francisca muñoz bravo msc computer science centro de modelamiento matematico (cmm) universidad de...

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

http://www.iai.int/index_body.htm

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


39x39 grid of 2x2km2

CO Emission inventory by hours, street bows -> grids of any size

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8 CO monitoring stations

ObjectiveObjectiveObjectiveObjectiveFORWARD

ADJOINT

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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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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


Validating the ScenarioValidating the ScenarioValidating the ScenarioValidating the Scenario+

0.1°x0.1°MATCHMATCH


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?



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




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


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


BLUE ValidationBLUE ValidationBLUE ValidationBLUE Validation


MATCH Adjoint ValidationMATCH Adjoint ValidationMATCH Adjoint ValidationMATCH Adjoint Validation


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


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.


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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


Boundaries Parallel Boundaries Parallel MATCHMATCH

Boundaries Parallel Boundaries Parallel MATCHMATCH


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.


Geography and Termic Geography and Termic InversionInversion

Geography and Termic Geography and Termic InversionInversion

francisca muñoz bravo msc computer science centro de modelamiento matematico (cmm) universidad de...

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