AIR POLLUTION

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AIR POLLUTION
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ATMOSPHERIC CHEMICAL TRANSPORT MODELS
Why models? incomplete information
(knowledge) spatial inference
prediction temporal inference
forecasting Mathematical models provide the
necessary framework for integration of our
understanding of individual atmospheric
processes. Classification of atmospheric models
Model Typical domain scale Typical
resolution Microscale 200x200x100 m 5
m Mesoscale(urban) 100x100x5 km 2 km
Regional 1000x1000x10 km 20 km
Synoptic(continental) 3000x3000x20 km 80 km
Global 65000x65000x20km 50 km
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PHYSICAL LAWS

• Momentum equations
• Air conservation
• Water conservation

• Energy conservation
• Reactive gas conservation
• Notations

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General circulation of the atmosphere
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Dimension-based model classification
0-D and 1-D models little information about a
problem or poor data for validation 2-D
models an horizontal dimension is important 3-D
models most complete answers are required
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0-D models

• Account for
• sources
• advection
• diffusion (entrainment/detrainment)
• reaction
• may be enhanced through a lagrangean approach

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1-D and 2-D models

• 1-D models
• ignore the horizontal transport and processes
• only vertical processes are modeled
• 2-D models
• ignore one horizontal dimension

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General methodology for air quality prediction
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General methodology for air quality prediction
(ctd.)

• Address the meteorological aspect of the problem
• determine (predict/ use meteorological products)
  the physical conditions (velocity fields
  temperatures, radiation etc)
• Identify the chemical processes and develop
  (include in the framework) numerical models to
  predict them
• Estimate the initial conditions and run the model
  in a predictive way
• Use observations to update the initial conditions
  and the state of the system

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Assimilation of Data in Models

• Example
• Data assimilation in a tropospheric ozone model
• Physical model
• Observations are provided by air quality
  monitoring stations and meteorological stations
• Special numerical technique are used to minimize
  Fobj

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Assimilation of Data in Models (ctd)

• Minimization of Fobj requires the derivative of
  F with respect to the initial conditions
• Direct evaluation of the gradient is not feasible
  due to the large number of components in the
  initial field (ex. 200x200 km domain with 2km
  grid size)
• Consider the general model
• with the observations
• The objective function is then

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Assimilation of Data in Models (ctd)

• The gradient of the objective function
• The gradient may be efficiently evaluated
  starting from the left-hand side (i.e. in a
  reverse manner)
• Then Fobj can be minimized using a standard
  optimization procedure

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Assimilation of Data in Models (ctd)
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WRAP-UP

• The pollution (chemical) problem needs to be
  connected to the physical (meteorological)
  problem
• In short (medium) term forecasts dynamics
  dominates and need to be properly capture
• In long term (climatic) forecasts the effect of
  gases on energy budgets are most important
• Data may be readily used to correctly initialize
  the models and get additional insight