1. No category

Atmospheric Environment 72 (2013) 1e9
Contents lists available at SciVerse ScienceDirect
Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Quantifying urban street configuration for improvements in air
pollution models
Marloes Eeftens a, *, Johan Beekhuizen a, Rob Beelen a, Meng Wang a, Roel Vermeulen a, b,
Bert Brunekreef a, b, Anke Huss a, Gerard Hoek a
a
b
Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
h i g h l i g h t s
< Air pollution models struggle to accurately predict concentrations in street canyons.
< We developed an automated GIS-based method to derive quantitative canyon indicators.
< Our approach is based on 3-D building data, which are increasingly available.
< The derived indicators may be used in both land use regression and dispersion models.
< The canyon indicators helped explain pollution contrasts of NO2 in the Netherlands.
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 16 October 2012
Received in revised form
21 January 2013
Accepted 5 February 2013
In many built-up urban areas, tall buildings along narrow streets obstruct the free flow of air, resulting in
higher pollution levels. Input data to account for street configuration in models are difficult to obtain for
large numbers of streets. We describe an approach to calculate indicators of this “urban canyon effect”
using 3-dimensional building data and evaluated whether these indicators improved spatially resolved
land use regression (LUR) models.
Concentrations of NO2 and NOx were available from 132 sites in the Netherlands. We calculated four
indicators for canyon effects at each site: (1) the maximum aspect ratio (building height/width of the
street) between buildings on opposite sides of the street, (2) the mean building angle, which is the angle
between the horizontal street level and the line of sight to the top of surrounding buildings, (3) median
building angle and (4) “SkyView Factor” (SVF), a measure of the total fraction of visible sky. Basic LUR
models were computed for both pollutants using common predictors such as household density, landuse and nearby traffic intensity. We added each of the four canyon indicators to the basic LUR models
and evaluated whether they improved the model.
The calculated aspect ratio agreed well (R2 ¼ 0.49) with aspect ratios calculated from field observations.
Explained variance (R2) of the basic LUR models without canyon indicators was 80% for NO2 and 76% for
NOx, and increased to 82% and 78% respectively if SVF was included. Despite this small increase in R2,
contrasts in SVF (10the90th percentile) resulted in substantial concentration differences of 5.56 mg m3 in
NO2 and 10.9 mg m3 in NOx.
We demonstrated a GIS based approach to quantify the obstruction of free air flow by buildings,
applicable for large numbers of streets. Canyon indicators could be valuable to consider in air pollution
models, especially in areas with low- and high-rise canyons.
Ó 2013 Elsevier Ltd. All rights reserved.
Keywords:
Street configuration
Aspect ratio
Urban morphometry
Land use regression
Air pollution
Geographic information systems
Canyon
Nitrogen oxides
1. Introduction
Abbreviations: AR, Aspect Ratio; GIS, Geographic information system; LUR, Land
use regression; SVF, SkyView Factor.
* Corresponding author. Tel.: þ31 30 253 94 96; fax: þ31 30 253 94 99.
E-mail address: [email protected] (M. Eeftens).
1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.atmosenv.2013.02.007
In recent years, the predictive ability of urban air pollution
models has improved substantially with the increasing use of
Geographic Information Systems (GIS) and common availability of
accurate digital geographical data (Hoek et al., 2008; Jerrett et al.,
2005a). While early air pollution studies compared concentration
2
M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
levels between cities (Dockery et al., 1993; Pope et al., 2002;
Götschi et al., 2005), focus has recently shifted towards exploring
intra-urban spatial contrasts in greater detail (Beelen et al., 2007;
Hoek et al., 2002; Jerrett et al., 2005b). Despite these efforts, estimating air pollutant concentrations in urban built-up areas remains
challenging, as many individual sources of pollution, meteorological conditions and different micro-environments may each affect
concentrations independently, as well as interacting with each
other (Vardoulakis et al., 2003). In built-up urban environments,
local meteorology is largely affected by buildings which cause
turbulence and alter airflow (Vardoulakis et al., 2003). Several
studies have used field observations and models to explore the
behaviour of air pollutants in street canyons; narrow streets lined
by tall buildings on either side (Solazzo et al., 2009; Eliasson et al.,
2006). In street canyons, pollutants emitted by road traffic are
trapped in a vortex formed within the canyon, limiting vertical air
exchange with the atmosphere above the buildings (Vardoulakis
et al., 2003). This results in high local pollutant concentrations
which often do not comply with environmental standards
(Vardoulakis et al., 2007). Local monitoring networks are often not
dense enough to capture the strong spatial gradients around urban
canyons, and often overlook these pollution hotspots (Vardoulakis
et al., 2011).
