1 / 18

NERAM 2006

This study explores exposure assessment methods for traffic-related air pollution, including GIS modeling and pollutant metrics, comparing acute vs. chronic impacts and local vs. long-range pollution sources. Results suggest that dispersion and land use regression models provide the best predictive capability for pollutant concentrations. The implications for health policy and the need for standardized emissions data are also discussed.

amandaflorence
Download Presentation

NERAM 2006

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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……..

More Related