AIR POLLUTION

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 : ModelTypical domain scale Typical resolution Microscale200x200x100 m 5 m Mesoscale(urban)100x100x5 km 2 km Regional1000x1000x10 km 20 km Synoptic(continental)3000x3000x20 km 80 km Global 65000x65000x20km 50 km

PHYSICAL LAWS Momentum equations Air conservation Water conservation Energy conservation Reactive gas conservation Notations

General circulation of the atmosphere

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

0-D models Account for –sources –advection –diffusion (entrainment/detrainment) –reaction –may be enhanced through a lagrangean approach

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

General methodology for air quality prediction

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

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 F obj

Assimilation of Data in Models (ctd) Minimization of F obj 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

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 F obj can be minimized using a standard optimization procedure

Assimilation of Data in Models (ctd)

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