In order to provide meaningful information for evaluating environmental consequences of alternative control strategies of air pollutant emissions, scientists have recently initiated numerous studies aiming at collecting and making use of regional data. New kinds of mathematical models have been developed that no longer are calibrated just for individual stands or catchments but can be applied on a large regional scale. Such regional models and data were reviewed at a meeting" Models to describe the geographic extent and time evolution of acidification and air pollution damage", organized by the Finnjsh Research Project on Acidification (HAPRO), and held in Aulanko, Hiimeenlinna, Finland, July 5-8, 1988. This volume is based on the outcome of that meeting. The chapters of the volume are written by leading scientists in the field. The main objective of this volume is to discuss the potential of regional models to analyze the geographic extent of acidification as well as the environmental damage and the time evolution of:the responses of the environment. First, in Part I of the volume, sensitivity distributions of ecosystems, forest soils and surface waters are described. The focus of interest is therefore extended from the most sensitive ecosystems to sensitive and moderately sensitive parts of the environment. When formulating national and interna tional targets, it is of crucial importance to know the whole distribution of critical loads within the area, and not only the level the most sensitive ecosystem can tolerate.
This overview of models for the assessment of regional acidification and other effects of air pollutants on the environment especially reviews the development and use of mathematical models. The main objective is to discuss the potential of the newly developed regional models for the analysis of the geographic extent of acidification and environmental damage as well as the evolution of environmental responses. Important aspects such as critical loads, sensitivity distributions, data acquisition, regional surveys and model uncertainty are discussed in connection with the application of dynamic models.
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