Figure 2.9: Substance Menu
The appearing substance dialog covers all directly related substance data split into seven categories. The dialog opens with the identification card. It is compulsory to give the substance a name; all other parameters which may ease identification are optional. Only three further cards are relevant at the current stage: sewage treatment removal, in-stream removal and market data.
As GREAT-ER also considers three plant types in the easiest sewage treatment submodel, the related elimination rates have to be entered. All other parameters are related to higher model modes. This also means that in-sewer removal is not considered in mode one. To work with in-sewer removal, set the sewer submodel mode to mode two (related dialog is mentioned later in this tutorial) and enter an elimination rate.
Please note that the plant types currently related to the treatment plant in the various catchments delivered with GREAT-ER always consider a primary settler as the first step in sewage treatment. If you only have an overall elimination rate for a plant or one explicitly without a primary settler, set the related rate to zero and enter the known elimination rate for the plant type in question.
The sewage treatment card also gives hints on another fundamental GREAT-ER concept (if you have selected the sample LAS substance data set). As described in the briefly under the basic concepts, the GREAT-ER models perform a stochastic simulation set on top of a deterministic model by the Monte-Carlo method. This covers the variations within the catchment's hydrologic regime but also considers other parameters. I.e. for the selected parameter it is possible to specify a distribution. This can be either normal, logarithmic normal or uniform. Depending on the selected distribution a set of values has to be entered to describe the distribution. Leaving the distribution dialog you may recognize that distributions are coded by the descriptive values separated by a semicolon. Do not try to specify a distribution by entering the values directly. Obviously further information is needed to describe a distribution. Always use the distribution dialog (invoked by the related button) if you want a parameter to be considered as distributed.
The in-stream removal card enables to you control the elimination processes within the river. Only an aggregated first-order elimination rate is needed for the simplest river fate model.
Another GREAT-ER feature is the option to enter a special comment for each value. This is useful to document the data sources for the various parameters. Try the comment button to obtain further information on the elimination rate.
The market data card enables you to specify a general consumption value for the substance. As the consumption values have to be related to both, a substance and a region, the parameter entered in the substance data dialog is used as a general value. The Edit Market Data dialog discussed later in this tutorial enables you to specify consumption values for selected discharge sites. The consumption values can not be considered as distributed.
Leaving the substance dialog, all substance settings for a first simulation run are made. All other items within the substance menu are mentioned briefly in the menu structure description and more detailed in the model section. If you want to learn more about substance data management within GREAT-ER please refer to the menu structure description (Section 3): The menu items invoke functions that are known from various windows applications managing documents, with the difference that they do not work for the whole data but for a subset of a scenario, the substance data. The Pick Substance and the Change Database function somewhat differently: The Pick Substance enables you to copy the substance data from another open scenario. This makes it easier to transfer specific parameter settings to several scenarios. The Change Database item enables you to change the database from which substance data are loaded. If you intend to work with an enormous amount of substances this feature gives users the opportunity to group substances by classes into different databases. This increases the transparancy of data storage and speeds up data retrieval.