pollution concentrations
at a given site based on surrounding land use and traffic
characteristics. Specifically, this method uses measured
pollution concentrations (y) at locations (s) as the
response variable and land use types (x) within circular
areas around s (called buffers) as predictors of the
measured concentrations (see Fig. 2). The incorporation
of land use variables into the interpolation algorithm
detects small-area localized variations in air pollution
more effectively than standard methods of interpolation
such as kriging (Briggs et al., 1997, 2000; Lebret et al.,
2000).
Mean NO2 estimates at 16 locations shown in Fig. 1,
operated by the Ontario Ministry of the Environment
(MOE), served as the dependent variable in the LUR.
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Fig. 2. Elements of a land use regression model showing monitoring locations for NO2 as the response variable and land use
characteristics within buffers as the predictor or independent variable.
P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–24092402
The 1999 NO2 annual mean values at the 16 MOE air
monitoring stations in the study area range between 12.4
and 28.4 parts per billion (ppb). Independent variables
were obtained by measuring the respective area and
length parameters within circular neighbourhood buffers
centred on the MOE station position. Accurate georeferencing
was essential for the locations of pollution
monitoring sites, roads and houses due to the highly
localized nature of the estimates. We used a global
position system to mark all the monitoring sites
(ground-level accuracy of 7 m or less). Field validation
of the DMTI land use coverage revealed high accuracy
in attribute classification and spatial coordinates.
Land use variables were calculated in hectares and
road lengths in kilometres. These variables were
measured under a circular buffer that extends from
each monitoring location out to a radius of 100 m. Road
length parameters were measured under two separate
buffers. A first circular buffer extends from each
monitoring point out to a radius of 50 m. A second
buffer is in the shape of an annulus (donut), with the
inner edge at a radius of 50 m and the outer at 200 m. All
area and length calculations were performed using
ArcView 3.2 software’s Spatial Analyst extension (ESRI
Corp., Redlands, CA, USA).
In total, 16 land use and transportation variables were
tested in a manual stepwise regression with S-Plus 6
software (Mathsoft, Cambridge, MA) to obtain a best
estimate pollution model. A bivariate ordinary leastsquares
regression analysis was used in the initial step to
develop a model that predicts the best pollution surface.
The variable selection criterion was set at po0:25
statistical significance for subsequent consideration of
the variable (Younger, 1985). The most statistically
significant variables were sequentially included until a
parsimonious model was achieved. While most variables
included in the model achieved statistical significance at
conventional levels (i.e., po0:05), we relaxed this
criterion if inclusion of the variable eliminated other
problems with outliers.
The resulting estimated LUR model was subsequently
used to predict NO2 values for all cells of a grid imposed
over Toronto at 5 m resolution. For a given grid cell,
values for independent variables of the LUR equation
were calculated within the same neighbourhood around
the centre of the grid cell as they were initially calculated
for inclusion in the regression equation. If in the
regression equation, for example, a significant independent
variable is the length of expressway portions that
fall within 50 m of a NO2 measuring station, then for the
prediction of NO2 for a given grid cell we construct a
50 m radius circle around the centre of the grid cell and
calculate the length of the expressways within that circle.
The 5 m resolution was chosen to allow a reasonable
approximation of the vector dataset into raster format.
The result of this exercise was a first approximation of a
pollution surface that is subsequently used to derive a
monitoring demand surface. Our first task towards this
end is to calculate the spatial variability of the pollution
surface at every single grid cell, as described in the next
subsection.
2.4. Creating a surface of monitoring demand
Air pollution over a study area is spatially continuous.
Consistent with geostatistical modelling, we assume that
any pollution measurement zðx
*
Þ at location x
*
is a
specific instance of a random variable Zðx
*
Þ at the same
location. The random variable Z over locations in the
study area represents the spatial stochastic process
under study. Because of the presence of second-order
effects (autocorrelation), we expect the correlation of
any two such random variables to diminish with
distance, while the variance will increase (Webster and
Oliver, 1990). Previous results of NO2 monitoring
(Hewitt, 1991; Gilbert et al., 2003) suggest that little
correlation exists if the distance between any two points
j h
*
j is ‘‘sufficiently large’’. Also, while correlation varies
according to topography and meteorology, 80–90% of
traffic related effects come from less than 150–300 m
away (English et al., 1999; Briggs et al., 2000). Thus, we
select as the maximum distance within which to examine
local variability of pollution to be 300 m.
