Fifth Symposium on Highway and Urban Pollution, May 22-24, 1995.
Spatial Distribution of urban pollution: civilizing urban traffic
Dr. Ben Croxford, Alan Penn, Prof. Bill Hillier.
The Bartlett School of Architecture and Planning
Philips House
University College London
Gower Street, London WC1E 6BT, ENGLAND.
tel.: +44 171 380 7890
fax: +44 171 916 1887
email: b.croxford@ucl.ac.uk
Abstract
This paper presents findings from the EPSRC funded research project,
"The Effects of street grid configuration on pedestrian
exposure to vehicular pollution: civilising urban traffic".
The project builds on work done at the Bartlett which has developed new configuration sensitive "space syntax"
models that describe and quantify the pattern properties of urban
space at the scale of the individual street segment. This model
can provide good predictions of both pedestrian and vehicular
flows throughout a city, and is currently being used commercially
to predict the effect on both pedestrian and vehicular traffic
flows of major urban projects. This paper will describe how the
model works and how measurements of urban pollution at street
level can be integrated into this model.
The main task of the project was to devise a method of measuring
urban pollution at the scale of the street segment, in a reliable,
accurate and cheap way. The monitoring equipment has been developed
here and is set up to measure Carbon Monoxide (CO), temperature,
relative humidity, light level, and wind speed at 6 minute intervals.
This paper reports early results from a small number of these
instruments.
Introduction
This paper presents findings from the EPSRC funded research project,
"The Effects of street grid configuration on pedestrian exposure
to vehicular pollution: civilising urban traffic". The project
itself addresses some of the many further areas for research suggested
in the first QUARG report, [QUARG 1993], which calls for more
data on a street by street basis, better understanding of meteorological
effects on pollutant levels, and assessing the impact of traffic
management options.
The problems of research into air pollution are large, and in
part reflect the immaturity of the whole field. Existing models
rely mainly on estimates and extrapolations, rather than observations,
of all variables including traffic flows, traffic speeds, emission
rates, and the effects of climatological variables. The error
on each estimate propagates through to produce a large total error
on the final model. There is also a problem relating to the measuring
of pollutants at enough sites, accurately enough, to validate
these models.
At the start of this project there was no cheap method
of accurately measuring pollutant concentrations at a large
number of sites simultaneously. All existing methods are unsatisfactory
in some way. Diffusion tubes provide single point measurements
representing a week or a fortnight's average, bag sampling provides
a cheap snapshot but is labour intensive [5], and other continuous
methods are too expensive and too big to install on many street
segments, so this project has developed a new method capable of
measuring differences in pollutant concentrations between closely
situated streets, and has validated this method against existing
continuous monitors. This is a first step to providing a method
of investigating spatial distributions of urban pollution, and
could be used to validate the existing pollution models.
The model used as a background to this work provides a measure
of how well a particular street "fits" into the whole
structure of the city [1]. This spatial configuration measure
can be used to provide good predictions of both pedestrian and
vehicular traffic flows, validated using actual counts
of both people and cars [6].
This paper has three parts, the first describes the previous
work at the Bartlett which explains how to quantify the
street configuration of a particular city and how this can be
used to predict traffic flows. The second part describes how
pollution can be measured in the street at the scale of the individual
street segment, leading to the third section that ties these two
parts together to investigate possible relationships between street
configuration and pollution.
How to describe the street grid configuration of a city
A method of quantifying how well a particular street segment in
a city is connected to the entire city's grid structure has been
developed here at the Bartlett. It is based on mapping
a city using longest lines of sight through that city then performing
calculations on each line of sight to provide a number relating
to how well "integrated"; that line of sight is within
the whole system. One of the consequences of this is to explain
numerically how navigation in a traditional town or city is easier
than in a new housing estate. Using this method it is possible
to analyse a whole city and produce a coloured map in which the
relative importance of a particular route within the system, derived
from the analysis, can be shown.
