Titlebook: Air Pollution Modeling and Its Application XII; Sven-Erik Gryning,Nadine Chaumerliac Book 1998 Springer Science+Business Media New York 19

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Welches Forschungsdesign plane ich?,ient Air Quality Objective of 82 ppb per hour is exceeded several times a year under high pressure ridge conditions (Taylor, 1991; McKendry, 1994). Modeling efforts are aimed at supporting local regulatory agencies in their attempt to apply appropriate control strategies to the problem. The complex

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Wie finde ich zu einem Forschungsthema?,onfidence bounds we can place on the model predictions of, for example, how ozone will respond to changes in emissions. These are presently unknown. The factors influencing prediction error can be classified as input errors, as model formulation errors, or as random stochastic processes. In the pres

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https://doi.org/10.1007/978-3-322-80281-1trol regulations. The response of the these model predictions to system parameters or emission controls provides valuable information for the strategy design to improve air quality. Such information can be pursued via sensitivity analysis, the systematic calculation of sensitivity coefficients, to q

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,Karl Weierstraß / Karl Weierstrass,e emission control measures for improving ambient ozone air quality. Recognizing the complexities associated with the nation’s ozone non-attainment problem, the 1990 Clean Air Amendments mandated the use of grid-based photochemical models for evaluating emission control strategies in urban areas hav

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https://doi.org/10.1007/978-3-531-92507-3 to the chemical composition of the atmosphere. Modelling, in the sense both of developing, using and applying models is a scientific activity as it is focused on answering fundamental questions in the field of atmospheric chemistry.

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