Direct and Inverse
CO Modeling
in Santiago de Chile
Francisca Muñoz Bravo
http://www.cmm.uchile.cl/umesam
MSc Computer Science
Centro de Modelamiento Matematico (CMM)
Universidad de Chile (UMR CNRS 2071)
E-mail: fmunoz@dim.uchile.cl
Outlook
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Objectives
Emission Inventory
Observations
What do we want to improve?
How to improve it?
To Do’s
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8 CO monitoring stations
Objective
1.30
1.20
1.10
1.00
*
Flujo variable normalizado
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
39x39 grid of 2x2km2
0
1
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5
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9
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19
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23
Hora
CO Emission inventory by hours, street bows -> grids of any size
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Emission Inventory
 MODEM is a model for the calculation of vehicle
emissions (CO, PM, HC, NOx, NO2, NH3, CH4, CC).
 Bottom-up methodology to estimate emissions
produced by on-road mobile sources in urban areas
 Temporal Variation: Emissions are considered the
same from Monday to Friday. Weeks and months are
invariable.
1.30
1.20
1.10
1.00
Flujo variable normalizado
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0
1
2
3
4
5
6
7
8
9
10
11
12
Hora
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13
14
15
16
17
18
19
20
21
22
23
Parque O’Higgins Diurnal Variation
Santiago CO Observations
Interannual Variation
 Hourly air quality data are available online, starting on 1997
 These data include: CO, PM10, PM2.5, NO2, SO2, O3 at 8 stations
 The stations are run by health authorities. The measurements and the
data are subject to independent assessments on a regular basis.
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www.sesma.cl
MATCH
+
0.1°x0.1°
Validating the Scenario
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What do we want to Improve?
 Magnitude
 by Zones
Sector 1
Providencia
Vitacura
Las Condes
Lo Barnechea
Sector 2
Ñuñoa
La Reina
Macul
Peñalolén
Sector 3
Santiago
Estación Central
Sector 4
Huechuraba
Recoleta
Independencia
Conchalí
Sector 5
Renca
Quinta Normal
Cerro Navia
Lo Prado
Pudahuel
Quilicura
Sector 6
Maipú
Cerrillos
Lo Espejo
Pedro Aguirre Cerda
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Sector 7
San Miguel
San Joaquín
La Cisterna
La Granja
Sector 8
San Ramón
La Pintana
El Bosque
San Bernardo
Sector 9
La Florida
Puente Alto
What do we want to Improve?
 Determine if there is Weekly or
Monthly variation
 Analize if the Diurnal estimated
variation corresponds
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Inverse Modeling
How to Improve the Inventory?
 BLUE (Best Linear Unbiased Estimator)
 Computationally inexpensive least
square method. Minimizes distance
between observations and model
results, and errors.
 MATCH Adjoint
 Adjoint Dispersion Model from SMHI
 Goes back in time through the derivate.
 Difficulty: Sources are co-located with the
measurement stations
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Inverse Modeling
BLUE Validation
 Parameters: Diurnal Variation
 Real Emissions: Fictitious scenario that
generated the observations
 Errors: 20% observations, 50% parameters
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Inverse Modeling
MATCH Adjoint Validation
 Parameters: Temporal and Geographical
variation
 Real Emissions: Fictitious constant scenario
that generated the observations
 Errors, Initial Guess: Non applicable
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To Do
 Forward Runs: Improve representation of
meteorological fields (dynamical interpolation
and by data assimilation of surface wind
data). Earlier runs for getting stable I.C.
 BLUE: Useful light weighted technique.
 MATCH Adjoint: further explorations with
more iterations and usage of initial guess.
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Emission Inventory
 MODEM is a model for the calculation of vehicle
emissions (CO, PM, HC, NOx, N2O, NH3, CH4, CC).
 Bottom-up methodology to estimate emissions produced
by on-road mobile sources in urban areas
CO EMISSIO NS
100%
80%
60%
20%
O
TE
CA
R
M
U
C
A
G
U
P
CE
O
N
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C
V
AL
P
0%
M
Light-w CAT-P
40%
R
Light-w NO CAT
Boundaries Parallel MATCH
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Topography and Dispersion
Santiago is a mega-city of 6 million
inhabitants, located within a basin
surrounded by the high mountain
chains, which reaches maximum
values of 4.500 m.a.s.l.
Stable conditions prevail all year around.
This is further enhanced by coastal
lows, which are associated with severe
pollution episodes.
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Geography and Termic
Inversion
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