A Simple Description of the Space Syntax method of city mapping
By taking an accurate small scale map of the city and tracing
over every street the longest line of sight possible,
the following map for London can be produced, see figure 1.
Figure 1: Black and white detail of colour axial line map of London
that contains over 16,000 lines of sight inside North and South
circular, the river Thames can be seen as a winding white space
through the map from left to right, other major white spaces are
parks.
This map is then digitised into a computer program and the number
of connections between streets is analysed. This enables the map
to be seen as in figure 2. The streets are coloured, (in this
case a grey scale), according to the number of changes of line
of sight away from Edgware road. The longest possible journey
would consist of 31 changes in direction of line of sight. Because
Edgware road is long and straight it is connected to many other
lines and is part of many possible routes through the city, thus
Edgware road is considered a street that is well "integrated"
into the city network.
Figure 2: Black and white version of colour axial line map of
London showing number of changes of line of sight from Edgware
Rd.
The next map, figure 3, is the same but coloured according to
the number of changes of line of sight away from Salter Street
in the Surrey Docks area, a very poorly "integrated"
street.
In the computer program, Axman [7] this analysis is done for each
line, then the results are all combined and normalised to produce
a value of "global integration" or "integration
radius n" reflecting the fact that the whole system is considered,
for each line. Figure 4 shows the "global integration"
map for all of London, the darkest streets are those which are
the best "integrated" into the street system.
Figure 3: Axial line map of London showing number of changes of
line of sight from Salter St., Surrey Docks. (Unavailable)
Other variations of the same map can be made. By colouring the
lines according to the number of routes consisting of three or
fewer changes in line of sight, known as "integration radius
3" the problem of edge effects can be avoided, i.e. where
the proximity of a line to the arbitrarily chosen boundary can
affect the results.
"Integration radius 3" is a "local integration"
measure and correlates strongly with pedestrian flows (annual
average daily flows), the "Integration radius 3" map
picks out the "local" major shopping streets in a city,
which are a focus for the small scale trips that pedestrians tend
to make. When vehicular traffic is also largely "local"
such as in the study area, a 1km2 area in North London, correlation
coefficients of r2=0.84 are found over 116 street segments [6].
This small area of North London is largely homogenous in terms
of street widths.
Figure 4: detail of colour axial line map showing
"Integration radius n" of central London. Regent's park
is the large white area in the upper left hand corner. Oxford
Street has been "cut" as this is closed to normal traffic.
(See also whole map on theSpace Syntax Laboratory home page
When larger systems are considered, usable street width is important
in the vehicular analysis. Counts of vehicles over several separate
and distinct areas of London are closely correlated with a combination
measure of just these two variables, relativised "Integration
radius 3" and relativised usable street width. The slopes
of the correlations from these separate areas all fall on the
same regression line, i.e. the method is valid for small areas
of London and for the whole system of London as far as the North
and South circular roads. Studies of more than 80 other cities
world-wide have confirmed the validity of this method which can
consistently predict movement with r2's of around 0.75 p<0.0001.
[1], [2]
This section has shown that both vehicular and pedestrian movement
are both strongly related to the street grid configuration. The
question this research addresses is how, and if, the pollution
mainly generated by traffic, affects the people who live, work,
and walk in the city. This method of city mapping is a powerful
tool to investigate possible effects of street configuration on
pollution concentrations in a city.
Pollution measurements
To correlate pollution measurements with these spatial measures
requires first that good pollution measurements are available
for every street segment in the study area. The project initially
specified the use of Carbon monoxide (CO) diffusion tubes, these
however were found to be a significant source of error and would
only provide, at best, one data point per street segment per week.
Instead it was realised that by using Carbon monoxide fuel cells
and a small datalogger, it would be possible to have near continuous
data for each street segment in the study area for a similar overall
cost to the diffusion tubes.
