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 16-18 th 2006

2. Some time of life questions 1.GIS and exposure modelling  LUR, focal sum techniques  Which methods work best – how can they be compared? 2.Time of life - what does it all mean?  Acute versus chronic  Long-range versus traffic-related  Spatial/temporal resolution 3.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 TypeMethodsExamples Monitor-basedNearest siteThiessen polygon Average of neighbouring sites City average Buffering Weighted averageIDW Kriging Indicator-basedSource proximityDistance to road Source intensityRoad density Traffic/truck volume Model-basedGIS modelsLand use regression Co-kriging Dispersion modellingADMS-Urban AERMOD

4. ADMS-modelled PM 10 concentration: London

5. Methods and metrics 1.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 2.Models  LURNO 2 – NO 2 concentration based on land use regression model  ADMSNO 2 and ADMSPM– NO 2 and PM modelled with ADMS-Urban 3.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) DistanceHGVnearTrafnearTrafdistRoads150Traf150 Distance 28221199 HGVnear 0.0348372129 Trafnear 0.060.80382235 Trafdist -0.450.400.523446 Roads150 -0.620.070.060.4581 Traf150 -0.420.330.430.600.80

8. Indicators: correlations with modelled traffic-related air pollution ADMS PMADMS NO 2 Distance-0.47 (-0.70*)-0.51 (0.69*) HGVnear0.340.37 Trafnear0.300.32 Trafdist0.720.73 Roads1500.740.75 Traf1500.710.73 * Power transformation (D -x)

9. Correlations with mean PM 10 concentration (2001-2004): N=71 R=-0.403 (0.473) R=0.314 R=0.297 R=0.400 R=0.370R=0.506 Distance HGVnear Trafnear Roads150 Traf150 ADMSPM

10. Land use regression R=0.88 R=0.61

11. Performance of exposure metrics: London MetricPM 10 (N=71)PM 10 (N=14)NO 2 (N=8) Distance-0.40 (-0.47*)-0.44 (-0.74*)-0.68 (-0.62*) HGVnear0.310.250.18 Trafdist0.300.610.56 Roads1500.400.460.70 Traf1500.370.360.53 ADMS0.510.810.72 LURN/A0.880.61 * Power transformation (D -x)

12. Conclusions so far…. 1.Indicators only weakly to moderately correlated 2.Reasonably strong correlations between some indicators – Distance (power transformed), Trafdist, Roads150 and Trafdist and modelled TRP 3.Variable capability to reflect geographic variations in PM 10 concentration:  HGV counts on nearest road poor predictor (despite widespread use)  Distance (power transformed) moderately predictive (R 2 ~0.2-0.5)  Dispersion and LUR seem to give best results (R 2 ~0.3- 0.6) BUT is monitored PM the gold standard?

14. Urban siteRural siteSpeciesConstantSlopeR2R2 Ratio (urban/rural) RochesterPM 2.5 2.450.970.821.17 Bloomsbury (urban centre) RochesterPM 10 8.461.060.621.25 HarwellPM 10 6.260.860.611.37 Kensington (kerbside) RochesterPM 10 5.360.910.631.16 Marylebone (urban background) RochesterPM 10 20.91.060.312.05 Relationships between rural and urban monitoring sites (n=365 days)

15. Conclusions 1 1.Monitored PM dominated by long-range particles  ~100% in urban background  <80% in urban centre  >50% in kerbside 2.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

16. Conclusions 2. 3.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 4.Very localised  Exposures therefore mainly in streets/transport environments  Short duration – high concentration

17. Conclusions 3 5.What are implications for health?  Spatial clustering (e.g. near-road studies)  Are toxicologies of local and long-range components different? 6.What should policy focus on?  Local policy = small, local effects  More emphasis on transport environments  Is hotspot policy appropriate

18. Thank you Time for bed……..