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|authors= [[User:Mikko Pohjola|Mikko V. Pohjola]], [[User:Jouni|Jouni T. Tuomisto]] 
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This page (including the files available for download at the bottom of this page) contains a draft version of a manuscript, whose '''final version is published and is available in the [http://www.bmj.com/content/343/bmj.d4521]'''. If referring to this text in scientific or other official papers, please refer to the published final version as: '''David Rojas-Rueda, Audrey de Nazelle, Marko Tainio, Mark J Nieuwenhuijsen: The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study. BMJ 2011;343:d4521 {doi|10.1136/bmj.d4521}'''.

Extra material supplied by the author (see [http://www.bmj.com/content/343/bmj.d4521/suppl/DC1])
Assumptions and calculation steps used to derive model inputs from available data
Conceptual framework for assessment of health impact

==Title==

Editing The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study

==Authors and contact information==

:David Rojas-Rueda predoctoral researcher, correspondence author
:(Rojas-Rueda drojas@creal.cat)
:(Center for Research in Environmental Epidemiology, Barcelona, Spain)
:(Municipal Institute of Medical Research (IMIM-Hospital del Mar) Barcelona, Spain)
:(CIBER Epidemiology and Public Health (CIBERESP) Madrid, Spain)

:Audrey de Nazelle researcher
:(Center for Research in Environmental Epidemiology, Barcelona, Spain)
:(Municipal Institute of Medical Research (IMIM-Hospital del Mar) Barcelona, Spain)
:(CIBER Epidemiology and Public Health (CIBERESP) Madrid, Spain)

:Marko Tainio researcher
:(Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
:(Department of Environmental Health, National Institute for Health and Welfare, Kuopio, Finland)

:Mark J Nieuwenhuijsen research professor
:(Center for Research in Environmental Epidemiology, Barcelona, Spain)
:(Municipal Institute of Medical Research (IMIM-Hospital del Mar) Barcelona, Spain)
:(CIBER Epidemiology and Public Health (CIBERESP) Madrid, Spain)

==Abstract==

'''Objective''' To estimate the risks and benefits to health of travel by bicycle,
using a bicycle sharing scheme, compared with travel by car in an urban
environment.

'''Design''' Health impact assessment study.

'''Setting''' Public bicycle sharing initiative, Bicing, in Barcelona, Spain.
Participants 181 982 Bicing subscribers.

'''Main outcomes measures''' The primary outcome measure was all cause
mortality for the three domains of physical activity, air pollution (exposure
to particulate matter <2.5 μm), and road traffic incidents. The secondary
outcome was change in levels of carbon dioxide emissions.

'''Results''' Compared with car users the estimated annual change in
mortality of the Barcelona residents using Bicing (n=181 982) was 0.03
deaths from road traffic incidents and 0.13 deaths from air pollution. As
a result of physical activity, 12.46 deaths were avoided (benefit:risk ratio
77). The annual number of deaths avoided was 12.28. As a result of
journeys by Bicing, annual carbon dioxide emissions were reduced by
an estimated 9 062 344 kg.

'''Conclusions''' Public bicycle sharing initiatives such as Bicing in
Barcelona have greater benefits than risks to health and reduce carbon
dioxide emissions.

==Introduction==

Bicycle sharing schemes have become increasingly popular in
countries throughout Europe, Asia, and America to encourage
cycling as an alternative means of transport in urban areas.
Large low cost rental systems (between 1000 and 50 000
bicycles) aimed at encouraging cycling for short urban trips and
multimodality (cycling along with another mode of transit) for
longer trips have been implemented by cities such as Lyon
(2005), Stockholm (2006), Barcelona (2007), Seville (2007),
Paris (2007), Toulouse (2007), Hangzhou (2008), Milan (2008),
Brussels (2009), Montreal (2009), Mexico City (2010), London
(2010), and Guangzhou (2010). In the United States, such large
scale initiatives are being considered for Los Angeles and New
York. The general impetus for these policies is more often the
reduction of traffic congestion than the promotion of health.

