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Francisca Muñoz Bravo http://www.cmm.uchile.cl/umesam MSc Computer ScienceCentro de Modelamiento Matematico (CMM) Universidad de Chile (UMR CNRS 2071) E-mail: [email protected]
Direct and Inverse Direct and Inverse CO ModelingCO Modeling
in Santiago de Chilein Santiago de Chile
La Serena November 2004
OutlookOutlookOutlookOutlook
Objectives Emission Inventory Observations What do we want to improve? How to improve it? To Do’s
Objectives Emission Inventory Observations What do we want to improve? How to improve it? To Do’s
La Serena November 2004
39x39 grid of 2x2km2
CO Emission inventory by hours, street bows -> grids of any size
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Flujo variable normalizado
8 CO monitoring stations
ObjectiveObjectiveObjectiveObjectiveFORWARD
ADJOINT
**
La Serena November 2004
Emission InventoryEmission InventoryEmission InventoryEmission 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.
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.
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Flujo variable normalizado
La Serena November 2004
Parque O’Higgins Diurnal Variation
Santiago CO ObservationsSantiago CO ObservationsSantiago CO ObservationsSantiago 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.
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.
www.sesma.cl
La Serena November 2004
Validating the ScenarioValidating the ScenarioValidating the ScenarioValidating the Scenario+
0.1°x0.1°MATCHMATCH
La Serena November 2004
Magnitude by Zones
Magnitude by Zones
Sector 1ProvidenciaVitacuraLas CondesLo Barnechea
Sector 2ÑuñoaLa ReinaMaculPeñalolén
Sector 3SantiagoEstación Central
Sector 4HuechurabaRecoletaIndependenciaConchalí
Sector 5RencaQuinta NormalCerro NaviaLo PradoPudahuelQuilicura
Sector 6MaipúCerrillosLo EspejoPedro Aguirre Cerda
Sector 7San MiguelSan JoaquínLa CisternaLa Granja
Sector 8San RamónLa PintanaEl BosqueSan Bernardo
Sector 9La FloridaPuente Alto
What do we want to What do we want to Improve?Improve?
What do we want to What do we want to Improve?Improve?
La Serena November 2004
Determine if there is Weekly or Monthly variation
Analize if the Diurnal estimated variation corresponds
Determine if there is Weekly or Monthly variation
Analize if the Diurnal estimated variation corresponds
What do we want to What do we want to Improve?Improve?
What do we want to What do we want to Improve?Improve?
La Serena November 2004
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
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
How to Improve the How to Improve the Inventory?Inventory?
How to Improve the How to Improve the Inventory?Inventory?
Inverse ModelingInverse Modeling
La Serena November 2004
Parameters: Diurnal Variation Real Emissions: Fictitious scenario that
generated the observations Errors: 20% observations, 50% parameters
Parameters: Diurnal Variation Real Emissions: Fictitious scenario that
generated the observations Errors: 20% observations, 50% parameters
Inverse ModelingInverse Modeling
BLUE ValidationBLUE ValidationBLUE ValidationBLUE Validation
La Serena November 2004
MATCH Adjoint ValidationMATCH Adjoint ValidationMATCH Adjoint ValidationMATCH Adjoint Validation
Inverse ModelingInverse Modeling
Parameters: Temporal and Geographical variation
Real Emissions: Fictitious constant scenario that generated the observations
Errors, Initial Guess: Non applicable
Parameters: Temporal and Geographical variation
Real Emissions: Fictitious constant scenario that generated the observations
Errors, Initial Guess: Non applicable
La Serena November 2004
To Do To Do To Do 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.
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.
La Serena November 2004
0%
20%
40%
60%
80%
100%
RM VALPCONCEPRCAGUATEMUCO
CO EMISSIONS
Emission InventoryEmission InventoryEmission InventoryEmission 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
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
Light-w NO CAT
Light-w CAT-P
La Serena November 2004
Boundaries Parallel Boundaries Parallel MATCHMATCH
Boundaries Parallel Boundaries Parallel MATCHMATCH
La Serena November 2004
Topography and Topography and DispersionDispersion
Topography and Topography and DispersionDispersion
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.