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NERAM 2006

NERAM 2006. Matching the metric to need: modelling exposures to traffic-related air pollution for policy support. David Briggs, Kees de Hoogh and John Gulliver Department of Epidemiology and Public Health Imperial College London. Vancouver, October 16-18 th 2006. Some time of life questions.

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NERAM 2006

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  1. NERAM 2006 Matching the metric to need: modelling exposures to traffic-related air pollution for policy support David Briggs, Kees de Hoogh and John Gulliver Department of Epidemiology and Public Health Imperial College London Vancouver, October 16-18th 2006

  2. Some time of life questions • GIS and exposure modelling • LUR, focal sum techniques • Which methods work best – how can they be compared? • Time of life - what does it all mean? • Acute versus chronic • Long-range versus traffic-related • Spatial/temporal resolution • The GEMS study • Locally-driven versus long-range episodes versus ‘normal’ pollution periods • Linkage of local and long-range models and air pollution data

  3. Methods of exposure assessment

  4. ADMS-modelled PM10 concentration: London

  5. Methods and metrics • Indicators • Distance – to nearest main road (metres) • Trafnear – traffic flow (vehicles) on nearest main road • HGVnear – heavy goods vehicles on nearest main road • Trafdist – Trafnear/Distance • Roads150 – road density (length/area) within 150 metres • Traf150 – vehicle km travelled (flow*length) in 150 metres • Models • LURNO2 – NO2 concentration based on land use regression model • ADMSNO2 and ADMSPM– NO2 and PM modelled with ADMS-Urban • Monitoring • Fixed site PM10 and NO2 concentrations - annual averages based on hourly

  6. Scatterplots: Indicators

  7. Indicators: correlations (bottom left) and % in same quintile (top right)

  8. Indicators: correlations with modelled traffic-related air pollution * Power transformation (D-x)

  9. R=0.297 R=0.314 R=-0.403 (0.473) R=0.370 R=0.506 R=0.400 Correlations with mean PM10 concentration (2001-2004): N=71 Distance Trafnear HGVnear Roads150 ADMSPM Traf150

  10. Land use regression R=0.88 R=0.61

  11. Performance of exposure metrics: London * Power transformation (D-x)

  12. Conclusions so far…. • Indicators only weakly to moderately correlated • Reasonably strong correlations between some indicators – Distance (power transformed), Trafdist, Roads150 and Trafdist and modelled TRP • Variable capability to reflect geographic variations in PM10 concentration: • HGV counts on nearest road poor predictor (despite widespread use) • Distance (power transformed) moderately predictive (R2~0.2-0.5) • Dispersion and LUR seem to give best results (R2~0.3-0.6) BUT is monitored PM the gold standard?

  13. Relationships between rural and urban monitoring sites (n=365 days)

  14. Conclusions 1 • Monitored PM dominated by long-range particles • ~100% in urban background • <80% in urban centre • >50% in kerbside • Little within-city/regional variation in long-range component, but drives temporal variation: • Time-series studies therefore valid in assigning constant exposure across city • But mainly detect effects of long-range component

  15. Conclusions 2. • Traffic-related particles represent a small add-on • Accounts for majority of spatial variation • Modelled by dispersion/LUR models • But need for more standardisation • Emissions data are the weak element • Very localised • Exposures therefore mainly in streets/transport environments • Short duration – high concentration

  16. Conclusions 3 • What are implications for health? • Spatial clustering (e.g. near-road studies) • Are toxicologies of local and long-range components different? • What should policy focus on? • Local policy = small, local effects • More emphasis on transport environments • Is hotspot policy appropriate

  17. Thank you Time for bed……..

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