THE INTRINSIC RANDOM-FIELD MODEL IN THE STUDY OF SULFATE DEPOSITION PROCESSES

This work is concerned with the application of advanced stochastic ana lysis in atmospheric environment problems. In particular, the focus is the study of spatially non-homogeneous sulfate deposition processes i n the conterminous U.S.A. by means of intrinsic random fields. The int rinsic model used here is significantly more general in theory and als o more powerful in practice than many of the conventional methods. It provides spatial correlation tools which can efficiently represent the complex non-homogeneous features of the sulfate deposition data. The intrinsic approach, which has been developed in response to experiment al findings, utilizes the spatial variability characteristics of the e nvironmental process and the mathematical characteristics of the rando m field models to fit these models to the observed data. The results a re represented in terms of maps which provide a quantitative descripti on of the spatial distribution of both the degree of non-homogeneity a nd the correlation features of sulfate deposition over the conterminou s U.S.A. An optimal spatial estimation method for non-homogeneous sulf ate deposition processes is also discussed. The estimation inputs refl ect important variability patterns of sulfate deposition and are opera tionally executable in the real world. The estimation outputs are deta iled maps of sulfate deposition together with the associated maps of 9 5% confidence interval width. These maps offer valuable information re garding the non-homogeneous distribution of the sulfate deposition val ues in the various parts of the U.S.A. The practical implementation of the intrinsic model involves an automatic procedure which operates at a local scale providing a detailed description of the spatial structu re of sulfate deposition.