Towards improved characterization of high-risk releases using heterogeneous indoor sensor systems

Sreedharan, Priya; Sohn, Michael D.; Nazaroff, William W.; Gadgil, Ashok

doi:10.1016/j.buildenv.2010.08.006

Cited by 23 publications

(12 citation statements)

References 15 publications

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“…In the second stage, Bayesian updating of the probability for each collected data is obtained after the event detection. See Sreedharan et al [4,5,6,7] for details of recent applications of the Bayes Monte Carlo method.…”

Section: Motivationmentioning

confidence: 99%

“…Multizone, zonal and computational fluid dynamics (CFD) models are used for simulation of indoor airflow and contaminant dispersion patterns [3,1]. Owing to ease of implementation and computational efficiency, multizone models are most widely used for predicting the contaminant dispersion and source localization/characterization [2,4,5,6,7]. A multizone model represents any building as a network of well-mixed zones connected by flow paths like doors, windows, leaks etc.…”

Section: Introductionmentioning

confidence: 99%

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A Gaussian process emulator approach for rapid contaminant characterization with an integrated multizone-CFD model

Tagade

Jeong

Choi

2013

Building and Environment

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This paper explores a Gaussian process emulator based approach for rapid Bayesian inference of contaminant source location and characteristics in an indoor environment. In the pre-event detection stage, the proposed approach represents transient contaminant fate and transport as a random function with multivariate Gaussian process prior. Hyper-parameters of the Gaussian process prior are inferred using a set of contaminant fate and transport simulation runs obtained at predefined source locations and characteristics. This paper uses an integrated multizone-CFD model to simulate contaminant fate and transport. Mean of the Gaussian process, conditional on the inferred hyper-parameters, is used as an computationally efficient statistical emulator of the multizone-CFD simulator. In the post event-detection stage, the Bayesian framework is used to infer the source location and characteristics using the contaminant concentration data obtained through a sensor network. The Gaussian process emulator of the contaminant fate and transport is used for Markov Chain Monte Carlo sampling to efficiently explore the posterior distribution of source location and characteristics. Efficacy of the proposed method is demonstrated for a hypothetical contaminant release through multiple sources in a single storey seven room building. The method is found to infer location and characteristics of the multiple sources accurately. The posterior distribution obtained using the proposed method is found to agree closely with the posterior distribution obtained by directly coupling the multizone-CFD simulator with the Markov Chain Monte Carlo sampling.

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Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Gaussian process emulator approach for rapid contaminant characterization with an integrated multizone-CFD model

Tagade

Jeong

Choi

2013

Building and Environment

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Section: Detectionmentioning

confidence: 99%

“…Like with specific sensor, non-specific sensors can act as a disruption measure for a bioaerosol attack. Bayesian modeling using inputs from a number of different sensor types has been suggested as a method for improving the characterization of a release [33].Alert and warning: Alert and warning systems: A detection system must provide a rapid warning of the release of BWA into the indoor environment, while minimizing the number of false positives and negatives. The need to maintain a central monitoring system for biosensors placed at strategic locations throughout the building is necessary in order to monitor the building for bioattack.…”

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confidence: 99%

The Role of Protection Measures and their Interaction in Determining Building Vulnerability and Resilience to Bioterrorism

Taylor¹,

Margaritis²,

Nasir³

et al. 2013

J Bioterror Biodef

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Understanding the risk presented to buildings by bioterrorism has been a topic of research interest in recent years. Risk assessment methodologies and guidelines for the protection of buildings have been developed by a number of researchers, but it remains difficult to quantify the vulnerability and resilience of a building. It is an important issue from both biosecurity investment and operation point of view because different measures have different cost and operation implications. This paper reviews existing risk assessment methodologies, introduces a novel framework for classifying protection measures, and determines the inter-relation of the framework components and building vulnerability and resilience using a fault tree analysis within a biosecurity network system. Future work will develop weighting values for the different protection measures within the framework, allowing for the calculation of bioterrorism resilience of existing buildings, and to provide a decision making guide for building architects, builders, and managers for new and retrofitted buildings. The physical decay of BWA in the indoor environment refers to the loss attributable to physical factors such as unrestricted diffusion, surface impaction, and fall-out. Unrestricted diffusion allows the movement of aerosolized particles out of the zone where the pathogen was released into other areas within the building, or the external environment. This dilutes the concentration of the particle in the indoor air, but can also lead to the spread of the pathogen to areas of the building not directly targeted. Surface impaction refers to the attachment of contaminants to surfaces, while fall out refers to the deposition of the contaminant particles on surfaces due to gravity. Physical decay does not necessarily eliminate the threat, but may remove it from the indoor air, although the risk of re-suspension can remain.The biological decay of BWA within the built environment depends on the type of pathogen, and the surrounding environmental conditions. Potential pathogens used in bioterrorism can exhibit a range of survival times in the environment. The persistence of pathogens is dependent on environmental conditions, including the temperature and relative humidity of the indoor environment and solar radiation levels [3]. Fungi and some bacterial species, for example Anthrax, can form spores, allowing them to survive desiccation and remain viable for years. The nature of the BWA and the means of delivery of the contaminant to the building will dictate the threat and the consequences of an attack. Threat analysis for buildingsA successful bioattack on a building will be a product of a number of factors, including the terrorists' resources, capabilities, and decisions, actions by other individuals and/or groups, and the buildings resilience. Assessing terrorism risk in specific buildings is often considered to be a combination of the threat of an attack, the vulnerability to the attack, and the expected consequences of theattack. Risk threat vuln...

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“…Some representative work in this area includes the Bayesian interpretation approach (see [11] and [12]), to assess the effect of various sensor characteristics on the overall system performance regarding the time needed to characterize the release (location, amount released and duration). The optimal sensor placement, however, was not investigated.…”

Section: Related Workmentioning

confidence: 99%

Security-oriented sensor placement in intelligent buildings

Ηλιάδης

Michaelides

Panayiotou

2013

Building and Environment

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Intelligent buildings are beginning to utilize sensor networks for monitoring and protecting indoor air quality against contamination events. This paper presents a methodology for determining where to install such sensors. In particular, a multi-objective optimization problem is formulated for minimizing the sensor cost, the average and the worst-case impact damage corresponding to a set of contamination event scenarios. Each contamination scenario is comprised of parameters characterized by some given probability distribution. Based on these distributions, a set of representative contamination scenarios is constructed through grid and random sampling, and the overall impact of each scenario is computed, thus providing a solution to the sensor placement problem. The proposed methodology is illustrated by two case studies, a simple building with five rooms and a realistic building with 14

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Towards improved characterization of high-risk releases using heterogeneous indoor sensor systems

Cited by 23 publications

References 15 publications

A Gaussian process emulator approach for rapid contaminant characterization with an integrated multizone-CFD model

A Gaussian process emulator approach for rapid contaminant characterization with an integrated multizone-CFD model

The Role of Protection Measures and their Interaction in Determining Building Vulnerability and Resilience to Bioterrorism

Security-oriented sensor placement in intelligent buildings

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