Motivated by the growing challenges of global obesity and
climate change, international organisations have been calling
for multisectoral and multidisciplinary approaches to increase
physical activity and reduce reliance on cars.2-5 In 2005 the
European Union formulated an important area of action
“addressing the obesogenic environment to stimulate physical
activity” (Commission of the European Communities 2005).
The Transport Health and Environment, Pan-European Program
(THE PEP) provides guidance to policymakers and local
professionals on how to encourage cycling and walking along
with an instrument, the health economic assessment tool, to
estimate the health benefits and cost effectiveness of cycling.6
Similarly, in the United States the Centers for Disease Control
and Prevention has developed guidelines for the prevention of
obesity.3 Integrating the promotion of walking and cycling into
daily life (for example, as part of commuting) is a promising
way to increase physical activity across a population. Cycling
does, however, have some potential risks such as increased road
traffic incidents and exposure to air pollution.

We estimated the effect on health of Bicing, the public bicycle
sharing initiative in Barcelona, Spain (see web extra appendix).
As direct outcomes on health are hard to measure, we estimated
the effects by studying all cause mortality using a newly
developed health impact model to integrate recently developed
tools, existing data from scientific studies, and local data. We
focused on the three domains of exposure to air pollution,
physical activity, and road traffic incidents. We also estimated
the reduction in carbon dioxide emissions.

==Methods==

Bicing, the public bicycle sharing initiative in Barcelona, Spain,
was introduced in March 2007 to improve the use of different
types of transport, promote sustainable transport, create a new
individual public transport system, promote the bicycle as a
common means of transport, improve air quality, and reduce
noise pollution. By August 2009, 182 062 people had subscribed
to Bicing (11% of the population in Barcelona municipality),
with 68% of trips being used for commuting to work or school
and 37% combined with another mode of travel. The mean
distance travelled by Bicing on a working day was 3.29 km
(mean duration 14.1 minutes) and at weekends was 4.15 km
(17.8 minutes).7

==Framework==

We used a health impact assessment framework to estimate the
potential effects on health of cycling compared with travel by
car (see web extra appendix figure 1). Exposure-response
functions were derived from existing studies and calibrated for
current exposure and health conditions in Barcelona. We chose
to model the effects of all cause mortality due to physical
activity, road traffic incidents, and exposure to air pollution
based on discussions held among experts during a workshop in
Barcelona in 2009 that suggested these domains would have
the greatest impact and best available data.8 Recent publications
provided further guidance.6 9 10 We focused on residents of
Barcelona who started cycling regularly using Bicing after its
implementation. Therefore we assessed the additional benefits
from physical activity and additional risks due to incremental
inhalation of air pollution and increased exposure of new cyclists
to road traffic incidents compared with previous exposures as
car users. We did not consider the benefits of decreased car use
to the general population of Barcelona because of Bicing. We
also estimated savings in carbon dioxide emissions.

Table 1 summarises the main input data used in the model. The
web extra appendix provides a detailed description of
assumptions and calculation steps used to derive the model
inputs from available data outlined in this section.

{|{{prettytable}}
|+ '''Table 1.''' Main input data for model to assess health impact of Bicing initiative in Barcelona, Spain
|-
!Input variables
!Value
|------
!Trip duration (minutes):
|
|-----
|Bicycle
|14.1*
|-----
|Car
|8.4†
|-----
!Distance travelled per trip (km):
|
|-----
|Bicycle
|3.29*
|-----
|Car
|3.29†
|-----
!Average speed (km/h):
|
|-----
|Bicycle
|14†
|-----
|Car
|23.5‡
|-----
!Fatal road traffic incident rate in Barcelona (deaths/billion km travelled):
|
|-----
|Bicycle
|4.54 §
|-----
|Car
|3.72§
|-----
!No of trips per day:
|
|-----
|Bicycle
|37 669*
|-----
|Car
|363 863‡
|-----
!Percentage of vehicles in Barcelona:
|
|-----
|Diesel
|44¶
|-----
|Petrol
|66¶
|-----
!Efficiency of vehicle fleet in Barcelona (L/100 km):
|
|-----
|Diesel
|7¶
|-----
|Petrol
|9¶
|-----
!Carbon dioxide emissions (kg/L):
|
|-----
|Diesel
|2.61**
|-----
|Petrol
|2.38**
|-----
|Expected mortality in 16-64 age population per year (deaths/1000 inhabitants)
|2.05††
|-----
|No of trips per person per day
|1.5†
|-----
|Total population of Barcelona (inhabitants)
|1.6 million‡
|-----
|Population using Bicing each day
|28 251†
|-----
|Population changing from car to bicycle
|25 426†
|}
:<nowiki>*</nowiki> = Average values of Bicing system in 2009.
:† = Assumptions (see web extra for description).
:‡ = Data from Barcelona council 2009.
:§ = Data from Barcelona Public Health Agency, 2002-10.
:¶ = Data from Spanish traffic department, 2008.
:<nowiki>**</nowiki> = Catalan Office for Climate Change, 2010.
:†† = Catalan Population in 2007.''

