neram 2006
DESCRIPTION
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 PresentationTRANSCRIPT
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
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
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
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
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
Scatterplots: Indicators
Distance HGVnear Trafnear Trafdist Roads150 Traf150
Tra
f150
Roa
ds15
0T
rafd
ist
Tra
fnea
rH
GV
near
Dis
tanc
e
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
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)
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
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
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)
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?
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
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)
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
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
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
Thank you
Time for bed……..