This study presents a set of new geostatistical methodologies for modelling air quality, mainly based on stochastic simulation algorithms. A methodology is proposed that introduces the time component in a geostatistical simulation of spatial processes via a deterministic model of fugitive dust emissions. Also, a methodology for stochastic simulation of air quality, combining hard data - field measurements of the number of epiphytic lichen species - and soft data - remote sensing images of the region - is presented. A new approach is proposed for the estimation of local conditional distribution functions with hard and soft derived data by a classification algorithm - Probability Neural Networks - for calibration between the hard and soft data. A comparative study between sequential indicator simulation, with and without correction for local probabilities, and probability field simulation is performed to determine their ability to reproduce non-stationary spatial patterns. Finally, it is shown how application of cost funsctions to a set of stochastic images allows an uncertainty assessment of the impact of air pollution. All these algorithms and methodologies have been applied to a particular real case study, but they can also be applied to a large number of other applications or even to other fieldsAvailable from Fundacao para a Ciencia e a Tecnologia, Servico de Informacao e Documentacao, Av. D. Carlos I, 126, 1200 Lisboa / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga
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