==Cycling and car use==

We obtained statistics on travel by car, cycling, and Bicing use
in Barcelona from a combination of data provided by the Bicing
management company, Barcelona de Serveis Municipals (B:SM)
and from travel surveys carried out by the city and by the
metropolitan area transportation departments.11 12 Based on these
sources of data, we estimated the mean number of trips daily
and duration of trip by travel mode in the city. We calculated
that 28 251 Bicing members used the scheme regularly. We
assumed that 90% of these users (n=25 426) were new cyclists
who had shifted travel mode from cars and that their current
Bicing trips replaced the same trips previously made by a
car—the same number of trips and same distance for each trip.
We also carried out sensitivity analyses using different scenarios
to assess the impact of shifting from other modes of transport,
with 10% changing from cars, 60% from public transport, and
30% from walking, based on figures in a UN report (see web
extra table 9).1

==Air pollution==

For the domain of air pollution we considered exposure to
particulate matter less than 2.5 μm, which has shown strong
associations with all cause mortality.13-15 We assessed the levels
of exposure and inhaled dose in car users compared with Bicing
users (see web extra appendix figure 4). Concentrations for each
mode were obtained from a study carried out in Barcelona.16
We assumed that the relative concentrations between modes
were representative of annual average relative concentrations.
We estimated yearly inhaled doses of these contaminants,
accounting for mode specific inhalation rates, exposures, and
duration of trip, as in a previous study.17 To simplify, we
assumed non-travel times to be spent resting and sleeping while
exposed to background annual concentrations of particulate
matter less than 2.5 μm. To estimate the relative risk of mortality
associated with incremental intake of pollutant for cyclists
compared with car users, similar to another study,9 we applied
the ratio between the estimated inhaled dose for cyclists and for
car users to exposure-response functions reported in the
literature. We used the update by previous researchers,18 of the
most commonly used relative risk functions in risk assessment
of exposure to particulate matter less than 2.5 μm.19 As a
sensitivity analysis, we also carried out this calculation for
mortality risks associated with increments of black smoke
inhalation (a mix of particles and carbon fume resulting from
the incomplete combustion of fossil fuels) and tested the
hypothesis that traffic related air pollution may be more toxic
than the ambient concentrations found in the cities from which
the relative risk functions were derived.13 14

==Traffic mortality==

For road traffic incidents we used data from the Barcelona public
health agency.20 We derived mortality from incidents per billion
kilometres travelled by bicycle and by car from estimates of
total yearly distance travelled by bicycle and car linked to traffic
mortality data by mode in the past nine years. We then calculated
relative risks of all cause mortality in a road traffic crash for
cyclists compared with car drivers, assuming the same distance
travelled for each mode, as in a previous study.9 (See web extra
figure 5.)

==Physical activity==

To quantify the benefits of physical activity, we followed the
approach presented in the health economic assessment tool for
cycling project.6 This instrument uses relative risks of all cause
mortality for commuters who use bicycles compared with other
modes of transport derived from a study in Copenhagen of the
largest health cohort that specifically considered health effects
of commuting by bicycle.21 As in the health economic
assessment tool for cycling, we adjusted the relative risk function
for daily average distances cycled in Barcelona compared with
Copenhagen (see web extra figure 6).

==Mortality rates==

Using the relevant relative risk functions from our three domains
derived for our study conditions in Barcelona, we calculated
the change in mortality (increment or decrement) associated
with travel by cycling using the Bicing initiative. To obtain the
population attributable number of deaths as in other classic risk
assessment frameworks, we applied the relative risk to the
number of deaths in our population of interest.22 23 To quantify
the number of deaths expected in the Bicing population assumed
to have started cycling when the initiative was implemented
(90% of users in our scenario), we used all cause mortality rates
years) in the Barcelona region (2.05 deaths per 1000 inhabitants
per year in 2007), reported by the Statistical Institute of
Catalonia.24 We had no information on the specific age
distribution of our Bicing population, so we carried out a
sensitivity analysis to quantify the effects on mortality related
to physical activity for the scenario using a younger age
distribution (average 33 years) based on a report on cycling25
and older age distribution (average 48 years) (see web extra
table 10 and figures 8-10).