Street canyons are commonly defined by the aspect ratio (AR);
the ratio between building height (H) and street width (W). The
aspect ratio is an indicator of the amount of interference with
synoptic wind patterns, and notably affects the strength and
number of vortices inside the canyon (Vardoulakis et al., 2003).
Most case studies focussed on relatively deep canyons (AR > 1.3),
which are uncommon in the Netherlands. The trapping properties
of low-rise canyons (w0.5 AR 1) have largely been overlooked
while these are more typical for medium-size (European) cities
(Vardoulakis et al., 2007).
1.1. Canyons in land use regression
Land-use regression (LUR) models have become an increasingly used method to assess spatial contrasts in air pollution
levels (Hoek et al., 2008; Jerrett et al., 2005a). An increasing
number of epidemiological studies use LUR models for estimating individual exposure levels for non-network locations,
such as the addresses of cohort subjects (e.g. (Morgenstern et al.,
2007; Beelen et al., 2008)). LUR models use a spatially dense
network of measured pollution concentrations. Each monitoring
site is characterized by a set of potential predictors, such as
population density, land use, physical geography and various
traffic-related variables, obtained through a GIS. Stochastic
modelling is used to determine which predictors best explain the
pollution concentrations (Hoek et al., 2008). Several LUR studies
have identified difficulties to model the increased level of pollutants in street canyons. Beelen et al. (2007) concluded that it
was impossible to predict extremely high concentrations of NO
and black smoke at a moderate-traffic monitoring site located in
a street canyon. In the TRAPCA study (1999e2000), LUR models
improved in both Munich and Stockholm if an indicator variable
for canyon was included (Brauer et al., 2003). However, the
canyon indicator was obtained through manual classification,
which made it unfeasible to use this predictor for estimating
exposure for large cohorts. A study by Su et al. (2008) was the
first to use GIS-derived ARs in a LUR model for downtown Vancouver, using high-resolution satellite (ETMþ) photographs to
identify buildings and their shadows to estimate building height
and calculate aspect ratios. LUR models improved from 56% to
67% for NO2 and from 72% to 85% for NO after inclusion of the
aspect ratio predictor variables (Su et al., 2008). This approach is
however not easily applicable in cities with irregular street patterns, such as most European cities.
1.2. Canyons in dispersion modelling
While only few LUR studies have incorporated street configuration in their models, it is a common variable used in dispersion
modelling (Vardoulakis et al., 2007). Examples include the OSPM
(Operational Street Pollution Model) (Hertel and Berkowicz, 1989)
and the Dutch CAR II (Calculation of Air pollution from Road traffic)
model (Eerens et al., 1993). A major limitation is that input data on
street configuration are obtained through manual characterization.
1.3. Quantifying the canyon effect
Although air pollution models benefit from including canyon
indicator variables (Brauer et al., 2003) or aspect ratios (Su et al.,
2008), these metrics cannot take into account other important
characteristics such as the length of the canyon, (a)symmetry of
building heights, and whether or not the building rows are interrupted. Air pollution models may benefit from taking a 3dimensional approach in characterizing all shelter from surrounding buildings, assuming that all obstacles to vertical air exchange are indicative of pollution trapping.
In this paper we present a novel approach to quantitatively
derive different indicators of pollution trapping using 3dimensional building data available in a GIS. Besides the aspect
ratio, we derive three other proxies for vertical air exchange by
incorporating all building obstruction in a 360-degree circle around
each site. One of those is the SkyView Factor (SVF), described previously by Souza and Rodrigues (2003); Souza et al., (2003), and
shown to be a good proxy for vertical air exchange (Gál et al., 2009).
The SVF was originally developed for estimation of urban heat island effects: the rise of temperatures in urban environments
compared to relatively natural surroundings. We tested whether
these “canyon indicators” improve the prediction of measured air
pollutant concentrations, using land use regression (LUR)
modelling.
2. Methods
We derived all canyon indicators for a monitoring network of
144 sites, spread over the Netherlands, including large and
medium-size Dutch cities (Eeftens et al., 2011). The original
network included 26 regional background sites, 78 urban background sites and 40 sites close to major roads. The network
included street sites in canyons, as well as wider streets lined by
fewer buildings.