The major part of this project has been concerned with developing
a method of measuring urban pollution based on these fuel cells,
in a cheap, reliable and accurate way. The fuel cells themselves
are relatively cheap and extremely accurate, the quoted specifications
are +/-5% from 0 to 4000 ppm. These cells are accurate right down
to background levels and lower, and have a detection limit of
less than 0.1 ppm.
Running six of these sensors side by side gives extremely close
agreement and no apparent drift even over a period of 6 months
or more (picture not in original paper), the maximum error between any one sensor and the average
of all six, is approximately 5%. These CO sensors have been combined
with sensors measuring wind speed, light, humidity, and temperature
and connected to a datalogger. All the components have been fitted
into a small, (171 x 122 x 55 mm) weather proofed box powered
by a battery that is kept "topped up" via a photo-voltaic
panel. The result is a totally self-contained pollution monitoring
instrument, the Street Box, which can be fixed anywhere.
Two of these Street Boxes have been run against a Thermo Electron
type, CO monitor based on the non dispersive infra red method
(NDIR). This particular instrument is sited at a background site
in North London and is maintained by the South East Institute
for Public Health (SEIPH). The results of this test are shown
below. Despite the very low levels of CO (< 2 ppm) the correlation
is excellent, with an r2 of better than 0.8, p<0.0001, for
both boxes. See figure 6.
Figure 6: Comparison between the Street Box and the Islington
Council government approved Thermo Electron type sensor. Good
correlation with an r2 of 0.8 can be seen despite the low levels
of CO present.
Wind speed is measured using an experimental sensor, it has no
moving parts, and has been tested against a conventional anemometer,
the results are shown in figure 7. The good correlation gives
confidence in the assigning of a qualitative category to each
street measured at a particular time. In this case streets can
be typed into calm, breezy or windy that is important for analysis.
Both temperature and humidity levels are recorded with an accuracy
of +/-2% or better. Light level is measured as just a number and
has yet to be calibrated to global solar radiation.
The equipment has thus been designed, built and validated, it
can measure six variables simultaneously, and at present is programmed
to provide 6 minute averages of one minute samples. The variables
measured are :- Carbon monoxide, maximum, minimum, average,
Wind speed, max., min, average, Internal and external
temperature, averages, Relative humidity, maximum
and minimum and Light, average.
Figure 7: Correlation between the novel wind sensor used in the
Street Box and a conventional anemometer.
At this frequency of measurement the Street Box can store data
for up to six weeks, the data can be offloaded at any time up
to this point using a por able computer, the offloading procedure
taking approximately one minute per week of data.
The datalogging board has only integer mathematic functions and
uses only 8 bit numbers, (0->255), the practical consequences
of this are that time is scaled from 0 to 240, so the ten's figure
is the hour and the last digit expresses fractions of an hour,
so 175 is 5:30 pm, 178 is 5:48 pm. The Carbon monoxide value also
has to be scaled to fit into one of 255 levels, I have arbitrarily
set this to be from 0 to 25.5 ppm, so the measurement is CO concentration
in parts per million times 10.
Results from pollution modelling at the street segment scale.
The use of some of these street boxes in sites around University
College London, has produced a complex dataset, the data from
which may be presented in several ways. The next graphs, figures
8,9, show results for several Street Boxes, measuring simultaneously,
for periods of one day for each graph.
Figure 8: At the start of the survey, five Street Boxes were together,
then as they were installed on lamp posts they began to show the
clear differences between individual streets. Peaks can be seen
relating to rush hours or traffic jams.
Figure 9: Pollution by day. A bad day on Euston Road, Euston road
reads much higher than the other streets for the whole day, this
is perhaps due to a constant high level of slow moving traffic.
The next graphs, figures 10,11, show pollutant levels for one
street for 5 days, but these graphs also show when the street
was windy. Clearly when the weather is windy the CO values are
lower. All the major pollution peaks occurred at times of calm
or breezy weather.
Figure 10: Splitting the data into three categories, windy, calm
and an in-between state called breezy, allows the following analyses
to be made. Euston road with wind effects shows how increased
wind reduces pollutant concentrations, and how all the pollution
peaks are on calm days.
