Francisca Muñoz Bravo MSc Computer Science Centro de Modelamiento Matematico (CMM) Universidad de Chile (UMR CNRS 2071) Francisca Muñoz Bravo MSc Computer Science Centro de Modelamiento Matematico (CMM) Universidad de Chile (UMR CNRS 2071) Direct and Inverse CO Modeling in Santiago de Chile

La Serena November 2004 OutlookOutlook  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 x39 grid of 2x2km 2 CO Emission inventory by hours, street bows -> grids of any size 8 CO monitoring stations ObjectiveObjective FORWARD ADJOINT *

La Serena November 2004 Emission Inventory  MODEM is a model for the calculation of vehicle emissions (CO, PM, HC, NOx, NO 2, NH 3, CH 4, 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, NO 2, NH 3, CH 4, 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.

La Serena November 2004 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, NO 2, SO 2, O 3 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, NO 2, SO 2, O 3 at 8 stations  The stations are run by health authorities. The measurements and the data are subject to independent assessments on a regular basis.

La Serena November 2004 Validating the Scenario + 0.1°x0.1° MATCH

La Serena November 2004  Magnitude  by Zones  Magnitude  by Zones Sector 1 Providencia Vitacura Las Condes LoBarnechea 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 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?

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 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 Inventory? Inverse 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 Modeling BLUE Validation

La Serena November 2004 MATCH Adjoint Validation Inverse 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  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 Emission Inventory  MODEM is a model for the calculation of vehicle emissions (CO, PM, HC, NOx, N 2 O, NH 3, CH 4, 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, N 2 O, NH 3, CH 4, CC).  Bottom-up methodology to estimate emissions produced by on-road mobile sources in urban areas Light-w NO CATLight-w CAT-P

La Serena November 2004 Boundaries Parallel MATCH

La Serena November 2004 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 m.a.s.l. Stable conditions prevail all year around. This is further enhanced by coastal lows, which are associated with severe pollution episodes.

La Serena November 2004 Geography and Termic Inversion