Can air pollution negate the health benefits of cycling and walking?

Abstract

Active travel (cycling, walking) is beneficial for the health due to increased physical activity (PA). However, active travel may increase the intake of air pollution, leading to negative health consequences. We examined the risk–benefit balance between active travel related PA and exposure to air pollution across a range of air pollution and PA scenarios.

The health effects of active travel and air pollution were estimated through changes in all-cause mortality for different levels of active travel and air pollution. Air pollution exposure was estimated through changes in background concentrations of fine particulate matter (PM2.5), ranging from 5 to 200 μg/m3. For active travel exposure, we estimated cycling and walking from 0 up to 16 h per day, respectively. These refer to long-term average levels of active travel and PM2.5 exposure.

For the global average urban background PM2.5 concentration (22 μg/m3) benefits of PA by far outweigh risks from air pollution even under the most extreme levels of active travel. In areas with PM2.5 concentrations of 100 μg/m3, harms would exceed benefits after 1 h 30 min of cycling per day or more than 10 h of walking per day. If the counterfactual was driving, rather than staying at home, the benefits of PA would exceed harms from air pollution up to 3 h 30 min of cycling per day. The results were sensitive to dose–response function (DRF) assumptions for PM2.5 and PA.

PA benefits of active travel outweighed the harm caused by air pollution in all but the most extreme air pollution concentrations.

Full text

Brief Original Report
Can air pollution negate the health benefits of cycling and walking?
Marko Tainio
a,
⁎, Audrey J. de Nazelle
b
, Thomas Götschi
c
, Sonja Kahlmeier
c
, David Rojas-Rueda
d,e,f
,
Mark J. Nieuwenhuijsen
d,e,f
, Thiago Hérick de Sá
g
,PaulKelly
h
, James Woodcock
a
a
UKCRC Centre for Diet and Activity Research, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
b
Centre for Environmental Policy, Imperial College London, London, UK
c
Physical Activity and Health Unit, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
d
Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
e
Universitat Pompeu Fabra (UPF), Barcelona, Spain
f
Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
g
Centre for Epidemiological Research in Nutrition and Health, School of Public Health, University of São Paulo, São Paulo, Brazil
h
Physical Activity for Health Research Centre (PAHRC), University of Edinburgh, UK
abstractarticle info
Article history:
Received 9 October 2015
Received in revised 28 January 2016
Accepted 1 February 2016
Available online xxxx
Active travel (cycling,walking) is beneficialfor the health due toincreased physicalactivity (PA). However, active
travel may increase the intake of air pollution, leading to negative health consequences. We examined the risk–
benefit balance between active travel related PA and exposure to air pollution across a range of air pollution and
PA scenarios.
The health effects of active travel and air pollution were estimated through changes in all-cause mortality for dif-
ferent levels of active travel and air pollution. Air pollution exposure was estimated through changes in back-
ground concentrations of fine particulate matter (PM
2.5
), ranging from 5 to 200 μg/m3. For active travel
exposure, we estimated cycling and walking from 0 up to 16 h per day, respectively.These refer to long-term av-
erage levels of active travel and PM
2.5
exposure.
For the global average urban background PM
2.5
concentration (22 μg/m3) benefits of PA by far outweigh risks
from air pollution even under the most extreme levels of active travel. In areas with PM
2.5
concentrations of
100 μg/m3, harms would exceed benefits after 1 h 30 min of cycling per day or more than 10 h of walking per
day. If the counterfactual was driving, rather than staying at home, the benefits of PA would exceed harms
from air pollution up to 3 h 30 min of cycling per day. The results were sensitive to dose–response function
(DRF) assumptions for PM
2.5
and PA.
PA benefits of active travel outweighed the harm caused by air pollution in all but the most extreme air pollution
concentrations.
© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Keywords:
Physical activity
Air pollution
Bicycling
Walking
Mortality
Health Impact Assessment
Risk–Benefit Assessment
Introduction
Several health impact modelling (HIM) studies have estimated the
health benefits and risks of active travel (cycling, walking) in different
geographical areas (Mueller et al., 2015; Doorley et al., 2015). In most
of these studies, the health benefits due to physical activity (PA) from
increased active travel are significantly larger than the health risks
caused by increases in exposure to air pollution.
Most of the existing active travel HIM studies have been carried out
in cities in high incomecountries with relatively low airpollution levels
(Mueller et al., 2015; Doorley et al., 2015). This raises the question on
the risk–benefit balance in highly polluted environments. Health risks
of air pollution are usually thought to increase linearly with increased
exposure for low to moderate levels of air pollution, whereas the bene-
fits of PA increase curvy-linearly with increasing dose (Kelly et al., 2014;
World Hea lth Organization , 2014). Thus,at a certain level of background
air pollution and of active travel, risks could outweigh benefits, which
would directly imply that,from a public health perspective, active travel
could not be always recommended.
In this study we compare the health risks of air pollution with the
PA-related health benefits from active travelacross a wide range of pos-
sible air pollution concentrations and active travel levels. We use two
thresholds to compare PA benefits and air pollution risks (Fig. 1): At
the “tipping point”an incremental increase in active travel will no longer
lead to an increase in health benefits (i.e. max. benefits have been
reached). Increasing active travel even more could lead to the “break-
even point”, where risk from air pollution starts outweighing the bene-
fits of PA (i.e. there are no longer net benefits, compared to not engaging
in active travel).
Preventive Medicine xxx (2016) xxx–xxx
⁎Corresponding author.
E-mail address: mkt27@medschl.cam.ac.uk (M. Tainio).
YPMED-04520; No of Pages 4
http://dx.doi.org/10.1016/j.ypmed.2016.02.002
0091-7435/© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Contents lists available at ScienceDirect
Preventive Medicine
journal homepage: www.elsevier.com/locate/ypmed
Please cite this article as: Tainio, M., et al., Can air pollution negate the health benefits of cycling and walking?, Prev. Med. (2016), http://
dx.doi.org/10.1016/j.ypmed.2016.02.002