On the basis of these observations, a suitable
operational estimator of local variability of NO2
pollution at location x
*
is obtained from Eq. (1) by
fixing the maximum distance j h
*
j to be equal to 300 m:
^gðx
*
Þ ¼
1
2
X
j h
*
jp300 m
zðx
*
Þ À zðx
*
þ h
*
Þ
 2
. (3)
It is worth noting that in Eq. (3), location x
*
is
represented by the centre of a 5 m grid cell. Variability
for a given grid cell x
*
is calculated by applying the
summation in the right-hand side of Eq. (3) over the
pairs that are formed between x
*
and all the cells within
300 m from x
*
: By contrast, for a semivariogram
estimator, the summation in the right-hand side is over
all location pairs that are a certain lag j h
*
j apart, and the
calculation is perfomed over several lags to determine
the semivariogram function. Thus, while Eq. (3)
resembles the standard semivariogram equation, its
usage in the present context is different. To determine
the surface of the monitoring demand, Eq. (3) is applied
for all locations x
*
within the study region R.
2.5. Augmenting demand for socio-demographics
The demand intended for sampling stations computed
via Eq. (3) accounts for the need to represent pollution
variability. When deriving pollution exposures for
epidemiological studies, however, it may be desirable
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P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–2409 2403
to measure more intensively in areas where the population
possesses particular socio-demographic (SD) characteristics
of interest. To achieve area-specific weighting
of the sampling network, we aim at increasing demand
in areas with high concentration of the target populations
and decreasing it in areas of low concentration.
We start with the density of the population of interest
at the CT level making use of standard tabulation data
provided by Statistics Canada. We chose the CT level
because it provides a compromise between spatial detail
and area homogeneity. Statistics Canada’s CTs are
equal in size to neighbourhood-like areas, having
approximately 2500–8000 people (average population
$4000). CT boundaries generally follow permanent
physical features, such as major streets and railway
tracks, and attempt to approximate cohesive socioeconomic
areas, but may not do so because of
subjectivity in unit selection or neighbourhood change
over time.
We transform the CTs population densities into
population counts at the level of grid cells with 5 m
resolution. We then aggregate the population counts to
grid cells of 2500 m resolution. This is done to account
for the spatial subjectivity built into the CT unit. A
further advantage of re-aggregating the CT-derived
population surface is that the weighted surface is
unlikely to have gaps in areas where the population is
low. The re-aggregation thus ensures that the final
demand surface represents both the population density
and pollution variability.
The weighting was implemented as a bivariate linear
rescaling of the pollution variability surface. This
process conserved the total amount of demand in the
initial variability surface. The exact formula we used is
an adaptation of Eq. (2) as follows:
W2500 m ¼
P2500 m=PT
^g2500 m=^gT
, (4)
where P2500 m is to population of a grid cell at 2500 m
resolution and PT is the total population of the study
area calculated as the sum of populations for all grid
cells at the 2500 m resolution. ^g2500 m is the variability in
a 2500 m resolution grid cell. For the purposes of this
paper, the population at risk was children 6 years of age
or younger. By multiplying the derived W2500 m value
with the variability value for all grid cells, a modified
demand surface is derived. In subsequent health studies,
this exposure assessment will assist with testing the
association between onset of childhood asthma and
traffic pollution in a case-control design.
2.6. Computing monitoring locations using demand
The discussion of computing demand does not
address the issues of calibration, reliability or validation
of the NO2 measurements. For purposes of reliability, it
is advisable to deploy two or more passive samplers at a
single location; the measured value at a location is taken
to be an average of the measurements at that location. A
further level of accuracy is added to our network by
calibrating the measurements against the collected MOE
data using continuously operating active monitors. This,
however, entails that the MOE stations be used as
sampling locations in our network.
