Chance‐Constrained Dynamic Model of Air Quality Management

Abstract

This paper presents a modeling approach based on chanceconstrained programming, that determines least‐cost time‐linked air pollution emission controls, while accounting for the dynamic and stochastic character of meteorological conditions and respecting different averaging‐time ambient air quality standards with exogenously specified reliabilities. The methodology is numerically illustrated with actual meteorological data in the case of an isolated pollution source and of monthly and annual air quality standards. Areas for further research are also delineated.