Reports and Papers

Fedra, K. (1995)
Decision support for natural resources management:
Models, GIS and expert systems.
AI Applications, 9/3 (1995) pp 3-19.




Application Examples
An Air Quality Management System

Another typical example of a natural resource in dire need of better management is clean air. The problem owner here is usually a regulatory agency that controls various sources of emissions, or a major industry or thermal power plant that has to comply with regulatory standards. While several novel approaches, including direct market mechanisms and economic incentives have been discussed, the majority of systems still rely on simple source by source emission control versus ambient air quality standards.

An example system designed for a typical regulatory agency integrates a set of simulation and optimization models for air quality management. They are built for the assessment of emission sources and the design and evaluation of pollution control strategies.

The system brings together data bases (emission inventories, meteorological data, and model scenarios), a geographical information system, simulation models, an optimization model, and an expert system for the estimation of point and area source emissions.

The geographical information system provides tools to display, access, and manipulate spatially distributed data that are either used for the models directly (eg., a digital elevation model, or land cover used to estimate surface roughness and surface temperature differentials), data used to derive emission estimates (eg., urban areas and major traffic arteries used to estimate area source emission), data for impact assessment such as different land use of different vulnerability to various pollutants, and finally geographical background data for the spatial orientation of the user, the location of sources, and as a spatial reference for model results.

The emission inventories are available either through the map display by picking sources for display and editing of their characteristics, or from a parallel listing of named sources. Basic source characteristics such as location, emission for various pollutants, stack parameters, and cost functions for alternative pollution abatement technologies that are applicable for a given source are stored in the inventories. An embedded expert system can be used to derive emission estimates from basic technological data such as fuel consumption and characteristics, or production technologies and volumes.

The meteorological data base allows the display of weather data and the selection of either a particular set of observations as the basis for a short-term model run, or the definition of a longer period, usually an entire year, for long-term simulation. In the latter case, a pre-processor generates the frequency distributions required by the long-term model from the time series of observations selected by the user.

The simulation models of the system include an implementation of EPA's Industrial Source Complex model ISC, a Gaussian model that can be run both for short episodes and long-term frequency data (USEPA 1979). Alternatively, a three layer finite element model, used conjunctively with a spatially distributed wind field generator can be used for dynamic short-term runs over a few days. These models describe pollutants such as SO2, NOx, or dust. For summer smog and ground level ozone, a photochemical box model simulating daily episodes based on EPA's PBM code is used (USEPA 1984). It is driven by the same weather scenarios and shares the emission inventories for point and area sources of NOx. Emissions volatile organic compounds are again estimated with an embedded expert system, using emission coefficients and a set of rules.

The output of any long-term model can be used as an emission and impact scenario for the optimization module. Using a source-receptor matrix and a spatially distributed, non-linear environmental impact function that can assign different weights to different land use or population zones, this component finds cost-effective strategies for pollution abatement. Each controlled source has a number of alternative control technologies available including the option in some cases to close a plant. Each option is associated with costs, and for a given overall budget the model finds the most effective (in terms of the environmental impact or just bulk emissions) investment strategy. Varying the budget, or the time horizon and discount rate for the cost estimates results in a large number of scenarios, that can be further analyzed by a discrete multi-criteria optimization tool (Zhao et al., 1985).

An ``optimal'' emission scenario can then be used again at the level of the simulation model, and tested with a broad range of individual short-term weather scenarios (rather than the frequency data used for the long-term model) to test the abatement strategy under specific, including worst case assumptions.

Model results are displayed as color coded overlays over the background maps in 2D and pseudo 3D displays over a digital elevation model (Figure 5, Figure 6), as a set of symbols representing emission reductions at the source locations in the optimization model, or as a set of time series displays and diagrams for the ozone model.


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