AirWare
air quality assessment & management
Reference and User Manual
AirWare   On-line Reference Manual
  Release Level 6.1
  Release Date 2012 05
  Revision Level 1.0
Last modified on:   Friday, 30-Nov-12 12:52 CET

Optimization: a summary Emission control technologies Emission control scenarios Preference structures
Optimization strategies Discrete multi-criteria DSS

Emission Control Optimization: Preference Structures

The basic idea of the Emission Control Optimization is to improve air quality - which primarily means to reduce emissions.

The goals of air quality improvement are to meet regulatory criteria (ensure compliance) but also to minimize exposure, and keep the costs of control measures low. This defines the preference structure, the goals and objectives and the constraints in terms of measurable criteria.

The elements of the preference structure are:

  • A set of measurable criteria
  • The "direction of optimization" (minimize or maximize) for each of the criteria, which includes a target value for the case of "minimizing deviation".
  • Constraints (upper or lower bounds of "acceptability") (optionally) defined for any or all of the criteria.

Criteria

The basic rule for any and all criteria is that they can be derived from the model result, directly or indirectly, i.e., they are measurable, and meaningful for the (implied) decision problem: how to best improve air quality.

Criteria a re defined on a case by case basis, as they reflect the specific physiographical, socio-economic and regulatory situation of each application.
Typical examples for criteria (for each pollutant/emissions) would be:

  • Emission related:

  • Costs:
    • Investment costs (minimize, possible budgetary constraint) i.e., user defined cost figures (adapted from the technology DEFAULTS are applied pro-rata, interpreted as 100% application rate/costs;
    • OMR costs (minimize, possible budgetary constraint) (same as above)
    • Cost efficiency (maximize, efficiency of emission reduction, absolute or relative, amounts per investment, OMR;

  • Concentration related:   (needs to run CAMx for selected scenarios)
    • Exceedances (minimize; this includes several "dimensions" or derived criteria in time and space, as well as function value:
      • minimize the (average) value of overall exceedences (AOT),
      • minimize the number (hours) or locations/area of exceedances;
      • minimize some combinations
    • Regulatory compliance (maximize at selected locations = monitoring stations, possible regulatory constraint)
    • Regulatory compliance (maximize in the overall domain)
    • Population exposure (minimize; may be weighted by population groups: children, elderly, special sensitive locations) (NO DATA YET !)
    • Exposure function (minimize, non-linear exposure function).

Direction of optimization

This can be:

  1. minimize (as small as possible)
  2. maximize (as large as possible)
  3. minimize the deviation from a target value

Constraints

Based on the direction of optimization (above), this number will define the (minimum or maximum) value for any one of the criteria or the deviation from a target value.
Criteria that exceed these constraints make the corresponding alternative infeasible and excluded from further consideration.


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