Methods
Our approach followed a general active travel HIM method (Mueller
et al., 2015; Doorley et al., 2015). Air pollution exposures due to active
travel were quantified by estimating the differences in the inhaled
dose of fine particulate matter (PM
2.5
) air pollution. We selected PM
2.5
because it is a commonly used indicator of air pollution in active travel
HIM studies (Mueller et al., 2015; Doorley et al., 2015), and because of
the large health burden caused by PM
2.5
(GBD 2013 Risk Factors
Collaborators et al., 2015). For both air pollution and PA we used all-
cause mortality as the health outcome because there is strong evidence
for its association with both long-term exposure to PM
2.5
(Héroux et al.,
2015) and long-term PA behaviour (Kelly et al., 2014).
The reduction in all-cause mortality from active travel was estimat-
ed by converting the time spent cycling or walking to metabolically
equivalent of task (MET) and calculating the risk reduction using
dose–response functions (DRFs) adapted from Kelly et al.'s
3
meta-
analysis. From the different DRFs reported in Kelly et al. (2014)we
chose the one with the “0.50 power transformation”as a compromise
between linear and extremely non-linear DRFs. Non-linearity in a DRF
means that the health benefits of increased active travel would level
out sooner and a tipping point would be reached earlier than with
more linear DRFs. See supplementary material for the sensitivity analy-
sis with different DRFs. To convert cycling and walking time to PA we
used the values of 4.0 METs for walking and 6.8 METs for cycling,
based on the Compendium of Physical Activities (Ainsworth et al.,
2011). The walking and cycling levels used in this study are assumed
to reflect long-term average behaviour.
The health risks of PM
2.5
were estimated by converting background
PM
2.5
concentrations to travel mode specific exposure concentrations,
and by taking into account ventilation rate whilst being active. For back-
ground PM
2.5
we used values between 5 and 200 μg/m3 with 5 μg/m3
intervals. We also estimated tipping points and break-even points for
the average and most polluted cities in each region included in the
World Health Organization (WHO) Ambient Air Pollution Database
(World Health Organization (WHO), 2014), which contains measured
and estimated background PM
2.5
concentrations for 1622 cities around
the world.
The mode specific exposure concentrations were estimated by mul-
tiplying background PM
2.5
concentration by 2.0 for cycling or 1.1 for
walking,based on a review of studies (Kahlmeier et al., 2014). The coun-
terfactual scenario for the timespent cycling or walking was assumed to
be staying at home (i.e. in background concentration of PM
2.5
). See
supplementary file for the sensitivity analysis with counterfactual sce-
narios where cycling time would replace motorised transport time.
The ventilation rates differences whilst at sleep, rest, cycling and walk-
ing were taken into account when converting exposure to inhaled dose.
For sleep, rest, walking and cycling we used ventilation rates of 0.27,
0.61, 1.37 and 2.55, respectively (de Nazelle et al., 2009; Johnson,
2002). The sleep time was assumed to be 8 h in all scenarios and the
resting time was 16 h minus the time for active travel.
For the PM
2.5
DRF we used a relative risk (RR) value of 1.07 per
10 μg/m3 change in exposure (World Health Organization, 2014). We
assumed that DRF is linear from zero to maximum inhaled dose. As a
sensitivity analysis we used non-linear integrated risk function from
Burnett et al. (2014) (see supplementary material for details).
The model used for all calculations is provided in Lumina Decision
Systems Analytica format in supplementary file 2 (readable with
Analytica Free 101, http://www.lumina.com/products/free101/), and a
simplified model containing the main results is provided in Microsoft
Excel format in supplementary file 3.
Results
The tipping point and break-even point for different average cycling
times and background PM
2.5
concentrations are shown in Fig. 2.Forhalf
an hour of cycling every day, the background PM
2.5
concentration
would need to be 95 μg/m3 to reach the tipping point. In the WHO
Ambient Air Pollution Database less than 1% of cities have PM
2.5
annual
concentrations above that level (World Health Organization (WHO),
2014). The break-even point for half an hour of cycling every day was
at 160 μg/m3 (Fig. 2). For half an hour of walking the tipping point
and break-even point appear at a background concentration level
above 200 μg/m3 (Fig. S3, supplementary file). For the average urban
background PM
2.5
concentration (22 μg/m3) in the WHO database, the
tipping point would only be reached after 7 h of cycling and 16 h of
walking per day.
Tables S2 and S3 (supplementary file) show the tipping point for cy-
cling and walking, respectively, in different regions of the world. In the
most polluted city in the database (Delhi, India, background concentra-
tion of 153 μg/m3), the tipping and break-even points were 30 and
45 min of cycling per day, respectively (Table S2, supplementary file).
In most global regions the tipping points for the most polluted cities
(44 μg/m3 to153 μg/m3) varied between 30 and 120 min per day for cy-
cling, and 90 min to 6 h 15 min per day for walking (Table S3, supple-
mentary material).
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600
Relative risk of all-cause mortality
Cycling (min./day)
Tipping point:
beyond this, additional PA will not lead to higher health benefits
Break-even point:
beyond this, additional PA will cause
adverse health effects
Increase in
risk due to
AP
Risk
reduction
due to PA
PM2.5 background level: 50µg/m3
Fig. 1. Illustration of tipping point andbreak-even point as measured by the relative risk(RR) for all-cause mortality (ACM) combining theeffects of air pollution (at 50 μg/m
3
PM
2.5
)and
physical activity (cycling).
2M. Tainio et al. / Preventive Medicine xxx (2016) xxx–xxx
Please cite this article as: Tainio, M., et al., Can air pollution negate the health benefits of cycling and walking?, Prev. Med. (2016), http://
dx.doi.org/10.1016/j.ypmed.2016.02.002

