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Traffic-Related Air Pollution and the
Local Burden of Childhood Asthma in
Bradford, UK
Haneen Khreis
Previous HIA of TRAP and Childhood Asthma
‱ Only four relevant studies, coming from the same
research group (Perez et al., 2009, Perez et al., 2013,
KĂŒnzli et al., 2008, Perez et al., 2012)
‱ Three from California and one European (10 cities)
‱ Exposure to TRAP was characterized by proximity to
major roadways
‱ Underlying exposure-response function sourced from
one Southern Californian cohort study (McConnell et
al. 2006)
‱ All examined prevalent asthma
Overarchingmethodology
COPERT (standard
European method)
PHEM (newly
developed method)
Overarchingmethodology
ESCAPE LUR
models
Study area
Traffic modelling
Simulation and Assignment of Traffic to Urban Road Networks model (SATURN)
4,500 road links
1,359 km coverage
3 simulation time periods (AM, Inter-peak, PM)
Traffic modelling
Outputs  Average link speed
(km/h), Link traffic flow (PCU/h)
Urban England (outside London) Vehicle Fleet
Composition - 2009
Passenger cars (82.26%)
55.43% petrol
26.83% diesel
Light Duty Vehicles
(12.54%)
0.73% petrol
11.81% diesel
Heavy Duty Vehicles
(2.13%)
1.70% diesel rigid HDVs
0.43% diesel artic HDVs
Buses and Coaches
(1.55%)
1.12% buses
0.43 coaches
Motorcycles (1.51%)
Traffic fleet mix
167 vehicle classes
National Atmospheric Emissions Inventory and Ricardo Energy and Environment (2014)
Overarchingmethodology
Dieselcarsexample
Air pollution dispersion modelling
Dispersion modelling
Overarchingmethodology
Land-use regression modelling
‱ NO2
‱ NOx
‱ Black Carbon (BC)
‱ PM2.5
‱ PM10
Exposure assignment
Overarchingmethodology
Black Carbon per 0.5 x 10-5 m-1
Nitrogen Dioxide per 4 ”g/m3
Nitrogen Oxides per 30 ”g/m3
PM2.5 per 1 ”g/m3
PM10 per 2 ”g/m3
Health impacts modelling
Overarchingmethodology
Baseline health data
136.6 asthma cases per 10,000 person-years, by the age of 18 years
Baseline health data
122.5 per 10,000 person-years, by the age of 7 years
Baseline health data
Wheezing disorders
based on treatment
which identifies the
existence of at least
two drug prescriptions
indicated for the
treatment of asthma a
minimum of 1 week
and maximum of 12
months apart -- 442 per
10,000 person-years, by
the age of 7 years
Health impacts modelling
‱ The population attributable fraction (PAF) -- the
proportional reduction in morbidity that would occur if
exposure to air pollution was reduced or to an alternative
ideal exposure scenario (zero)
‱ PAF =
(Risk estimateexposure difference -1)/Risk estimateexposure difference
‱ Attributable number of cases =
PAF*expected asthma cases due to all causes
‱ Sum-up all attributable number of cases across all census
tracts -- separately for each pollutant
Health impact modelling – LUR model
137 asthma cases per 10,000 person-years, by the age of 18 years
Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases
NO2 433 191 571 24%
NOx 685 -278 1191 38%
PM2.5 485 180 728 27%
PM10 608 277 861 33%
BC 276 112 444 15%
Health impact modelling – LUR model
123 per 10,000 person-years, by the age of 7 years
Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases
NO2 389 171 513 24%
NOx 615 -250 1070 38%
PM2.5 435 162 653 27%
PM10 546 249 773 33%
BC 248 100 398 15%
Health impact modelling – LUR model
442 per 10,000 person-years, by the age of 7 years
Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases
NO2 1398 615 1844 24%
NOx 2209 -898 3843 38%
PM2.5 1564 582 2347 27%
PM10 1961 894 2276 33%
BC 891 360 1432 15%
Health impact modelling – ADMS
137 asthma cases per 10,000 person-years, by the age of 18 years
Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases
COPERT
NO2
321 139 428 18%
COPERT
NOx
530 -201 976 29%
PHEM
NO2
317 137 423 17%
PHEM
NOx
523 -198 966 29%
Health impact modelling – ADMS
137 asthma cases per 10,000 person-years, by the age of 18 years
Pollutant
Attributable
cases
Attributable
cases lower CI
Attributable cases
upper CI
Percentage of
all cases
Cases attributable
to traffic
COPERT
NO2
394 173 520 22% 128 (7%)
COPERT
NOx
638 -256 1125 35% 219 (12%)
+ minor roads and cold starts
Summary
‱ First full-chain HIA of TRAP and childhood asthma with
validation, as possible, at each stage
‱ Comparing it to commonly used approaches (LUR models)
‱ These results indicate that air pollution is responsible for a
large (up to 38% of all cases), but preventable, asthma
burden
‱ This burden is higher than previous studies estimates ~ 14%
(Perez et al. 2013)
‱ Different exposure assessments (pollutant selections,
exposure models) result in different estimated burdens
‱ Different emission factors did not result in different
estimated burdens
James Tate, Institute for Transport Studies, Leeds
Luc Pellecuer, École de technologie supĂ©rieure, MontrĂ©al
Sally Jones, City of Bradford Metropolitan District Council -- ADMS-Urban
Graz University of Technology -- PHEM
World Conference on Transport Research Society -- Young Researcher Innovation Grant
Philadelphia University -- PhD Studentship
Natalie Mueller, ISGlobal -- CREAL
David Rojas-Rueda, ISGlobal -- CREAL
Montserrat De Castro, ISGlobal -- CREAL
Mark Nieuwenhuijsen, ISGlobal -- CREAL
Acknowledgements
Thank
you!
haneen.khreis@isglobal.org

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Traffic-related air pollution and the local burden of childhood asthma in Bradford, UK

  • 1. Traffic-Related Air Pollution and the Local Burden of Childhood Asthma in Bradford, UK Haneen Khreis
  • 2. Previous HIA of TRAP and Childhood Asthma ‱ Only four relevant studies, coming from the same research group (Perez et al., 2009, Perez et al., 2013, KĂŒnzli et al., 2008, Perez et al., 2012) ‱ Three from California and one European (10 cities) ‱ Exposure to TRAP was characterized by proximity to major roadways ‱ Underlying exposure-response function sourced from one Southern Californian cohort study (McConnell et al. 2006) ‱ All examined prevalent asthma
  • 3.