2.1. Estimation of canyon indicators
2.1.1. Reconstruction of building heights
Detailed data on buildings were available from the TOP10 vector
map for the year 2009, produced by the Dutch National Mapping
Agency (Kadaster). In total, 2,996,212 building blocks were available with an accuracy of 2 m (http://www.gdmc.nl/oosterom/
sdh98.pdf). No polygons were available for the towns of Heerlen
and Sibbe-Ijzeren, therefore we excluded the 4 monitoring sites in
these towns from the analysis, leaving 140 sites. Building block
polygons had no attribute of height attached, so we estimated
building height from a 5 5m height grid available at a national
level (Actueel Hoogtebestand Nederland, AHN). Data for this grid
were collected between 1996 and 2003. We used the geoprocessing
capabilities of Python 2,5 (Python Software Foundation) and ArcGIS
9.3.1 (Esri Redlands, CA, USA) to determine the height of each
M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
building block based on the height grid. First, we intersected the
building block outlines with the height grid and recorded the
heights of the grid cells that fell within each building. Taking the
average of these heights would underestimate the true building
height, as sometimes the building blocks included an enclosed
garden of lower height. Taking the maximum height however,
could result in overestimation of the building height as small onroof obstacles (e.g. elevator shafts, towers and antennas) are also
included in the height grid. We therefore used the 90th percentile
as the building height. Next, we calculated the surface height
around the building. While differences in altitude are small in the
Netherlands (between 7 and 323 m), they become important
when determining the height of a building relative to its surroundings. We created a 10 m buffer around each building block
polygon, and set the surface height to the 10th percentile of grid cell
heights within this buffer. Finally, the height of the building relative
to the ground was calculated by subtracting the surface height from
the building height. Final building heights were validated using 3Dbuilding data from EUROSENSE (Wemmel, Belgium) available for
the city of Amsterdam only. The overall agreement was moderate
(R2 ¼ 0.35), see Online Supplement A.
For 19 out of 140 sites, the heights of surrounding buildings
could not be calculated, because of missing building height information, either due to outdated height data (for 7 sites, buildings
were constructed after the creation of the height grid) or due to
flattening of the terrain (for 12 sites, buildings in rural environments were filtered in order to represent terrain height). We chose
to impute the missing building heights rather than exclude the sites
which were among buildings of unknown height, as this would
mean selectively excluding many regional sites with little sky
obstruction. Typical Dutch family homes have 2 to 3 stories. In rural
areas, high-rise buildings over three stories are uncommon. We
decided to use a cut-off of 2.0 m below which to set the building
height to 2.0 m, because the distribution plot suggested a natural
break between the “signal” and “noise” (Online Supplement B). In a
sensitivity analysis, we set all missing building heights to 6.0 m (the
median height of buildings when excluding building s < 2.0 m), see
Online Supplement C.
2.1.2. Positioning of the measurement sites
We manually checked if all measurement sites were positioned
correctly relative to building polygons and road segments using the
digital maps. Most samplers were placed at 30 cm distance from the
façade of a building. Eight points were excluded from the analyses,
because they were situated in back gardens, behind buildings and
were not in direct contact with the pollution source, leaving 132
sites.
The height at which a measurement took place (usually 1.5 m
above ground) was registered as an attribute (Zi) for each site i.
Seven background sites were situated on rooftops. For those sites,
the Zi-value was set to the height of the building þ 1 m (the height
of the equipment support stand).
2.1.3. Calculation of 360 skyview obstructions for each site
We selected subsets of all buildings within 200 m around each
on-road site, assuming that buildings further than 200 m away did
not obstruct the air flow around that site. From each subset, we
further eliminated buildings which were lower than Zi (the height
at which the measurement took place), as these would not obstruct
the skyview around site i. We then searched for buildings in horizontal direction for each site in 360 steps of 1, registering search
angle (a), height of each building (H) and the distance to the
building (D). The building angle b was then calculated from the
building height and distance, using Equation (1). All results are
stored in a “SkyView Table”. An example can be found in Online
3
Supplement D. Fig. 1 illustrates a search for the nearest building
for a moderate canyon site (A) and a wider street lined by separated
buildings (B).
ba;i ¼ tan1 Ha;i =Da;i
(1)
Because buildings have different heights, the nearest building is
not necessarily the one causing the largest skyview obstruction, as
illustrated by Fig. 2. If more than one building was found for a
specific search angle a, the building with the largest building angle
b was selected, so only the height, distance and building angle of
the building forming the highest obstruction were included in the
SkyView Table. Fig. 3 gives a 3-D impression of the statistics
calculated in the SkyView Table for several search angles.
2.1.4. Calculation of the aspect ratio
We defined the aspect ratio (AR) as the average height (H) of the
buildings on opposite sides of the street, divided by the sum of the
distances to the buildings on either side (D) (Equation (2). The AR
was calculated for all sites and search angles a between 0 and 179 .
The highest AR was then selected for each site. On a street lined on
both sides by buildings with uniform height, the maximum AR is
found for search angles perpendicular to the street.
