Asthma impacts 8% of the US population and may be exacerbated by allergens and mold. Mold growth is caused by excess moisture indoors, including elevated relative humidity (RH). This growth results of the emission of microbial volatile organic compounds (mVOCs) from microorganisms, and the detection of moldy odor in a home is strongly associated with negative health effects. However, we do not understand how changes in RH impact microbial growth and the subsequent production of mVOCs in building materials. The goal of my research is to develop a framework to quantify microbial growth due to elevated RH and characterize the corresponding release of mVOCs. I utilized the “time-of-wetness” framework which calculates expected growth from the fraction of time the RH is above an 80% threshold. Samples of carpet and dust were collected from homes in Ohio and incubated at varying RH conditions (50%, 85% or 100%) to simulate the home environment. Results indicated growth in carpet follows the twoactivation regime model within the time-of-wetness framework. Location of collection was a strong indicator of species composition (P = 0.001, R² = 0.461) compared to moisture condition (P = 0.001, R² = 0.021). Samples of drywall, carpet and dust were incubated at RH conditions of 50%, 65%, 70%, 75%, 80%, 85% and 95% for two weeks to determine the relationship between mVOC emissions and moisture. A proton transfer time-of-flight mass spectrum (PTF-ToF-MS) was utilized to measure emissions of mVOCs, and the quantity of microbial growth and composition of species was measured. The fungal growth in drywall occurred at ≥85% RH, while fungal growth in carpet dust occurred at ≥75% RH. The concentration of mVOCs is likely small compared to the concentration of compounds released from the building materials themselves. However, known mVOCs such as limonene and dimethyl sulfide were associated with dust in carpet when compared to autoclaved (non-microbial) carpet. Continued work will allow us to identify which microbes are metabolically active and release mVOCs to create a framework for detection in homes. Ultimately, this information will allow for the prediction of how moisture influences a given indoor environment and impacts human health.