==Carbon dioxide emissions==

We estimated the savings in carbon dioxide emissions following
guidelines and emission factors provided by the Catalan Office
of Climate Change.26 These values were calibrated to the
Barcelona vehicle fleet (number of vehicles using diesel or
petrol, and engine efficiency).27

==Statistical analysis==

We linked the various model parts in a quantitative model built
in Analytica 4.2 (Lumina Decisions Systems, CA, 2010) Monte
Carlo simulation program. The main model uses average values
for the variable inputs.

We carried out a sensitivity analysis to test the effect of using
alternative values for 15 of the variable inputs. These variables
were either used to construct other variables or used directly in
the analysis. The sensitivity analysis was meant to assess the
stability of our results when using a range of possible input
values, provide a range of likely outcomes as a simple form of
uncertainty and variability analysis, and identify which inputs
are most influential in determining the outcomes. The range of
values included for each tested variable depended on the type
of information available on the probability distribution for each
variable (see web extra table 8). When the data were available,
we used the mean and standard deviation to create a normal
distribution. If data were not available or the source of data did
not include information from distribution variables, we used
triangular distributions. If it was possible to select a minimum
and maximum from the data or to compare the results of
different datasets, we chose a triangular distribution with a
maximum and minimum based on the data. For input variables
without any other information than the mean, we assumed the
triangular distribution with minimum and maximum values to
be 50% around the mean value. This uncertainty range was
chosen to avoid underestimation of the uncertainty. We
identified input variables of highest importance by calculating
rank order correlations between each variable and the model
outcome. A tornado plot illustrated the effect of uncertainty,
which shows the effect on the overall results in the model of
varying each input variable individually.

==Results==

The estimated relative risk for all cause mortality associated
with physical activity among the residents of Barcelona who
travelled by bicycle (Bicing initiative) compared with by car
was 0.80, resulting in an attributable fraction of 0.23 avoided
deaths in the Bicing population who had shifted mode of
transport from the car. An estimated 12.46 deaths were avoided
each year. The relative risk of all cause mortality related to the
incremental inhalation of particulate matter less than 2.5 μm
was 1.002. The corresponding mortality attributable fraction
was 0.002, leading to an estimated 0.13 expected annual number
of deaths from air pollution in the Bicing population. The
relative risk of road traffic crashes for the Bicing population
compared with car users was 1.0007, with an associated
attributable fraction of 0.0007, resulting in 0.03 extra deaths
per year from road traffic incidents in the Bicing population.
As a result, 52.15 deaths would have been expected each year,
but because cycling was used as a typical means of transport,
the number of annual deaths was reduced by 12.28 to 39.87
(table 2).

{|{{prettytable}}
|+'''Table 2.''' Main results from health impact assessment of Bicing initiative in Barcelona
|-
!Variables
!Relative risk*
!AF<sub>exp</sub>†
!Deaths/year
|-----
|Road traffic injury
|1.0007
|0.0007
|0.03
|-----
|Air pollution (particulate matter <2.5 μm)
|1.002
|0.002
|0.13
|-----
|Physical activity
|0.80
|−0.23
|−12.46
|-----
|Carbon dioxide emissions saved (kg/year)‡
|—
|—
|9 062 344
|}



The results were most sensitive to the variations imposed on
the relative risk associated with physical activity and mortality,
derived from a previous study,21 the average duration of the trip
by bicycle, the number of days travelled by bicycle per year per
person in Barcelona, and the proportion of cyclists who started
cycling when Bicing was implemented. These four variables,
in that order, had the highest correlation with the combined
mortality and led to the greatest variations in net number of
deaths per year in the new cyclist population (see figure and
web extra figure 7). Using black smoke instead of particulate
matter less than 2.5 μm as a proxy for air pollution slightly
reduced the negative effects of cycling (0.04 v 0.13 deaths/year)
(see web extra table 4) despite the greater contrasts between
exposures during cycling and travel by car, because of the lower
relative risk reported in the limited literature on black smoke.
Applying a factor of 5 to account for the assumed higher toxicity
of traffic related exposure to particulate matter less than 2.5
μm14 increased all cause mortality to 0.52 people each year (see
web extra table 5). When the effects of varying the input
variables within the ranges specified in the web appendix were
analysed, the effect on all cause mortality ranged between 4 and
22 deaths avoided.

