neram 2006

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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

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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. - PowerPoint PPT Presentation

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Page 1: 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 16-18th 2006

Page 2: NERAM 2006

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

Page 3: NERAM 2006

Methods of exposure assessment

Type Methods ExamplesMonitor-based

Nearest site Thiessen polygon

Average of neighbouring sites

City averageBuffering

Weighted average IDWKriging

Indicator-based

Source proximity Distance to road

Source intensity Road densityTraffic/truck volume

Model-based GIS models Land use regressionCo-kriging

Dispersion modelling ADMS-UrbanAERMOD

Page 4: NERAM 2006

ADMS-modelled PM10 concentration: London

PM10 monitoring sites

Mean 2001 -2004 (ug/m3)

22.0 - 26.0

26.1 - 31.0

31.1 - 39.0

39.1 - 47.0

47.1 - 65.0

PM10 (ug/m3)

0 - 0.39

0.4 - 1.4

1.5 - 3.5

3.6 - 6.8

6.9 - 11

12 - 33

´0 3 6 9 121.5

Kilometers

Page 5: NERAM 2006

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 LURNO2 – NO2 concentration based on land use

regression model ADMSNO2 and ADMSPM– NO2 and PM modelled with

ADMS-Urban

3. Monitoring Fixed site PM10 and NO2 concentrations - annual

averages based on hourly

Page 6: NERAM 2006

Scatterplots: Indicators

Distance HGVnear Trafnear Trafdist Roads150 Traf150

Tra

f150

Roa

ds15

0T

rafd

ist

Tra

fnea

rH

GV

near

Dis

tanc

e

Page 7: NERAM 2006

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

right)

Distance

HGVnear

Trafnear

Trafdist

Roads150

Traf150

Distance 28 22 11 9 9

HGVnear 0.03 48 37 21 29

Trafnear 0.06 0.80 38 22 35

Trafdist -0.45 0.40 0.52 34 46

Roads150 -0.62 0.07 0.06 0.45 81

Traf150 -0.42 0.33 0.43 0.60 0.80

Page 8: NERAM 2006

Indicators: correlations with modelled traffic-related air

pollution

ADMS PM ADMS NO2

Distance-0.47 (-0.70*)

-0.51 (0.69*)

HGVnear 0.34 0.37

Trafnear 0.30 0.32

Trafdist 0.72 0.73

Roads150 0.74 0.75

Traf150 0.71 0.73

* Power transformation (D-

x)

Page 9: NERAM 2006

Correlations with mean PM10 concentration (2001-2004):

N=71

0 100 200 300 400 500 600

Distance

20

25

30

35

40

45

50

Me

an

PM

10

0 1000 2000 3000 4000 5000

HGVnear

20

25

30

35

40

45

50

Me

an

PM

10

0.0 10000.0 20000.0 30000.0 40000.0 50000.0 60000.0

Trafdist

20

25

30

35

40

45

50

Me

an

PM

10

0.0 500.0 1000.0 1500.0 2000.0

Roads150

20

25

30

35

40

45

50

Me

an

PM

10

0 10000000 20000000 30000000 40000000 50000000

Traf150

20

25

30

35

40

45

50

Me

an

PM

10

0 1 2 3 4 5 6

ADMSPM

20

25

30

35

40

45

50

Me

an

PM

10

R=-0.403(0.473)

R=0.314 R=0.297

R=0.400 R=0.370 R=0.506

Distance

HGVnear

Trafnear

Roads150 Traf150 ADMSPM

Page 10: NERAM 2006

40 50 60 70

LUR NO2 (ug/m3)

30

40

50

60

70

80

90

Mea

n N

O2

(ug

/m3)

Land use regression

40 45 50 55 60 65 70

LUR NO2

25

30

35

40

45

Mea

n P

M10

(u

g/m

3)

R=0.88R=0.61

Page 11: NERAM 2006

Performance of exposure metrics: London

Metric PM10 (N=71)

PM10 (N=14)

NO2 (N=8)

Distance -0.40 (-0.47*)

-0.44 (-0.74*)

-0.68 (-0.62*)

HGVnear 0.31 0.25 0.18

Trafdist 0.30 0.61 0.56

Roads150 0.40 0.46 0.70

Traf150 0.37 0.36 0.53

ADMS 0.51 0.81 0.72

LUR N/A 0.88 0.61

* Power transformation (D-

x)

Page 12: NERAM 2006

Conclusions so far….

1. Indicators only weakly to moderately correlated2. Reasonably strong correlations between some

indicators – Distance (power transformed), Trafdist, Roads150 and Trafdist and modelled TRP

3. 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?

Page 13: NERAM 2006

Rochester PM10 (ug/m3)

6050403020100

Blo

om

sbu

ry P

M1

0 (

ug

/m3

)

70

60

50

40

30

20

10

0

Rochester PM10 (ug/m3)

6050403020100

Ma

ryle

bo

ne

PM

10

(u

g/m

3)

120

100

80

60

40

20

0

Harwell PM10 (ug/m3)

6050403020100

Blo

om

sbu

ry P

M1

0 (

ug

/m3

)

70

60

50

40

30

20

10

0

Rochester PM2.5 (ug/m3)

403020100

Blo

om

sbu

ry P

M2

.5 (

ug

.m3

)

50

40

30

20

10

0

Page 14: NERAM 2006

Urban site Rural site

Species Constant

Slope R2 Ratio (urban/rural

)

Rochester

PM2.5 2.45 0.97 0.82 1.17

Bloomsbury (urban centre)

Rochester

PM10 8.46 1.06 0.62 1.25

Harwell PM10 6.26 0.86 0.61 1.37

Kensington (kerbside)

Rochester

PM10 5.36 0.91 0.63 1.16

Marylebone(urban background)

Rochester

PM10 20.9 1.06 0.31 2.05

Relationships between rural and urban monitoring sites (n=365

days)

Page 15: NERAM 2006

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

Page 16: NERAM 2006

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

Page 17: NERAM 2006

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

Page 18: NERAM 2006

Thank you

Time for bed……..