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Potential events involving biological or chemical contamination of buildings are of major concern in the area of homeland security. Tools are needed to provide rapid, on- site predictions of contaminant levels given only approximate measurements in limited locations throughout a building. In principal, such tools could use calculations based on physical process models to provide accurate predictions. In practice, however, physical process models are too complex and computationally costly to be used in a real-time scenario. In this paper, we investigate the feasibility of using machine learning to provide easily computed but approximate models that would be applicable in the field. We develop a machine learning method based on support vector machine regression and classification. We apply our method to problems of estimating contamination levels and contaminant source location.