Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals

Liu, X.; Zhai, Zhiqiang

doi:10.1111/j.1600-0668.2007.00497.x

Cited by 133 publications

(69 citation statements)

References 42 publications

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“…The method, named as the adjoint probability method, was first proposed by Wilson (1999 and2001) to track groundwater pollutant sources. The method can, in theory, identify pollutant source location, flux, and release time with least priori information, and is faster than other inverse modeling algorithms (Liu and Zhai 2007a). One key concept (or variable) in this method is the location probability density function (LPDF), which describes the possibility of a contaminant parcel released from a source location s x at 0 t = to reach an observation/ sensing location o x in the…”

Section: Principles Of Probability-based Inverse Modeling Methodsmentioning

confidence: 98%

“…Inverse modeling of advection-dispersion problems has been studied for decades. A few modeling theories and methods have been developed and applied to predict atmosphere and ground water pollutions, as reviewed by Liu and Zhai (2007a). For indoor air quality applications, three studies have been found in the literature: Sohn et al (2002 and2003), Zhang and Chen (2007), and Vukovic and Srebric (2007).…”

Section: Principles Of Probability-based Inverse Modeling Methodsmentioning

confidence: 99%

“…The adjoint probability method solves an adjoint probability equation that is derived from the conventional contaminant transport equation with the sensitivity analysis approach of Sykes et al (1985). Liu and Zhai (2007a) presented the mathematics details of deducing the adjoint equation. In essence, the general contaminant transport governing equation, together with the initial and boundary conditions, can be expressed as…”

Section: Principles Of Probability-based Inverse Modeling Methodsmentioning

confidence: 99%

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Principles and applications of probability-based inverse modeling method for finding indoor airborne contaminant sources

Zhai

Liu

2008

Build. Simul.

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Building indoor air quality (IAQ) has received growing attentions lately because of the extended time people spend indoors and the increasing reports of health problems related to poor indoor environments. Recent alarms to potential terrorist attacks with airborne chemical and biological agents (CBA) have further highlighted the research needs on building vulnerability and protection. To maintain a healthful and safe indoor environment, it is crucial to identify contaminant source locations, strengths, and release histories. Accurate and prompt identification of contaminant sources can ensure that the contaminant sources can be quickly removed and contaminated spaces can be effectively isolated and cleaned. This paper introduces a probability concept based prediction method -the adjoint probability method -that can track potential indoor airborne contaminant sources with limited sensor outputs. The paper describes the principles of the method and presents the general modeling algorithm and procedure that can be implemented with current computational fluid dynamics (CFD) or multi-zone airflow models. The study demonstrates the application of the method for identifying airborne pollutant source locations in two realistic indoor environments with few sensor measurement outputs. The numerical simulations verify the feasibility and accuracy of the method for indoor pollutant tracking applications, which forms a good foundation for developing an intelligent and integrated indoor environment management system that can promptly respond to indoor pollution episodes with effective detection, analysis, and control.

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Section: Principles Of Probability-based Inverse Modeling Methodsmentioning

confidence: 98%

Section: Principles Of Probability-based Inverse Modeling Methodsmentioning

confidence: 99%

Section: Principles Of Probability-based Inverse Modeling Methodsmentioning

confidence: 99%

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Principles and applications of probability-based inverse modeling method for finding indoor airborne contaminant sources

Zhai

Liu

2008

Build. Simul.

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“…This assessment requires information about number of sources, their locations, emission rates, time, and duration of releases. However, in most of the realistic events, the difficulty increases when distributed contaminant sensor networks detect concentrations over threshold value but have no particular idea about the releases including their origin (LIU and ZHAI, 2007). This necessitates the development of a methodology that can help in identifying potential contaminant sources from limited concentration measurements.…”

Section: Introductionmentioning

confidence: 99%

Least Square Data Assimilation for Identification of the Point Source Emissions

Sharan

Singh

Issartel

2011

Pure Appl. Geophys.

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The identification of single and multiple-point emission sources from limited number of atmospheric concentration measurements is addressed using least square data assimilation technique. During the process, a new two-step algorithm is proposed for optimization, free from initialization and filtering singular regions in a natural way. Source intensities are expressed in terms of their locations reducing the degree of freedom of unknowns to be estimated. In addition, a strategy is suggested for reducing the computational time associated with the multiple-point source identification. The methodology is evaluated with the synthetic, pseudo-real and noisy set of measurements for two and three simultaneous point emissions. With the synthetic data, algorithm estimates the source parameters exactly same as the prescribed in all the cases. With the pseudo-real data, two and three point release locations are retrieved with an average error of 17 m and intensities are estimated on an average within a factor of 2. Finally, the advantages and limitations of the proposed methodology are discussed.

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“…indoor environment with passive or active airflows [7], [8]. These contaminant events could be the result of an accident, such as carbon monoxide (CO) leakage from a malfunctioning furnace, or a terrorist attack.…”

mentioning

confidence: 99%

Distributed Contaminant Detection and Isolation for Intelligent Buildings

Kyriacou

Michaelides

Reppa

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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Abstract-The automatic preservation of the Indoor Air Quality is an important task of the intelligent building design in order to ensure the health and safety of the occupants. The indoor air quality, however, is often compromised by various airborne contaminants that penetrate the indoor environment as a result of accidents or planned attacks. In this work, we provide the detailed analysis, implementation and evaluation of a distributed methodology for detecting and isolating multiple contaminant events in large-scale buildings. Specifically, we consider the building as a collection of interconnected subsystems and we design a contaminant event monitoring software agent for each subsystem. Each monitoring agent aims to detect the contaminant and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighbouring agents. For configuring the subsystems, we implement both exact and heuristic partitioning solutions. A main contribution of this work is the investigation of the impact of the partitioning solution on the performance of the distributed Contaminant Detection and Isolation scheme with respect to the detectability and isolability of the contaminant sources. The performance of the proposed distributed contaminant detection and isolation methodology is demonstrated using models of real building case studies created on CONTAM 1 .

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Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals

Cited by 133 publications

References 42 publications

Principles and applications of probability-based inverse modeling method for finding indoor airborne contaminant sources

Principles and applications of probability-based inverse modeling method for finding indoor airborne contaminant sources

Least Square Data Assimilation for Identification of the Point Source Emissions

Distributed Contaminant Detection and Isolation for Intelligent Buildings

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