When we assumed that time spend cycling would replace time driv-
ing a car, benefits always exceeded the risks in the background air pol-
lution concentrations below 80 µg/m3, a concentration exceeded in
only 2% of cities (World Health Organization (WHO), 2014). Other sen-
sitivity analyses showed that the results are sensitive to the shape of the
DRF functions. With the linear DRF for active travel the break-even point
would be reached with background PM
2.5
concentrations of 170 μg/m3
regardless of the active travel time (Fig. S4, supplementary material); a
level not currently found in any of the cities in the WHOair pollution da-
tabase (World Health Organization (WHO), 2014). With the most
curved DRF (0.25 power) the PM
2.5
concentration where harms exceed
benefits for 1 h of cycling per day would drop from 150 μg/m3 to 130 μg/
m3 (Fig. S4, supplementary material), a level currently found only in 9
cities (World Health Organization (WHO), 2014). With a non-linear
DRF for PM
2.5
the break-even point was not reached in any background
PM
2.5
concentration when using “power 0.50”DRF for cycling and walk-
ing. Other input value modifications had small or insignificant impact to
the results.
Discussions
This study indicates that, practically, air pollution risks will not ne-
gate the health benefits of active travel in urban areas in the vast major-
ity of settings worldwide. Even in areas with high background PM
2.5
concentrations, such as 100 μg/m3, up to 1 h 15 min of cycling and
10 h 30 min of walking per day will lead to net reduction in all-cause
mortality (Fig. S5, supplementary material). This result is supported
by epidemiological studies that have found the statistically significant
protective effects of PA even in high air pollution environments
(Matthews et al., 2007; Andersen et al., 2015). However, a small minor-
ity engaging in unusually high levels of active travel (i.e. bike messen-
gers) in extremely polluted environments may be exposed to air
pollution such that it negates the benefits of PA.
Some considerations of the limitations and the strengths of our
study need to be applied when generalising these findings.
In this analysis we took into account only the long-term health con-
sequences of regular PA and chronic exposure to PM
2.5
. Impacts of
short-term air pollution episodes, where concentrations significantly
exceed the average air pollution levels for a few days, may induce addi-
tional short term health effects. We have also only worked with all-
cause mortality and have, thus, not taken into account the morbidity
impact.
For the health risks of air pollution we only estimated the increased
risk during cycling and walking, not the overall health risk from every-
day air pollution. Airpollutioncauses a large burden of diseases all over
the world (Burnett et al., 2014) and reducing air pollution levels would
provide additional health benefits. Since transport is an important
source of air pollution in urban areas, mode shifts from motorised trans-
port to active travel would not only improve health in active travellers,
but also help to reduce air pollution exposures for the whole population
(Johan de Hartog et al., 2010).
The results are sensitive to assumptions of the linearity of dose–re-
sponse relationships between active travel-related PA and health bene-
fits, and between PM
2.5
and adverse health effects. With linear DRFsfor
PA the benefits always exceeded the risks at all levels of PM
2.5
concen-
trations. Evidence for a linear DRF for high PM
