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Concentrations (CTM)
Concentrations d’exposition
Modèle Monte-Carlo
Expositions personnelles
Fonctions d’exposition-risque
déterministe/statistique
non-déterministe
statistique
Concentrations
Exposure
Risk
démographie
exposition
occupation du solémission
sfine échelle
Exposure-concentrations
Personal Exposures
Exposure-response Function
Sub-grid Model
deterministic/statistical
Monte-Carlo Model
non-deterministic
Regression/Health data
statistical
<1 km1-10 km
Chemistry Transport Modeling (CTM)
E
1-10 km
E
1-10 km
C E´
transport chemistry & depositionmixing
emissions
Desegregating emissions
∆x emissions [km]
Higher resolution
Model Error [ O3] ∆x [CTM]=6km∆x meteo=6km
Rel
ativ
e di
ffere
nce
with
mea
sure
men
ts (
%) ✕
✕
✕
✕
✕✕ ✕✕
✕
✕
✘CTM
Direct ‘Downscaling’
✘CTM
Sub-grid modeling / urban environments
Statistical representation of E´
Concentrations per environnementEmissions per sector
2)Re-organisation1)Desagregation
+high resolution emission data
+statistical info(land-use)
Calcul dans des micro-environnements
•Deterministic CTM-modeling >1km:
•Sub-grid CTM-modeling <1km:
Local emission Sub-grid mixing
i = Park, residential, traffic, etc.
31/7 1/8 2/8 3/8 4/8
Background Stations
1/7 3/7 5/7 7/7 9/7
ResidentialGrid-averagedMeasurements
ResidentialMeanMeasurements
31/7 1/8 2/8 3/8 4/8
1/7 3/7 5/7 7/7 9/7
Traffic Stations TrafficGrid-averagedMeasurements
TrafficGrid-averagedMeasurements
Concentrations
Demography
Val d’Oise
Essone
Yvelines
Seine-et-Marne
Hauts-de-Seine
Seine-Saint-Denis
Val-de-Marne
>15%8-15%2-5%
Towards Paris
Out of Paris
Hauts-de-Seine
Seine-Saint-Denis
Val-de-Marne
Essone
Val d’Oise
Yvelines
Seine-et-Marne
10-12%5-6%
Essone
Val d’Oise
Yvelines
Seine-et-Marne
>10%8-10%5-8%1-5%<1%
>80%70-80%
Between Administrative Units
Demographic data Activity data
004
12
20
816
TransitOffice00
4
12
20
816 ☀12
16 8
004
12
20
816
★
Home00
20 4
=1% of the Paris population
Monte-Carlo Model
Demographic data
Monte-Carlo Model
004
12
20
816
TransitOffice00
4
12
20
816 ☀12
16 8
004
12
20
816
★
Home00
20 4
=1% of the Paris population
Hourly Concentrations
Exposure
Activity data
Tri-modal exposureO3 (µg/m 3)
June 2001
June 2002
June 2003
Number of persons
Exposure level
Low
Medium
High
Tri-modal exposureO3 (µg/m 3)
June 2001
June 2002
June 2003
Number of persons
Exposure level
Tri-modal exposureNO2 (µg/m 3)
HighMedium
Low
Number of Persons
June 2001
June 2002
June 2003
Exposure level
O3, 2004-07-07
Fre
qu
ency
HighMedium
Low
RuralPeriurban
Urban
Exposure Modes geographical area
Exposure to [O 3] (µg/m 3)
Pop
ulat
ion
Fra
ctio
n
σ2
f2
µ1 µ2 µ3
?
?
? ??
Exposure to [NO 2] (µg/m 3)
FaibleMoyenFortEntier
Pop
ulat
ion
Fra
ctio
n
Exposure to [PM 2.5] (µg/m 3)
FaibleMoyenFortEntier
Pop
ulat
ion
Fra
ctio
n
•One fit per day:
•Time series for 9 parameters
µ3, σ3, f3
tµ2, σ2, f2
µ1, σ1, f1
Generalized Additive Model (GAM)
Monitor data (concentrations)
concentrations CTM
Generalized Additive Model (GAM)
Monitor data (concentrations)This study
004
12
20
816
TransitOffice00
4
12
20
816 ☀12
16 8
004
12
20
816
★
Home00
20 4
Demographic/activity data
Emploi du temps
Monitor data (concentrations)
Generalized Additive Model (GAM)
‘Exposure’
Generalized Additive Model (GAM)
Monitor data (concentrations)
Generalized Additive Model (GAM)
ParametrizationMonitor data (concentrations)
Generalized Additive Model (GAM)
N number of monitors
Generalized Additive Model (GAM)
Area-aggregated surrogate Spatially resolved surrogate
Two-step comparison
1.Mono-pollutant model (one regression per pollutant)[APHEA Project]
...
...
...
Mono-pollutant model
Pa r
tial v
aria
bili
ty
[NO2] (µg/m 3)
[APHEA Project]
-Area-aggregated surrogate-Cette étude
[NO2] (µg/m 3)
-Area-aggregated surrogate-Spatially resolved surrogate
Mono-pollutant model
Pa r
tial v
aria
bili
ty
[O3] (µg/m 3)
-Area-aggregated surrogate-Cette étude
Mono-pollutant model
Pa r
tial v
aria
bili
ty
[APHEA Project]
[O3] (µg/m 3)
-Area-aggregated surrogate-Spatially resolved surrogate
Mono-pollutant model
Pa r
tial v
aria
bili
ty
[PM2.5] (µg/m 3)
-Area-aggregated surrogate-Cette étude
Mono-pollutant model
Pa r
tial v
aria
bili
ty
[APHEA Project]
[PM2.5] (µg/m 3)
-Area-aggregated surrogate-Spatially resolved surrogate
Mono-pollutant model
Pa r
tial v
aria
bili
ty
Spatially resolved surrogate•Less strong effects•Larger uncertainty•Effect of PM2.5 > NO2
Conclusions for the mono-pollutant model
Area-aggregated (monitor data)•Effect of NO2 > PM2.5
•Impossible to separate the effects of the co-pollutants
...
2.Tri-pollutants model (only one regression)
[NO2] (µg/m 3)
-Area-aggregated surrogate-Cette étudeEffect of NO2 over-estimated
Tri-pollutant model
Pa r
tial v
aria
bili
ty
[APHEA Project]
[NO2] (µg/m 3)
-Area-aggregated surrogate-Spatially resolved surrogate
Tri-pollutant model
Pa r
tial v
aria
bili
ty
[O3] (µg/m 3)
-Area-aggregated surrogate-Cette étude
[APHEA Project]
Tri-pollutant model
Pa r
tial v
aria
bili
ty
[O3] (µg/m 3)
-Area-aggregated surrogate-Spatially resolved surrogate
Tri-pollutant model
Pa r
tial v
aria
bili
ty
-Area-aggregated surrogate-Cette étude
[PM2.5] (µg/m 3)
Negative effect for PM 2.5
[APHEA Project]
Tri-pollutant model
Pa r
tial v
aria
bili
ty
[PM2.5] (µg/m 3)
-Area-aggregated surrogate-Spatially resolved surrogate
Tri-pollutant model
Pa r
tial v
aria
bili
ty
✘ ✘Spatially resolved surrogate•PM2.5 & NO2 indicators less correlated (0.72 0.6)•Positive effects for all 3 pollutants •Effect of PM2.5 > NO2
Conclusions for the tri-pollutants model
✔ ✔