  • 7. Traffic modelling Simulation and Assignment of Traffic to Urban Road Networks model (SATURN) 4,500 road links 1,359 km coverage 3 simulation time periods (AM, Inter-peak, PM)
  • 8. Traffic modelling Outputs  Average link speed (km/h), Link traffic flow (PCU/h)
  • 9. Urban England (outside London) Vehicle Fleet Composition - 2009 Passenger cars (82.26%) 55.43% petrol 26.83% diesel Light Duty Vehicles (12.54%) 0.73% petrol 11.81% diesel Heavy Duty Vehicles (2.13%) 1.70% diesel rigid HDVs 0.43% diesel artic HDVs Buses and Coaches (1.55%) 1.12% buses 0.43 coaches Motorcycles (1.51%) Traffic fleet mix 167 vehicle classes National Atmospheric Emissions Inventory and Ricardo Energy and Environment (2014)
  • 15.
  • 16. Land-use regression modelling ‱ NO2 ‱ NOx ‱ Black Carbon (BC) ‱ PM2.5 ‱ PM10
  • 19.
  • 20. Black Carbon per 0.5 x 10-5 m-1
  • 21. Nitrogen Dioxide per 4 ”g/m3
  • 22. Nitrogen Oxides per 30 ”g/m3
  • 23. PM2.5 per 1 ”g/m3
  • 24. PM10 per 2 ”g/m3
  • 27. Baseline health data 136.6 asthma cases per 10,000 person-years, by the age of 18 years
  • 28. Baseline health data 122.5 per 10,000 person-years, by the age of 7 years
  • 29. Baseline health data Wheezing disorders based on treatment which identifies the existence of at least two drug prescriptions indicated for the treatment of asthma a minimum of 1 week and maximum of 12 months apart -- 442 per 10,000 person-years, by the age of 7 years
  • 30. Health impacts modelling ‱ The population attributable fraction (PAF) -- the proportional reduction in morbidity that would occur if exposure to air pollution was reduced or to an alternative ideal exposure scenario (zero) ‱ PAF = (Risk estimateexposure difference -1)/Risk estimateexposure difference ‱ Attributable number of cases = PAF*expected asthma cases due to all causes ‱ Sum-up all attributable number of cases across all census tracts -- separately for each pollutant
  • 31. Health impact modelling – LUR model 137 asthma cases per 10,000 person-years, by the age of 18 years Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases NO2 433 191 571 24% NOx 685 -278 1191 38% PM2.5 485 180 728 27% PM10 608 277 861 33% BC 276 112 444 15%
  • 32. Health impact modelling – LUR model 123 per 10,000 person-years, by the age of 7 years Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases NO2 389 171 513 24% NOx 615 -250 1070 38% PM2.5 435 162 653 27% PM10 546 249 773 33% BC 248 100 398 15%
  • 33. Health impact modelling – LUR model 442 per 10,000 person-years, by the age of 7 years Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases NO2 1398 615 1844 24% NOx 2209 -898 3843 38% PM2.5 1564 582 2347 27% PM10 1961 894 2276 33% BC 891 360 1432 15%
  • 34. Health impact modelling – ADMS 137 asthma cases per 10,000 person-years, by the age of 18 years Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases COPERT NO2 321 139 428 18% COPERT NOx 530 -201 976 29% PHEM NO2 317 137 423 17% PHEM NOx 523 -198 966 29%
  • 35. Health impact modelling – ADMS 137 asthma cases per 10,000 person-years, by the age of 18 years Pollutant Attributable cases Attributable cases lower CI Attributable cases upper CI Percentage of all cases Cases attributable to traffic COPERT NO2 394 173 520 22% 128 (7%) COPERT NOx 638 -256 1125 35% 219 (12%) + minor roads and cold starts
  • 36. Summary ‱ First full-chain HIA of TRAP and childhood asthma with validation, as possible, at each stage ‱ Comparing it to commonly used approaches (LUR models) ‱ These results indicate that air pollution is responsible for a large (up to 38% of all cases), but preventable, asthma burden ‱ This burden is higher than previous studies estimates ~ 14% (Perez et al. 2013) ‱ Different exposure assessments (pollutant selections, exposure models) result in different estimated burdens ‱ Different emission factors did not result in different estimated burdens
  • 37. James Tate, Institute for Transport Studies, Leeds Luc Pellecuer, École de technologie supĂ©rieure, MontrĂ©al Sally Jones, City of Bradford Metropolitan District Council -- ADMS-Urban Graz University of Technology -- PHEM World Conference on Transport Research Society -- Young Researcher Innovation Grant Philadelphia University -- PhD Studentship Natalie Mueller, ISGlobal -- CREAL David Rojas-Rueda, ISGlobal -- CREAL Montserrat De Castro, ISGlobal -- CREAL Mark Nieuwenhuijsen, ISGlobal -- CREAL Acknowledgements