The remainder of this section details a technique for
computing the locations of n number of monitoring
stations, assuming that n is a specified constant before
the computation. The computation of the locations
begins by calculating the locational demand for a grid at
500 m resolution. For Toronto, the centroid of each of
the grid cells results in a lattice of 2537 potential
locations, which we refer to as ‘‘candidate locations’’.
The goal of our L-A procedure is to select n primary
station locations taking into account the demand surface
for monitoring that we have created. In formulating this
problem as a classic facility location problem, we
constrain ourselves to problems solved using the ESRI’s
ARC/INFO software. This software offers two options:
the P-Median Problem or P-MP (ReVelle and Swain,
1970) and the Attendance Maximizing Problem or AMP
(Holmes et al., 1972). For our particular problem, we
have 2537 candidate locations for 100 monitors to be
located (n ¼ 100). The candidate locations also act as
the demand locations. Each demand location is weighted
by the value of the demand surface in that location.
The P-MP places the 100 primary stations in a way
that the sum of weighted distances for all demand
locations from their nearest station is minimized. The
AMP, on the other hand, locates stations so as to
maximize an objective function of the form
Z ¼
Xk
i¼1
Xm
j¼1
wi 1 À bdij
À Á
xij, (5)
where k is the number of demand locations and m is the
number of candidate locations. In our case k ¼ m ¼
2537: The weight wi at location i represents demand,
while dij is the distance between locations i and j: xij is
the allocation decision variable attaining the value of 1 if
demand location i is served by a station in j and 0
otherwise. Attendance linearly decreases with distance at
the rate of parameter b; the value of which is determined
by the particular problem in hand. For our case, we
assume that attendance becomes negligible or 0 for a
distance of 1.5 km, thus b is determined by solving the
Eq. (1)Àb1.5 ¼ 0, which leads to b ¼ 0.67.
The above discussion suggests that the AMP formulation
provides better control and is thus better
suited than the P-MP formulation for our particular
problem. The assumption that attendance becomes zero
for a distance beyond 1.5 km is justified on the basis of
previous research. Findings suggest that concentrations
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P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–24092404
of NO2 and other pollutants decrease away from
polluting sourses (English et al., 1999; Gilbert et al.,
2003). The measured decay of concentration around
Canadian highways is such that no correlation exists
between the highway and a point 300 m away from the
highway. For the purposes of the model we assumed this
distance to be 1500 m. The definition of the allocation
decision variable xij above provides one of the five
constraints of the constrained optimization AMP. Other
constraints restrict the number of stations to be located
to exactly n ¼ 100, and ensure that no two stations are
located in the same place.
The actual computation was performed using the L-A
functionality of ESRI’s ArcPlot module of ArcGIS. We
set the optimization criteria to be the ‘‘Maximum
Attendance (MaxAttend)’’ problem, and then ran the
L-A function. Near-optimal or ‘‘heuristically optimal’’
solutions to these types of computationally intensive
problems can be found in a reasonable amount of time
using proven heuristic techniques. The Global Regional
Interchange Algorithm (GRIA) (Densham and Rushton,
1992) is appropriate for large datasets because
running time is linear with respect to n, which allows for
computation with current desktop computing technology.
The grid resolution affects the optimality of the
solution. Given n ¼ 100, the choice of a 500 Â 500 m2
grid of demand locations made for a problem of
reasonable size for desktop computers that could be
easily replicated by others.
3. Results
3.1. Monitoring location selection
Results of the initial LUR are displayed in Table 1
and include regular diagnostic values such as sum of
squares (SS), degrees of freedom (df), mean
square (MS), and a inter-variable collinearity
factor called the various inflation factor (VIF). The
table shows that commercial, government and institutional,
expressway (0–50 m), and expressway (50–200 m)
are significant land-use and transportation variables in
predicting NO2 with the available dataset. Similar to
other LUR results (Briggs et al., 2000), some coefficients
take an unexpected sign (i.e., 0–50 is negative), but this
appears to balance the other positive signs in the
equation, and this model produced maximal prediction
with minimal bias (measured by the Mallow’s Cp
statistic) compared to other models we tested. The
pollution surface generated from this model had high
pollution concentrations predicted in the vicinity of the
expressways and relatively low pollution predictions in
other areas.