AR ¼
ðHa þ Haþ180 Þ=2
Da þ Daþ180
(2)
2.1.5. Mean and median building angle
The mean and median of the building angles b for the 360 search
angles were calculated.
2.1.6. Calculation of the SkyView Factor
The SkyView Factor (SVF) is a measure of the fraction of visible
sky from the position of an observer on the ground, inside the street
canyon. Following (Gál et al., 2009), the SVF was calculated as follows (Equation (3):
SVF ¼ 1 359
X
a¼0
sin2 ba *ð1=360Þ
(3)
2.2. Validation of aspect ratio calculations
All sampling locations were characterized on-site following the
CAR II classification (Eerens et al., 1993), registering width of the
street, height of buildings on both sides of the street, separation of
the buildings and street type. We compared the ARs computed in
ArcGIS to the ARs calculated when determining street width and
building height at the actual measurement sites. Even though
building heights and street widths were determined on-site, these
were rough approximations: it was not feasible to directly measure
the height of a building, so often the number of floors was counted
as a proxy. The width of the street was determined by the number
of steps it took to cross the road. Differences between the on-site
and the GIS determined ARs are therefore caused by errors in
both measures.
2.3. Sensitivity of canyon indicators to distance to the façade
The indicators are derived to assess the pollution trapping
properties of the street and should not change with the site’s position closer to or further away from the building façade. Online
Supplement E illustrates that especially for points which are positioned very close (<0.50 m) to the façade, the derived canyon indicators are sensitive to slight positioning differences. This is less of
an issue for points which are situated in the middle of the street
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M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
Fig. 1. A&B: Bird’s-eye views of the search angles in GIS, and radar plots of the building angles and SkyView Factor for a low street canyon (A) and a wider street lined with separated
buildings of varying height (B).
canyon. To investigate the potential impact of the position, we
automatically determined the nearest point on a road for each site,
using the “Near (analysis)” Tool in ArcGIS. The median distance
between the original point and its on-road location was 10.0 m
(range 2.4 me62.3 m). Only 7 out of 132 points were further than
25 m from a road. In a sensitivity analysis we calculated the canyon
indicators for these on-road points, and compared them to those
derived for the original points (Online Supplement E).
2.4. Air pollution measurements
Measured air pollution data were available from the TRACHEA
(Traffic Related Air pollution & Children’s respiratory HEalth and
Allergies) study (Eeftens et al., 2011). Briefly, NO2 and NOx measurements were taken at 144 sites using Ogawa badges, a passive
sampler which collects nitrogen oxides through diffusion on a precoated filter (http://www.ogawausa.com). All 144 sites were
Fig. 2. Calculation of the largest building angle. In this situation, the nearest building has a building angle of 36.9 . However, a taller building further away has a building angle of
48.0 . For this search angle, the height, distance and building angle of the taller building are selected for the SkyView Table. Fig. 3 gives a 3-D impression of the statistics calculated
in the SkyView Table.
M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
5
Fig. 3. Calculation of the different elements of the SkyView Table. Illustration of the statistics stored in the SkyView Table: the height of the buildings at a search angle of 210 and
270 , the distance to the buildings at search angles of 270 , 300 and 330 , and the building angles calculated from search angles of 90 , 120 and 150 . The highest AR is calculated
along the green lines (from search angle a ¼ 90 and opposite angle of 270 ). (For interpretation of the references to colour in this figure legend, the reader is referred to the web
version of this article.)
measured simultaneously during four periods of a week in different
seasons of 2007. As mentioned earlier, a total of 12 sites were
excluded (4 sites because there were no building polygons available, 8 because samplers were situated in back gardens), leaving
132 sites for the analyses.
2.5. Predictors for land-use regression models
Besides the urban canyon indicators, a total of 44 potential
predictor variables were generated for model development. As
“background” predictors we collected data on land use, population,
home address density and regional variability. Land use variables
were obtained from the CORINE 2000 database in buffers of 100,
300, 500, 1000 and 5000 m for the four land use categories residential, industry, ports and urban green/natural land. The population and home address density within 100, 300, 500, 1000 and
5000 m buffers were calculated, using a national 100 100 m grid
obtained from the National Institute of Public Health and the
Environment. Because of the large geographical size of the study
area, the LUR models had to account for regional variation, which
could not be captured by any of the GIS variables because of a
maximum range of 5000 m. An inverse distance weighted concentration measured at regional background sites was used to
represent regional variation.
