Rapid data assimilation in the indoor environment: Theory and examples from real-time interpretation of indoor plumes of airborne chemical

Abstract

Releases of acutely toxic airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are identified and implemented. Commonly, possible responses include conflicting strategies, such as shutting the ventilation system off versus running it in a purge (100% outside air) mode, or having occupants evacuate versus sheltering in place. The right choice depends in part on quickly identifying the source locations, the amounts released, and the likely future dispersion routes of the pollutants. This paper summarizes recent developments to provide such estimates in real time using an approach called Bayesian Monte Carlo updating. This approach rapidly interprets 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. As an illustration, two specific applications of the approach are described.