Routes to Clean Air 2019 Imperial College London

1. Making the case for holistic thinking
about urban air quality policies
Audrey de Nazelle
Senior Lecture, Centre for Environmental Policy, Imperial College
Institute of Air Quality Management
Routes to Clean Air, London, September 16th 2019

6. Health impact modelling of traffic reductions: Barcelona, Spain
Rojas-Rueda et al. Environment International 49 (2012) 100-109
Modelling benefits of mode shifts from cars to active travel

7. x 20
Source: Andrea Calderon, PhD work in progress
Policy scenarios in London: comparing
avoided deaths
Tech
Behav

8. Behavioural vs Technological approaches in London
©
Woodcock et al. 2009 The Lancet , v3674, 9705: 1930-1943

9. Behaviour change
scenario Technological
change scenario
538 17
Behavioural vs Technological approaches in London
Avoided premature deaths / million
Data from Woodcock et al. 2009 The Lancet , v3674, 9705: 1930-1943
©

10. ©
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Photos: Gil Garcetti
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11. Avila-Palencia et al. (2018) The effects of
transport mode use on self-perceived
health, mental health, and social contact
measures: A cross-sectional and
longitudinal study.
Environment International 120
Transport mode use
(days/month)
Self-perceived healtha
OR (CI 95%)
Car 1·00 (0·99, 1·02)
Motorbike 1·02 (0·99, 1·04)
Public transport 0·99 (0·98, 1·01)
E-bike 0·99 (0·96, 1·02)
Bicycle 1·07 (1·05, 1·08)**
Walking 1·02 (1·00, 1·03)*
Table 3. Regression models assessing associations between the different transport modes and the health outcomes, adjusted for all the potential confounders
aMixed-effects logistic regression models. bLinear regression models. cLogistic regression models. All models were adjusted by age, sex, education, nationality, employment status, and city. Sample sizes:
Self-perceived health (n=8218); Perceived stress (n=3241); Mental Health (n=3243); Vitality (n=3243); Loneliness (n=3247); Contact with friends/family (n=3247). *p-values<0·05, **p-value<0·001.
Perceived stressb
coef (CI 95%)
-0·003 (-0·019, 0·013)
0·006 (-0·018, 0·031)
-0·002 (-0·016, 0·011)
-0·025 (-0·052, 0·003)
-0·016 (-0·028, -0·004)*
-0·005 (-0·019, 0·010)

12. Transport mode
use
(days/month)
Mental Healthb
coef (CI 95%)
Vitalityb
coef (CI 95%)
Lonelinessb
coef (CI 95%)
Contact with
friends/family c
OR (CI 95%)
Car 0·03
(-0·05, 0·12)
-0·02
(-0·12, 0·07)
-0·04
(-0·06, -0·02)**
1·02
(0·99, 1·05)
Motorbike -0·06
(-0·19, 0·07)
-0·09
(-0·24, 0·06)
-0·04
(-0·07, -0·00)*
1·01
(0·97, 1·06)
Public transport 0·00
(-0·07, 0·07)
-0·05
(-0·13, 0·030)
-0·02
(-0·03, 0·00)
1·00
(0·98, 1·02)
E-bike 0·12
(-0·02, 0·27)
0·13
(-0·04, 0·30)
-0·02
(-0·06, 0·01)
1·01
(0·97, 1·05)
Bicycle
0·11
(0·05, 0·18)**
0·14
(0·07, 0·22)**
-0·03
(-0·05, -0·01)**
1·02
(1·00, 1·04)
Walking
0·05
(-0·03, 0·13)
0·14
(0·05, 0·23)*
-0·02
(-0·04, 0·00)
1·03
(1·00, 1·05)*
Table 3. Regression models assessing associations between the different transport modes and the health outcomes, adjusted for all the potential confounders
aMixed-effects logistic regression models. bLinear regression models. cLogistic regression models. All models were adjusted by age, sex, education, nationality, employment status, and city. Sample sizes:
Self-perceived health (n=8218); Perceived stress (n=3241); Mental Health (n=3243); Vitality (n=3243); Loneliness (n=3247); Contact with friends/family (n=3247). *p-values<0·05, **p-value<0·001.
Avila-Palencia et al. (2018) The effects of
transport mode use on self-perceived
health, mental health, and social contact
measures: A cross-sectional and
longitudinal study.
Environment International 120

13. Dons et al. (2018) Transport mode choice
and body mass index: Cross-sectional and
longitudinal evidence from a European-
wide study. Environment International 119
BMI difference per additional day of travel per month by mode

14. BMI and travel mode
longitudinal analysis:
Impact of an additional day
per month by mode
BMI and travel mode
longitudinal analysis:
Impact of change in cycling
Dons et al. (2018) Transport mode choice
and body mass index: Cross-sectional and
longitudinal evidence from a European-wide
study. Environment International 119

15. ©

16. Alcock et al. 2014 Longitudinal effects on mental health of
moving to greenr and less green urban areas ES&T 48
Moving to greener or less green area and
mental health scores in the UK

17. Co-benefits: greenspace
• Reduce the heat island effect
• Decrease energy consumption
• Improve drainage, stormwater
management
• Provide food supply
• Improve biodiversity (beware
of trade-offs)
• Increase social capital – which
may also increase resilience to
CC
• Increase physical activity
Maciel et al. 2013 International Journal of
Low-Carbon Technologies

18. Quantifying potential co-benefits of planning strategies…
Estimated preventable deaths under compliance with exposure
recommendations by exposure domain in Barcelona, Spain.
Mueller et al. 2016 Urban and Transport Planning Related Exposures and
Mortality: A Health Impact Assessment for Cities. EHP

19. Co-benefits?
• Climate change
• Greenspace
• Biodiversity
• Noise
• Physical activity
• Traffic injuries
• Diet
• Etc
Trade-offs?
• Cooling agents
• Air pollution inhalation
• Traffic injuries
• Pollen
• Etc.
Air pollution strategies: conclusion
With holistic thinking we identify that
urban design strategies can provide additional benefits
compared to air pollution technological solution

21. Audrey de Nazelle
anazelle@imperial.ac.uk

22. Extras

23. Rojas-Rueda et al. Environment International 49 (2012) 100-109
Impacts of mode shift from cars to active travel (Barcelona, Spain)

25. Air pollution and physical
activity:
Epidemiologicalevidence
Andersen et al. 2015; Kubesch et al. 2014 European Journal of Preventive
Cardiology; Kubesch et al. 2014 Occupational Environmental Medicine; Cole-
Hunter et al. 2015 J of Exposure Science and Environmental Epidemiology;
Matt et al. 2016 Environment International; Laeremans et al. MSSE 2018

26. London air pollution cancels positivehealth effects of
exercisein over 60s
Experimental study comparing health effects of a walk in Hyde park vs Oxford St
Synharay et al. The Lancet 2017