As “local” predictors, we calculated the total traffic load in
vehicle m, defined as the length of a road segment * the traffic
intensity on that road segment. The traffic load was computed for
buffers of 25, 50, 100, 300, 500 and 1000 m. Traffic and road data
were obtained from a National Road Database with linked traffic
intensities from the National Institute of Public Health and the
Environment. From these 7 traffic predictors, the 50 m traffic load
buffer was selected for further model development as it showed the
highest correlation with total traffic intensity within the adjacent
street (R2 ¼ 0.98).
We selected the buffer sizes for land-use, population and
households to reflect known distances of pollutant dispersion,
taking into account the resolution of the available maps. The same
buffer sizes and variables were used in the Dutch LUR modelling for
the ESCAPE project (Eeftens et al., 2012). An overview of all variables and their distribution can be found in Online Supplement F.
2.6. Land use regression (LUR) models
We first developed LUR models without the canyon indicators,
based on 132 sites. We then added each canyon indicator separately
to these basic LUR models to evaluate the additional predictive
value of the four canyon indicators.
The basic LUR model was developed in two steps. First, a model
for the background was developed for NO2 and NOx using the 37
available background predictors. Each potential predictor was first
evaluated individually, and the one giving the highest adjusted R2
was selected. Additional predictors were selected in a stepwise
process if they added more than 1% to the adjusted R2 of the previous model. New variables were only included if their direction of
effect was specified a priori (e.g. positive for traffic intensity,
negative for urban green and natural land use), and if their addition
did not change the direction of effect of previously included variables. Second, traffic load in a 50 m buffer was added to the background models as a variable representing the additional pollution
caused by traffic in the street in which the sampling site was
located. We specifically added this traffic variable to the model as
we also characterized the street configuration of this street and the
hypothesis was that including street configuration in addition to
the traffic load would improve the model.
We then added the 4 different canyon variables to the basic
background þ traffic models one at a time, and evaluated their
significance, contribution to the explained variability and direction
of effect. In a sensitivity analysis, we evaluated this for the on-road
points also (Online Supplement E). We also evaluated the SkyView
Factor in classes, and the aspect ratio as determined in the field.
We further explored if there was evidence of interaction effects
between traffic load in a 50 m buffer and the significant canyon
indicators, because poor dispersion related to canyons may be more
important in high traffic streets. We evaluated interactions using
the product of the linear terms “traffic intensity in a 50 m buffer”
and the product of traffic intensity and SVF classified in quartiles.
2.7. Model validation
In addition to assessing the contribution of canyon indicators to
the model R2, we also assessed whether they improved validation
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M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
Table 1
Distribution of derived canyon indicators (n ¼ 132).
Variable
Mean
Minimum
10th percentile
25th percentile
Median
75th percentile
90th percentile
Maximum
Aspect ratio
Mean building angle ( )
Median building angle ( )
SkyView Factor (SVF)
0.296
18.4
17.7
0.841
0
0
0
0.472
0.0748
1.69
1.06
0.619
0.144
11.5
7.08
0.772
0.253
17.9
16.3
0.871
0.387
25.3
26.9
0.946
0.598
34.7
37.5
0.999
0.933
45.9
56.4
1.00
statistics. All models were primarily validated using leave-one-out
cross validation (LOOCV), successively leaving out one site and
estimating the concentration at this site using models based on the
other 131 sites. The correlation (R2) and root mean squared error
(RMSE) between the measured and estimated concentrations were
recorded for each model. Although this approach is often used in
the LUR literature (Hoek et al., 2008), we found that LOOCV R2 gives
an overestimation of the accuracy with which we can predict
concentration at locations not used in fitting the LUR model
(Basagaña et al., 2012; Wang et al., 2012). Using an independent
“hold-out” validation set may give a more realistic estimate of the
model accuracy (Wang et al., 2012). Therefore, in an additional
analysis, we also assessed whether canyon indicator improved
hold-out validation statistics. We fitted the models on a randomly
selected “training” subset of 66 sites (50% of the total 132 sites),
keeping the model structure the same and varying only the coefficients. The random selection was stratified by site type to ensure
that regional background, urban background and street sites were
equally represented in both training and test dataset. Similar to the
original (132-site) models, we cross-validated the 66-site models
and recorded the LOOCV R2. We then predicted concentrations for
the complementary 66 sites which were not used to fit the model
(“test” subset), and calculated the correlation (R2) with the
measured concentrations. This process was repeated 10 times for
different random selections. We calculated the average and range of
the 66-site model R2’s, training subset LOOCV R2’s and the test
subset hold-out validation.
3.2. Air pollution measurements
Distributions of NO2 and NOx concentrations are shown in Fig. 5
for all 132 sites. Substantial variation was present, partly related to
differences between regional background, urban background and
street sites.
