Electronic Thesis/Dissertation


Early Identification of At-Risk Facilities to Prevent Air Quality Issues Open Access

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Numerous government facilities in the Washington metropolitan area are experiencing air quality issues that pose a health risk to occupants. The current Facility Condition Assessment process focuses on rating facility components to determine an overall condition and estimate costs for sustainment, renovation and modernization. This process works to minimize costs while maintaining the expected service life of the facility. This process does not include a capacity to determine when a facility is at risk for mold growth and indoor air quality issues which can have significant impacts on occupant health and facility operations. This research develops a statistical method to determine which facilities are at risk for indoor air quality issues by applying a Logistic Regression model to facility condition inspection variables already in use. This research leverages applicable knowledge at the nexus of mycology, epidemiology, and Facility Management processes so that mold growth in facilities is not an unanticipated issue. The analysis and tools developed herein are based on the current Facility Condition Assessment process and are intended to supplement existing procedures based on facility classification from modeling output and a proposed Focused Inspection Process. These tools will allow facility managers to establish protocols for building inspection-based mold growth scenarios that are utilized to plan maintenance and repair activities. Early interventions will allow identified risks to be mitigated prior to impacting occupant health or deterioration of overall facility quality. The end product identifies early signs of conditions that lead to mold growth in order to protect human health and minimize remediation and renovation costs.

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