Master of Science / Department of Biological & Agricultural Engineering / Stacy L. Hutchinson / The shortage of clean, usable water is a global problem (Millennium Ecosystem Assessment, 2005). As much as 80% of the world’s population has been reported to be in areas of high water security risk due to a convergence of factors, such as watershed disturbance, pollution, water resource development and biotic factors (Voeroesmarty et al., 2010). Water reuse technologies are a potential solution to this problem. However, implementation of treatment technologies for improved water reuse require rapid, effective monitoring techniques capable of insuring treatment quality. Fluorescence spectroscopy has shown potential for wastewater treatment monitoring due to its sensitivity, selectivity, and capacity to be employed in-situ. Online fluorescence data and full fluorescence excitation-emission matrices coupled with parallel factor analysis (PARAFAC) were employed to evaluate the treatment performance of a membrane bioreactor (MBR) at Fort Riley, KS. Specific research goals were to evaluate the effectiveness of fluorescence for monitoring wastewater treatment and to determine the contamination detection limit of fluorescence techniques in a non-potable reuse scenario. Study results revealed a two-stage startup period, the first 60 days indicated membrane cake layer formation and the first 90 days showed signs of oxic tank maturation. Fluorescence was found to be effective at monitoring carbon concentration trends throughout the MBR system, showed preferential removal of protein-like dissolved organic matter (DOM), and an increase in biodegradation of DOM as the oxic tank matured. A ratio of the humic-like fluorescent components to the protein-like fluorescent components correlated to TOC removal (R² = .845, p < .001). Also, fluorescence was able to detect contamination in the effluent at the 0.74-1.24 mg C/L level using two wavelength pairs, indicating that effective real-time monitoring for contamination can be accomplished with minimal instrumentation and post-processing of data.