Abstract
Abstract A cost-effectiveness model for air pollution control is constructed in which the total cost of abatement is minimized for a given set of air quality goals and for varying degrees of confidence that the goals will be achieved. The probabilistic element is confined to a single stochastic variable, annual average wind velocity. While this is a simplified model, the results indicate that the cost of increased certainty rises rapidly. This suggests that air quality goals should be expressed not only in terms of maximum pollutant concentrations but also the minimum probabilities that these maximums will not be exceeded.
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