3.3. Land use regression models
3.3.1. Basic land use regression models
The basic LUR model is presented in Table 3. The same three
background predictors (regional background estimate, industry in a
5000 m buffer and household density in a 500 m buffer) were
selected for NO2 and NOx, in addition to traffic load in a 50 m buffer.
These basic models explained 79% of the spatial variation for NO2
and 75% for NOx.
3.3.2. Basic models with canyon indicators
Mean building angle and SkyView Factor variables contributed
significantly to the NO2 and NOx LUR models. All effects were in the
expected direction, adding between 1 and 2% to the explained
variance (Table 4). The SVF was evaluated both as a continuous and
a class variable (with 4 classes made up of quartiles). SVF quantiles
were only significant for NO2. The difference between the 10th and
90th percentile of the mean building angle was equivalent to a
difference in concentration of 4.63 mg m3 of NO2 and 8.66 mg m3
3. Results
3.1. Estimation of canyon indicators
Estimates of canyon indicators were calculated for all 132 sites,
and showed clear contrasts between moderate canyons and wider
streets (Table 1; Fig. 1). Busier streets had significantly higher
aspect ratios (p ¼ 0.0009), higher mean (p < 0.0001) and median
(p ¼ 0.0007) building angles and lower SkyView Factors
(p < 0.0001), but the correlations between traffic load and each of
these predictors were very low (with R2’s of 0.08, 0.17, 0.08 and 0.18,
respectively). The correlations between the different indicators
were moderate to high, especially between the mean building
angle and SkyView Factor (SVF) (Table 2). Maximum aspect ratios
estimated by GIS agreed well with those from on-site observations
(R2 ¼ 0.49) (Fig. 4).
Table 2
Correlation between the different canyon indicators, R2-values (n ¼ 132).
Aspect ratio
Mean building angle
Median building angle
SkyView Factor (SVF)
Aspect
ratio
Mean
building
angle
Median
building
angle
SkyView
Factor (SVF)
e
0.73
e
0.90
0.73
e
0.68
0.95
0.63
e
Fig. 4. Comparison between aspect ratios obtained from field observations and those
calculated from GIS (R2 ¼ 0.49, n ¼ 132). If we do not consider the points which were
among buildings of imputed height (grey squares), the agreement between the
methods improves and R2 increases to 0.53.
M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
7
140
120
100
80
60
40
20
0
TOTAL
RB
UB
ST
Fig. 5. Adjusted mean NO2 and NOx concentrations for the 132 sites, stratified for 25 regional background (RB), 68 urban background (UB) and 39 street sites (ST). Median, and inter
quartile ranges are shown in the box, whiskers indicate 10th and 90th percentiles and individual outliers are shown as points.
of NOx. The difference between the 10th and 90th percentile of the
SkyView Factor was equivalent to a decrease of 5.56 mg m3 of NO2
and a decrease of 10.9 mg m3 of NOx. The maximum aspect ratio
and median building angle did not improve the LUR models, and
neither did the aspect ratio as derived from field observations
(Table 4).
In a sensitivity analysis, canyon indicators derived for the onroad points were highly correlated to those derived for the original points (Online Supplement E). Improvement of model performance was also virtually the same for the original and on-road
points (Online Supplement E).
Interactions between traffic and canyon indicators were evaluated for those canyon indicators for which the main effects were
significant (mean building angle, SVF and SVF class for NO2). Model
and cross-validation R2 did not improve and the interaction terms
were not significant (Online Supplement G).
We also performed hold-out validation to test the hypothesis
that a model identified with canyon indicators reflects physical
reality better and therefore can be transferred better to sites not
used in model development. The average hold-out validation R2 for
NO2 was higher for LUR models with a continuous SkyView factor
(R2 ¼ 0.79) than for the basic model (R2 ¼ 0.78) (Online Supplement
H). For the other canyon indicators hold-out validation was not
better than for the basic model.
4. Discussion
We demonstrated a novel approach to estimate the extent of
pollution trapping by urban street canyons in a quantitative way,
using 3-dimensional building data. Digital building data are
becoming more widely available in GIS environments, creating
possibilities for those who develop air pollution models to consider
the aspect of pollution trapping. Aspect ratios derived from our GIS
calculations were comparable to those from in-field observations.
Our approach does not require manual classification of roads by
time-consuming site visits, and can therefore also be applied to
extract canyon indicators for large numbers of non-monitoring
locations (e.g. cohort subjects). This method may be used in
epidemiological research to better characterize individual exposure
to air pollutants. Furthermore, the presented canyon indicators
may also be applied in dispersion modelling. In contrast to the
-commonly used- aspect ratio, which is calculated only from
buildings on opposite sides of the street, our canyon indicators
mean/median building angle and SkyView Factor consider the
obstruction by all surrounding buildings. While our basic LUR
models explained a large amount of spatial contrast without
including canyon indicators, we found that the inclusion of the
SkyView Factor or the mean building angle added significantly to
the models. Effects are consistent and in the expected direction. We
Table 3
Model description of NO2 and NOx models.
