ICUC8 – 8th International Conference on Urban Climates, 6th-10th August, 2012, UCD, Dublin Ireland.
522: A GIS model for commuters’ exposure to air pollutants
Francesco Pilla 1*, Brian Broderick 1
Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Ireland 1
fpilla@tcd.ie *
Abstract
The European-Commission has shown a greater concern in the development of actions that
allow increasing the knowledge on transport dynamics and related atmospheric pollution to
assure the accomplishment of legislation. At the same time, the directive 1996/62/EC
establishes the possibility of using modelling techniques to assess air quality.
The aim of the research presented in this paper is to understand the dynamics of air
pollution with the purpose of obtaining a precise estimation of the air quality through highresolution modelling and use it as a tool for managing transport and mobility and improve
the infrastructure towards a reduction of the cities’ inhabitants exposure to air pollutants.
Once the different air pollutants concentration levels are calculated, they are imported in
ArcGIS and dedicated layers are created along with thematic dynamic layers for traffic (and
time to drive a segment of road) and population density.
The layers are combined and an estimate of the population exposure varying with time and
location is calculated.
As such the model is able to predict the exposure to air pollutants of Dublin inhabitants and
also of commuters on their way in/out of the city according to traffic and weather forecasts.
Keywords: air quality modelling, ArcGIS, artificial neural networks, personal exposure
modelling, data mining.
1. Introduction
Living or attending school near major roadways
has been associated with numerous adverse
health outcomes in recent years, including
asthma [1], other respiratory illnesses [2] and
increased risk of mortality from cardiopulmonary
[3] and cardiovascular diseases [4]. While the
potential for a large public health impact has
been established and well documented, it
remains current practice to derive inhabitants’
exposure from the measurements of very sparse
fixed monitoring stations even for large size
cities. This approach does not fully capture
spatiotemporal patterns of air pollutants, and
causes the information on an individual’s
environmental exposure to be poorly aligned with
more detailed information on their personal
health.
As such, an ArcGIS model for Personal Exposure
(PALM-GIS) to Particulate Matter for the
inhabitants of the Dublin City is created and then
it is applied to the assessment of the personal
exposure to PM10 of commuters in Dublin.
2. The PALM-GIS personal exposure to
PM10 model
A number of air dispersion models are integrated
in ArcGIS. The advantage of this solution is that
the iteration of the modelling procedure for
different modelling tests and weather conditions
is carried out automatically by ArcGIS through a
Python code, which is used to automate timeconsuming and complex GIS workflows.
Figure 1: PALM-GIS model structure
This integration aims to provide the researchers,
local authorities and others with a tool to
calculate the concentration levels of PM and to
correlate them with other thematic layers, such as
land use and population density, in order to link
localised peaks in air pollutants with particular
activities. As such, the following outcomes are
obtained by using dedicated ArcGIS workflows
and tools:
1. Modelled background concentrations;
2. Modelled traffic related concentrations;
3. Modelled
industrial
sources
related
concentrations;
ICUC8 – 8th International Conference on Urban Climates, 6th-10th August, 2012, UCD, Dublin Ireland.
4. Modelled
domestic
sources
related
concentrations.
The concentration levels are then combined in
ArcGIS in order to obtain total concentrations at
specific locations in Dublin to calculate the
personal exposure of a commuter on his/her
route from habitation to workplace using the
PALM-GIS model (Figure 1). This ArcGIS
extension is able to calculate the exposure of the
commuter on the route according to the time
spent on each link, which is function of the
average speed on the same link. It is also
possible to calculate the route from a starting
point (the habitation) to a destination (the work
place) which would minimise the exposure by
using a cost function applied to the road network
and its characteristics, such as the average
speed and the pollutant concentration.
Figure 2: Commuting route - bus
Figure 3 and Figure 4 show the measured and
modelled PM10 concentrations respectively.
3. Validation of the model
A number of significant examples were selected
to be modelled and used as validation tests for
the model. For completeness purpose, the
commuters’ datasets to be modelled and
analysed are selected with different transport
modes and routes through the city. The following
examples are chosen:
1. Bus: southbound route to Trinity College with
bus;
2. Bicycle: westbound route to Trinity College
with bicycle;
3. Walk: northbound route from Trinity College.
The measurements are carried out with an
Aerocet-531 mass particle counter, which
averages the measurements over two minute
periods; these data are then combined with the
positions recorded by the GPS unit, (which was
carried by the commuter along with the PM10
monitoring device).