As the results were shown to be most sensitive to variations in
the relative risk from the physical activity benefits of cycling,
the levels of cycling activity were converted to hours of
metabolic equivalent tasks a week and the relative risk functions
applied from general physical activity (not specifically cycling)
reported in a meta-analysis.28 This analysis showed similar
benefits among the current population (−12.46 v −11.65 deaths).
Additional sensitivity analyses were based on age and shift of
travel mode scenarios, considering only physical activity, the
main driver of the results. Assuming a younger age population
distribution of Bicing users (average 33 years) an estimated
7.43 annual deaths would be avoided instead of the 12.46 from
the baseline analyses, whereas assuming a higher age
distribution (mean 48 years) an estimated 20.55 annual deaths
would be avoided (see web extra table 10 and figures 8-10).
Data on shifts in mode of travel as a result of the Bicing
initiative could not be found, but using an alternative scenario
with 60% of Bicing users having shifted from public transport,
30% from walking, and 10% from car, had little effect on the
number of deaths avoided (10.46 v 12.46) (see web extra table
9).

The annual reduction in carbon dioxide emissions resulting from
implementation of the Bicing initiative in Barcelona was
estimated to be 9062 metric tonnes (see table 2).

==Discussion==

The health benefits of physical activity from cycling using the
bicycle sharing scheme (Bicing) in Barcelona, Spain, were large
compared with the risks from inhalation of air pollutants and
road traffic incidents (benefit:risk ratio 77). Also, the potential
reduction in carbon dioxide emissions from cycling instead of
travel by car represented 0.9% of emissions from all types of
motor vehicles in Barcelona in 2009.

==Comparison with other studies==

Our results corroborate the findings of the two other published
assessments of multiple risks and benefits of active
transportation. One study found that the health benefits of
cycling would be larger (3-14 months gained) than the risks of
road traffic incidents (5-9 days lost) and exposure to air
pollutants (0.8-40 days lost) if car journeys were substituted by
cycling trips.9 The other study found that if urban trips in private
motor vehicles were replaced by active travel this would result
not only in important health gains but also in reductions in
carbon dioxide emissions.10 We built on previous studies6 9 10
by linking a specific and newly introduced policy in a real life
setting to the effects on health. The preceding studies considered
the impacts of hypothetical changes in choice of travel mode
not associated with an implemented policy or programme and
modelled populations much larger than in our study, generating
perhaps unrealistic estimates compared with integrating observed
usage numbers as in our study.

==Strengths and limitations of the study==

Along with our efforts to assess the effects of an implemented
policy, we incorporated data and measurements relevant to the
local context, such as air pollution concentrations, measured
previously in different transportation microenvironments in
Barcelona. Grounding this work in a real life setting with
observed measurements presents an important strength of the
study design. We chose all cause mortality as our main outcome
to provide the most robust results possible, given the strongest
evidence found in the epidemiological literature for that
outcome.

As in all risk assessments, our study was limited by the
availability of data and the necessity to make assumptions to
model likely scenarios. We carried out sensitivity analyses to
assess the stability of our results and tested effects of deviations
from our main assumptions and data choices. Included in the
sensitivity analysis were, for example, relative risk functions
from the literature, choice and toxicity of pollutants, age
distribution, shift in mode of travel, and environmental and
travel conditions in Barcelona. Importantly, we found that in
all the scenarios we tested a net benefit was always evident for
Bicing users.

Not all assumptions and data inputs, however, could be tested
in the sensitivity analysis, as some remain difficult to quantify
owing to lack of knowledge in the research area or the added
complexity for modelling, going beyond the scope of this first
pass assessment. For example, the benefits of physical activity
may be a function of baseline levels of physical activity and
health status, although the shape of dose-response functions for
changes in physical activity at different baseline levels is not
well established, especially for active travel.21 It is possible that
people who had more sedentary lifestyles could have benefited
more from the shift to cycling than those who already participate
in sports and exercise activities,29 but we did not have this
information.

If shifts in mode of travel were generated predominately from
walking and public transit, the savings in carbon dioxide
emissions would also be lower than our estimations. Hence we
could potentially be over-estimating benefits of the Bicing
initiative on carbon dioxide emissions in our central calculations
of value. However, we did not consider the possible positive
effects of the Bicing initiative in encouraging cycling not using
Bicing thus triggering a larger number of shifts in mode of
travel, nor the potential for cycling trips to replace longer trips
in vehicles (there may be a leveraging effect of the cycling
policy), which would all lead to estimating greater benefits on
emissions. Another factor not accounted for in the analysis is
the ability of cyclists to choose less trafficked routes, hence
reducing their exposures. In Barcelona, however, bicycle lanes
tend to be situated in roads with heavy traffic and these present
likely travel routes for cyclists. Anyhow, varying the exposure
concentration of particulate matter less than 2.5 μm in the
sensitivity analysis had minor effects on overall mortality.