NO2
Adj R2 ¼ 0.79
RMSE ¼ 4.55
R2 ¼ 0.80
LOOCV Adj R2 ¼ 0.77
LOOCV RMSE ¼ 4.80
LOOCV R2 ¼ 0.77
Variable
Coefficient
Standard error
Coefficienta (90the10th percentile)
Intercept
Regional background NO2
Industry 5000 m
Household density 500 m
Traffic load 50 m
0.636
0.903
0.000000808
0.00150
0.00000572
1.87
0.0987
0.000000171
0.000294
0.000000512
e
9.93
5.78
5.47
9.20
NOX
Adj R2 ¼ 0.75
RMSE ¼ 10.5
R2 ¼ 0.76
LOOCV Adj R2 ¼ 0.72
LOOCV RMSE ¼ 11.1
LOOCV R2 ¼ 0.73
Variable
Coefficient
Standard error
Coefficienta (90the10th percentile)
Intercept
Regional background NOX
Industry 5000 m
Household density 500 m
Traffic load 50 m
4.02
0.949
0.00000161
0.00285
0.0000136
4.14
0.161
0.000000398
0.000679
0.00000118
e
14.2
11.5
10.4
21.9
a
Coefficients and standard errors were multiplied by the difference of the 10th and 90th percentile of the predictors.
8
M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
Table 4
LUR model performance with and without canyon indicators.
Coefficient
(standard error)
NO2
No canyon indicators
Aspect ratio
Mean building angle
Median building angle
SVF
SVF classc
Aspect ratio from field observations
NOx
No canyon indicators
Aspect ratio
Mean building angle
Median building angle
SVF
SVF classc
Aspect ratio from field observations
a
b
c
Model
LOOCV
a
R2
Adj R2
R2
Adj R2
e
1.82 (1.29)
4.63 (1.47) b
1.13 (1.33)
5.56 (1.45) b
Q2: 2.12 (1.29)
Q3: 2.74 (1.36)b
Q4: 3.25 (1.47)b
1.46 (1.19)
0.80
0.80
0.82
0.80
0.82
0.81
0.79
0.80
0.81
0.79
0.81
0.80
0.77
0.77
0.79
0.77
0.79
0.77
0.77
0.77
0.78
0.77
0.79
0.77
0.80
0.79
0.76
0.76
e
1.85 (3.01)
8.66 (3.46) b
0.930 (3.10)
10.9 (3.41) b
Q2: 3.92 (3.00)
Q3: 5.17 (3.18)
Q4: 5.99 (3.42)
1.46 (2.83)
0.76
0.76
0.77
0.76
0.78
0.76
0.75
0.75
0.76
0.75
0.77
0.75
0.73
0.72
0.73
0.72
0.74
0.72
0.72
0.72
0.73
0.72
0.74
0.72
0.76
0.75
0.70
0.70
Coefficients and standard errors were multiplied by the difference of the 10th and 90th percentile of the predictors.
Canyon indicator significant (p < 0.05).
Class 1 (lowest SkyView Factor) is the reference class.
did not find an improvement of the LUR model using the aspect
ratio used in previous studies, suggesting that it is worthwhile to
also derive indicators such as mean building angle and SkyView
Factor.
SVF has been used to represent urban heat island effects and
may better reflect the impact of multiple buildings (Souza and
Rodrigues, 2003). The lack of model improvement using aspect
ratio is in contrast to a study in Vancouver, which found improvements of R2 of LUR models from 72 to 85% for NO and from
56 to 67% for NO2 by including aspect ratios derived from satellite data (Su et al., 2008). This difference could be due to the
presence of more and deeper canyons in the Vancouver study. In
our study, all aspect ratios were below 1 (Table 1), indicating low
to moderate canyons only. Buildings in many Dutch city centres
date from the 17th century and have 4 or 5 stories at most.
Consequently, the layout of streets has remained largely unchanged for centuries. In more recently built residential neighbourhoods, most homes have 2 or 3 stories. High-rise apartments
and office buildings were mainly built in the 20th and 21st
century and are usually not built close to each other. Better
assessment of the aspect ratio is an unlikely explanation, as the
Vancouver paper reports a correlation (R) of 0.41 for aspect ratios
derived from satellites and field observations, compared to an R2
of 0.49 in our study. The higher R2 of our basic models may also
have contributed to a lower opportunity for increase in R2.
