Development of a rapid sampling and analysis methodology to measure microbially generated volatile organic compounds for the assessment of indoor mold contamination

Mold contamination in the indoor environment can cause health problems, property damage, and unpleasant living conditions for occupants. Current mold sampling techniques require complicated materials and techniques that are often time-consuming, costly, and may not accurately reflect the presence of mold in the home. It is well known that characterization of airborne bioaerosols in indoor environments is a challenge because of inherent variability in concentrations, which are influenced by many environmental factors. Visual mold inspections and bioaerosol monitoring do not adequately assess the presence of hidden mold contamination nor do they provide an efficient means of quickly assessing mold contamination in built environments. Identification of mold contamination in built environments based on the chemical signature that mold emits may be a viable alternative to current bioaerosol assessment methodologies. The primary goal of this research was to develop a new sampling system using volatile organic compounds generated by mold as an indicator of the presence of mold contamination in built environments. Understanding the strengths and weaknesses of current mold sampling techniques has enabled the development of this novel exposure assessment strategy to assess indoor mold concentrations using microbially generated volatile organic compounds (MVOCs).;The initial research phase quantified the seasonal and day-to-day variability of airborne fungal concentrations in a single residential environment. Interday variability observed during this study suggests that indoor air quality practitioners must adjust their exposure assessment strategies to ensure that the temporal variability in bioaerosol concentrations is considered.;In the second phase, the goal was to evaluate the ability of an evacuated glass bottle, Entech Bottle-Vac, to collect representative field MVOC samples in indoor environments.;In the third phase of the research, the use of a pattern recognition program was proposed to classify chemical signatures produced by mold growth into categories related to the bioaerosol characterization of a building. This aspect of the research demonstrated that FFBP ANN may be used in conjunction with MVOC sampling in built environments to predict the presence of mold growth in a more timely and cost-effective manner than bioaerosol monitoring.;This research resulted in the development of a new sampling system and means of assessing mold contamination in built environments. A correlation was developed between bioaerosol characterization and chemicals emitted by mold through the use of artificial neural networks; the results of the study are promising and may redefine current mold assessment strategies. Chemical sampling by the resident could replace bioaerosol sampling by professionals thereby reducing the cost and increasing the availability of mold assessments to the non-industrial sector.