3.1 Commuting by bus
The route goes through different air quality
environments (Figure 2):
 The residential area, characterised by roads
flanked by trees and low rise detached and
semi-detached buildings with gardens;
 A heavily trafficked road (Rathmines Road),
characterised by a building height to street
width ratio which is typical of the “urban street
canyons” and may lead to the generation of
recirculation street vortexes;
 A park in an urban area, characterised by an
open green area surrounded by heavily
trafficked roads.
Figure 3: Measured PM10 – bus
Figure 4: Modelled PM10 - bus
3.2 Commuting by bicycle
This route goes through very heterogeneous
environments, which require completely different
air pollutants dispersion modelling approaches
(Figure 5):
 The residential area around Castleknock,
characterised by roads flanked by trees and
low rise detached and semi-detached
buildings with gardens;
 The rural environment from the M50 Flyover
and the residential area surrounding Phoenix
Park, characterised by very sparse houses,
open fields and trees along the road;
 The open park (Phoenix Park), characterised
by open spaces with no buildings;
 The heavily trafficked road (the Quays),
characterised by a ratio building height street
width which is typical of the “urban street
ICUC8 – 8th International Conference on Urban Climates, 6th-10th August, 2012, UCD, Dublin Ireland.
canyons” and may lead to the generation of
recirculation street vortexes;
Figure 5: Commuting route - bicycle
Figure 8: Commuting route - walking
Figure 6 and Figure 7 show the measured and
modelled PM10 concentrations respectively.
Figure 9 and Figure 10 show the measured and
modelled PM10 concentrations respectively.
Figure 6: Measured PM10 - bicycle
Figure 9: Measured PM10 - walk
Figure 7: Modelled PM10 - bicycle
Figure 10: Modelled PM10 - walk
3.3 Walking
The route goes through an urban environment,
with a building height to street width ratio which is
typical of “urban street canyons”. This test case
was also chosen because it was carried out
around 6pm, which is generally “rush hour” in
Dublin and therefore a period where there is likely
to be high exposure to road traffic pollutants timeframe for pedestrians in that part of the city
(Figure 8):
4. Performances of the model
The measured and modelled data are averaged
over 5 minute periods in order to minimise the
influence of noise and short-term variations
between the compared two averaged datasets.
The PALM-GIS model reproduces with very high
accuracy the measured levels for the three test
cases, with a coefficient of correlation R2 which
ranges between 0.74 and 0.96.
Figure 11: Comparison between measured and
modeled concentrations - bus
ICUC8 – 8th International Conference on Urban Climates, 6th-10th August, 2012, UCD, Dublin Ireland.
Table 1: calculated dose for measured (meas) and
modelled (mod) data [µg].
Transport
mode
Bus
Bicycle
Walk
Meas
Mod
24.38
25.12
36.21
1.23
2.33
1.79
Ratio
mod/meas
57.24
47.57
54.12
Figure 12: Correlation coefficient between
measured and modeled concentrations - bus
5. Conclusions
Figure 13: Comparison between measured and
modeled concentrations – bicycle
Figure 14: Correlation coefficient between
measured and modeled concentrations - bicycle
In personal exposure studies, inhalation is
considered as a major route of exposure for an
individual, who breathes in polluted air which
enters the respiratory tract. Identification of the
pollutant uptake by the respiratory system can
determine how the resulting exposure contributes
to the dose. In toxicology, dose may refer to the
amount of a harmful agent to which an organism
is exposed: as such, the mechanism of pollutant
uptake by the respiratory system can be used to
predict potential health impacts within the human
population.
It is clear from the results presented in the table
above that the personal exposure model highly
performs in predicting the inhaled dose for the
individuals while commuting to work with four
different transport modes.
6. Acknowledgements
This project is founded by the Irish E.P.A. under
the STRIVE Program.
7. References
Figure 15: Comparison between measured and
modeled concentrations – walk
Figure 14: Correlation coefficient between
measured and modeled concentrations - walk
In order to have a better estimate of the
performances of the personal exposure model for
the test cases presented above, the dose is
calculated for the measured and modelled data.
The calculation is performed for each presented
test case and the results are presented in the
table below.
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