Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases

Abstract

Optimizing real-time sensor systems to detect and identify relevant characteristics of an indoor contaminant event is a challenging task. The interpretation of incoming sensor data is confounded by uncertainties in building operation, in the forces driving contaminant transport, and even in the physical parameters governing the transport. In addition, any simulation tools used by the sensor interpretation algorithm will introduce modeling uncertainties. This paper explores how the time scales inherent in contaminant transport influence the information that can be extracted from real-time sensor data. In particular, we identify three time scales (within room mixing, room-to-room transport, and removal from the building) and study how they affect the ability of a Bayesian Monte Carlo (BMC) sensor interpretation algorithm to identify the release location and release mass from a set of experimental data, recorded in a multi-floor building.The research shows that some limitations in the BMC approach do not depend on details of the models or the algorithmic implementation, but rather on the physics of contaminant transport. 2This in turn has implications for the design of sensor systems.

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

“…However, both networks characterize the released mass with reasonable confidence intervals within 10 min, likely because releases of similar magnitude originating from the different locations generate similar concentrations at the sensed locations. This result is also consistent with the notion that information pertaining to the release mass persists even after information about the release location dissipates [3].…”

“…The library and likelihood function from Sreedharan et al [3], which were developed for the same field experiments, are used in this section. Table 1 , and τ release Throughout this paper, the release time is treated as an additional unknown parameter that is estimated using the BMC algorithm.…”

“…Instead, an integrated-design approach that considers contaminant transport processes, sensor characteristics, and sensor interpretation algorithms can help to develop a system that performs well in diverse conditions. The present paper builds on our previous efforts to develop system design methods and model-based algorithms capable of reconstructing and estimating the unobserved parameters of a release event [1][2][3].…”

State-space methods for calculating concentration dynamics in multizone buildings

“…Multizone models [1,2] and computational fluid dynamics (CFD) [3,4] are widely used for such studies and they can readily be applied to examine specific cases. However, CFD studies are limited by their speed of set up and execution for whole building studies, typically limiting their use to a relatively small set of scenarios.…”

Updating aB. anthracisRisk Model with Field Data from a Bioterrorism Incident

“…56 Although Bayesian approaches have been applied to indoor environmental models to identify indoor contaminant source using tracer-gas data along with sensor network observations, 22,23 and to estimate concentrations of chlorinated volatile organic chemicals as a result of vapor intrusion, 21 studies are lacking on the application of Bayesian approaches to the fate and transport of B. anthracis spores.…”