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. The approach is employed, as illustration, to conduct two specific investigations under different situations.
BT - Air Pollution Modeling and its Applicatiion XIX CY - New York LA - eng N1 -This book chapter was presented by Ashok J. Gadgil, Michael D. Sohn and Priya Sreedharan at the 2008 29th NATO/SPS International Technical Meeting on Air Pollution and its Application in Aveiro, Portugal.
N2 -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. The approach is employed, as illustration, to conduct two specific investigations under different situations.
PB - Springer Science + Business Media B.V. PP - New York PY - 2008 SP - 263 EP - 277 T2 - Air Pollution Modeling and its Applicatiion XIX TI - Rapid Data Assimilation in the Indoor Environment: theory and examples from real-time interpretation of indoor plumes of airborne chemicals ER -