Modelled pollution values near expressways ranged
from 29.5 to 1502 ppb, while in other areas the pollution
estimates ranged from 18.1 to 34.5 ppb. Given the range
of the MOE NO2 monitoring concentrations and those
noted in other studies (Hewitt, 1991), such high levels
are unrealistic. Yet, independent of the actual values, an
important characteristic was preserved in the surface,
namely the simple relative pollution variability caused
by the different land uses—an indication that some areas
have higher pollution variability than others. The
variability was obtained by Eq. (3) using the obtained
pollution surface. The obtained surface of variability
appears to have much the same characteristics as the
pollution surface, with high values near expressways and
low values in other areas.
Proximity to expressway features dominated sampling
locations selected by the L-A model, as seen in Fig. 3.
About 95% of the stations were assigned to areas that
coincided with expressway features in the pollution
model. The other 5% of the locations satisfied the
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Table 1
Initial land use regression model used for location allocation procedure to locate samplers
Source SS df MS
Regression 213.89 4 53.47 Number of obs ¼ 16
Residual 130.68 11 11.88 F(4,11) ¼ 4.5
Total 344.58 15 Prob4F ¼ 0.021
R2
¼ 0.621
Adj. R2
¼ 0.483
Variable Coefficient Std. error t Prob4t VIF
NO2 (ppb)
Constant 18.058 1.353 13.348 0.000
Commercial 5.223 2.080 2.511 0.029 1.0
Government/institutional 1.748 1.065 1.641 0.129 1.1
Expressway (0–50 m) À592.725 228.883 À2.590 0.025 2.3
Expressway (50–200 m) 253.155 68.532 3.694 0.004 2.4
P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–2409 2405
attendance demand created by the surrounding expressways
and consequently were located centrally.
In contrast, the population weighting method (PWM)
produced a well-distributed monitoring network, as
shown in Fig. 3. There is a low-level clustering in the
central west part of the city, an area with the highest
aggregation of child populations.
Once the L-A procedure was complete and alternative
monitoring networks were configured, it was necessary
to ascertain the monitoring networks’ capacity to
represent key land use and transportation criteria. Table
2 presents a comparison of the network developed by
taking into account only the pollution surface variability
criterion with the network that also took account of the
population distribution criterion (children of age 0–6).
The comparison of the networks is based on ten land-use
criteria that were used in the LUR model, as shown in
the first column of the Table. The second and third
columns are based on characteristics of all the 2537
candidate locations. They indicate that 245 of the
candidate locations are between 50 and 200 m
away from an expressway, while 96 of them are qwithin
50 m from an expressway. Similarly, the pollution
variability criterion alone gives rise to a network
whereby 94 of the 100 stations are located between
50 and 200 m away from an expressway as compared to
32 of the 100 stations located within the same distance
band from an expressway in the case where the
population criterion is also taken into account. This
indicates that the variability criterion alone produces a
much more concentrated network pattern around the
expressways.
This conclusion is strengthened by the observation
that in the variability criterion case 58 of the 100 stations
are located within 50 m of an expressway, compared
with 32 stations in the case where population is taken
into account. Similarly, the population-weighted surface
allocates more stations in residential areas and fewer
stations in industrial areas. The latter is probably
because expressways go through industrial areas.
Comparing the mean distance values of the two
networks with those of all the candidates reveals that
in six out of ten land use cases, the means are
significantly different for the variability criterion,
but in only two land use cases the difference in
means is statistically significant when population
weighting is used. We thus conclude that the population-weighted
surface produces a better representation
of the land use and transportation characteristics of the
study area.
3.2. Results from the Toronto deployment
Of the 100 monitors deployed in the Toronto area, 95
produced valid measurements. These locations were
used in a second LUR model to derive a predicted
surface of NO2. The arithmetic mean of NO2 values
(measured in ppb) for these 95 records was 32.71 ppb,
with values ranging from 17.41 to 77.31 ppb, which is
larger than the value we observed through the government-monitoring
network. In total, 79 land use,
transportation, population, and physical geography
variables were tested (see Jerrett et al. 2003 for more
detailed descriptions of the variables).
Logarithmic NO2 concentrations were negatively
correlated with the distance with the nearest expressway
and the area of open space within 400 m, while they were
intuitively correlated with road measures and dwellings
in the vicinity. The final regression model (shown below)
yielded a R2
of 0.633 (see Table 3).
