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Mathematical Models for Planning and Controlling Air Quality documents the proceedings of an IIASA Workshop on Mathematical Models for Planning and Controlling Air Quality, October 1979. The Workshop had two goals. The first was to contribute to bridging the gap between air-quality modeling and management. The second was to consider an unusual air-quality control strategy: namely, real-time emission control. The book is organized into two parts, corresponding roughly to the two goals outlined above. Part One examines the role of mathematical models in air-quality planning and includes: a presentation of a decision maker's viewpoint; illustrations of various types of models (descriptive and/or decision models) available to decision makers; assessments of the role of models in actual decision making; and two papers on the more traditional question of the significance and range of application of descriptive models, i.e., of models that represent the physics of the air-pollution phenomenon. Part Two is devoted primarily to real-time control. It includes a presentation of the IIASA case study of the Venetian lagoon; and papers on various aspects of this research; on alternative concentration predictors; and descriptions of implementations of real-time forecast and control schemes in Japan and Italy.
This book has a dual objective: first to introduce the reader to some of the most important and widespread environmental issues of the day; and second to illustrate the vital role played by mathematical models in investigating these issues. The subjects covered are ground water contamination, air pollution, and hazardous materials emergencies. These issues are presented in their full real-world context and are used to develop important classical mathematical themes. The emphasis throughout is on fundamental principles and concepts, not on achieving technical mastery of state-of-the-art models.
The protection of our environment is one of the major problems in the society. More and more important physical and chemical mechanisms are to be added to the air pollution models. Moreover, new reliable and robust control strategies for keeping the pollution caused by harmful compounds under certain safe levels have to be developed and used in a routine way. Well based and correctly analyzed large mathematical models can successfully be used to solve this task. The use of such models leads to the treatment of huge computational tasks. The efficient solution of such problems requires combined research from specialists working in different fields. The aim of the NATO Advanced Research Workshop (NATO ARW) entitled “Advances in Air Pollution Modeling for Environmental Security” was to invite specialists from all areas related to large-scale air pollution modeling and to exchange information and plans for future actions towards improving the reliability and the scope of application of the existing air pollution models and tools. This ARW was planned to be an interdisciplinary event, which provided a forum for discussions between physicists, meteorologists, chemists, computer scientists and specialists in numerical analysis about different ways for improving the performance and the quality of the results of different air pollution models.
"Models are often the only way of interpreting measurements to in vestigate long-range transport, and this is the reason for the emphasis on them in many research programs". B. E. A. Fisher: "A review of the processes and models of long-range transport of air pollutants", Atmospheric Environment, 17(1983), p. 1865. Mathematical models are (potentially, at least) powerful means in the efforts to study transboundary transport of air pollutants, source-receptor relationships and efficient ways of reducing the air pollution to acceptable levels. A mathematical model is a complicated matter, the development of which is based on the use of (i) various mechanisms describing mathematically the physical and chemical properties of the studied phenomena, (ii) different mathematical tools (first and foremost, partial differenti al equations), (iii) various numerical methods, (iv) computers (especially, high-speed computers), (v) statistical approaches, (vi) fast and efficient visualization and animation techniques, (vii) fast methods for manipulation with huge sets of data (input data, intermediate data and output data).