Finally, there are many other benefits and some potential risks
associated with cycling that were not integrated in this
assessment. We estimated savings in carbon dioxide emissions
but did not measure reductions in other emissions or the potential
associated health effects or cost savings of reductions in
emissions. Based on the small 4% reduction in car journeys in
Barcelona according to our calculations on shift in travel mode,
it is unlikely that savings on emissions would be sufficient to
have a meaningful effect on population exposures and their
health implications, especially considering the high uncertainty
associated with estimating health impacts of probable unequal
distribution of reduction to exposures. Considering possible
larger implications of cycling on shifts in mode of travel (for
example, a trend phenomenon leading to more cycling in general
in the city) and the increase in replacement of car journeys by
cycling trips over the years, it may be worth accounting for
changes in exposures in the general population in future work.
A similar argument can be made about reductions in exposure
to road traffic crashes and noise that can accompany a reduction
in car use—for example, fewer cars on the road increases traffic
safety for the general population. Future assessments may take
such effects in the general population into account, along with
other exposures or behaviours potentially affected by the active
travel policy, such as heat or recreational cycling. Other future
improvements in the modelling framework include estimating
morbidity, given that mortality represents only a part of the
underlying groups of diseases associated with the three main
domains. Morbidity also involves greater uncertainty in the
calculations but can show larger effects on public health for
both risks30 and benefits.31-33 Furthermore, analyses of years of
life lost or disability adjusted life years are likely to show even
more pointedly the benefits of the policy, given the numbers of
avoided deaths in relatively young populations. Follow-up of
this work should also include a more detailed uncertainty and
variability analysis, so that the distribution in the population of
exposures and behaviours and different categories of
demographics and health status can be accounted for rather than
providing only central estimates (albeit with sensitivity
analyses).

==Conclusion and policy implications==

The Bicing initiative is a policy measure that has been highly
successful in terms of number of subscribers and led to a large
increase in trips on bicycles, which is often hard to achieve.34
A previous study showed that interventions generally led to an
average 3% increase in the prevalence of cycling in the
population. Bicing so far has increased the number of cycling
trips by 30%. Eleven per cent of the population in Barcelona
subscribes to Bicing, although based on our estimates only 1.7%
of the population are regular users.

We provide the first assessment of multiple risks and benefits
of a policy implemented to promote cycling. Our work has
shown that low cost public bicycle sharing systems aimed at
encouraging commuters to cycle are worth implementing in
other cities, not only for the health benefits but also for potential
co-benefits such as a reduction in air pollution and greenhouse
gases.35 Many cities worldwide have shown an interest in
developing bicycle sharing schemes, and this assessment
provides useful data in support of such solutions. Future work
should refine the assessment and integrate a more comprehensive
array of domains and outcomes (including, for example,
morbidity), which will provide further indications of how to
implement best active travel policies. This initial assessment is,
however, important now to encourage cities to follow the lead
of Barcelona and other major cities as a cost saving solution for
alternative transportation and promotion of health

==Contributors==

AdeN and MJN conceived and designed the study. DR-R
and AdeN collected the data. All authors analysed and interpreted the
data, wrote the manuscript, and edited and approved the final version
for submission. AdeN and MJN are guarantors.

==Funding==

This work is part of the European wide project Transportation
Air pollution and Physical ActivitieS: an integrated health risk assessment
progamme of climate change and urban policies (TAPAS), which has
partners in Barcelona, Basel, Copenhagen, Paris, Prague, and Warsaw.
TAPAS is a four year project funded by the Coca-Cola Foundation,
AGAUR, and CREAL.

==Competing interests==

All authors have completed the ICMJE uniform
disclosure form at www.icmje.org/coi_disclosure.pdf (available on
request from the corresponding author) and declare: no support from
any organisation for the submitted work; no financial relationships with
any organisations that might have an interest in the submitted work in
the previous three years; no other relationships or activities that could
appear to have influenced the submitted work.

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Accepted: 13 June 2011