These results confirm that in addition to contributing
to regional air pollution, urban traffic is a strong
determinant of intra-urban variation in air pollution.
Results also indicate that a combination of traffic and
land use variables could be used to estimate exposure to
traffic-related air pollution in epidemiologic studies on
the effects of air pollution on health. Unlike the initial
model based on the sparse monitoring network, which
we used to derive the variability of the initial pollution
surface, all the coefficients take the expected sign. This
LUR model also resulted in a predictive pollution
surface with the expected characteristics (see Fig. 4) with
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Fig. 3. Comparison of unmodified sampling locations to population weighted locations.
P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–24092406
only slight over prediction near the expressways. Areas
in proximity to expressways and in the downtown core
appeared to have higher levels of NO2, while areas with
less development in the northeast of the city exhibit
lower levels.
4. Conclusion
With the increasing need to understand the spatial
distribution of air pollution at the intra-urban scale, the
methods developed here for locating air pollution
monitors have proven to be an effective and systematic
technique to maximize sampling coverage in relation to
important socio-demographic (SD) characteristics and
likely pollution variability. The location-allocation (LA)
approach offers flexibility to integrate an assortment
of variables into the demand surface that can reconfigure
the resulting monitoring network. In this application,
the variable integration has been implemented with
a SD weighting of children 6 years of age and under.
Because we had to rely on sparse initial pollution data,
the population-weighted pollution surface also ensured
that the subsequent sampling locations would yield
useful results for human exposure assessment.
To validate this method, we plan to replicate the
entire modelling process using the pollution surface
generated from the 95 NO2 observations collected in
Toronto from our first round of monitoring. Assessing
the impact of availability of better pollution data on the
monitoring locations might have important implications
for the generalizability of this method. If we achieve
significantly different results from an improved input
pollution surface, then this L-A approach would be
effectively limited to the few places where spatially
extensive air pollution data are available or to the
second round of monitoring. If, however, the model
performs adequately with weaker pollution data combined
with SD population data, then the method may
have wide applicability to most urban areas. Given the
large influence of population weighting in our findings
and the subsequently reasonable results from the first
round of monitoring in Toronto, the method appears to
have considerable promise for improving the assessment
of exposure to ambient air pollution in epidemiologic
studies.
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Table 2
Land use and transportation criteria represented by location-allocation models compared to candidate location values
Mean std. deviation Candidate N Variability criterion (VC) N VC+popul age 0–6 N
Distance 1783.6 2537 158.5 100 1631.7 100
1381.2 496.1 1620.2
Expressway 50–200 263.2 245 417.3 94 380.4 32
190.4 189.3 207.7
Express 0–50 44.8 96 52.2 58 50.2 20
29.7 32.5 30.7
Major 50–200 90.6 1151 91.9 58 100.5 63
45.3 46.0 49.9
Major 0–50 19.0 344 18.3 21 17.4 21
8.6 9.9 9.2
Minor 50–200 229.3 2264 128.8 76 200.4 93
119.6 93. 5 107.0
Minor 0–50 24.1 1474 18.3 30 22.9 52
10.7 10.6 10.1
Residential 100 856.8 1779 541.5 53 672.8 73
425.3 439.2 410.1
Industrial 100 660.8 774 594.0 45 649.3 36
459.2 403.5 453.8
Commercial 100 375.8 235 353.6 10 481.1 39
349.3 311.8 402.0
Gov. Inst. 100 415.7 434 659.3 10 451.1 19
394.0 469.7 435.1
P.S. Kanaroglou et al. / Atmospheric Environment 39 (2005) 2399–2409 2407
Acknowledgments
We are grateful to Health Canada and the Canadian
Institutes of Health Research for funding this work. We
also thank Pat De Luca, Dan Crouse, Norm Finkelstein,
and Chris Giovis for assistance with the GIS modelling. The
first author is grateful to the Social Sciences and Humanities
(SSHRC) Canada Research Chairs (CRC) program.
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Fig. 4. Land use regression model of NO2 concentrations (ppb) using 95 sampled values.
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