Stochastic Models for Analysis and Control of Air Pollution in a Manufacturing System

Pollution of air resulting from toxic wastes emitted by large manufacturing plants and power-generation plants is a much-studied topic. The issue of health hazards arising from water pollution (see Wagner, Shamir, and Marks, 1994), storage and transportation of hazardous materials, and genetically-engineered food products has been a topic of intense debate and considerable research for the last few years. A great deal of attention has been paid to reprocessing of wastes before they are discharged into the ground from industries. An equally important topic is the pollution of air inside a manufacturing plant which typically also houses the source of pollution. This subject deserves close research attention because it is a matter of public health and also one of concern to the managers of almost every manufacturing plant. The persons who work in such plants are exposed to toxics and can suffer long-term ailments as a result of the exposure. Air pollution has been identified as the root cause of a very large number of chronic diseases. Laws have been passed in almost all countries in the world in recent times to minimize the deleterious effects of air pollution. The evidence gathered to measure the effects of air pollution has played a significant role in the passage of such laws. These laws have had a significant impact on reducing the occurrence of related diseases and on improving the average health index of individuals in that country. Not surprisingly, operations-research models have provided important tools in measuring and controlling air pollution. An example is the formula of Grandell (1985) that is used commonly by meteorologists. In this paper, we provide an operations-research perspective to address an important problem of air pollution in a static mass of air that occupies the inside of manufacturing plants. The problem of air pollution inside manufacturing plants is of serious public concern in the rapidly-growing semi-conductor industry and the plastics industry. In the semi-