The Submodules

Having given a more detailed account on the decision variables and the quantification of the effects to be minimized, we can now turn our attention to the simulation of the fate of a pollutant through the biosphere. It was chosen to develop a general modeling system to simulate the following subsystems of the environment and the fate of the pollutant in them:

1. atmosphere,
2. land-based ecosystems,
3. individuals, and
4. individual organs.

Of course, this list of subsystems is not exhaustive. We deliberately skipped the geosphere, the aquatic distribution of pollutants, the corresponding water-based ecosystems, the simulation of the effects of a pollutant on cells, etc.

Note that the meteorological conditions of the atmosphere are independent of the fate of the pollutant and of the location of the facility, but not vice versa. It makes therefore sense to calculate the meteorological variables needed over the time period of interest beforehand and use this data in subsequent simulation runs inside the optimization module.

The discussion above leads in a natural way to a breakdown of the software system into the following submodels and modules.

1. The meteorological preprocessor, which uses observational meteorological data to compute all the meteorological parameters needed for the time period of interest in the spatial domain considered,
2. the air dispersion model, in which the distribution of the pollutant in the atmosphere is simulated,
3. the ecosystem dispersion model, which simulates the fate of the pollutant in the ecosystems considered, through different trophic levels, through food chains, etc.,
4. the chemokinetic model, in which the concentration of the pollutant in different organs of different individuals, living in the abovementioned ecosystems, is traced,
5. the cytodynamic multistage model, which simulates the effects of the pollution on different cell populations in the organs of the individual life forms considered,
6. the effect quantification module, agglomerating the cytodynamic effects on the individuals to an effect at the population level, and finally
7. the optimization module, which minimizes this effect by optimizing the decision variables specified in Section 1.2.1.

The corresponding data flow between the submodules is depicted in Figure 1.3.

 
Figure 1.3: Submodules and data flow.  

In the following chapters, each of these parts is described in detail.