Identification of indoor contaminant source location by a single concentration sensor

Sensor Location Design for Contaminant Source Identification in Water Distribution Systems

A general method is proposed to identify appropriate network locations for placing water quality monitoring sensors to identify the sources of accidental or deliberate contaminations. First, the contamination source identification problem is discussed for an assigned set of monitored locations. If the contaminant is conservative or its reaction rate in water is first order, the problem can be e...

Probabilistic Contaminant Source Identification in Water Distribution Infrastructure Systems

Large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. As contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. In this paper, a methodology...

Identification of contaminant sources in enclosed spaces by a single sensor.

UNLABELLED To protect occupants from infectious diseases or possible chemical/biological agents released by a terrorist in an enclosed space, such as an airliner cabin, it is critical to identify gaseous contaminant source locations and strengths. This paper identified the source locations and strengths by solving inverse contaminant transport with the quasi-reversibility (QR) and pseudo-revers...

An Indoor Contaminant Sensor Placement Toolbox for Critical Infrastructure Buildings

A robust geostatistical approach to contaminant source identification

[1] Estimation under model uncertainty remains a practical concern in many scientific and engineering fields. A commonly encountered example in groundwater remediation is the contaminant source identification problem. Like many other inverse problems, contaminant source identification is inherently ill posed and is sensitive to both data and model uncertainties. Model uncertainties, which may b...