A model for the evaluation of alternative policies for atmospheric pollutant source emissions (Masc model)

  • R. Aguilar
  • L. F. Escudero
  • J. F. G. de Cevallos
  • P. G. de Cos
  • F. Gómez-Pallete
  • G. Martínez Sánchez
Environmental Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4)


The model presented in this paper should be considered an effective tool in the establishment of bases for certain corrective alternatives for abatement of an atmospheric pollution problem. It should be regarded in addition as a truly valuable instrument for quantifying, within the development policies of a given area, ever more essential criteria concerning the protection of the environment.

It should be pointed out that the fundamental statistical parameter considered in the formulation of the model is the maximum admissible probability of the concentration in a given grid exceeding the maximum permitted limit, as contrasted with models which consider quality standards on the basis of mean values. In this way we avoid masking the hazard of large concentrations with situations of low concentration.

The criteria to be minimized are: a) Weighted emission reduction for each grid, according to the incidence of the latter on the pollutant concentration in the total number of grids comprising the polluted area, and/or b) Probability of the concentration exceeding the adopted limit.

The possibility arises of considering these criteria simultaneously, optimizing them in uniform fashion.

Lastly, the results provided by this model are formulated as emissions reduction percentages at grid level, for the entire period studied, without considering to what extent nor with that concrete technical alternatives each pollution source present in the grid should correct its respective emissions. Emission reduction values are expressed in either discrete or continuous scale fashion.


Emission Reduction Pollutant Concentration Mixed Integer Linear Programming Polluted Area Basic Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1973

Authors and Affiliations

  • R. Aguilar
    • 1
  • L. F. Escudero
    • 1
  • J. F. G. de Cevallos
    • 1
  • P. G. de Cos
    • 1
  • F. Gómez-Pallete
    • 1
  • G. Martínez Sánchez
    • 1
  1. 1.Madrid Scientific CenterIBMSpain

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