Date of Award

2011

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mechanical and Aerospace Engineering

Advisor(s)

Jensen S. Zhang

Keywords

Material emission signature, PTR-MS, Signal processing, Source identification, VOC

Subject Categories

Engineering

Abstract

One of the recent important challenges in the research field of indoor air quality is the identification of indoor Volatile Organic Compound (VOC) emission sources to clearly pinpoint the sources of concern in a field condition. This study represents the first attempt in developing a new technique to find the sources that may be invisible or hidden based on the inspection even of experts when a building with problems of indoor air quality is suspected. The objectives of this study were 1) to determine VOC emission signatures specific to nine typical building materials by using an on-line analytical monitoring device, Proton Transfer Reaction - Mass Spectrometry (PTR-MS), 2) to explore the correlation between the PTR-MS measurements and the measurements of acceptability by human subjects, 3) to develop and evaluate a methodology to identify individual sources of VOC emissions based on the measurements of mixed air samples and the PTR-MS material emission signatures, 4) to determine the long-term variation of VOC emission signatures over time, and 5) to develop a method to account for the long-term variation of emission signatures in the application of the emission source identification method. Samples of nine building materials were tested individually and in combination, including carpet, ceiling material, gypsum board, linoleum, two paints, polyolefine, PVC and wood. VOC emissions from each material were measured in a 50-liter small-scale chamber. Chamber air was sampled by PTR-MS to establish a database of emission signatures unique to each individual material. Sorbent tube sampling and TD-GC/MS analysis were also performed to identify the major VOCs emitted and to compare the resulting data with the PTR-MS emission signatures. The data on the acceptability of air quality assessed by human subjects were obtained from a previous experimental study in which the emissions from the same batch of materials were determined under the same area-specific ventilation rates as in the case of the current measurements with PTR-MS. The same task was performed to measure combined emissions from material mixtures for the application and validation of a signal separation methodology and its source identification enhancement by the consideration of long-term emissions. The methodology was developed based on signal processing principles by employing the method of multiple regression least squares (MRLS) and a normalization technique. Source models were employed to track the change of individual material emission signatures by PTR-MS over a long period of time. It is concluded that: 1) PTR-MS can be an effective tool for establishing VOC emission signatures of material types, and there were sufficient correlations (i.e. Correlation coefficient r < -0.92 ) between the PTR-MS measurements and the acceptability of air quality for the nine materials tested when the sum of selected major individual VOC odor indices was used to represent the emission level measured by PTR-MS; 2) the proposed method for source identification could identify the individual sources at high success rates under laboratory conditions with two, three, five and seven materials present; and 3) the long-term (over nine months) variation of emission factors of the tested materials could be well represented by an empirical power-law model or a mechanistic diffusion based model, and the model coefficients could be estimated based on relatively a short-term set of emission measurements (i.e. within 28 days). The source models could also be used to predict the variation of material emission signatures, which could in turn be used for source identification. Further experiments and investigation are needed to apply the presented source identification method under real field conditions.

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