2.5
concentrations is
small and, for example, the Global Burden of Disease study applied
non-linear, disease specificDRFsforPM
2.5
(Burnett et al., 2014). If the
risks of PM
2.5
level out after PM
2.5
concentrations over 100 μg/m3, the
health benefits of PA would always exceed the risks of PM
2.5
.
It should also be taken into account that the results are based ongen-
erally representative values without detailed information on local con-
ditions, or from the background PA and disease history of individuals.
For individuals highly active in non-transport domains the benefits
from active travel will be smaller, and vice versa.
Conclusions
The benefits from active travel generally outweigh health risks from
air pollution and therefore should be further encouraged. When
weighing long-term health benefits from PA against possible risks
from increased exposure to air pollution, our calculations show that
promoting cycling and walking is justified in the vast majority of set-
tings, and only in a small number of cities with the highest PM
2.5
con-
centration in the world cycling could lead to increase in risk.
Author contributions
MT made the calculations and drafted the first version of the manu-
script. AJN, TG, MJN, SK, THS, DRR, PK and JW participated in designing
the scope of the study. AJN and TG helped to clarify the message of the
study. All authors contributed to the writing of this paper. All authors
approved the final version to be submitted for consideration of
publication.
Conflict of interest statement
The authors declare that there are no conflicts of interests.
Transparency document
The Transparency document associated with this article can be
found, in the online version.
Acknowledgments
MT and JW: The work was undertaken by the Centre for Diet and Ac-
tivity Research (CEDAR), a UKCRC Public Health Research Centre of Ex-
cellence. Funding from the British Heart Foundation, Cancer Research
UK, Economic and Social Research Council, Medical Research Council,
the National Institute for Health Research, and the Wellcome Trust,
under the auspices of the UK Clinical Research Collaboration, is grateful-
ly acknowledged.
AJN, DRR, MJN, SK and TG: The work was supported by the project
Physical Activity through Sustainable Transportation Approaches
(PASTAs)funded by the European Union's Seventh Framework Program
under EC-GA no. 602624-2 (FP7-HEALTH-2013-INNOVATION-1). The
Fig. 2. Tipping and break-even points for different levels of cycling (red dashed line and
blue solid line, respectively) (minutes per day, x -axis) and for different background
PM
2.5
concentrations (y-axis). Green lines represent the average and 99 th percentile
background PM
2.5
concentrations in World Health Organization (WHO) Ambient Air
Pollution Database (World Health Organization (WHO), 2014).
3M. Tainio et al. / Preventive Medicine xxx (2016) xxx–xxx
Please cite this article as: Tainio, M., et al., Can air pollution negate the health benefits of cycling and walking?, Prev. Med. (2016), http://
dx.doi.org/10.1016/j.ypmed.2016.02.002

sponsors had no role in the study design; in the collection, analysis, and
interpretation of data; in the writing of the report; and in the decision to
submit the article for publication.
JW is supported by an MRC Population Health Scientist fellowship.
THS is supported by the Brazilian Science without Borders Scheme
(process number: 200358/2014-6) and the Sao Paulo Research Founda-
tion (process number: 2012/08565-4).
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