Finally, our study area was larger than the Vancouver urban area
as smaller towns were also included. Brauer et al. (2003) reported increases in R2 of 4% and 6% by adding canyon indicators
derived from direct field observations to basic LUR models for
PM2.5 absorbance (a marker for soot) for Munich and Stockholm,
but no improvement in the Netherlands. The basic LUR models
for PM2.5 were improved by 7% for Munich and 13% for Stockholm (Brauer et al., 2003). Munich and Stockholm also included
significantly deeper (AR 1.5) canyons than discussed in this
paper and both Stockholm and Munich included more of them
(21% of sites in Stockholm and 20% in Munich) than the Dutch
study area (only 5% of sites).
Despite the small increase in model R2, the difference of
5.56 mg m3 in NO2 and 10.9 mg m3 in NOx between moderate
canyons and open streets was substantial and comparable to the
predictor variables industry and household density in the basic
model (Table 3). The presence of other sources of variation (e.g.
urban-rural, regional variation) in this national dataset may have
contributed to the small increase in R2. The estimates were comparable to predictions of the widely used CAR-II dispersion model
for the traffic related contribution to air pollution in the
Netherlands (Eerens et al., 1993). A recent monitoring study in the
Netherlands found differences of more than 10 mg m3 in annual
average NO2 concentration between two canyon type streets and
more open streets with similar traffic intensity (Boogaard et al.,
2011).
Several factors may explain the small improvement in model R2.
Other variables like household density (which is generally higher in
built-up areas) may have accounted for some of the canyon effect in
the basic model. The need to estimate building heights in our study
may have decreased the predictive value of our indicators. In cities
with detailed building height data, indicators may results in more
improvement. Furthermore, there were limitations in the building
heights available for regional areas, resulting in a large number of
buildings for which the height was imputed. Since most of our
monitoring sites were in urban areas (where contrast in pollution is
highest), this only affected 19 out of 132 sites, and there were small
differences in the results depending on whether the missing
building heights were imputed at 2 m, or at 6 m (Online
Supplement C).
Thus, the model improvement in terms of explained variance by
including canyon indicators differs per study area. As canyon indicators may be time-consuming to obtain, the potential merit of
adding them should be carefully assessed. Studies which are conducted in a small area (e.g. single city) including streets with a large
variation in street configurations, deep canyons, may benefit most
from including canyon indicators.
We did not consider the effects of other obstacles than buildings
on vertical air exchange. It has been suggested in literature that
trees can either trap pollution or increase turbulence, enhance
deposition and even absorb some pollutants (Baldauf et al., 2008,
2009) and that noise barriers and parked vehicles could trap
pollution on the roadway, while acting as a shield and reducing
pollution levels on the footpath (Baldauf et al., 2008; Gallagher
et al., 2011).
M. Eeftens et al. / Atmospheric Environment 72 (2013) 1e9
Most case studies of street canyons so far have focused on
“ideal” canyon settings of deep canyons, sided by uninterrupted
lines of equally high buildings on both sides, without side streets
(Vardoulakis et al., 2007). Many of these studies of street canyons
have considered prevailing wind direction, atmospheric stability,
orientation of the street and the placement of the monitor on the
windward versus the leeward side. While these issues could affect
pollution concentrations inside the canyon on the short term, we
expect that on average, on the long term, more pollutants will
accumulate at sites with low vertical air exchange. Wind direction
and speed in the Netherlands is highly variable, as observed during
the measurement periods for this study (Online Supplement I). In
this paper, we evaluate the effect of urban morphology on longterm average air pollution, and therefore have not considered
these factors in the current study design. Our study also deals with
real-life settings which are more difficult to model, particularly in
combination with site-specific factors such as traffic induced turbulence, on which no data are available. We have shown that on the
long term there are higher levels of air pollution in streets where
buildings obstruct vertical air exchange, implying that pollution
generated by on-road traffic is indeed trapped by surrounding
buildings.
5. Conclusion
We demonstrated a GIS-based method to derive quantitative
indicators for pollution trapping by buildings. Digital building data
are increasingly available, and could be used to incorporate pollution trapping by buildings in both dispersion and land use regression air pollution models. In addition to the classical aspect ratio,
our approach also derives three indicators which consider all surrounding buildings: the mean and median building angle and the
SkyView Factor. The mean building angle and SkyView Factor
accounted for substantial concentration differences in NO2 and NOx
in a LUR model including moderate canyons in the Netherlands.
6. Competing interest
The authors declare they have no competing financial interest.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.atmosenv.2013.02.007.
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