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Releases of airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are taken. However, possible responses can include conflicting strategies, such as shutting the ventilation system off versus running it in a purge mode or having occupants evacuate versus sheltering in place. The proper choice depends in part on knowing the source locations, the amounts released, and the likely future dispersion routes of the pollutants. We present an approach that estimates this information in real time. It applies Bayesian statistics to interpret measurements of airborne pollutant concentrations from multiple sensors placed in the building and computes best estimates and uncertainties of the release conditions. The algorithm is fast, capable of continuously updating the estimates as measurements stream in from sensors. We demonstrate the approach using a …