(Also Figure 11 showing Tottenham Court Road. Not in original paper)
Figure 12: Gower St with wind effects, shows the same pattern
as before. Gower St is aligned North-South whereas Euston Rd is
aligned West-East, the wind during this period was mainly from
the West.
A novel vector based visualisation program developed at the
Bartlett is being used to display results in ways that
may show up unexpected patterns, this view, figure 14, shows blocks
representing streets. These blocks can represent several variables
at once, for example, width could represent traffic flow, block
height could represent pollutant levels and colour could represent
wind speed.
Figure 14: Visualisation program with blocks representing streets
and the height, width and colour of the block representing variables
related to that street. This visualisation program can animate
an Excel spreadsheet, so each frame of the animation would show
one row of 6 minute averages.
Pollution results overview
The turbulent effects of air movement mean that two measurements
taken at the same junction can differ enormously from each other
with only a small difference in distance. In addition the stop-start
nature of traffic at junctions adds confusion to the data. I have
therefore tried to measure the pollution at a point in the middle
of a street segment. All the street boxes are mounted at the same
height using the same criteria, that they should be at about 2.5m
from the ground, near the road, away from the possibility of stationary
traffic, and as far from any junctions as possible.
How representative these data are of the pollution level in a
particular street and whether they can be used to correlate with
spatial variables is a matter of concern and will be investigated,
however it is felt that using wind speed data to select calmer
periods it will be possible to find sufficient, accurate data
to allow valid inter-street comparisons to be made.
The results from this initial survey show that
- spatial differences in pollutants exist and can be measured.
- pollutant levels are affected by local climatic variables applied
to the street segment, as much as by traffic conditions.
Conclusion
The spatial configuration analyses described in this paper show
how, by using only two variables, usable street width and a measure
of how well connected a particular street is within a city system,
("Integration radius 3"), a strong prediction of vehicle
movement can be made for each line in a system.
This paper has also presented data which confirms that urban
pollution is not a homogeneous quantity, it varies on a street
by street basis, even streets spatially close can have very different
levels of pollution. A method has been outlined whereby these
spatial differences can be investigated and related to the configuration
of the street.
Due to the ability to select calm or windy days it is possible
to compare the pollutant levels of different street segments in
different climatic conditions. This enables a picture of the pollution
distribution over a small area of London to be built up. Initial
results using data from only 8 streets are insufficient to correlate
with "integration", the next phase using 30 monitors
will provide more detailed data and be used to guide the third
monitoring phase which will consist of 60 monitors and be used
to monitor up to 60 street segments within a small 1km2 study
area.
Later work will investigate how well these variables can be used
to predict other pollutants.
References
1. Hillier B., Penn A., Hanson J., Grajewski T., Xu J., (1993),
Natural Movement: or configuration and attraction in urban
pedestrian movement, Planning and Design: Environment and
Planning B, Pion, London.
2. Penn, A.,Banister D., Hillier B., et al, (1991), The relationship
between vehicular and pedestrian movement in the smaller scale
urban grid: a pilot study, SERC report GR/G 23609.
3. Stonor T., Hillier B., Penn A., Karvoutzi K., (1993) The
Manchester pedestrian and vehicular movement study, Internal
Report.
4. QUARG group (Jan 1993), Urban Air Quality in the United
Kingdom
5. L.Y.Chan, W.Y. Wu, (1993) A Study of bus commuter and pedestrian
exposure to traffic air pollution in Hong KongÕ, Environment
International, Vol 19, pp121-132, .
6. Penn A., Dalton N., (1994). The architecture of society,
stochastic simulation of urban movement., Simulating Societies,
University College London Press pp 85-126.
7. Dalton N., (1991) Axman computer program, used to generate
spatial configuration models. Bartlett, University College London.
Note
Links to other world wide
web pages for colour figures and further explanations, relevant
pages are as follows.
Spatial Configuration or "space syntax"
Axman computer program
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