Emissions Inventories for SP (Brasseur) ODP

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Sergio Espinosa

Emissions

Sciences

Universidade de São Paulo
Instituto de Astronomia, Geofísica e Ciências Atmosféricas
Air pollution modeling in the Estado de São Paulo
using a bottom-up emissions inventory
Orientadora: Rita Yuri Ynoue
Aluno: Sergio Ibarra Espinosa
2/10/14

1 Background
 Expanded Area: Sao Paulo,
Campinas, Santos and more

1 Background
E=FE∗VF∗TV∗L
Activity Level

Bottom-up
Dynamics of
vehicular flux
should be
incorporated to
emissions models

Metodos
206 points for
counting
Regressions:
●Distribution Poisson
●Zero Inflated
Negative Binomial
●Normal

Model
βo: intercept; β1: estimate; TR: Type of Street; ε: Error
0 5 10 15 20 25 30
2.5
2
1.5
1
0.5
0
Distribuição normalizada 800:900 segunda São Paulo
RM São Paulo
seg
ter
qua
qui
sex
sab
dom
0 5 10 15 20 25 30
3
2.5
2
1.5
1
0.5
0
Distribuição normalizada 800:900 segunda São Paulo
RM Campinas
seg
ter
qua
qui
sex
sab
do
m

RMSP RMC RML RMS RMSP RMC RML RMS Santiago AMBA
IAG CETESB DICTUC D'Angiola
1400000
1200000
1000000
800000
600000
400000
200000
0
Emissions inventories (t/y)
CO (t/a) Nox (t/a) COV (t/a)

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Emissions Inventories for SP (Brasseur) ODP

1. Universidade de São Paulo Instituto de Astronomia, Geofísica e Ciências Atmosféricas Air pollution modeling in the Estado de São Paulo using a bottom-up emissions inventory Orientadora: Rita Yuri Ynoue Aluno: Sergio Ibarra Espinosa 2/10/14

2. 1 Background  Expanded Area: Sao Paulo, Campinas, Santos and more

3. 1 Background E=FE∗VF∗TV∗L Activity Level

4. Top-Down Where are the cars?

5. But in reality...

6. Bottom-up Dynamics of vehicular flux should be incorporated to emissions models

7. 1 Background E=FE∗VF∗TV∗L Activity Level

8. Road Network Activity Vehicular flow Vehicular Technology Standards Municipality Street Models Traffic Counts Regression and Traffic Simulations Statistical Evaluation IAG HBEFA Emissions Estimation Vehicular flow Copert 4 CETESB WRF Chem 24 hours profile Meteorology Emissions Street-hour

9. Metodos 206 points for counting Regressions: ●Distribution Poisson ●Zero Inflated Negative Binomial ●Normal

10. Model βo: intercept; β1: estimate; TR: Type of Street; ε: Error 0 5 10 15 20 25 30 2.5 2 1.5 1 0.5 0 Distribuição normalizada 800:900 segunda São Paulo RM São Paulo seg ter qua qui sex sab dom 0 5 10 15 20 25 30 3 2.5 2 1.5 1 0.5 0 Distribuição normalizada 800:900 segunda São Paulo RM Campinas seg ter qua qui sex sab do m

11. RMSP RMC RML RMS RMSP RMC RML RMS Santiago AMBA IAG CETESB DICTUC D'Angiola 1400000 1200000 1000000 800000 600000 400000 200000 0 Emissions inventories (t/y) CO (t/a) Nox (t/a) COV (t/a)

Emissions Inventories for SP (Brasseur) ODP

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